Optimal Utilization of Battery Energy Storage System

for Power Losses Minimization in an Active Distribution Network

Table of Contents

1. Introduction.......................................................................................................... 1

1.1 Background and Problem Description........................................................1

1.2 Battery Energy Storage System...................................................................3
1.3 Research Aim and Objectives......................................................................7
1.4 Research Questions......................................................................................8
1.5 Significance of Study....................................................................................9
2. Literature Review..................................................................................................9

2.1 Introduction....................................................................................................9
2.2 Historical Background..................................................................................9
2.2.1 Development.....................................................................................10

2.3 BESS Prevalence.........................................................................................14

2.4 Battery Energy Storage System.................................................................17
2.4.1 BESS Applications...........................................................................20

2.5 Optimal Placement...................................................................................... 23

2.6 Optimal Capacity......................................................................................... 26
2.7 Optimal Operation....................................................................................... 29
2.8 Applications to aid the integration of renewables....................................37
2.9 Lithium Batteries vs Lead Acid Batteries..................................................40
2.10..............................................................Comparison between Technologies
43
2.10.1 Application Fields............................................................................43

2.10.2 Power, energy and discharge time.................................................44

2.10.3 Yield and lifespan.............................................................................45

2.10.4 Weight and volume density.............................................................45

2.10.5 Energy and power density...............................................................45

2.10.6 Investment costs..............................................................................46

 2.11....................................................................................................Gap Analysis
46
3. References..........................................................................................................49

1. Introduction

1.1 Background and Problem Description

Renewable Over the past few years, environmental concerns have led to a

significant expansion in deploying renewable energy sources (RES). The total power

generated by photovoltaics (PV) worldwide was 486 gigawatts in 2018, up from 41

gigawatts in 2010. There has been a rise of nearly eleven times this amount in just eight

years. To wit, wind power surged from 180 GW in 2010 to 564 GW in 2018energy

sources (RES) deployment has been considerably expanding throughout the previous

years due to environmental concerns. In fact, the accumulated capacity of global

photovoltaics (PV) power increased from 41 gigawatt in 2010 to 486 gigawatt in 2018.

This represents an increase by almost 11 times more in 8 years. Correspondingly, the

[1]
wind power increased from 180 gigawatt in 2010 to 564 gigawatt in 2018 . Because

RES are unstable due to their reliance on weather conditions, which can change

drastically in a short time, this increases the risk of grid instability This raises the

possibility of grid instability as RES are stochastic because of their dependence on

[2]–[8]
weather conditions which can significantly vary in a short period of time .

1
 At high RES penetration levels, the unpredictable nature of RES leads to grid

instability and inconsistent dispatchable electricity. Consequently, an over-provisioned

demand-generation balance setup usually results from building up the power supply to

handle the worst-case scenario of power demand. Regardless of the amount each end

user contributes to the over-provision, all end users equally share the increased

expenses and price fluctuations caused by over-provisionThe stochastic nature of RES

causes grid instability, but it also results in inconsistent dispatchable power, particularly

at high levels of RES penetration. Consequently, the power supply has to be built up to

handle the worst-case scenario of power demand, which typically leads to an over-

provisioned demand-generation balance configuration. Because of over-provision,

pricing fluctuate and additional costs are incurred, which all end users equally share,
[9]] –[– [12]
regardless of how much they contributed to the over-provision. .

There are multiple potential approaches to mitigate this problem, from whichand

the integration of a battery energy storage system (BESS) is one of the best solutions to
[13]–[16]
mitigate reduce many multi-faced techno-economic problems issues . BESS is

used for multiple purposes in a distribution network, such as load levelling, frequency
[17]
regulation, peak shaving, and spinning reserve .

2
 Given the unpredictable and uncontrollable characteristics of power demand as

well as RES, peak shaving is the most adopted method which improves the power

quality by reducing the unpredictable and uncontrollable characteristics of power

demand as well as tnd RES, peak shaving is the most adopted method which improves

the power quality by reducing the unpredictable and uncontrollable characteristics of

[18]–[22]
power demand andhe uncertainty of power generation in RES . In peak

shaving, the BESS operates as an energy buffer where it accumulates the surplus

energy during low power demand and discharges it back to the network during high

power demand; therefore, the load peaks are removed, which increases the stability
[23]–[25]
and reliability of the grid .

[1.2] Battery Energy Storage SystemConceptual Framework

1.1.1 Battery Energy Storage System (BESS)

The standard term in the business world for a collection of components that connect to

an electrical grid and store energy in batteries for later use is a Battery Energy Storage

System (BESS). Here are the parts that make up a standard BESS: The system for

managing and controlling batteries; an inverter or power converter for alternating

voltage; protective circuits and switchgear; a transformer; and a system for controlling

and managing the system [26]. Decentralization of the grid opens up new opportunities

for users to put stored energy to use, which in turn increases the ROI in areas like

optimizing power quality, minimizing carbon emissions, and keeping operations running

smoothly in the face of power outages or fluctuations [27-29] Benefits in particular:

3
 Users in the residential and commercial sectors can reap the rewards of backup

power, reduced energy bills, and off-grid power when they integrate solar power with

energy storage for load management [30]. S—services related to utilities, such as

frequency regulation and emergency power. When the sun doesn't shine, the stored

capacity of renewable energy can be used as backup power for utilities. With a buffer in

place, renewable energy generation is less likely to be unpredictable [31-35]. Give a

hand with the charging infrastructure for electric vehicles that are unpredictable.

Decrease or eliminate power expenses associated with temporary surges in demand.

In order to facilitate the transfer of power and ensure the consistent operation of

the electrical network, a variety of equipment within the electric system provides

ancillary functions. Energy storage that can supply ancillary services is expected to

have increased demand in the coming decade due to the predicted increase in the

technological necessity for such services [66]. [67] In this article, we will go over a few

of the most important supplementary services that energy storage systems that use

batteries can offer to the power grid. Figure 1.4 shows a timeline summary of the most

prevalent electrical services from.

The capacity of a system to remain operationally stable in the face of a large

disturbance is known as transient voltage stability. (Commonly involving the linking or

disconnecting of massive generators). In order to reduce voltage disturbances brought

about by connecting the doubly-fed induction generator wind turbine to the grid, BESS

was employed in the study by [48].

4
 In the event of a frequency disruption, primary frequency control can assist with

reestablishing the frequency within a matter of seconds. When controlling the main

frequency of a generator, droop control is the method of choice. The ability of BESS to

effectively offer main frequency control during a disturbance was demonstrated in [49].

Under the Voltage Control system service, BESS is used to monitor and control

voltage sags, swells, and other voltage-related issues. Optimal voltage management

with battery energy storage is proposed in papers [50] [51]. Grids with dispersed

generation (renewables) are the ones that are being analyzed.

1.1.2 Grid

"The grid" refers to the electric power value chain, which also goes by the name

ofalso called the electricity value chain. Power generation, transmission, and distribution

are the three main parts that work together to supply consumers with electricity [36-40].

Figure 1 shows the many parts of the electricity value chain, the uses of batteries, and

the functions of the batteries in each part.

5
Figure 1 Electricity value chain (Grid). Battery application and purpose of use at each
segment

Most power sources, including batteries, generate electricity using direct current

(DC), which only flows in one direction. While megavoltage direct current (DC) is used

at the major grid transit points, the majority of the system operates on alternating

current (AC), which allows power to flow in both directions [37, 38]. On the electricity

grid in the United States, the direction of alternating current changes about 60 times

every second, while in other regions of the world, like Europe, it changes about 50 times

per second. As a result, converting DC battery power to AC grid power requires

specialist power conversion equipment [39].

1.1.3 Types of Batteries

A battery energy storage system relies on batteries, which are crucial but not

sufficient on their own. Power conversion devices, such as DC-AC inverters and DC-

DC, AC-DC, or AC-AC converters, or loads directly draw energy from batteries. The

size and power of batteries can vary greatly, depending on whether they are mechanical

(flywheels, water storage, gravity systems, etc.), electrochemical (lithium-ion, sealed

lead acid, etc.), or electrical (super/ultra-capacitors) [40-43].

The following types of batteries are used in various applications: mobile phone

battery storage systems that use 3.7V DC lithium-ion technology; solar generator

batteries that can run small appliances with a few kilowatts of power; sealed whole

house PV installation batteries that contain lithium and lead acid; data center centre

batteries ranging from several hundred watts to 10MW; and finally, batteries that

6
support networks at thousands of volts and have storage capacities in the gigawatt

range [44-49].

No matter the size or shape, the idea behind batteries is the same: they store

energy, which may be utilized to generate electricity in a matter of seconds, minutes,

hours, days, or even longer [50]. Figure 2 shows the most common kinds of grid energy

storage batteries grouped by use case, discharge time (the amount of power a battery

can power a load), and storage capacity. In terms ofRegarding practical use,

mechanical batteries are more common in the 10MW-1GW range, while lithium-ion

batteries are more common in the 1kW-10MW range [51, 52].

Figure 2 Battery types and capacities. Illustrates how they are used and their power
capabilities

1.1.4[1.2.1] Factors Influencing BESS Performance

Size, weight, power, cost, operating conditions, voltage, discharge and charge

rates, and other metrics determine the many uses for batteribattery uses. All of tThese

things work together to influence the engineer's design decisions about which batteries

to use for specific loads [53-55]. One example use case of a battery is shown in Table

7
1, which is based on the battery storage capacity axis in Figure 2, which ranges from 1

kW (1,000 watts) to 1 GW (1,000,000,000 watts) [56-59]:

Table 1 Battery applications and usage across storage capacity segments, 1 kWh - 1
GWh.

Rang 1kWh - 5kWh - 10kWh - 100kWh - 10MWh and

e 5kWh 10kWh 100kWh 10MWh more

Off-grid

solar Residenti Commerci

generators; , al PV al and
Data center
VR power peak residential Utility
UPS;
supply; , shaving, backup, Subtransmissi
Use Subtransmissi
Utility load load on and
cases on support,
vehicle leveling levelling Transmission
and; Telecoms
power, and; and off- and off- Carrier
Backup
Marine grid grid

energyEner power. power.

gy

1.2[1.3] Research Aim and Objectives

8
This This research aims to determine how a Battery Energy Storage System canbasic

aim of this research is to find out how can Battery Energy Storage System be utilized

optimally for Power Losses Minimizloss minimization in an Active Distribution Network.

The objectives are:

 To find out how a Battery Energy Storage System uses the RES Model for Power

Losses Minimization in an Active Distribution Network

 To find out how a Battery Energy Storage System uses Load Model for Power

Losses Minimization in an Active Distribution Network

 To find out how a Battery Energy Storage System uses the BESS Capacity

Model for Power Losses Minimization in an Active Distribution Network

 To find out how a Battery Energy Storage System uses the BESS Location

Model for Power Losses Minimization in an Active Distribution Network

 To find out how a Battery Energy Storage System uses the BESS Operation

Model for Power Losses Minimization in an Active Distribution Network

1.3[1.4] Research Questions

This research will address the following main question:

 How can a Battery Energy Storage System be utilized optimally for Power

Losses Minimization in an Active Distribution Nbattery energy storage system be

utilized optimally for power loss minimization in an active distribution network?

Sub Sub-questions include:

9
  How can a Battery Energy Storage System be utilized according to the RES

Model for Power Losses Minimization in an Active Distribution Network?

 How can a Battery Energy Storage System be utilized according to RES Model

optimallyoptimally according to the RES Model for Power Losses Minimization in

an Active Distribution Network?

 How can a Battery Energy Storage System be utilized according to Load Model

for Power Losses Minimization in an Active Distribution Nbattery energy storage

system be utilized according to the load model for power loss minimization in an

active distribution network?

 How can a Battery Energy Storage System be utilized according to the BESS

Capacity Model for Power Losses Minimization in an Active Distribution Network?

 How can a Battery Energy Storage System be utilized according to BESS

Location Model optimallyoptimally according to BESS Location Model for Power

Losses Minimization in an Active Distribution Network?

 How can a Battery Energy Storage System be utilized according to BESS

Operation Model optimallyoptimally according to BESS Operation Model for

Power Losses Minimization in an Active Distribution Network?

1.4[1.5] Significance of Study

In order to keep off-grid systems and small electrical loads running smoothly and

efficiently, batteries play an increasingly important role in the whole value chain of

power and energy storage [60-63]. The technology is crucial to the generation and

10
delivery of energy globally because of the increasing demand for end-user electricity

and the subsequent rise in the importation of batteries to assist decarbonization, grid

decentralization, and stability while alsogenerating and delivering energy globally

because of the increasing demand for end-user electricity and the subsequent rise in

the importation of batteries to assist decarbonization, grid decentralization, and stability

while reducing energy costs [64-67].

