sustainability

Review
Virtual Power Plant Operational Strategies: Models, Markets,
Optimization, Challenges, and Opportunities
Mohammad Mohammadi Roozbehani 1 , Ehsan Heydarian-Forushani 1, * , Saeed Hasanzadeh 1
and Seifeddine Ben Elghali 2, *

1 Department of Electrical and Computer Engineering, Qom University of Technology, Qom 37195-195, Iran
2 Laboratory of Information & Systems (LIS-UMR CNRS 7020), Aix-Marseille University,
13007 Marseille, France
* Correspondence: heydarian@qut.ac.ir (E.H.-F.); seif-eddine.ben-elghali@univ-amu.fr (S.B.E.)

Abstract: High penetration of distributed generation and renewable energy sources in power systems
has created control challenges in the network, which requires the coordinated management of these
resources. Using virtual power plants (VPPs) on a large scale has solved these challenges to a
significant extent. VPPs can be considered systems consisting of distributed generations, energy
storage, controllable loads, electric vehicles (EVs), and other types of resources to provide energy and
ancillary services. VPPs face various challenges such as energy management, operation, resource
uncertainty, participation in electricity markets, etc. This paper discusses an overview of the basic
challenges of VPPs, including control and communication issues, electricity markets, its different
models, and energy management issues. The main purpose is to investigate the performance of VPP
in different markets, energy management of VPP in different operating conditions and strategies,
and compare different planning methods for VPP. Note that the application of blockchain to control
Citation: Roozbehani, M.M.; and improve VPP performance has been investigated, taking into account the different layers of
Heydarian-Forushani, E.;
this technology.
Hasanzadeh, S.; Elghali, S.B. Virtual
Power Plant Operational Strategies:
Keywords: virtual power plant; energy management; resource uncertainty; electricity market; blockchain
Models, Markets, Optimization,
Challenges, and Opportunities.
Sustainability 2022, 14, 12486.
https://doi.org/10.3390/
su141912486
1. Introduction
The need for higher electricity demand and increasing environmental concerns, on the
Academic Editor: Pablo
one hand, and the complexity of energy distribution networks, on the other hand, have
García Triviño
led to the focus of many distribution network designers on MGs as a source of electrical
Received: 31 August 2022 energy with high reliability [1]. The available challenges in environmental issues and recent
Accepted: 27 September 2022 advancements in the field of power electronics increase the penetration rate of distributed
Published: 30 September 2022 energy resources in distribution networks [2]. In the last decade, RESs have been considered
Publisher’s Note: MDPI stays neutral
the closest alternative to the current power systems due to their high flexibility of operation.
with regard to jurisdictional claims in However, the high penetration of these resources may provide great challenges for the
published maps and institutional affil- power grid [3]. The VPP is an effective solution to solve this problem. VPPs can be a
iations. combination of sources such as WT, PV, MT, ES, interruptible loads, etc. [4]. Although
the outputs of DERs may be intermittent and have uncertainty, the total behavior of a
VPP is more certain [4]. The VPPs have several advantages such as reducing the number
of outages, reducing network recovery time, integrating DGs, reducing line congestion,
Copyright: © 2022 by the authors. reducing peak demand, etc. [5,6].
Licensee MDPI, Basel, Switzerland. With the expansion of DERs in distribution networks, new ideas for using these
This article is an open access article resources have been reviewed in various papers; one is using VPP. The challenges in VPPs
distributed under the terms and have caused different VPP modes that must be evaluated. These challenges could be control
conditions of the Creative Commons
and operation, power exchange, and required communication and telecommunication
Attribution (CC BY) license (https://
systems. Reference [3] proposed a completely distributed control strategy for several
creativecommons.org/licenses/by/
DGs so that DGs can easily form a VPP. Reference [4] has proposed a general method to
4.0/).