[2.] Literature Review

[2.1] Introduction

Energy storage through batteries is characterized by its wide range of

applications, and can be used by consumer units – in small electronic devices – and by

large plants in electricity distribution, transmission and generation systems. There is a

great diversity of technologies, such as lead-based, lithium-based, sodium-based,

nickel-based batteries, electrochemical capacitors and vanadium flow batteries (redox

vanadium) [68-70].

Batteries can be used for voltage and frequency regulation and control, reduction

of demand peaks, emergency supply, price management, wind and solar intermittency

control, load balancing, grid stabilization and black-start. These applications can be

centralized (on the network or system) or distributed (behind the meter). Centralized use

is programmed and managed by the system operator who, depending on the structure

of the energy market, can be a generation, transmission or distribution operator [71-76].

[2.2] Historical Background

11
 Electrical energy can be generated, transmitted and transformed relatively easily.

However, its storage is still a challenge as it is an impractical, expensive and, at times,

difficult process. This means that energy must be generated according to demand and,

consequently, ERVs will need support from storage systems to be integrated into the

electrical system, avoid energy discharges at times of low load and provide greater

efficiency to the electrical system [77-81].

In a world in full transition from fossil energy to renewable sources, such as wind and

solar energy, an improvement in electrical energy storage would be vitally important to

support these technologies, ensuring that grid systems are balanced and contributing to

taking advantage of make the most of each green megawatt generated [82-85].

Storing energy is not a new topic. According to Greek mythology, to harness the

strength of the winds, the god Aeolus decided to imprison them in an amphora. In turn,

had managed to establish the alternation between day and night by dragging the Sun

with his winged car [86, 87]. Leaving the Mythology aside, history shows that humanity

has been searching for centuries for ways to store and better utilize natural resources

and the energy associated with them [88].

[2.2.1] Development

• 4000 BC: it is said that the oldest structure to store energy resources was the

Jawa Dam, known as the first masonry dam to be built by man to divert the course of

the Nile River and direct water to complex irrigation systems of the time, in order to

transform unproductive regions into rich fertile plains [89].

12
• 247 BC – 224 AD: in 1938 the German archaeologist Wilhem König analyzed an

object that was considered the first rudimentary battery in history, the so-called

Baghdad Battery. The object was a terracotta jar equipped with a copper cylinder, which

in turn contained a single iron bar. Experiments were conducted and showed that the

jar, filled with an electrolyte, acted like an electrochemical cell [90].

• Middle Ages: in order to defend medieval fortifications, logs or rounded stones

were installed on top of walls and hills. In the event of an eventual attack, the

associated potential energy was released, transforming into kinetic energy used to repel

the invaders' attacks [91].

• 1799: Italian physicist Alessandro Volta builds the first battery that could provide

direct current to an electrical circuit. The so-called Volta Pile was the first static electrical

energy generator to be created [92]. The battery consisted of a circuit with two different

metals separated by a piece of fabric or paper soaked in electrolyte. This circuit

generated electrical current. By stacking these components, Volta was able to adjust

the amount of electricity produced according to his wishes [93-94].

• 1866: French engineer Georges Leclanché invents the Leclanché cell. This

device is made up of a metallic zinc cylinder, which functions as an anode, separated

from the other chemical species present in the battery by a porous paper, immersed in a

container with ammonium chloride solution. The cathode is the central electrode,

consisting of graphite covered by a layer of manganese dioxide, which also had carbon

mixed in. This battery provided a voltage of 1.4 V [95].

13
• 1888: German physicist Carl Gassner invents the dry battery, that is, completely

free of

liquids and therefore easily transportable and usable. Its composition is similar to the

Leclanché battery, however its cathode contains powdered charcoal and a wet paste

containing ammonium chloride and zinc chloride [96]. Like the Leclanché battery, this

battery has an acidic character, due to the presence of ammonium chloride, and cannot

be recharged, as an irreversible reduction half-reaction occurs when used [97].

• 1893: English chemist Edward Weston invents an even smaller wet cell. The

Standard Stack of Weston will serve as a reference for laboratories for calibrating

measuring instruments, such as voltmeters [98]. This battery is composed of mercury-

cadmium, produces a voltage of 1.0183V and has a very low temperature coefficient.

The voltage decreased very little over time – about 0.08 mV per year [99].

• 1896: dry batteries begin to enter the market. The National Carbon Company –

now Energizer – begins distributing dry batteries in the American market [100].

• 1900 – 1950: in a trend of evolution and reduction of materials, different materials

for new types of batteries such as zinc-air (1914), methane (1936), mercury (1942) and

the first alkaline battery (1950). The objective is to obtain increasingly smaller products

with the same storage capacity, capable of adapting to different purposes [101].

• 1907: the Engeweiher plant is built in Switzerland, the first reversible

hydroelectric plant, which uses two reservoirs – one lower and one upper – to store

water and, sometimes generating energy through falling water, sometimes pumping

water to the upper reservoir to use again. This type of plant allows energy to be

14
generated at times when energy is more expensive, and at times when energy is

cheaper, water is pumped. It was the first large-scale electrical energy storage

mechanism to be implemented in the world [102-104].

• 1957: the first mercury batteries enter the market. The most common are round

and flat, typical of wristwatches, calculators and other portable electrical devices [105].

• 1963: fuel batteries are used for the first time in the Gemini and Apollo, both from

NASA replacing conventional fuel cells [106].

• 1978: the first CAES (Compressed-air energy storage) which uses nuclear

energy to compress and inject air into two displacement caverns with a volume of

310,000 m³ [107].

• 1990: Weston stack was banned due to new research findings on toxicity of

mercury and cadmium, both elements present in high concentrations in batteries [108].

• 1991: after 20 years of studies, the first rechargeable lithium-ion batteries begin

to be commercialized: they allow large amounts of energy to be stored several times

and are the basis of today's main communication and work instruments. In 2019, John

B. Goodenough, M. Stanley Whittingham and Akira Yoshino were awarded the Nobel

Prize in Chemistry for the discovery [109, 110].

• 2008: the so-called Project Barbados comes into operation in November. This is

the first lithium-ion battery system connected to the electrical grid for strictly commercial

purposes [111].

15
• 2014: Europe's first commercial-sized electrochemical storage system opens in

Germany, near Schwerin. The plant hosts 25,600 lithium-ion batteries to store non-

programmable wind and photovoltaic output [112].

[2.3] BESS Prevalence

According to the United States Department of Energy (DOE) database, there are

767 battery storage system projects currently operating around the world, resulting in

1.79 GW of installed power. The country with the largest number is the United States,

with 321 projects and 793.7 MW, followed by Japan with 42 systems and 242.4 MW. In

South America, the highlight is Chile, which, although it only has three projects in

operation, total 32.18 MW. The largest storage system in operation in the world has a

nominal power of 100 MW/129 MWh and is located in Australia, in the city of

Jamestown, in the Hornsdale wind farm (called Hornsdale Power Reserve). The system

uses Tesla's lithium-ion batteries applied to essential grid support services [113-117].

The largest battery manufacturers are Asian countries, notably South Korea,

Japan and China. The largest of them is LG Electronics, a Korean company with 202.11

MW of battery capacity in use in several projects around the world, followed by the

Japanese company NGK, 178.90 MW, and, in third, another Korean company,

Samsung, with 174.72 MW of batteries installed worldwide. Also mentioned is the

Chinese company BYD, 168.35 MW; Tesla (United States), 143.67 MW; and Japan's

A123/ NEC, 95.78 MW [118-121].

The majority of projects in operation (57.08%) use lithium ion batteries; batteries

with more consolidated technology, such as lead acid, are present in 10.14%.

16
Technologies with a growth trend are lithium polymer, sodium and vanadium redox.

There are 28 systems with lithium polymer batteries in operation (3.89%), 27 of which

were manufactured by the French company Blue Solutions and one by Samsung-SDI.

Sodium ion batteries are already on the market, manufactured by the company Aquion

Energy, with eight projects in operation, totaling 830 kW. Other promising technologies

are zinc air, aluminum ion and sodium ion7 (Irena, 2019a; 2019b) [122- 125].

The current scenario indicates a growing market, with several technological

alternatives, seeking to increase efficiency and reduce costs, resulting in increased

research and development (R&D) activity in this area. From 2000 to 2018, there was a

680% increase in patent applications for electrical storage technologies. Until 2010,

most innovations served portable devices; in 2011, patent applications for electric

vehicles took the lead. In 2018, the number of patent applications for batteries for

electric vehicles reached 738, followed by portable equipment (298) and stationary

systems (94). The most researched technologies are lithium and lithium ion, with 2,547

patent applications, while the other technologies totaled 462 (IEA, 2020) [126-130].

The leading countries in the production of knowledge in the area are South Korea

and Japan. Of the ten main patent applicants related to batteries, nine are based in

Asia, seven of which are Japanese – with 967 applications, led by Panasonic and

Toyota – and two Korean companies – Samsung and LG Electronics, with 986 orders.

Bosch, a German company, is the only non-Asian company to appear on the list, with

186 patent applications [131-133].

17
 The cost of using batteries varies depending on the application, the power of the

system and the type of technology used. The lowest cost for centralized use of batteries

occurs when used jointly with large photovoltaic plants. For this application, considering

a system with 20 MW of storage and 40 MW of solar photovoltaic, the cost of lithium

batteries varies between US$ 108/MWh and US$ 140/MWh; with zinc batteries, the cost

would be between US$115/MWh and US$137/MWh; and the highest cost arises from

the use of vanadium batteries, between US$ 133/MWh and US$ 222/MWh [134-136]

The levelized costs for distributed applications are higher than those for

centralized applications. However, the price variation is similar when used in

conjunction with photovoltaic systems. For smaller systems, such as residential projects

with 0.01 MW of storage and 0.02 MW of solar PV, lithium batteries have the lowest

levelized cost, ranging between US$476/MWh and US$735/MWh, followed by

advanced lead with a cost between US$498/MWh and US$675/MWh; using lead

batteries the cost varies between US$512/MWh and US$707/MWh [137].

As for storage systems installed in consumer units, the applications with the

highest installed power are in commercial and industrial installations, generally to

guarantee autonomy and energy reserve. In this way, there is no associated use of

energy generation, which increases the cost of the storage system. Considering 1 MW

systems, the levelized cost of lithium batteries varies between US$829/MWh and

US$1,152/MWh, while with advanced lead batteries it varies between US$ 1,005/MWh

and US$ 1,204/MWh and with lead batteries the cost is between US$ 1,076/MWh and

US$ 1,225/MWh [138-139]

18
 Battery life is fifteen years for most technologies, except zinc bromide batteries

with a ten-year life. In terms of energy efficiency, the highlight is vanadium redox

technologies with 95% efficiency, that is, 5% loss of stored energy, followed by other

technologies with 90%; the lowest energy efficiency is 75% for zinc bromide. The use of

batteries is part of the low- carbon energy transition agenda, but the disposal of battery

waste at the end of its life cycle must be considered in its emissions balance [140].

Battery recycling is essential for economies to live with the increased share of

lithium batteries. Global battery recycling capacity is currently approximately 180 kilo

tons per year (kt/year), and 50% of this total is carried out by China. Most of the

companies involved are independent refiners, but a broad spectrum of players from

battery manufacturers, original equipment manufacturers, miners and processors are

starting to show interest in this market, especially in Europe. An alternative that should

be considered is the reuse of batteries: those used in electric vehicles, after their useful

life for a quality standard for this application, can, in general, be used for stationary use

in electrical networks [141-143].

[2.4] Battery Energy Storage System

For nearly a century, power systems around the world have focused on three key

functions: generating, transmitting and distributing electricity as a real-time commodity.

Physics requires that generation is always balanced with the load, regardless of its

variability, on time scales of the order of milliseconds. With the increase in generation

penetration from variable renewable sources, as discussed [144], reduction of

generation based on fossil fuels and the increasingly varied consumption profile, system

19
operators are using new methods to maintain the network balance. One of the most

recent and promising methods is the Battery Energy Storage System (BESS) [145].

BESS is a fundamental energy storage system to meet the objective of

decarbonizing activities in the electricity sector. Battery storage systems are capable of

storing excess electrical energy produced by variable renewable plants and their

operation is similar to that of batteries present in our everyday devices [146]. They are

capable of converting a chemical reaction into electrical energy, storing the energy that

will be released as needed, just like a power bank when our electronic devices enter the

reserve. Its participation in the electrical system is proposed as an important approach

to provide ancillary services, improve ERV generation and, consequently, reduce

energy consumption from fossil sources [147].

The BESS is composed of a Battery Bank, a Battery Monitoring System (BMS), a

Power Conversion System (PCS), an Energy Management System (EMS) ), and by

auxiliary components such as sensors and fire extinguishers. A BESS has a useful life

of between 10 and 15 years, which may exceed, depending on construction

characteristics, such as the type of chemical battery, usage patterns, operating and

maintenance conditions [148].

The BMS is responsible for monitoring and controlling battery parameters during

BESS operation. PCS aims to convert alternating current energy into direct current and

vice versa through inverters and converters. The function of the EMS is to monitor and

manage the performance of the BESS and its devices [149].