Sustainability 2022, 14, 12486. https://doi.org/10.3390/su141912486 https://www.mdpi.com/journal/sustainability

Sustainability 2022, 14, 12486 2 of 23

investigate the effect of combining energy storage elements in a VPP model. This study
tries to increase generation power based on existing storage devices. The communication
systems and protocols that could be used in VPPs are investigated in [5]. In this study, a
new method for two-way communication in VPPs has been proposed. The control aspect
of VPP as a basic challenge has been discussed in [6] and various control strategies have
been proposed. The authors in [7] investigated the impacts of the uncertainty of PV and
WT sources using the Monte Carlo method and evaluated the control strategies related to
these resources within VPP. The operation of VPP in a disconnected mode from the main
grid has been evaluated in [8].
The authors in [9] evaluated the challenges that VPP faced in telecommunication
and system operations. The reference [10] has three main parts: optimization, generation
planning, and VPP classification. In the optimization part, the main objective is to reduce
pollutant emissions and planning costs. In the generation planning part, the main purpose
is to satisfy load and generation balance constraints. The authors in [11] presented a novel
solution to solve the problems related to energy deficit and excess. In this regard, the VPP
communicates with different available resources, such as PVs and battery energy storage,
to respond to power deviations. The authors in [12] provided a new approach for the
simultaneous management of responsive loads and EVs in an industrial VPP (IVPP) to
reduce the load of industrial centers to enhance the system’s profit and reliability. The
objective function assigns to short-term generation scheduling of IVPP with the aim of
profit maximization, taking into account DERs, conventional resources, DR, and EVs.
Uncertainty management is one of the most important issues affecting the optimal
scheduling of DERs [13]. The VPP brings together different ENs to enable them to partici-
pate in energy markets in an integrated manner. The VPP manages the generation of each
DER and encourages the DER owners to participate in the electricity market. Penalties and
incentives have also been considered for DERs, even those with a low-power generation
capacity. Today, business models are lacking in achieving win-win benefits for all stake-
holders [14]. This study described the structural models of energy markets, market services,
and future market mechanisms in the design of VPP. The authors in [15] presented a new
model to use a large number of distributed energy resources in rural areas with rooftop PV
resources and distributed wind turbines. This new structure has been designed in order to
absorb the carbon emission of the gas power plant. Revenue maximization and carbon emis-
sion minimization are the objective functions, eventually leading to stakeholder satisfaction.
A new approach for the investment planning of a VPP in the market environment has
been presented in [16]. In [16], different VPP models and structures have been evaluated
with multiple resources. Investment decisions have also been made under the long-term
uncertainty of the energy market. The authors in [17] analyzed various VPP models taking
into account carbon absorption devices and a comprehensive, responsive load mechanism.
The paper used a risk-based model considering the uncertainty of the electricity market
price as well as the price of natural gas through the value of the risk index.
The large-scale VPP (LSVPP) concept has been proposed in [18] so that different
generation resources, loads, and storage devices are distributed in a wide geographical area
while each of them could have a separate connection point. It is noteworthy that although
the mentioned resources may have separate owners, all resources are managed through
one VPP. A price-based unit commitment model has been developed in [19] in order to
determine an optimal strategy for VPP in the electricity market. The presented model takes
into account the constraints such as generation and load balance, technical limitations of
DER units, security constraints of VPP, and network constraints. In the proposed model, it
is possible for VPP to participate in the market as a producer or consumer. According to
the direction of power exchange within the main grid, the VPP could play different roles in
the market. The VPP model presented in [20] aggregates the available DGs installed in a
wide geographical area, and the output power of the aggregated DGs can be controlled
like a large central power plant. The paper has employed a distributed control strategy to
optimize the VPP output and converge to an optimal operating point.
Sustainability 2022, 14, 12486 3 of 23

The current paper intends to analyze the behavior of VPPs in different structures of
energy markets. In addition, the power management of VPPs and their challenges are
also analyzed. Finally, the utilization of blockchains is examined in the context of VPP.
Therefore, the contribution of the paper can be summarized as follows:
• To analyze different VPP models for different market mechanisms. In this regard, this
paper describes different energy market structures and thoroughly explains the role of
VPP in each market.
• To address different energy management algorithms within VPP considering vari-
ous resources such as renewable-based/conventional DGs, battery energy storage,
responsive loads, and EVs.
• To compare different planning methods of VPPs in terms of solution methods to
optimize VPPs.
• To examine the use of blockchain in the structure of VPPs and the benefits of using
this technology.
The rest of this paper is organized as follows: the concept of VPP is fully investigated
in Section 2. In Section 3, the uncertainties in the context of VPP are explored. In Section 4,
the energy management approaches in the VPP are reviewed. Section 5 assigns to the
planning of VPPs in the power system. Section 6 addresses the participation of VPP in the
electricity markets. Section 7 is related to the basic challenges of VPP and the application of
blockchain in VPP. Finally, Section 8 concludes the paper and remarks on future directions.

2. VPP Definition
Awerbuch firstly defined the concept of VPP as a “virtual distribution company”
that creates attitudes about changing the paradigm in distribution companies. Since
then, the concept of VPP has been extended by various researchers. Recently, various
definitions have been presented for VPPs, and in all these definitions, there is a common
point that VPP is a set of conventional DGs and renewable (RES) resources that could be
controlled as a single power plant in order to perform better in a supplying load [21–29].
The authors in [24] have defined VPP: “A set of DG units, controllable loads and energy
storage system are integrated, which act as a power plant with less uncertainty”. This study
also emphasizes that the generators in the definition of VPP can be renewable-based or
fossil fuel-based. The heart of a VPP is an EMS that coordinates generators, controllable
loads, and storage devices. In [25], VPP is defined as “A set of DERs that include DER
with different technologies, responsive loads and storage elements, which by integrating
these sources, flexibility, and controllability similar to large conventional power plants are
obtained.” In [26], VPP is defined as an information and communication system with a
focus on a set of DGs, controllable loads, and storage elements. In [27], VPP refers to a set
of DERs mutually connected and controlled through a central entity. This study further
explains that a VPP can be replaced with a conventional power plant to achieve greater
efficiency and flexibility. A set of different DER types that may be dispersed at different
points of the distribution networks is called VPP [28].
From the definitions presented for VPP, a more comprehensive and complete definition
can be defined as follows: a set of controllable and uncontrollable DGs, energy storage
systems, and flexible loads, together in the presence of information and communication
technologies to form a single imaginary power plant. The VPP could schedule and monitor
the performance of its elements and coordinate their operation in order to minimize the
generation costs, minimize the production of greenhouse gases, or maximize profits within
the electricity market. The conceptual schematic of a typical VPP has been illustrated in
Figure 1.
Sustainability 2022,
Sustainability 14,14,
2022, 12486
x FOR PEER REVIEW 4 of 244 of 23

Figure 1. Conceptual model of VPP.