20
 Maintaining the safety and reliability of the storage system operation is the

responsibility of auxiliary services. The expansion of storage systems is closely linked to

technological innovation and product sustainability [150]. The most popular modalities

are currently based on lithium battery systems, used in combination with other emerging

technologies that will make the storage systems of the future even more advantageous

and with increasingly optimized performance. Despite having a low weight and high

efficiency, one of the biggest obstacles to their greater dissemination in the electrical

power system is their high cost [151].

However, this situation appears to be changing. According to a recent study by

the BloombergNEF organization (BNEF), the cost of lithium ion batteries will reduce

considerably in the coming years, in addition to the 85% reduction that occurred

between 2010 and 2018. More specifically, BNEF predicts a reduction to half the costs

of lithium-ion batteries per kW/h by 2030 as demand increases in two different markets:

stationary storage and electric vehicles [152-155].

This will facilitate energy storage facilities worldwide to multiply exponentially,

from a modest 9 GW/ 17 GWh implemented as of 2018 to 1,095 GW/2,850 GWh in

2040. This significant increase will require an investment of around 662 billion dollars

[156].

The global BESS market offers a large choice of product options, which vary in

chemistry, scale, functionality, intended use and price. Some of the biggest players in

this market are: ABB, NextEra Energy, BYD, Panasonic, Toshiba, Fuence (joint venture

21
of AES and Siemens), Samsung, LG Chem, General Electric, Hitachi, Tesla, NEC

Corporation, Johnson Controls [157].

The purposes of BESS include balancing the network through frequency control,

load shifting, energy reserve, supply reliability, energy quality, contracted demand

management and tariff management. The different applications will depend on the

location of the BESS installation, whether it will be “before or behind the meter”. Each of

these points will be addressed next [158].

[2.4.1] BESS Applications

Before discussing the different uses of BESS, it is important to mention that there

are three areas of application: “behind the meter” systems, “before the meter” systems,

and off-grid systems. Each of these systems will determine the size of the energy

storage project as well as its applicability [159]. “Behind the meter” systems are small

and medium-sized systems installed by individual consumers. These are focused on

aspects of management, reliability and energy quality for the consumer. “Before the

meter” systems are large ones applicable in large generation centers and energy

transmission and distribution networks in order to avoid lines and carry out ancillary

services. Off-grid systems, in turn, are those that operate outside the grid electrical

[160-162].

In California, Southern California Edison (SCE) is a distributor that has been

using a capacity of around 10 GW in energy storage through distributed energy

resources, as an example of “after the meter” applications. A growing number of

22
Californians have adopted distributed energy resource technologies such as solar

generation, energy efficiency devices, electric cars and energy storage [163].

SCE conducted testing to determine the ability of a full-scale BESS to assist with

a load curve flattening and power factor correction service. Curve flattening will help the

distributor avoid operational complexities associated with the process of starting and

stopping plants during heavy load and light load hours, preventing overloading of

equipment in parts of the system and postponing maintenance on transmission and

distribution systems. The use of the battery bank to correct the power factor proposes

the improvement of the voltage profile, the reduction of energy losses and the increase

of load capacity in the distribution circuits [164-167].

In order to synchronize generation assets for grid operation, alternating current

must be controlled within tolerance limits. To carry out this control, the inertia of the

generators and the addition and subtraction of generation in the network are among the

most common methods. Among new methods, the use of energy storage systems

represents great potential for regulating the network frequency [168]. In Kyushu, Japan,

a project that has a 50 MW BESS and a capacity of 300 MWh is being implemented.

When the grid frequency decreases due to high energy demand, the battery is able to

start supplying the stored energy in a few seconds; In the event of an increase in

frequency due to a drop in demand, the battery is recharged with the excess energy.

This is an essential dual function for the stabilization of electrical networks [169]. One of

the benefits of BESS in this application is that the frequency regulation response per

ramp of generation assets is on the order of seconds to minutes, while the storage is

capable of regulating in milliseconds.

23
 Another approach to using BESS is to shift the generation curve by a few hours,

when it is most necessary or to take advantage of arbitrary pricing. A portfolio of three

plants with variable renewable sources, of installed capacity of 723 MW and Physical

Guarantee of 302 MW, will be integrated with distributed energy storage resources

totaling 100MW. In a given week in August 2022, wind generation was lower than

average during heavy load hours. Knowing that generation pricing is valued at the

Difference Settlement Price (PLD) and this varies hourly, the aim is to optimize the

operation by shifting the transmitted energy curve to peak hours [170].

In the early hours of the day, PLD is at its lowest price. On August 16, 2022,

when the lowest generation occurred during peak hours, the PLD was varying between

R$ 67.39 /MWh and R$ 68.48 /MWh. In order to maintain the minimum generation

equal to the Physical Guarantee and knowing that the BESS takes 5 hours to be

charged, the surplus of the Physical Guarantee is used in the first 5 hours of the day to

charge the storage system and is discharged into the times when the PLD is higher.

Based on price distribution, the best window occurs between 4pm and 9pm [171].

Although BESS became quite cost effective in recent years with maturing battery
[172]
technology , its high capital investment combined with operational cost inculcates

the need for appropriate designs to optimize the integration of BESS into an active

distribution network (ADN), a distribution network that has a combination of distributed

energy resources (DERs) such as diesel generators and renewable energy sources, in
[173]–[174]
order to harvest its most benefits in resolving techno-economic problems . In

this respect, optimal integration of BESS is an important parameter that inevitably

quantifies the efficacy of the BESS with respect to its applications in demand-side

24
management, energy markets, and large-scale of RES deployments [175]. Optimal

integration of BESS is established based on three prime parameters: optimal

placement, optimal capacity and optimal operation.

[2.5] Optimal Placement

Integrating a BESS at sub-optimal locations in an AND causes additional

problems such as increment in system loss and voltage congestion. Therefore, optimal
[176]
placement of BESS is critical . In order to reach an optimal placement of BESS in

an AND, BESS should be allocated such that it can reduce most of the system losses.

Several studies in the area of optimal placement of BESS have been

successfully concluded. However, the idea of optimal placement of distributed

generation as well as shunt capacitors serves as a cornerstone for numerous methods

[177]–[178]
for optimal placement of BESS in a power distribution network .

In [36], the optimal allocation of BESS is achieved by using non-radial distribution

system for voltage regulation. Likewise, a technique is established based on

classification of distribution main transformer (MTr) in order to identify the optimal BESS
[179] [180]
allocation in a grid is introduced in . The study in presents a methodology to
[181]
reduce the feeder losses based on an optimal BESS placement. The study in

presents a voltage sensitivity approach that relates the distance between the load and

power supply in determining the suitable network buses for the integration of BESS that

contribute towards the improvement of the power quality of the network. Similarity, the
[182], [183]
greedy algorithm framework proposed in suggests the optimal allocation of

BESS based on the loss sensitivity analysis in order to decrease the network losses.

25
 A battery is made up of several electrochemical cells that are connected in series

and/or parallel to achieve a certain level of voltage and capacity of storage, respectively

[184]. The cell as a basic electrochemical unit is responsible for providing a source of

electrical energy through direct conversion of chemical energy. This is made up of 3

main components [185]: Anode - Corresponds to the negative electrode that transfers

electrons to the external electrical circuit and is oxidized during electrochemical

reactions; Cathode - Corresponds to the positive electrode that accepts electrons from

the external electrical circuit and is reduced during electrochemical reactions;

Electrolyte - Corresponds to the ionic conductor that provides the medium through

which there is an exchange of ions between the anode and the anode, while the

electrons flow through the external circuit. Batteries also have an important component

that is the separators are normally made up of a porous material that allows the

exchange of ions but which guarantees electrical isolation between the anode and the

cathode. An electrolyte must present high ionic conductivity, however it must not

present electrical conductivity. It must not reveal reactivity with the materials that make

up the electrodes, it must be safe and show slight or no changes in its properties when

faced with temperature fluctuations. The cathode, in turn, as an oxidizing agent must be

efficient in this function and be stable in contact with the electrolyte. Finally, the anode

must be efficient as a reducing agent, present a high load transfer per unit of weight

(Ah/g) when operating the battery and be stable. Therefore, the right choice of these

three components of a cell is one of the main factors in defining a successful battery.

However, this ideal combination of properties is reflected in the cost of the batteries

[186]. It is mainly the different combination of materials of these three components that

26
justifies the existence of different types of batteries with different characteristics and

applications. ˜ The set of cells is sealed and connected in order to generate cell

modules that, connected together in an enclosure, create a battery ready to be

connected to an external source or load. However, a BESS is not limited to just the

battery [187]. A BESS contains, in addition to the battery itself, a series of other

components responsible for monitoring and controlling some properties of the battery or

container, as well as circuit breakers and power electronics, namely the power

conversion system (converter). The main objective of existing monitoring and control

systems is to manage battery operation in order to maximize its technical-economic

performance and guarantee the necessary safety conditions for the correct functioning

of the system. Some of the main control actions include ensuring that the discharge

depth of the cells does not exceed certain limits defined by the system operators or that

the cells do not become overloaded and heat up am, controlling their loading and

unloading. There are several particular control strategies for each battery technology

according to the main drawbacks to which its use is subject. In the particular case of

lithium batteries, they require particular monitoring and control of their operating

temperature. Given the combustibility of lithium, operation at high temperatures can

lead to the battery overheating and potentially igniting, hence the importance of having

a temperature control system [188]. The power conversion system is a fundamental

component of the battery. This is responsible for the ability to transmit energy

bidirectionally between the grid and the battery [189]. The battery is a system that

delivers electricity in the form of direct current (DC). Therefore, for interconnection with

the electrical network to be possible, which operates in alternating current (AC), a power

27
conversion system is necessary. The converter to be applied must be capable of

functioning as a rectifier in a battery charging situation, converting the AC current into

DC so that energy can be stored in the battery. In the discharge situation, on the

contrary, the DC current that comes out of the battery terminals has to be converted into

AC so that it can be used by the grid, with the converter having to behave like an

inverter. Hence the reference to the need for an inverter with bidirectional behavior.

[2.6] Optimal Capacity

The optimal capacity of a BESS is not less important than its optimal placement.

Plenty of studies recommended that the capability of a BESS is improved by using the
[190]–[194]
optimal capacity . In fact, network losses and voltage congestion increase
[195]
with the integration of undersized or oversized BESS .

An intelligent algorithm using grasshopper optimization (GO) technique is

[196]
proposed in in order to establish an economic framework of BESS sizing in a RES

standalone microgrid. The authors consider a hybrid power system that consists of solar

and wind RES as well as diesel generators and a BESS that aims to supply power to

five residential load demands. This research considered the probability of power supply

deficiency and low energy cost. The proposed GO technique is found to be effective,

where it was compared against two heuristic algorithms namely: cuckoo search

optimization and particle swarm optimization.

Furthermore, the study in [182] posits a methodology for voltage regulation

through a technoeconomic placement of a BESS in a radial distribution network with

high PV penetration. However, maintaining a reduced system losses in the presence of

28
high PV variability can be ideal only with the optimal size of the BESS. This proves that

the optimal capacity of BESS is not less important than its optimal location.

Furthermore, the authors in [197] presented an optimal capacity utilization of

BESS by establishing a non-linear dynamic programming approach for optimal sizing

and scheduling of BESS in wind farms. This research considers the depth-of-discharge

and lifetime of BESS to formulate the optimization framework. The authors present the

validity of the proposed methodology considering standalone and grid-connected

scenarios of wind diesel penetration power networks.

The study in [198], an economic oriented BESS utilization considering technical

efficacy in terms of peak regulation and system profits through optimal sizing of a

vanadium flow batteries (VFB). It is found that BESS built using VFB are bed to have a

capacity ranging from 17 to 40 MWh and efficiencies of about 70–80% in order to be

[199]
used in power systems .

Despite the predominance shown by hydroelectric pumping systems in terms of

installed storage capacity globally, there is widespread recognition that batteries can

present a series of interesting opportunities (as BESS) to the network. BESS are an

energy storage technology that presents unique versatility, allowing them to be applied

in the most diverse areas, from the industrial sector to everyday life. The hope placed in

the imposition on electric/hybrid vehicles, the need to create portable devices with

prolonged operation and the increasing use in electrical energy networks have been

some one of the main reasons for the accelerated growth of the battery sector in recent

decades [200]. With the growing interest in renewable energies, the use of power

29
electronics in generation/transmission systems has become increasingly fundamental

[201]. Thus, with the evolution of technologies associated with batteries and converters,

it is possible to present BESS that have multiple application possibilities. Compared to

the BESS presented in the next section (Section 2.8), batteries are the technology that

stands out for their modularity, scalability and ability to serve a series of different

applications, throughout of various time periods from seconds to hours. These

applications can range from auxiliary quality of service services to energy management

applications. The versatility of a battery from an operational point of view is one of the

main arguments for being the technology chosen to accommodate the integration of

renewable sources into the grid. Optimizing the variable injection of power resulting

from renewable sources, absorbing or injecting power according to the load/generation

balance or responding quickly to voltage dips are just some of the uses in which a

battery can participate. Thus, this combination of 19 applications ends up benefiting the

installation in economic terms [202]. Not all batteries are the same and since they are

composed, in part, of chemical agents, their performance is dependent on their

electrochemical characteristics. The batteries that are of interest for this type of

applications are secondary batteries (rechargeable), since primary batteries are

incapable of being recharged, as such, they are ineffective. Useful for BESS that require

several cycles of use. Among the secondary batteries, those that deserve the most

attention are the lead-acid (PbA), lithium (Li-ion) and nickel-cadmium (NiCd) batteries,

which are part of the so-called low temperature and sodium-sulfur (NaS), known as

high-temperature batteries [203]. Of the existing low-temperature battery technologies,

the one with the highest installed capacity in electrical energy systems is lead-acid

30
batteries, largely due to their great commercial and technical maturity and consequent

reduced investment costs [204]. Despite this, lithium-ion batteries have proven to be the

technology that technically presents the best characteristics. The biggest problem is the

BESS technology, which generally presents a higher investment cost. This is the main

factor that has been responsible for hindering the imposition of lithium batteries as the

dominant technology among secondary batteries. There are several global lithium

battery projects, among other technologies, that demonstrate the ability of batteries to

successfully facilitate and assist the transition to an energy system with greater

penetration, renewal and renewal. Some of these cases are presented). In recent years,

the BESS market has recorded significant growth. According to a study carried out by

the consultancy Navigant Research, in 2014, revenues from batteries used in

applications in the energy sector across the globe were around 200 million euros and

are expected to reach 2023 reach 16 billion euros [206]. With this growth in the battery

market, their costs have varied inversely, decreasing. The increase in the

implementation of batteries in electrical networks to support the integration of renewable

energy combined with the increasing development of electric vehicles has had a direct

impact on progress and advancement from a technological point of view. ´ logic ´ of the

batteries. These factors make mass production a reality that allows battery costs to be

reduced, which has particularly benefited lithium-ion battery technology. Proof of this is

the fact that lithium batteries have seen the sharpest drop in cost in recent years [207].

Technologically, increasingly advanced, improving their performance in terms of life

cycle and efficiency, and economically increasingly attractive, lithium batteries are a

safe bet among BESS. The following subsections present the main components of a

31
battery, its operating principles and an overview of the main technical characteristics

that distinguish the 2 main secondary battery technologies: ion batteries lithium and

lead acid.

[2.7] Optimal Operation

The control theory to achieve optimal operation of BESS is important to enable

[50]
its accelerated applications in the modern power system . Optimal operation

ensures proper charging and discharging of the BESS and subsequently prolonged its
[208]
lifetime .

Numerous studies have proposed novel operational control schemes for energy

storage. A novel technique to eliminate the imbalance between Lithium-ion (Li-ion)

[209]
battery cells is presented in in order to improve the state of charge (SoC)

balancing of the battery pack. This study proposes a dual screening technique, namely

capacity screening and resistance screening, to determine the stable battery

configuration that can be used in accordance with the system requirements. The

relationship between the open circuit voltage and the SoC that is important for SoC

balancing is established by the capacity screening. Furthermore, by applying pulse

charge/discharge currents at multiple SoC points, resistance screening finds the battery

cells with comparable voltage variance. Reference to the experimental analysis that is

presented, the suggested methodology, with an extended BESS lifetime and stable

configuration, guarantees battery security and protection against sudden failures.

A power frequency (P-f) droop control theory for active power deployment and a
[210]
reactive power-voltage (Q-V) droop control strategy presented in in order to

32
dispatch the reactive power support from BESS are the basis for the SoC balancing.

Plug-and-play functionality of the BESS and the suggested approach are made possible

by the suggested decentralized control framework, which eliminates the requirement for

cutting-edge communication. Moreover, the authors attain a higher level of controllability

over the charge/discharge of BESS with improved reactive power sharing and SoC

balancing among BESS in contrast to traditional droop control theory. Comparably, the

idea of BESS monotonic operation assumes that BESS are used in a coordinated and

optimal manner according to their specific applications while simultaneously keeping a

smooth charge/discharge curve that guarantees the best possible use of their life cycle
[211]–[214]
. Installing a minimum of two batteries in the BESS is necessary for its

continuous functioning. Grid variability, such as load variation, is used to identify the

BESS's operation mode (charge or discharge), along with its capacity.

Furthermore, the research study in [215] proposes a SoC balancing technique

based on sliding mode control theory and hierarchical control strategy for multi-module

BESS. The control framework that is being presented creates a central controller that

controls the output power of the BESS by using the generated and identified nominal

reference value. Based on generated values and in conjunction with interconnected

BESS local communication and SoC values, a coordinated output power from the BESS

is extracted to maintain a balanced SoC. The authors argue that the suggested control

strategy, as opposed to a conventional linear controller, preserves the

charging/discharging integrity of BESS by selective continuous charging or discharging,

which guarantees a specific optimal life cycle utilization of BESS in accordance with the

load demand of the system.

33
 A dual property of monotonicity for BESS operation is introduced in the study in
[216]
. The authors take into account BESS integration in order to reduce load demand

at peak times. As a result, the first monotonicity property determines the state of the

BESS according to the load and system requirements at a specific moment. The second

monotonicity property guarantees load smoothing and peak clipping by coordinating the

load demand based on the processed load profiles. The authors postulate that

expanding the size of the BESS further improves the peak clipping operation of the load

profile. This hypothesis is reached after testing and validating the suggested approach

on a stand-alone power network.

The authors in [217] propose monotonic operation of BESS for smoothing the

output power of wind energy based electric power generators. The presented

framework is designed with a large-scale wind farm integrated with large BESS that are

connected in parallel to fulfill the corresponding load profile. The author designed a

model predictive controller (MPC) based on the formulated monotonic equations that

generates an error signal of power surplus or power deficit in order to request BESS

intervention of charging or discharging. By coordinating with the grid and wind farm, the

MPC selects the suitable battery in the BESS in order to fulfill the load requirement.

Using actual Australian market pricing data, a subjective and economic analysis of this
[218]
approach is provided in , confirming an increase in revenue for large-scale wind

farms based on the monotonic operation of BESS.

The optimal placement of BESS is achieved through the voltage sensitive

approach. The prime difference of locating the BESS in this research from these
[182]–[186]
methods presented in is that they are not tested in large-scale distribution

34
network. This research tests the voltage sensitivity approach method for optimal BESS

placement on a 14-bus distribution network that includes different types of RES.

Similarly, the optimal capacity of a BESS is determined by analyzing the power

demands of the system based on the concept of the curves arriving in the network
[213]
calculus . A fundamental difference between our work and the previous works in
[198]–[180]
is that we consider the load inelastic which must be instantly fulfilled. In

addition, the peak demand is to be fulfilled by the RES and the BESS integrated to the

distribution network while the power supply remains unchanged. The RES as well as

the BESS are accordingly considered as distributed generation (DG) units. We

accomplish implementation of peak shaving by first forecasting the load and then

integrating the PV systems and the BESS.

Furthermore, the lifetime of a BESS is severely affected during the intensive

[216]
charging and discharging process in peak shaving . Therefore, the BESS in this

research is controlled in a way that facilitates a smooth charging and discharging curves

of the battery from the lowest to the highest point, or from the highest to the lowest

point. Meaning that a battery in the BESS will not discharge until it is fully charged and

will not charge until it is fully discharged. This control methodology is referred to as

monotonicity: monotonic charging and discharging. A fundamental difference between

[214]–[217]
our work and the previous works in is that we consider four batteries

integrated into the BESS, instead of two batteries, in order to ensure monotonic

operation for the worst case of power demand.

35
 Secondary battery cells have the ability to be recharged by storing electricity in

the form of chemical energy. As seen previously, each cell is composed of a positive

and a negative electrode where oxidation-reduction (redox) reactions occur. These

electrodes, in turn, are connected by an external circuit that allows the circulation of

current (electrons), which occurs due to chemical reactions that occur simultaneously in

both electrodes. These chemical reactions, in turn, are reversible reactions allowing the

battery to be recharged by applying an external voltage to the battery terminals [219]

The typical operating principle of the battery is explained in its 2 operating modes,

discharging and recharging: ˜ Discharging: in the process of discharging a battery, the

electrons accumulated by the anode (reducing agent) are given up and flow through the

external circuit to the cathode where they are accepted and consequently the cathode

material is reduced. The complete electrical circuit ends with the exchange of positive

ions (cations) for the anode and negative ions (anions) for the anode. “Charging: in the

process of recharging a battery, the methodology is the opposite of that which occurs in

the discharge operation mode. It is worth noting that when the battery charges, the

polarity of the anode and cathode changes, that is, the anode becomes positive and the

anode becomes negative. Thus, the current flows in the opposite direction, oxidation

occurs at the positive electrode and reduction occurs at the negative terminal. By

definition, the anode is the electrode where oxidation occurs and the anode is the

electrode where reduction occurs. Thus the positive electrode is now the anode and the

negative electrode is the anode [220]. The charge and discharge cycle of a battery can

be repeated several times, since the chemical reactions that occur in batteries are

reversible. Even so, despite being made of elements that can be recombined

36
repeatedly, rechargeable batteries have a certain useful life. With use, they begin to

gradually lose their ability to retain their charge, as discussed in the following section.

Characteristics such as power/energy density and specific power/energy that affect the

volume/size of the battery and the nominal power that affects the power that the battery

can inject. Also, storage capacity, system cost, performance and self-discharge are

important considerations when selecting a battery. Now, in this section, the approach is

taken taking into account that the chosen BESS is a battery. Therefore, it is interesting

to investigate the specific attributes and characteristics of the different BESS

technologies that have not previously been explored, such as the depth of discharge

(PD), the memory effect, operating temperature power and voltage to the cell terminals.

Batteries, because they have chemical components, are storage systems that are

greatly influenced by the conditions in which they operate, which can affect their

technical-economic performance, cost and useful life. The PD corresponds to the

amount of energy that can/should be used in relation to the total capacity of the battery.

It is notable that the higher the PD of a battery, the shorter its useful life, which

corresponds to a lower number of cycles that it is capable of completing. Typically, for

high discharge depth values (>85%), the lifespan of a battery is considerably reduced,

this is because it contributes to the degradation of the cells. Often people also talk about

state of charge (SOC), which is an exactly opposite concept, that is, a SOC of 100%

corresponds to a PD of 0% [221]. The memory effect is another of the typical

characteristics of a BESS, however not all of them present such a drawback. This effect

is reflected in the considerable decrease in the effective capacity of a battery when it is

continuously subjected to a certain incomplete charge and discharge cycle. The fact

37
that a battery is repeatedly recharged without being completely discharged, that is, with

a PD less than 100%, affects the electrochemical properties of some battery

technologies. The technology that most suffers from this effect are nickel-cadmium

(NiCd) batteries, on the other hand, lithium-ion batteries do not have a memory effect

[220]. Regarding the operating temperature, its control is essential for prudent battery

operation. Although batteries have a relatively wide range of operating temperatures,

generally between 0 and 45 ˜ oC, for the charge/discharge process to be efficient the

range must be shortened. Especially when recharging the batteries, the situation is

more delicate. 24 The increase in operating temperature reduces the voltage at the

battery terminals, which makes the recharging process more inefficient and time-

consuming [222]. Additionally, operation at high temperatures leads to cell degradation,

loss of effective capacity, corrosion, gas emission and consequently a reduction in its

useful life cycle. Due to all these factors, many battery system installations have

integrated temperature control and management systems in order to guarantee safety

and optimize their performance [223]. Finally, each technology presents typical nominal

voltage values at which its cells are discharged/charged, which depend on the potential

difference generated by the materials used in the anode and anode. . The voltage that

appears at the terminals of a cell, at any time, depends on the load current, its internal

impedance, the operating temperature, the SOC and the aging of the cell [224]. During

discharge, the voltage at the battery terminals tends to decrease while during

recharging it tends to increase. The typical characteristic of the voltage along the cell

discharge is particular to each technology, and may present a more or less pronounced

slope. Lithium batteries are the technology that presents higher operating voltage

38
ranges and also whose voltage at the terminals is more invariable in the discharge

cycle, which allows for faster and long-lasting operation of these battries.. Batteries

whose discharge curve slopes are steeper, as is the case with lead acid batteries, result

in a decrease in the power delivered during the discharge cycle, which is inconvenient

[225] [226]. These are some of the main considerations when selecting a battery.

Additionally, for the battery to be recommended, the performance requirements required

by the intended application must be taken into account. For example, for regulation and

mitigation applications, it is important to have a fast response time and the ability to

withstand a high number of charge/discharge cycles. Alternatively, in energy

management applications where it is necessary to “shift” the energy over time, the

charge/discharge cycles must be capable of being extended over time 227]. In this

sense, the following subsection addresses the main applications in which batteries can

be advantageous.