Figure 1. Conceptual model of VPP.
Operating Strategies of VPPs
Operating Strategies of VPPs
VPP is a new power generation entity that aggregates and optimizes the scheduling of
VPP is a new power generation entity that aggregates and optimizes the scheduling
DGs, energy storage, and loads. The VPP can significantly improve the power system’s
of DGs, energy storage, and loads. The VPP can significantly improve the power system’s
flexibility, help to better use distributed demand-side resources, and promote the devel-
flexibility, help to better use distributed demand-side resources, and promote the devel-
opment of the electricity market [29,30]. To facilitate the application and deployment of
opment of the electricity market [29,30]. To facilitate the application and deployment of
VPP, powerful and reliable VPP platforms are essential. However, currently, VPP is still in
VPP, powerful and reliable VPP platforms are essential. However, currently, VPP is still
the early
in the stages
early stagesofofits
itsdevelopment [31]. There
development [31]. Thereisisstill
still
nono systematic
systematic andand comprehensive
comprehensive
participation
participation mechanism, control algorithm,
mechanism, control algorithm,or orsupporting
supportingsoftware
softwarefor
forVPP.
VPP.InInthis
thissec-
section,
practical
tion, practical applications of VPP are reviewed. The practical worldwide applications of
applications of VPP are reviewed. The practical worldwide applications of VPP
are shown in Table 1.
VPP are shown in Table 1.
In recent years, many VPP projects have been deployed around the world, including
Table 1. Completed projects of VPP in the world.
the European FENIX project [32], the Danish Edison V2G/VPP project [33], the Shanghai
Targets
Huangpu Commercial Building
Construction Time
VPP project [34],
VPP Name
and the South Australian
Type of DER
Tesla VPP pro-
Country/Countries
ject. Table 1 shows the completed projects of VPPs around the world. DG technologies in
Development,
implementation, and testing VPPs currently offer a wide range of active and reactive power generation options. The
of the VPP as well as whether final2001–2005
structure and operation strategy of a VPP depends
VDCPP Fuel cell on the interests
Germany,ofNetherlands,
stakeholders
Spain
fuel cells can be installed in and owners of different resources involved in the electric power supply, such as system
residential areas
operators, DG owners, energy suppliers, customers, and supervisory centers [35]. There-
Provide market mechanism 2005–2007 PM VPP µCHP Netherlands
fore, the optimal operation of VPPs can have economic, technical, and environmental
Choosing DER-based systems goals. Figure 2 summarizes VPP operation strategies.
in order to choose a solution
for EU electricity supply with 2005–2009 FENIX µCHP, GB, Spain, France
Table 1. Completed projects of VPP in the world. PV, WT
low cost, safety, and
high reliability
Construction
Providing the balancing Targets VPP Name Type of DER Country/Countries
power needed to increase the 2009–2012 Time
EDISON EVs Denmark
Development,
use of wind powerimplementation, and testing of the
Germany, Nether-
VPP as
Active wellsupply
power as whether
and fuel cells can be installed in 2001–2005 VDCPP WT Fuel cell
2010–2013 FLEX POWER lands, Spain
Denmark
residential
small-scale areas
generation
Provide market
Implementation of mechanism
2010–2015 2005–2007
WEB2ENERGY PM VPP μCHP
CHP, PV, WT Netherlands
Germany, Poland
“Smart Distribution”
Choosing DER-based systems in order to choose a
Advanced integration of μCHP,
solution
wind for EU electricity supply with
turbines low cost,
2012–2015 2005–2009
TWENTIES FENIX WT GB, Spain,
Belgium, France
Germany, France
PV, WT
safety, and high reliability
Providing network support
services, helping to secure 2016–2018 SA VPP PV, battery Australia
demand, and saving
customers’ energy costs
 - In the economic objective, the objective function is to minimize the total costs wit
respect to less impact on the network. This option may be considered by DG owner
or operators. The main limitations in the economic viewpoint are the physical limita
tions of DGs which may affect the economic dispatch. The impact of VPP on reducin
losses cost is because of the elimination of the transmission lines since these resource
Sustainability 2022, 14, 12486 are close to the load location. In fact, when transmission lines are removed,
5 of 23 power i
generated near local loads, which can reduce losses. In this case, the network operato
can also benefit from this issue. As a result, electric power can be delivered to th
In recent years,customer
many VPP at aprojects
lower price
havedue
beento deployed
reduced losses.
around Exploiting
the world,VPP could also reduc
including
the failure cost and the number of emission pollutants that enter the air; therefore
the European FENIX project [32], the Danish Edison V2G/VPP project [33], the Shanghai
the cost of these items will be avoided or will be very low.
Huangpu Commercial Building VPP project [34], and the South Australian Tesla VPP
- From a technical point of view, the network performance is improved. The purpos
project. Table 1 shows the completed projects of VPPs around the world. DG technologies
of network performance is to minimize power losses and improve voltage fluctua
in VPPs currently offer a wide range of active and reactive power generation options. The
tions and network congestion without considering resource costs or revenues. Thi
final structure and operation strategy of a VPP depends on the interests of stakeholders
option is mostly considered by system operators [37].
and owners of -different resources involved
The environmental in the
objective electric
function is power supply,
considered such asofsystem
regardless the economic o
operators, DG owners, energy suppliers, customers, and supervisory centers
technical aspects and only based on the need for reducing [35]. Therefore,
greenhouse gases. Thi
the optimal operation of VPPs
option is fullycan have economic,
supported technical,
by regulatory and environmental goals.
schemes.
Figure 2 summarizes VPP operation strategies.

Figure 2. VPPs operation strategies.

Figure 2. VPPs operation strategies.

Depending on the stakeholders involved in the planning and operation of VPP, four
objectives have been identified in the operation of VPP [36]. The economic, technical, and
environmental goals of VPP are the three important purposes of VPP operation, while the
fourth objective is a combination of the previously mentioned goals [36].
- In the economic objective, the objective function is to minimize the total costs with
respect to less impact on the network. This option may be considered by DG owners or
operators. The main limitations in the economic viewpoint are the physical limitations
of DGs which may affect the economic dispatch. The impact of VPP on reducing
losses cost is because of the elimination of the transmission lines since these resources
are close to the load location. In fact, when transmission lines are removed, power is
generated near local loads, which can reduce losses. In this case, the network operator
can also benefit from this issue. As a result, electric power can be delivered to the
customer at a lower price due to reduced losses. Exploiting VPP could also reduce
the failure cost and the number of emission pollutants that enter the air; therefore, the
cost of these items will be avoided or will be very low.
- From a technical point of view, the network performance is improved. The purpose of
network performance is to minimize power losses and improve voltage fluctuations
and network congestion without considering resource costs or revenues. This option
is mostly considered by system operators [37].
- The environmental objective function is considered regardless of the economic or
technical aspects and only based on the need for reducing greenhouse gases. This
option is fully supported by regulatory schemes.