[2.8] Applications to aid the integration of renewables

The applications in which a BESS can assist can be grouped into three different

categories according to their nature. This sub-section presents the main applications for

which a battery is suitable. BESS can be used in any of the existing groups of

applications covering periods of operation from milliseconds to a few hours. These

applications, as a general rule, are associated with the need to compensate for the

variable nature of wind and solar energy [228]. From the contribution to auxiliary

services, such as the regulation and control of frequency and voltage of networks with

high renewable penetration, to the contribution to balancing load/generation supplying

or absorbing energy when imbalances occur, are applications that a battery can

39
perform. The fact that a single BESS is capable of multiple uses, as is the case with a

battery, benefits the economy of the installation [229]. One of the vital reasons for

installing a battery is to contribute to a reliable increase in renewable penetration. In the

particular case of an island, this issue takes on a greater dimension and batteries are

seen as a unique opportunity from a technical, economic and environmental

perspective. As the islands generally have a high renewable potential, have electrical

systems that are dependent on conventional generation and lack of interconnections,

they benefit from the installation of a storage that allows the reliable integration of

renewables, that provides flexibility of use and allows the reduction of dependence on

fossil fuels. It is evident that with the installation of a battery the dependence on

conventional generation decreases 26 at the expense of an increase in renewable

penetration. The battery ensures load/generation balance and stable grid operation in

parallel with maximizing renewable penetration. Additionally, the batteries also function

as a reserve, thus helping to satisfy generation losses or load variations. The fact that a

battery does not have rotating masses (kinetic energy) means that the concept of

rotating reserve is not fully applicable in this case, which is why it is usual to refer to this

reserve as non-reserve. Another type of application consists of accommodating the

variability characteristic of renewable sources and consequently of their energy

production. In this case, the batteries act on the generation side of renewable energy.

As the wind or solar irradiance has a stochastic and variable character, it presents more

or less severe fluctuations over the hours, which affect the stability of the electrical

system. The use of a battery makes it possible to smooth out the fluctuations typical of

renewable production through the absorption/injection of power in short periods. This

40
way the injected energy is more level, allowing the network to operate in a stable and

reliable manner [229] In red we have the power generated by the Photovoltaic Panel

( PV) on the AC side, in black we show the power imposed by the battery and in blue,

the resulting power that is injected into the network with the battery accommodating the

fluctuations. Below is the variation in energy stored in the battery over time. It appears

that the necessary charge/discharge cycles are many, however the depth of discharge

in this type of applications is generally reduced. Less frequent, but feasible, is the use of

batteries for applications related to energy storage over long periods of time. Among

them there is seasonal storage (months) and temporal displacement of energy (a few

minutes to hours). In the latter, the objective is to take advantage of the capabilities of a

battery in order to benefit technically and economically. On the one hand, it allows

renewable installations to remain in service even when their generation exceeds

demand, using the battery as a load, absorbing excess production. ˜ 27 On the other

hand, it allows for economically justifiable storage in periods when demand and the

price of electricity are low so that it can later be injected into the grid. The energy stored

in the battery can then be injected during a period when electricity demand and prices

are higher, which generally occurs at the peak. Temporal displacement includes

applications such as peak management or load leveling, both of which are a way of

smoothing the typical hill and valley shape of the demand curve, thus allowing

generation at most constant [221]. Finally, batteries are recognized as a notable BESS

in collaboration in ancillary services. ˜ Voltage and frequency regulation and control

support are the main applications included here. In these applications, response times

must be fast in order to remedy imbalances in active and/or reactive power that result in

41
instabilities in the network, which, if ignored, can lead to load shedding and ultimately

case, to the collapse of the network. Regarding frequency control, the strategy used is

to maintain a balance between the active power generated and consumed. As such, the

most commonly used technique in batteries is similar to the speed regulator strategy of

a conventional machine, through static control. Therefore, depending on the frequency

deviation that occurs in the network, the battery is capable of injecting or absorbing

energy in order to balance the frequency. In turn, batteries have faster response times

than conventional machines, which benefits the grid in quickly and accurately

compensating for the frequent fluctuations of renewable generation, maintaining the

system frequency within the required limits. High frequencies indicate excess

generation compared to the load, which causes the battery to charge to repair such

disturbance, on the contrary, low frequencies indicate a generation deficit compared to

the load, so the battery provides power in response. ˆ With regard to voltage control,

this aims to maintain the network voltage within acceptable limits, guaranteeing its

stability, since operation outside the limits can cause damage and affect performance if

it continues over time [230]. This control is based on the management of reactive power

that is injected into the network. The reactive power capacity is 28 of the battery is

associated with the capacity of the energy conversion system to which it is connected. It

is power electronics that provides the ability to regulate voltage, largely independent of

active power supply or consumption. A four-quadrant converter through high switching

speed makes it possible to independently control the active and reactive power injected

with the possibility of generating and absorbing both by the battery. This functionality is

extremely valuable for improving voltage stability [231].

42
[2.9] Lithium Batteries vs Lead Acid Batteries

It is considered important to compare the two low temperature battery

technologies that have greater relevance in the electrical market, namely power

systems energy storage: lead-acid and lithium batteries. If in the former we can see an

unrivaled maturity reflected, in the latter a promising future is projected based on the

technical-economic successes achieved. Lead acid batteries are the cheapest and most

mature rechargeable battery technology among the different types of batteries on the

market. Generally, the cathode is composed of lead dioxide (PbO ´ 2), the anode is

composed of spongy lead (Pb) and the electrolyte is made of liquid sulfuric acid (H ´

2SO4) [232]. The nominal voltage of a typical PbA cell is around 2V [233]. Lead acid

batteries are characterized by having a relatively low cost compared to other battery

technologies, around $50-$400/kWh [234]. On the other hand, the efficiency of this

technology is in the order of 70-85%. The short response time, the advantageous

cost/performance ratio and the relatively low self-discharge are also characteristic

factors of this type of batteries. These can be used in energy management applications,

due to their low self-discharge which makes them suitable for energy storage

applications over longer periods of time. On the other hand, there are some factors that

limit its more widespread use. The degradation of its performance and the effect on its

lifespan depend on the operating temperature, making it necessary to have a

temperature control system, which increases the costs required. But the main

disadvantages of this technology are the low specific energy (25-50 Wh/kg), the high

toxicity of lead and the reduced useful life, between 600 and 1800 cycles, depending on

the depth of discharge [21]. These batteries can currently be found, already integrated

in some installations such as, for example, in Chino, California, where a PbA battery
43
with 10MW of nominal power and a capacity of 40MWh is responsible for providing

reserve rotating and for participating in load leveling applications [31]. As for lithium

batteries, the first commercial ones appeared in 1990, produced by Sony [15]. Since

then, this technology has shown notable technical-commercial growth, and can currently

be used in a wide range of applications, from cell phones to medium/high power

applications for storage systems in electrical systems (electric vehicles or electrical

grid), with capacities that can reach 30MWh [22]. These are batteries in which the

anode is made up of lithium metal oxide, the anode is made of graphitic carbon and the

electrolyte is based on a solution of lithium salts. lithium with a mixture of organic

solvents. The nominal voltage of a typical lithium cell is around 3.7V [19], being higher

than other battery technologies, which means that the number of cells to interconnect in

s erie to obtain a given operating voltage and lower. The main characteristics of lithium-

ion batteries are the high specific energy (75-200Wh/kg) superior to any other

technology, fast response times and high performance (80-97%) [15, 20]. The life cycle

may vary depending on the PD to which the battery is subjected, but as a rule, it is

between 1000 and 10000 cycles [15]. The typical self-discharge observed is 85%)

without significantly affecting the performance and lifespan of the battery. Furthermore,

the operating temperature is also a factor that can influence the lifespan and safety of

lithium batteries, if this is not controlled through monitoring cell temperature. However,

the two main disadvantages of this technology lie in the high price of batteries and their

safety. Batteries with high capacities have a high cost (>$600/kWh), which is the main

obstacle to their commercial growth in large-scale systems. The high cost is related to

the need for cells to have internal protection circuits against overloads. ˜ Overloads are

44
prone to occurring in lithium batteries due to the high energy density and combustibility

of lithium, which leads to the possibility of overheating of the cells, putting the safety of

the installation at risk to the. ˜ Li-ion batteries are seen as the main candidates for

applications where short response time and small dimensions are determining factors.

Currently research and research focuses on increasing the capacity of lithium batteries,

reducing their cost and increasing their useful life. AES Energy Storage, a company

based in the USA, has already been responsible for installing several lithium ion storage

systems, including one in Laurel Mountain, where a battery system was installed lithium

battery with a nominal power of 32MW and a capacity of 8MWh to support a wind farm

with 98MW, providing flexibility in stabilizing the grid and allowing energy management

[20]. It is clear that from a technical and operational point of view, lithium-ion batteries

have a clear advantage over lead-acid batteries in terms of performance, power/energy

density, specific power/energy logic, useful life cycle or voltage at the terminals of high

cells. The biggest problem arises from an economic point of view in that lithium batteries

appear as the technology that requires the highest investment among batteries.

However, as already mentioned, it has been verified that the trend and so that with the

imposition of lithium batteries as one of the solutions with the best practical results

among BESS, their prices fall with the increase in production and emergence of

economies of scale.

[2.10] Comparison between Technologies

The comparative study between the technologies mentioned in the previous point

is complicated, as not all are suitable for the same types of applications. Initially, it is

important to carry out a general survey of certain characteristics, such as cost, capacity

45
storage, autonomy, discharge time, energy and power density, performance, durability,

among others, for all existing technologies. Based on this idea, the comparison among

storage technologies is divided into the following characteristics [235]:

• Application Fields;

• Power, energy and discharge time;

• Income and lifespan;

• Weight and volume density;

• Energy and power density;

• Investment costs;

[2.10.1] Application Fields

Regarding the fields of application of storage technologies, these are divided into

three large groups:

• Quality of service: the stored energy is applied for a few seconds, to

ensure continuity of service quality;

• Binding power: The stored energy is applied in an interval between

seconds and a few minutes, to ensure continuity of service, when

switching between energy sources;

• Energy management: Storage is used to decouple energy generation

from consumption, particularly for load leveling;

The application fields appear related to the power level of each storage

technology. The most suitable technologies for power quality applications, which are

those with lower power levels and faster discharge times are high-power

46
supercapacitors, Flywheels and some batteries (those with a faster discharge time).

Batteries with higher discharge rates, together with magnetic superconductors, are

indicated for connection power applications, while compressed air systems and

pumping, due to its high size, power and discharge time, are the most suitable for

energy management application fields.

[2.10.2] Power, energy and discharge time

In the following analysis, the energy and power that each storage device has.

The arrangement of technologies depending on the mentioned parameters, also adding

the response time. In a brief analysis, it appears that energy storage technologies

potential (CAES, PHS), together with fuel cells (hydrogen), are the ones that they have

greater energy and power and a slower discharge time. The opposite situation occurs in

magnetic superconductors, Flywheels and supercapacitors, which have the lowest

energy and power ratings and the fastest response time. Most batteries existing ones

presents average values of the three parameters under analysis.

[2.10.3] Yield and lifespan

Another comparison that is made between technologies is their positioning in

relation to the income and lifespan of each one. Yield can be defined generically, as the

relationship between the energy supplied and the energy stored. However, the notion of

yield in storage technologies is complex and can be defined in different ways, according

to the process in question, the elements taken into account and the period of time in

study. Since storage devices have losses during periods of loading and unloading, with

also losses due to self-discharge, the analysis considers a cyclical yield, that is, the

comparison between technologies is made with their yield in depending on the number

47
of charge/discharge cycles to which each one may be subjected [2]. It should be noted

that this analysis does not consider the duration of each cycle, that is, a device with a

higher number of cycles than another, does not necessarily mean that it has a higher

life.

[2.10.4] Weight and volume density

In certain types of applications, the weight and volume of devices can also be

considered to assess the viability of a given application. Therefore, technology storage

are classified depending on energy availability and maximum power per volume

(volume density) or per kilogram (weight density).

[2.10.5] Energy and power density

Regarding energetic properties, storage technologies are divided into high

energy and high power technologies, depending on their application. Based on

graphical interpretation, it can be seen that the energy and power densities are

inversely proportional to the volume of the devices. Compressed air and hydro systems

pumping systems, which are the most voluminous technologies, present power

densities and energy, as opposed to most battery technologies available. They exist

also technologies that present disproportionate density values, such as magnetic

superconductors and supercapacitors that have higher values of power density, while

fuel cells present high values only of energy density.

[2.10.6] Investment costs

As with any application in terms of technology, the economic factor is It is also

considered in the area of energy storage. The cost is influenced by amount of energy

48
that the device can store and its maximum power, being still influenced by the cost of

building the device itself. The comparison is made based on in the price per unit of

stored energy (kWh) and in the capital cost (kW).