3. Uncertainties in the VPP

By increasing the share of large-scale grid-connected RESs and the emerging electricity
market, the uncertainties in the power system are gradually increasing, making the power
grid’s safety and stability more challenging. Therefore, it is necessary to identify the
uncertainty sources and choose the appropriate solution to describe these sources [38]. In
Sustainability 2022, 14, 12486 6 of 23

this section, we will examine the available uncertainties for VPPs. These uncertainties are
categorized in three different sections as follows:

3.1. Uncertainty of Renewable Energy Resources

The uncertainty of RESs is mainly reflected in the time-varying characteristics of
wind and solar energy. In order to enable the VPP to operate in a steady state, various
characteristics of RESs must be fully considered in the design process. Currently, the
accuracy of forecasting the output of renewable resources is still not satisfactory and the
error of forecasting wind and solar energy is around 20% to 30% [39]. The uncertainty of
wind power generation is mainly due to the randomness of wind speed, which is influenced
by environmental factors such as location and climate. Similarly, solar radiation is the most
important factor affecting solar energy production. Seasonal and daily modes characterize
solar radiation. These sources are also affected by sudden weather changes, especially
cloud changes. When the installed capacity of RESs to the grid increases, the output power
increases randomly, which will have a special effect on the grid’s safety, stability, and
economic performance. This special effect could be categorized from the consumer and
producer’s points of view. For example, by installing high-capacity energy sources, the
manufacturer will generally eliminate the transmission lines and save costs. From the
consumer’s viewpoint, purchasing energy from renewable energy sources is cheaper than
conventional units. Finally, RESs could affect the system reliability so that in a blackout
situation, these units can operate separately from the main grid and supply the load.

3.2. Market Price Uncertainty

The market price is another uncertain resource when dealing with VPP. During the
lifetime of the energy system, market prices, for example, power prices, gas prices, and oil
prices, do not remain constant and will change with changes in energy markets, weather
conditions, and global and local policies [40]. Market prices can be determined only after
the market clearing procedure. It should be noted that the prices are also affected by
the transmission network, the demand elasticity, and the supply capacity. Therefore, the
electricity market prices are different from the price of physical goods and have strong
fluctuations. According to the Department of Energy of the United States report, the
fluctuations in electricity prices in the United States can reach up to 359.8% [41]. Under
such extreme volatility, market participants may face huge losses. In addition, the crisis in
the electricity market may impose great losses on both consumers and resource owners.

3.3. Load Uncertainty in VPPs

Load uncertainty is one of the main factors in VPPs. The load amount is composed
of two parts, including deterministic and stochastic parts. The deterministic part can
be repeated and depends on factors such as time and geography. The stochastic part is
obtained from measuring and estimating information. Uncertainty in the VPP environment
increases with the appearance of controllable loads such as EVs. At the same time, VPP
motivates customers to change their electricity consumption and actively participate in
smart electricity distribution. However, load demand fluctuates not only with seasonal
changes but also with consumer habits, economic situations, production activities, and
emergency conditions. These characteristics make the load demand uncertainty more
complicated [42].
In general, the three main groups of uncertainties in the VPP decision-making problem
and the factors affecting them are summarized in Figure 3. In fact, the factors affecting
uncertainty tend to present in more than two characteristics in the VPP optimization
scheduling process, and these characteristics are subject to real physical features. For
instance, the output of DG units is affected by the external system [43]. Therefore, familiarity
with uncertainty modeling and its impressive factors, along with combining multiple
uncertainties with physical limitations, is one of the essential subjects in the field of VPP
uncertainty. In addition to modeling an uncertainty factor, in some cases, the relationship
 In general, the three main groups of uncertainties in the VPP decision-making prob-
lem and the factors affecting them are summarized in Figure 3. In fact, the factors affecting
uncertainty tend to present in more than two characteristics in the VPP optimization
scheduling process, and these characteristics are subject to real physical features. For in-
stance, the output of DG units is affected by the external system [43]. Therefore, familiarity
Sustainability 2022, 14, 12486 with uncertainty modeling and its impressive factors, along with combining multiple un- 7 of 23
certainties with physical limitations, is one of the essential subjects in the field of VPP
uncertainty. In addition to modeling an uncertainty factor, in some cases, the relationship
between the uncertainty coefficients should also be considered. For example, the coeffi-
between
cients the uncertainty
affecting coefficientsinshould
multiple uncertainties alsoturbine,
the wind be considered. For together
which work example,asthe coefficients
a cou-
affecting multiple uncertainties in the wind turbine, which work together
ple, have a high impact on the turbine’s power and should be taken into account. as a couple, have
a high impact on the turbine’s power and should be taken into account.

Figure 3. Uncertainty resources in VPPs.

Figure 3. Uncertainty resources in VPPs.
3.4. Modeling Uncertainties
3.4. Modeling Uncertainties
In order to measure the effects of uncertainties in VPP, many explanations have been
In order
given to measure
in recent the effects
research of uncertainties
[44]. Here, we divide in VPP, many explanations
the explanations have
related to been
uncertainty
given in recent research [44]. Here,
modeling into two different parts. we divide the explanations related to uncertainty mod-
eling into two different parts.
(A) Probability distribution: The probability model is a classical method. The most
(A) Probability distribution: The probability model is a classical method. The most
important part of this approach is to extract characteristics of coefficients affecting uncer-
important part of this approach is to extract characteristics of coefficients affecting uncer-
tainty [45]. In modeling random parameters of VPP, probability density function (PDF) as
tainty [45]. In modeling random parameters of VPP, probability density function (PDF) as
well as normal PDF, uniform PDF, and exponential (logarithmic) PDF are used to identify
well as normal PDF, uniform PDF, and exponential (logarithmic) PDF are used to identify
the input parameters. For example, the Weibull PDF is used for modeling wind speed
the input parameters. For example, the Weibull PDF is used for modeling wind speed
uncertainty
uncertainty as Equation
as in in Equation
(1). (1).
k v k v  k−1 v k (1)
PDF( PDF
) = ((v))(= ( )(
c c c c
e e(−( c ) ) (1)
where k is the shape, c is the scale coefficients of the Weibull PDF, and v represents the
where k is the shape, c is the scale coefficients of the Weibull PDF, and v represents the
wind speed in the area where the study was conducted [43]. The normal distribution is
wind speed in the area where the study was conducted [43]. The normal distribution is
used to describe the uncertainty of electricity market price and its PDF can be expressed
asused to describe
in Equation (2). the uncertainty of electricity market price and its PDF can be expressed as
in Equation (2). 1 ( )
f(x) = 1 exp − (x−µ2)
2 (2)
f(x) =σ√2 √
π exp 2 σ (2)
where x is the market price, μ is the position σ parameter,
2π and σ is the size parameter. It
should
where bexnoted
is thethat the expressed
market price, µ PDFs
is theare the input
position variables of
parameter, probability
and σ is the distribution
size parameter. It
description
should be noted that the expressed PDFs are the input variables ofPDD
(PDD). If this situation is not acceptable, i.e., PDFs are not input varia-
probability distribution
bles, the following
description (PDD).explanation can be considered
If this situation [46]. i.e., PDFs are not PDD input variables,
is not acceptable,
the following explanation can be considered [46].
(B) Feasibility description: In many cases, due to incomplete information, it is difficult
to extract the PDF of the uncertainty resource, and only its approximate value can be
predicted. Therefore, a different linguistic category with fuzzy boundaries is used to
describe uncertainty [44]. Assume that the function Y = f(X), where X is the input vector of
uncertainty coefficients and Y is the output vector. Many MFs, such as the trapezoidal MF,
can be applied to describe the membership degrees of probabilistic uncertainty coefficients.
The MF of a fuzzy set is a generalization of the characteristic function in classical sets.
In fuzzy logic, this function represents the degree of truth as an extension of evaluation.
The degree of truth is often confused with probabilities. However, these are two separate
concepts because fuzzy truth refers to membership in vaguely defined sets rather than the
Sustainability 2022, 14, 12486 8 of 23

probability of certain events or conditions. In addition, the historical database and relevant
forecasting tools are also used to obtain the minimum, maximum, and most probable range
of load demand and output power.