Within the price range presented, it appears that most batteries present average

values in terms of energy cost (around hundreds of €k/ Wh), and medium/high values

for the capital cost (values that can range from €300k/ W for lead acid batteries, up to

€3000k/ W for ion batteries lithium). Magnetic superconductors, supercapacitors (high

power) and Flywheels (high power), present the highest energy cost (values that can go

up to 8000 €k/ Wh in the case of magnetic superconductors and 5000 €k/ Wh in

supercapacitors and Flywheels).

This economic analysis can be complemented by considering the cost of energy

per cycle charge/discharge of each storage technology. These conclusions are directly

related to the life cycle of each technology.

[2.11] Gap Analysis

After analyzing the fundamental parameters of the storage technologies under

study and from the comparison between them, the main conclusions of the analysis of

the state of art. Flywheels, Supercapacitors and magnetic superconductors have high

performance and very fast response time, but they are the ones with the most energy

expensive (€k/ Wh) due to its low energy density and consequently faster

charge/discharge cycles. It is important to note that the energy storage time of

Flywheels are limited mainly by high friction losses due to the presence of the machine

rotary.

49
 Reversible water use is by a large margin the one with the greatest capacity of

storage, however presenting a very low energy density. These together with

compressed air storage and fuel cells are the technologies that can supply energy for

longer periods of time. Regarding batteries, lithium-ion, nickel-cadmium and sodium-

sulfur batteries appear inserted in applications with higher power density. Within this

group, ion batteries lithium are the most promising for use as they also have a high

density of energy and higher performance. Despite their good energy characteristics,

they present as inconveniences a high cost) and the inability to discharge 100%.

Nickel-Cadmium and lead-acid batteries have a relatively high, but their use is

limited due to their high self-discharge rates and the fact containing toxic heavy metals

in their composition. Sodium-sulfur batteries, despite having much smaller in size and

lighter than nickel-cadmium ones, they operate at temperatures very high and require a

constant heating system to maintain the molten state of electrolytes. Zinc-Air batteries

are cheap, have a long life cycle and a high density high energy, however it is difficult to

recharge them and the performance is low compared to other battery technologies.

Flow cells are promising for long-term storage because of their rate self-discharge is

very low. They have high performance and a short response time. In In terms of

disadvantages, they have a low energy density and some toxic materials.

Optimal location and sizing of BESS is obviously a fascinating topic, particularly

when it comes to renewable energy integration. Since oversizing might result in

additional investment expenses, there is a strong commercial case for optimizing the

placement and sizing of BESS. There have been a number of optimization strategies

suggested for determining where and how much energy storage should be located in

50
distribution networks, but it is not always easy to tell which is best because it depends

on so many factors. As far as the author is aware, the method has not been extensively

tested on small-scale distribution grids. Lessening the system's expenses and losses

has been the primary focus of most BESS research. As this research has shown, there

are some fascinating areas to explore within BESS, including placement and sizing

optimization, power loss mitigation, and voltage support in distribution grids.

2. Literature Review

Numerous advantages can be achieved by utilizing energy storage devices, and

experts in the field have catalogued numerous potential applications [68], [69]. Different

51
markets and regions, as well as different types of sectors (transmission, distribution,

commercial/industrial, and residential), reap differing degrees of profit from these use

cases. Local market dynamics, tariff structures, regulatory requirements, available

subsidies, and a host of other factors determine the advantage in each scenario.

For instance, the PJM market in the US deployed more than 160 MW of energy

storage in 2015 [70], and frequency regulation has been named as one of the most

profitable uses for energy storage systems [71].

While behind-the-meter applications are anticipated to be highly lucrative in

Australia in the near future, according to industry research, the residential market will

drive the energy storage market's growth. One study predicted that the residential

market would increase from 1.9 megawatts in 2015 to 44 megawatts in 2016. From 9–

12 years in 2015 to 4-6 years in 2035, according to a new study, the payback period for

newly installed storage and PV systems on a flat tariff will decrease [72]. These

payback durations could decrease even further with time-of-use tariffs. Consequently, a

plethora of energy storage companies are penetrating the Australian residential market,

where there is a lot of interest in residential storage systems that can be integrated with

solar photovoltaic (PV) installations already in place and in brand new developments

[73].

The need of "value stacking"—utilizing a single energy storage system to reap

the benefits of numerous revenue streams—in maximizing profitability and return on

investment is universally acknowledged across all industries and regions. While one

study indicated that only one use case for energy storage devices may be viable when

52
looking at them in isolation, another study that looked at value stacking showed that

multiple additional applications could be economically attractive right now [74].

Although BESS became quite cost effective in recent years with maturing battery
[75]
technology , its high capital investment combined with operational cost inculcates

the need for appropriate designs to optimize the integration of BESS into an active

distribution network (ADN), a distribution network that has a combination of distributed

energy resources (DERs) such as diesel generators and renewable energy sources, in
[26]–[79]
order to harvest its most benefits in resolving techno-economic problems . In

this respect, optimal integration of BESS is an important parameter that inevitably

quantifies the efficacy of the BESS with respect to its applications in demand-side
[80]
management, energy markets, and large-scale of RES deployments . Optimal

integration of BESS is established based on three prime parameters: optimal

placement, optimal capacity and optimal operation.

2.1 Optimal Placement

Integrating a BESS at sub-optimal locations in an AND causes additional

problems such as increment in system loss and voltage congestion. Therefore, optimal
[81]
placement of BESS is critical . In order to reach an optimal placement of BESS in an

AND, BESS should be allocated such that it can reduce most of the system losses.

Several studies in the area of optimal placement of BESS have been

successfully concluded. However, the idea of optimal placement of distributed

generation as well as shunt capacitors serves as a cornerstone for numerous methods

[82]–[84]
for optimal placement of BESS in a power distribution network .

53
 In [85], the optimal allocation of BESS is achieved by using non-radial distribution

system for voltage regulation. Likewise, a technique is established based on

classification of distribution main transformer (MTr) in order to identify the optimal BESS
[86] [87]
allocation in a grid is introduced in . The study in presents a methodology to
[88]
reduce the feeder losses based on an optimal BESS placement. The study in

presents a voltage sensitivity approach that relates the distance between the load and

power supply in determining the suitable network buses for the integration of BESS that

contribute towards the improvement of the power quality of the network. Similarity, the
[89], [90]
greedy algorithm framework proposed in suggests the optimal allocation of

BESS based on the loss sensitivity analysis in order to decrease the network losses.

In recent years, there has been a lot of focus on finding the ideal location and

size for battery energy storage. For example, the grid may have negative consequences

from the integration of renewables if BESS is not appropriately sized and placed.

Economic issues and grid power-related difficulties are the two main foci of the research

[91]. In order to achieve this goal, an optimization technique is employed to determine

the most suitable size and location for energy storage. To solve the optimization

problem, a number of heuristics are employed, including Genetic Algorithm (GA),

Particle Swarm Optimization (PSO), Integer Programming (IP), and countless more.

There are a lot of variables that go into determining where BESS should be located on a

grid-connected or isolated power system, including the distribution network type, the

size and type of the loads connected to the buses, the voltage levels and fluctuations on

each bus, and the distribution network itself.

54
 A lot of writers thought about GA as a tool for optimization. An ideal sizing model

taking into account the network's power losses and voltage drops was proposed in [92]

and [93]. Simultaneously, a cost-based multi-objective planning approach was

suggested in [94]. When compared to the results achieved using Hybrid Genetic

Algorithm (HGA), the model in [95] yielded superior efficiency, particularly for bigger

systems, when evaluated on an IEEE 33-node network. Models were suggested in [96]

and [97] to determine the best place to put electric vehicle (EV) batteries and how much

power they should have. Since the investment costs were considered, the optimization

models have numerous constraints. Since PSO demonstrated superior responsiveness

compared to other optimization methods when dealing with various constraints, it was

selected for both investigations. Ant Colony Optimization (ACO) is another heuristic

optimization method that is employed to assess the best location and size for BESS. In

[98], the authors determined the best spot for EVs on the distribution grid by reducing

overall expenses and actual power loss while keeping traffic flow and security under

control. When compared to other optimization approaches, this one is slow, which is its

biggest drawback. To address the optimization issues, other strategies were employed

alongside the heuristic optimization techniques.

Renewable energy sources, such as solar and wind power, are seen as a

possible solution to both the energy crisis and environmental problems. Pv electricity, or

photovoltaics, has many applications [99]. As both energy users and suppliers,

residences with PV-battery systems are an essential component of smart grids and

have been the subject of substantial research [100]. Nevertheless, the economic

advantages will be diminished as a result of low usage, as PV power generation is

55
certain to fluctuate seasonally and hourly. The self-consumption rate of PV power can

be enhanced by battery energy storage, which acts as an energy buffer. It stores

excess power and releases it when needed. An essential metric to ascertain is the

capacity of BESSs, or battery energy storage systems. Inadequate battery capacity will

lower the self-consumption rate of PV power, while excessive capacity will lead to

resource waste and excessive investment costs. In order to ensure the necessary

performance at an affordable investment cost, it is crucial to determine the ideal size of

BESSs. The ideal battery capacity for long-term financial advantages, however, will vary

according to changes in load demand and power generation under various seasons and

climates [101, 102]. Therefore, in order for a BESS to function optimally over the long

term, it needs to be able to handle the seasonal change.

Furthermore, the entire home system's energy management strategy (EMS)

relies heavily on the battery capacity. The EMS regulates the BESS to support the

home appliances and account for variations in PV power output. The BESS's maximum

allowable energy output is directly proportional to its battery capacity [103]. Therefore,

to maximize economic benefits, it is necessary to optimize both the battery size and the

EMS simultaneously.

The ideal EMS and battery storage size should be developed with the long-term

in mind. In order to determine the best amount of storage for the long run, two primary

approaches are available. The first is to determine the worldwide optimum storage

capacity and EMS over an extended period of time, say, a year. Although the

optimization problem is computationally hard owing to the enormous memory need, the

scale will expand dramatically with the long-term horizon [104]. It is also not possible to

56
apply this ideal EMS online. It is usual practice to manage home energy systems online

using declining horizon control (RHC), which includes model predictive control (MPC)

and similar technologies. To find the best storage capacity for a long-term operation, the

second approach is to use the RHC technique, which typically has a time horizon of 24

hours. The authors of Ref. [105] used a smart grid to combine optimal storage

architecture with RHC-based energy management for 365 consecutive days. The ideal

design parameter and receding control outputs are determined annually using the bi-

level optimization methodology. But the nested loop for long-term planning significantly

increased the computation time, therefore two loops were required. A great deal of

research has been published in an effort to resolve the aforementioned two issues.

Below you may see the literature review.

One group of methods aims to minimize memory requirements by calculating the

ideal battery storage size by simplifying the original large-scale problem. This is done to

handle the large-scale problem with a long-term perspective. With the use of the

clustering technique k-means, Gabrielli et al. [106] chose 48 days that were

representative of the year in order to capture the seasonal variance. In order to make it

look like a multi-energy system is running continuously from hour to hour, these days

are then combined together. That is to say, the energy storage system's initial condition

is the same as the energy state in the last hour of the day. The ideal amount of battery

storage for each season can be determined by solving the resulting mixed-integer linear

programming (MILP) problem. In order to decrease the time resolution of energy

models, Pfenninger [107] examined various approaches, including representative days

or weeks. According to the findings, there aren't any one-size-fits-all methods for

57
addressing long-term unpredictability while simultaneously decreasing time resolution.

For a home system, Saez-de-Ibarra et al. [108] split the year input data into 12 months.

On an inexpensive scale, there were twelve optimization issues that could be resolved

hourly. Then, for every month, the 12 best sizes of used batteries were determined. The

end result of the year-round preparation was the average size. Despite the fact that the

aforementioned methods can accomplish problem size reduction and input data

seasonal fluctuation reflection, the downside is that the resultant storage size is no

longer appropriate.

For long-term planning, the ideal storage size and EMS of an online RHC

strategy are often designed using the bi-level optimization framework, which falls under

the second category of techniques. The bi-level approach makes use of two nested

loops. Heuristic techniques like genetic algorithm (GA) and particle swarm optimization

(PSO) are commonly employed by the outer loop to determine the storage size. After

receiving the storage size from the outer loop, the RHC strategies for the long-term are

calculated in the inner loop. Using predictive control, Rullo et al. [109] optimized the size

and energy management of freestanding hybrid renewable energy systems

simultaneously. The outer loop's hybrid energy system size vector was computed using

the GA technique. In order to mimic the system's performance throughout the year,

economic model predictive control (EMPC) was employed, with a time horizon of 12

hours. The system was only controlled by the first output. In the end, this scaling

process will use 5 hours of CPU time. A bi-level optimization strategy involving GA in

the outer loop and MPC in the inner loop was implemented by Li et al. [110] to tackle

the calculation time issue caused by the nested loop in long-term planning. The annual

58
operation's simulation time was reduced by using a resolution of one week. After that,

about 30 minutes had passed in total computing time. We contrasted the RHC approach

using a resolution of 1 hour. According to the findings, the daily fluctuations in load

demand and PV power cannot be captured by a longer time horizon. Therefore, better

results can be obtained with a shorter time horizon. Hybrid electric vehicle size and

control also make use of the bi-level optimization methodology. For most driving cycles,

the global control methods were computed using dynamic programming (DP). The

computational time required for such a strategy will extend into several days [111, 112].