4. Energy Management of VPPs

Electric energy is essential in humans’ economic development and social life [47]. In
recent years, the need for electrical energy has increased by 1.3% throughout the year [48].
According to the American EIA, 62% of the electric power produced in America is gener-
ated from fossil fuels [49]. Buildings are a significant producer of greenhouse gases (GHG)
due to the fact that more than 50% of the electrical energy consumed in different countries
is consumed in buildings. The sharp increase in demand should be addressed through an
intelligent EMS, especially for residential loads, as the growth rate of household consump-
tion is above average. In addition, according to the Paris Agreement, many countries have
agreed to reduce their GHG emissions. Therefore, reducing pollutants is achievable by
using RESs and EVs [50]. In addition to the benefits of distributed and renewable resources,
the high penetration of these energy generation units in smart grids brings new challenges
due to the uncertainty in their nature. Conventional EMSs are no longer effective due
to new grid topology and different generation and consumption patterns combined by
RESs and EVs. On this basis, it is vital to develop an optimal scheduling algorithm for
integrating RES resources by considering geographic distribution and uncertainties [51].
The concept of VPP is one of the most practical solutions for energy management which
enables new features by integrating communication networks in the energy system. The
VPP facilitates the necessary interaction among all participants (energy producers and con-
sumers), performs real-time monitoring through a bidirectional energy flow, and improves
energy efficiency. This concept helps consumers to trade their surplus electrical energy in
the market without the intervention of a third party.
On the other hand, consumers who have installed RESs or storage devices (such as bat-
teries, EVs, etc.) can sell their excess power in the market. Consumers who do not have RESs
can also participate in load shifting. Finally, VPP can assist energy management policies by
optimal planning and enhancing the security level against physical cyber-attacks [52,53].
Building a centralized renewable-based power plant requires significant investment while
transmission losses and other costs are still present, making the development of small-scale
RESs widespread [54,55]. On the other hand, a comprehensive management solution is
required to overcome the three main challenges of a renewable-based grid: high risk of
market participation due to the uncertainties of RESs, energy pricing, and the complexity
of energy trading. The VPP, MG, ADN, and load aggregators are four main concepts that
have been used for energy management purposes in smart grids [56]. The VPPs do not
face geographical restrictions based on operating conditions; rather, these power plants
are suitable to supply consumers considering economic, technical, and security issues. In
VPPs, the RESs also appear to determine the energy price (price maker). In addition, MGs
and ADNs are limited according to the network topology, and only centralized control is
accessible in this manner. Figure 4 shows the diagram of energy management approaches
at different levels.
VPP is the only flexible alternative allowing the consumer to act simultaneously as a
price maker and price taker. Accordingly, as an organized platform, the VPP can implement
EMS to deal with the discussed challenges. The VPP activates an energy management
mechanism and analyzes information collected from all power system components to
control and coordinate supply and demand among all energy elements. Subsequently,
an EMS aims to improve the load curve as much as possible and reduce costs for either
providers or users. In fact, VPP is a suitable solution for EMS because of its particular
features such as real-time monitoring, energy transfer controlling, and operational planning
from the viewpoints of electricity producers and consumers, simultaneously. With the EMS
of VPP, all consumers can benefit. In addition, power companies can reduce their operating,
transmission, and maintenance costs and delay the need for new investments. It should
 an EMS aims to improve the load curve as much as possible and reduce costs fo
providers or users. In fact, VPP is a suitable solution for EMS because of its pa
features such as real-time monitoring, energy transfer controlling, and operation
ning from the viewpoints of electricity producers and consumers, simultaneousl
Sustainability 2022, 14, 12486 the EMS of VPP, all consumers can benefit. In addition, power companies can redu
9 of 23

operating, transmission, and maintenance costs and delay the need for new inves
It should be noted that EMS plays an important role in reducing GHG emissions
be noted that EMS plays an important role in reducing GHG emissions since it leads to
leads to declining fossil fuel use and makes RESs more useful and affordable.
declining fossil fuel use and makes RESs more useful and affordable.

Figure 4. Energy management approaches.