The bi-level optimization approach to long-term planning, in a nutshell, uses too much

processing time in its outer and inner loops because of the sequential calculation mode.

2.2 Optimal Capacity

The optimal capacity of a BESS is not less important than its optimal placement.

Plenty of studies recommended that the capability of a BESS is improved by using the
[113]–[115]
optimal capacity . In fact, network losses and voltage congestion increase
[116]
with the integration of undersized or oversized BESS .

An intelligent algorithm using grasshopper optimization (GO) technique is

[117]
proposed in in order to establish an economic framework of BESS sizing in a RES

standalone microgrid. The authors consider a hybrid power system that consists of solar

and wind RES as well as diesel generators and a BESS that aims to supply power to

five residential load demands. This research considered the probability of power supply

deficiency and low energy cost. The proposed GO technique is found to be effective,

where it was compared against two heuristic algorithms namely: cuckoo search

optimization and particle swarm optimization.

59
 Furthermore, the study in [89] posits a methodology for voltage regulation

through a technoeconomic placement of a BESS in a radial distribution network with

high PV penetration. However, maintaining a reduced system losses in the presence of

high PV variability can be ideal only with the optimal size of the BESS. This proves that

the optimal capacity of BESS is not less important than its optimal location.

Furthermore, the authors in [118] presented an optimal capacity utilization of

BESS by establishing a non-linear dynamic programming approach for optimal sizing

and scheduling of BESS in wind farms. This research considers the depth-of-discharge

and lifetime of BESS to formulate the optimization framework. The authors present the

validity of the proposed methodology considering standalone and grid-connected

scenarios of wind diesel penetration power networks.

The study in [119], an economic oriented BESS utilization considering technical

efficacy in terms of peak regulation and system profits through optimal sizing of a

vanadium flow batteries (VFB). It is found that BESS built using VFB are bed to have a

capacity ranging from 17 to 40 MWh and efficiencies of about 70–80% in order to be

[120]
used in power systems .

Many integration issues arise, particularly at high levels of penetration, from the

variable production of renewable energy sources. Just as the weather-based generating

facilities aren't always predictable, distribution system operations aren't always

predictable either. The primary factor influencing the expenses of electric utility

infrastructure is the requirement to supply the load during the period of peak demand.

Consequently, reducing peak demand is preferable since it delays the need to improve

60
transmission and distribution systems and lessens or eliminates the need to buy

significantly more expensive generation assets.

Battery Energy Storage Systems (BESS) are a great tool for lowering peak loads.

One possible solution to the issues caused by a high penetration of dispersed

generating is the inclusion of battery energy storage into distribution grids. Among

BESS's many applications outside load leveling are frequency management, voltage

regulation, and distribution system power quality improvement. A more stable and

dependable grid is the result of all these uses [121].

The distribution network may experience under-or over-voltages due to the

improper placement and sizing of the BESS. Because of its closeness to the load

centers, BESS also contributes to the losses. For this reason, you should think about

where in the home distribution system to put BESS so you can get the most out of it.

One crucial factor to optimize the benefits of the BESS in the system is the proper sizing

and placement of the BESS inside the distribution power system.

The best place for the BESS to reduce losses is on a bus, where it can do so

without disrupting the voltage profile. The literature proposes a number of techniques for

appropriate BESS sizing and location [22]. Despite widespread agreement among

researchers that a larger BESS enhances power system performance, no one has

offered a clear recommendation for where to put the BESS inside the distribution grid

[123].

For the purpose of regulating voltage, a non-radial distribution system has been

suggested as a means to locate energy storage units [124]. To find the best spot for the

61
BESS in the distribution system, a strategy is suggested in [125] that relies on

categorizing the main transformer (MTr) of the distribution substation. Optimal

placement of the BESS in the network is suggested as a means to decrease feeder

losses in [126]. The main problem with these approaches is that they might not work for

complicated distribution systems.

Renewable Energy Resources (RESs) have recently gained popularity as

electrical power system sources for a number of reasons, including their sustainability

and low environmental impact [127]. Renewable energy sources mostly include solar

panels and wind turbines. Nevertheless, due to its characteristics, power generation

from RESs is intermittent. Hence, the right management system is required before

renewable energy generation sources may be directly integrated into the current system

or utilized [128]. Microgrids are small-scale power systems that are designed to manage

renewable energy Distributed Generators (DGs) and load clusters. The microgrid is said

to be in "islanded mode" when it is disconnected from the bulk power supply during an

emergency, and "on-grid mode" when it connects to the system. Any microgrid that can

function independently of any other network is known as a standalone microgrid [129].

In more rural or out-of-the-way places, you might see the standalone microgrid.

Therefore, without the bulk power system's support, the stability of the isolated

microgrid is poor. The fundamental objective of a microgrid is to maintain a steady flow

of electricity by balancing demand and generation. In order to control the power of the

microgrid, it is required to have sources of manageable power generation, like the

BESS or diesel engine generators [130]. The usage of diesel generators is discouraged

for environmental reasons in favor of freestanding microgrids that incorporate

62
renewable energy sources, load clusters, and BESS [131]. Optimal capacity that takes

BESS lifetime into account is one of the major challenges for BESS applications. Due to

the expensive expense of the BESS, microgrids often use BESSs with a short lifespan.

Reliability and cost in relation to the system's reduced emissions of greenhouse gases

are the primary metrics used to evaluate the grid's performance [132]. In [133], the

authors laid out the ideal microgrid PV and BESS size to keep the BESS lifetime as long

as possible with just the startup cost and the Loss of Power Supply Probability (LPSP)

as indicators. The State of Health (SOH) of the battery was used to estimate the cost

reduction study, and the battery management system was assisted by artificial

intelligence. The lifespan and cost of a battery can be increased with an accurate

assessment of its health [134]. If you want your batteries to last as long as possible, you

need an accurate SOH. Using electrochemical impedance spectroscopy and relaxation

time distributions, we can follow battery degeneration and estimate SOH. Indicators

associated with decline were sought after using them [135]. In a PV system, [136]

demonstrated the BESS lifespan estimation using a realistic model. The writers took

into account the effect of BESS types and sizes on its longevity as well. There is a direct

correlation between the size of the BESS and both its longevity and its initial investment

cost. The authors also proposed a method to minimize the overall expense, which takes

into account both the initial investment and the cost to replace BESS. To estimate the

lifetime of lead-acid BESS in microgrids, we used the weighted Wh throughput

approach and varied the BESS's size according to the lifetime prediction method. The

authors neglected to take cost analysis into account while determining the appropriate

size, though [137]. An optimization of BESS on a microgrid utilizing PV and WT as

63
power generating sources is suggested in this research. Minimizing overall expenditures

is the primary goal of this research. The literature study indicated that the referenced

papers [138] employed a PV and BESS microgrid system. In order to cut expenses, the

SOH estimation approach was examined in references [139], however the optimization

method is used in this paper. The BESS lifespan estimation in a PV system was the

sole focus of the study in [140]. Also, in order to get the best size, the writers in [141]

didn't think about the cost analysis. Consequently, this study examines PV and WT

generating systems simultaneously. An optimization analysis was conducted to

determine the appropriate BESS sizing and NPV. The batteries are the most costly part

of a microgrid system, so it's important to plan for investment costs, profitability, and

maintenance over the system's lifetime before putting it into action. There will be an

increase to the system price if the battery is changed too soon. When planning the

battery's replacement and life cycle, it's common practice to employ forecasting and

estimate methodologies. In order to achieve optimal outcomes while minimizing the

overall cost of the BESS system, optimization is suggested in this study. To optimize the

cost of BESS, the weighted Wh approach and the PSO algorithm are used. Keeping

equipment in a standalone microgrid system for as long as possible helps keep

replacement costs down. But the storage systems need to be suitable in terms of size

and installation expenses. Consequently, in order to keep the microgrid system cost as

low as possible, this study offers a suitable weighting. Statistical analysis takes time and

isn't always correct, therefore we use the PSO algorithm to find the best weighting

parameter instead.

64
 In brief, the following are the main points of the paper: Considering the BESS

lifetime, this study proposes a method for optimizing capacity and doing a cost analysis

of the BESS. The weighted Wh throughput approach, which is detailed in [142], is used

to estimate the BESS lifetime. The goal is to calculate the overall cost of the microgrid

using the "Net Present Value" (𝑁𝑃𝑉) concept. The optimal capacity of the BESS is

found by solving the objective function with the help of the PSO algorithm. The study

utilizes the lead-acid battery because of its price and reliability. A cost-benefit study

indicates that it is the superior option compared to more contemporary batteries, such

as lithium-ion batteries [143].

Combining renewable energy with BESS has been popular in microgrids as a

solution to the unpredictability of renewable energy power supply and load demand

[144]. In addition, the BESS addresses the issues of accommodating renewable energy

sources in the microgrid and filling valleys [145]. It is critical, though, to maximize BESS

capacity and microgrid operational flexibility immediately.

A lot of research has gone into finding the best ways for the CCHPM to be able

to operate in a flexible manner. A decision-making evaluation technique is proposed in

[146] using a model that takes into account the economy-environment-energy

performance of CCHPM. Both [147] and [148] lay out a plan for the electro-thermal

home microgrid that takes demand-side management and energy balance forecasts into

account. The model, however, disregards the need-and-supply equilibrium of systems.

In the context of supply-demand energy matching, a technique for CCHPM design is

presented in references [149] and [150]. An ideal technique that takes into account

CCHPM in conjunction with renewable energy, hybrid storage, and multiple loads is
65
achieved under various operating modes in [151]. Furthermore, a battery energy

storage system and optimal scheduling for the short term are established in [152]. In

order to make multi-energy microgrids more efficient and cost-effective, a concept that

combines storage and energy sharing is suggested in [153]. Optimal cost management

of CCHPM in varied energy costs and prices is proposed in Ref. [154] based on heating

and cooling integration.

Applying BESS is also the subject of some works. The big hydro-wind-

photovoltaic complementing system with battery storage is suggested in [156] as an

evaluation model for the day-ahead. At the same time, the electric market is developing

utility-scale battery energy storage [157], [158]. Another player in the market for power

ancillary services is a wind power facility that uses batteries for storage [159].

Additionally, BESS, solar systems, and solid oxide fuel cells are integrated to provide a

zero net energy home grid [160].

2.3 Optimal Operation

The control theory to achieve optimal operation of BESS is important to enable

[161]
its accelerated applications in the modern power system . Optimal operation

ensures proper charging and discharging of the BESS and subsequently prolonged its
[162]
lifetime .

Numerous studies have proposed novel operational control schemes for energy

storage. A novel technique to eliminate the imbalance between Lithium-ion (Li-ion)

[163]
battery cells is presented in in order to improve the state of charge (SoC)

balancing of the battery pack. This study proposes a dual screening technique, namely

66
capacity screening and resistance screening, to determine the stable battery

configuration that can be used in accordance with the system requirements. The

relationship between the open circuit voltage and the SoC that is important for SoC

balancing is established by the capacity screening. Furthermore, by applying pulse

charge/discharge currents at multiple SoC points, resistance screening finds the battery

cells with comparable voltage variance. Reference to the experimental analysis that is

presented, the suggested methodology, with an extended BESS lifetime and stable

configuration, guarantees battery security and protection against sudden failures.

A power frequency (P-f) droop control theory for active power deployment and a
[164]
reactive power-voltage (Q-V) droop control strategy presented in in order to

dispatch the reactive power support from BESS are the basis for the SoC balancing.

Plug-and-play functionality of the BESS and the suggested approach are made possible

by the suggested decentralized control framework, which eliminates the requirement for

cutting-edge communication. Moreover, the authors attain a higher level of controllability

over the charge/discharge of BESS with improved reactive power sharing and SoC

balancing among BESS in contrast to traditional droop control theory. Comparably, the

idea of BESS monotonic operation assumes that BESS are used in a coordinated and

optimal manner according to their specific applications while simultaneously keeping a

smooth charge/discharge curve that guarantees the best possible use of their life cycle
[165]–[168]
. Installing a minimum of two batteries in the BESS is necessary for its

continuous functioning. Grid variability, such as load variation, is used to identify the

BESS's operation mode (charge or discharge), along with its capacity.

67
 Furthermore, the research study in [166] proposes a SoC balancing technique

based on sliding mode control theory and hierarchical control strategy for multi-module

BESS. The control framework that is being presented creates a central controller that

controls the output power of the BESS by using the generated and identified nominal

reference value. Based on generated values and in conjunction with interconnected

BESS local communication and SoC values, a coordinated output power from the BESS

is extracted to maintain a balanced SoC. The authors argue that the suggested control

strategy, as opposed to a conventional linear controller, preserves the

charging/discharging integrity of BESS by selective continuous charging or discharging,

which guarantees a specific optimal life cycle utilization of BESS in accordance with the

load demand of the system.

A dual property of monotonicity for BESS operation is introduced in the study in

[167]
. The authors take into account BESS integration in order to reduce load demand

at peak times. As a result, the first monotonicity property determines the state of the

BESS according to the load and system requirements at a specific moment. The second

monotonicity property guarantees load smoothing and peak clipping by coordinating the

load demand based on the processed load profiles. The authors postulate that

expanding the size of the BESS further improves the peak clipping operation of the load

profile. This hypothesis is reached after testing and validating the suggested approach

on a stand-alone power network.

The authors in [168] propose monotonic operation of BESS for smoothing the

output power of wind energy based electric power generators. The presented

framework is designed with a large-scale wind farm integrated with large BESS that are

68
connected in parallel to fulfill the corresponding load profile. The author designed a

model predictive controller (MPC) based on the formulated monotonic equations that

generates an error signal of power surplus or power deficit in order to request BESS

intervention of charging or discharging. By coordinating with the grid and wind farm, the

MPC selects the suitable battery in the BESS in order to fulfill the load requirement.

Using actual Australian market pricing data, a subjective and economic analysis of this
[169]
approach is provided in , confirming an increase in revenue for large-scale wind

farms based on the monotonic operation of BESS.

The optimal placement of BESS is achieved through the voltage sensitive

approach. The prime difference of locating the BESS in this research from these
[36]–[41]
methods presented in is that they are not tested in large-scale distribution

network. This research tests the voltage sensitivity approach method for optimal BESS

placement on a 14-bus distribution network that includes different types of RES.

Similarly, the optimal capacity of a BESS is determined by analyzing the power

demands of the system based on the concept of the curves arriving in the network
[170]
calculus . A fundamental difference between our work and the previous works is

that we consider the load inelastic which must be instantly fulfilled. In addition, the peak

demand is to be fulfilled by the RES and the BESS integrated to the distribution network

while the power supply remains unchanged. The RES as well as the BESS are

accordingly considered as distributed generation (DG) units. We accomplish

implementation of peak shaving by first forecasting the load and then integrating the PV

systems and the BESS.

69
 Furthermore, the lifetime of a BESS is severely affected during the intensive
[43]
charging and discharging process in peak shaving . Therefore, the BESS in this

research is controlled in a way that facilitates a smooth charging and discharging curves

of the battery from the lowest to the highest point, or from the highest to the lowest

point. Meaning that a battery in the BESS will not discharge until it is fully charged and

will not charge until it is fully discharged. This control methodology is referred to as

monotonicity: monotonic charging and discharging. A fundamental difference between

[54]–[57]
our work and the previous works in is that we consider four batteries

integrated into the BESS, instead of two batteries, in order to ensure monotonic

operation for the worst case of power demand.

Batteries are becoming more affordable, but they are still prohibitively expensive

for most consumers to justify using them for behind-the-meter applications. In most

cases, though, this is because the battery was either over-sized or had an overly

simplistic operating approach. To reduce operational costs and maximize economic

benefits for the client, the battery storage system should be sized and controlled

optimally to match its application goals.

A number of studies have investigated, using a range of optimization models,

how batteries should be operated for behind-the-meter applications. To maximize

battery capacity, [1] employ a rule-based system that takes into account on-peak and

off-peak circuits; however, they do not account for operational costs. Similarly, [2]

minimizes the battery's net present cost by using real-time pricing and a simple rule-

based system. Over varying time frames, [3] examine how an improved depiction of

energy storage systems impacts energy arbitrage income. Using linear programming,

70
[4] find the best time to schedule a battery so that the net load demand stays below a

certain power limit. The model is still overly simplistic, despite its improved performance

compared to the on-peak/off-peak algorithms.

In recent years, the excessive global increment of energy consumption and the

issue of environmental pollution has led to the shift towards a green economy. The

scientific community has put a significant amount of effort into improving the

manufacturing quality and performance of the devices which are utilized in renewable

energy systems (such as photovoltaics, wind generators and batteries). Nevertheless,

the implementation of control strategies in order to guarantee the optimization of the

system’s performance remains crucial.

In that context, battery energy storage systems (BESS) are going to play a key

role in the future in many sectors [1]. However, BESSs have not proven yet notable

performances in the household sector [1]. In [2,3] it is analyzed that the application of a

photovoltaic (PV) battery system in a household is a significantly challenging issue

regarding its sustainability. This is mainly due to the high investment costs for the

development of a BESS. Furthermore, in most countries single rate (SR) energy tariffs

and time-of-use (ToU) energy tariffs are used. This implies that applying a BESS

without renewable energy sources (RES) is at the first case (SR) infeasible and at the

second case (ToU) extremely challenging. Additionally, despite the continuous

decrease of the lithium-ion battery costs and the consecutive improvement of the

batteries’ performance, the value for money relation still cannot be considered ideal. In

order to overcome these difficulties, various economic and technical studies [4,5] have

been performed over the past years which have proved that it remains unclear whether

71
a residential PV battery system can operate profitably. Moreover, in [6] it is proved that

achieving 100% autonomy in a household with a PV battery system is not a realistic

scenario in most countries in Europe without oversizing the PV or the BESS. In addition,

in [7] the importance of developing management strategies for analyzing the

performance of the PV battery system is highlighted. Various scenarios are analyzed

such as operation of the system without PV and operation without BESS. Another

remarkable example of techno-economic studies is the work presented in [8], where a

decision support tool for a BESS with integrated photovoltaic is presented. This tool

aims to define the optimal sizing of BESS and optimal operation scheduling by taking

under consideration various parameters such as weather data, electricity pricing

environment and BESS specifications/costs. In addition, plenty methodologies for

calculating the levelized cost of energy (LCOE) have been developed. A methodology

which intends to minimize the LCOE by defining optimal photovoltaic (PV) rated power

and BESS capacity is analyzed in [9]. Methodologies for calculating LCOE, economic

and technical studies are implemented so as to define the optimal PV rated power and

BESS capacity by taking into account specific conditions like building dimensions,

geographical location, climate, energy consumption per season and energy market.

Therefore, they cannot be considered suitable for universal use. All the aforementioned

factors prove the difficulty of ensuring sustainability of BESSs in building applications

and hence they evince the requirement of applying efficient energy management

strategies (EMS).

A BESS can be used in a residential building with RES or without in case of the

existence of a ToU tariff or real time pricing (RTP) tariff. Nonetheless, efficient control of

72
BESSs plays a significant role in achieving sustainability regardless the existence of

RES [10,11]. A non-constrained operation will result in a long operation time for the

BESS and/or high operating currents and thus to significant degradation effects. In [12]

the economic impact of battery ageing in a residential PV battery system is highlighted.

Moreover, in [13] a methodology is presented for preventing battery degradation in a

residential BESS by using forecast-based operating strategies. This is achieved by

storing in the BESS only the amount of energy which is estimated to be needed during

the night. This yields lower depth of discharges and hence, reduced capacity losses for

the BESS. However, in case of inaccurate estimations for the needed energy,

decreased economic profit might be observed. Furthermore, in [14] is proposed a

cooperative energy management between a utility and households with PV battery

systems. The energy management is examined under RTP tariff and aims to operate

the BESS at minimum cost for each household.

Optimal management of energy storage assets has been the subject of extensive

research in several fields. Storage is commonly used in micro-grid operations [13]–[15],

in wind or solar PV integration [16]–[22], for price arbitrage [23]–[25], for peak shaving

[26], or for a mix of several advantages [27], [28]. In order to make energy storage

systems work better, many different strategies have been suggested:

Although dynamic programming is adaptable, it isn't well-suited to account for the

unavoidable unpredictability [21], [26]. While methods based on linear programming,

like [13], are efficient in terms of computing power, nonlinearities may necessitate pre-

and post-processing of input and output. Finding global optima can be a computational

ordeal, but methods like mixed integer linear (or quadratic) programming [24], [27] and

73
quadratic programming [22] can handle it. Although [29] suggest optimization condition

decomposition to break down a stochastic model predictive control problem involving

large storage and generation scheduling into smaller parts, allowing for parallel

computation to speed up solutions, even for a small number of scenarios, the solution

times are still quite long. Energy storage optimization methods based on stochastic

dynamic programming (SDP) are gaining popularity.

These approaches can accommodate for forecast uncertainty while still enjoying

the benefits of dynamic programming. The effect of prediction mistakes on the

operational optimization of distributed energy resources has been thoroughly examined

in several recent publications using various methodologies.

When optimizing the operation of controllable loads, [30] use fuzzy logic to

account for the uncertainty in PV projections. In this study, [31] optimize micro-grid

planning and operation while considering unpredictability in RES generation, load, price,

and islanding events. They achieve this by finding a workable solution in the operational

sub-problem, even under the most extreme circumstances. Using heuristic scheduling

algorithms that account for predicted forecast mistakes, [32] investigate optimal

generation and storage scheduling subject to RES prediction uncertainty. Considerable

research has made use of stochastic methods, which formally represent optimization-

related uncertain terms as variables with a certain probability distribution [16], [18], [23],

[28], [29].

2.4 Gap Analysis

74
 With an installed capacity of 340 MW in 2013 and a projected capacity of over 40

GW by 2022, the utilization of grid-scale battery energy storage systems (BESS) is

increasing at an exponential rate [171]. The multiple grid services provided by BESS,

such as energy arbitrage, frequency management, transmission deferral, and reactive

power assistance, among others, have contributed to its rapid expansion. Because of

their low cost per unit of energy, high power density, and lithium-ion (Li-ion) chemistry

based BESS are well-positioned to offer a considerable portion of this versatility among

the many storage technologies available.

There have traditionally been two primary areas of emphasis in BESS research:

(i) the chemistry and material properties (see, for example, references [172, 173, 174,

175, 176]), and (ii) the grid integration, operation, and economic performance (see, for

example, references [177]). The research community and commercial users of batteries

are well-aware of this gap. It is particularly problematic for grid applications, where

market-based decision-making tools employ oversimplified models that hinder the full

operational flexibility of the battery due to inadequate characterization of cycle-life

degradation and charging/discharging efficiencies. The development of conventional

empirical or theoretical degradation models, which are substantially non-linear (see, for

example, references [172, 173], [174], [175], [176], [178], [179], [180], [181], [182],

[183], [184], [185], [186]), is to blame for this disparity since it makes optimizing over

numerous time periods computationally burdensome. Typical models are complicated

because they have to forecast a lot of different physicochemical causes of deterioration,

such as active material loss, mechanical stress, SEI layer expansion, inventory loss of

lithium, and other similar issues.

75
 Although this level of detail is helpful for figuring out how batteries fail and for

estimating how much power they will have left in them, it makes it hard to generalize the

models to other chemistries and applications. Optimal approach for power grid

involvement at a high level is still unaffected by the additional mechanical data. The

decision-making processes are enhanced by integrating the economic exploitation of

BESS for grid services with a straightforward and descriptive data-driven evaluation of

important internal chemical features.

There have been initial efforts to connect grid economics with battery

degradation mechanisms (such as resistive surface film growth, degradative side

reactions, and lithium loss) [187, [188], [189], [190], [191], [192], [193]. Taking into

account both degradation of cycle life and power losses caused by charging and

discharging, BESS is investigated in the context of a microgrid in [187]. Nevertheless,

grid earnings that may be gained from variable C-rate operations are diminished

because the battery model is only supposed to charge at ±1C rates, meaning it can only

charge or discharge all of its capacity in 1 hour. Deterioration over the battery's cycle life

and the associated costs are also simplified by treating them as constants independent

of the battery's chemistry. As part of the model predictive control of a peak shaving

algorithm, an empirical cost function based on battery degradation as a function of

depth-of-discharge (DOD) was used in [188]. However, the effect of deterioration on

charging rates is also disregarded. In [189], an optimization model was created to run

BESS with wind resources that are stochastic and to account for degradation. A

maximum daily deterioration percentage was set in [190] to guarantee the BESS lasts

the projected number of years, even though there might be situations where it's

76
profitable to discharge it while taking the increased degradation into account. The

economy performs less than optimally when this strategy is used. For Li-ion batteries

used in electric vehicles (EVs), previous research has investigated the trade-off

between maximizing charge and minimizing degradation in the batteries [190], [191],

[192], and [193]. Instead of a data-driven method that can be used for any chemical,

empirical degradation models that are particular to one Li-ion chemistry are used in

[190]. Economic tradeoffs between deterioration and EV charge management are

implemented using thorough models in [192], [193]. The models are quite non-linear,

though, and variable C-rate operation was ignored.

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