Figure 4. Energy management approaches.
5. Planning of VPPs in the Power System
5. Planning of VPPs
Electric energy canin
bethe Power
supplied Systemways, but consumers want it with the
in different
highest quality, lowest cost, and highest reliability. MGs and VPPs are two important
Electric
solutions energy
for reliable cansupply
power be supplied
in a powerinsystem.
different
Since ways, but consumers
these structures want it w
include DERs,
highest quality, lowest cost, and highest reliability. MGs and VPPs are two impor
planning these resources is very important and should be considered [57]. MGs and VPPs
lutions for reliable
share some importantpower supply
features, such asinthe
a power
ability tosystem.
integrateSince
DERsthese
at the structures
distribution includ
level. These two platforms (MG and VPP) are active players in energy markets, but they
planning these resources is very important and should be considered [57]. MGs an
have some differences [58,59]:
share
-
some important features, such as the ability to integrate DERs at the distr
MGs can be operated connected to or disconnected from the main grid, while VPPs
level. can
These
only two platforms
operate (MG andmanner.
in a grid-connected VPP) are active players in energy markets, b
have
- some differences
MGs usually [58,59]:
require some level of energy storage. However, the presence or absence
of storage in VPPs is not very important.
-- MGs can beon
MGs depend operated
hardware connected
changes suchto as or disconnected
inverters and smart from thewhile
switches, main grid, whi
VPPs
can only
heavily operate
depend in a grid-connected
on smart metering and informationmanner. technology.
- - MGs usually require some level of energy storage. However,
MGs include a fixed set of resources in a limited geographic area, whilethe
VPPspresence
can or
of storage in VPPs is not very important.
combine a wide variety of resources in large geographic areas.
- MGs are usually traded in retail markets, while VPPs can also be traded in wholesale
- MGs depend on hardware changes such as inverters and smart switches, whi
markets.
- heavily
MGs maydepend
face legalon smart
and metering
political obstacles,andwhile information
VPPs could betechnology.
implemented based
- MGs
on theinclude a fixedand
current structure setlegal
of resources
tariffs. in a limited geographic area, while V
combine a wideofvariety
For the planning VPPs in of
theresources
power grid,inEMS large geographic
is needed. Figureareas.
5 shows a VPP
- MGs are usually traded in retail markets, while VPPs can also
energy management system. According to Figure 5, EMS is associated with be traded in wh
the electricity
market, load/DER/price forecast, consumers, and distribution network. EMS also receives
markets.
forecast information on energy resources, loads, consumers, etc., and creates its planning
policies based on this information.
Sustainability 14,x12486
2022, 14,
Sustainability 2022, FOR PEER REVIEW 11 of 24 10 of 23

Figure
Figure 5. 5. Intelligent
Intelligent management
management system
system of VPPs.
of VPPs.

Table 2 has examined various modeling approaches for the planning goals of DERs
5.1. Classical Method in Optimal Planning of VPPs
in the context of VPP. The planning of VPPs is a multi-objective problem its goals are to
The Linear Programming (LP) method is the simplest classical mathematical optimi-
maximize profit and, at the same time, minimize the cost of power generation, considering
zation method that is applied when all objectives and constraints are linear or assumed to
all the constraints. Two important aspects of VPP planning are technical and economic
be linear because the real relationships may be very complex [61]. The IP and MILP are
perspectives, as addressed in [60]. From the economic point of view, the operation of DERs
linear algorithms where all or some variables are integers. Based on the number of objec-
must be done to minimize costs or maximize revenues considering the environmental as-
tives and constraints, and type of variables, classical optimization methods in VPPs are
pects. From
formulated as afollows
technical
[62]:point of view, all elements’ physical constraints must be considered
to ensure secure and reliable network operation. There are different methods for VPP
planning, and we will evaluate three of them min
inPP, x paper.
this , ∈ℝ
objective function = min CC, R ∈ ℝ (3)
Table 2. Optimal planning methods in VPP. max SS ∈ N, x , ∈ℝ
Reference Solution Method Description
⎧ x Presenting
, ≤ L ∀t ∈aℋ,
newn strategy
∈ℚ for providing ancillary
[56] MINLP ⎪ energy services
constraints = P ×Maximizing
rate ≤ C ∀t (4)
∈ ℋ and minimizing pollutant
profits
[57] MILP ⎨ emissions in VPP
⎪ ξ × k ≤ θ ∀t ∈ ℋ, k ∈ N
[58] LP ,
⎩ Optimum scheduling of VPP with battery regardless of cost
[59] MINLP Planning industrial VPPs
[60] LP
x , , P , ξ Linear
, ≥ 0 programming of market optimization (5)
In (3), the variablesMILP
[61] p, c, and s stand for power consumption,
Optimum planningcost, and safety,
of day-ahead respec-
markets
tively. x is a set of functions
Mathematical and n is the number of elements. Different time periods can
[62] Optimal planning of VPP considering battery failure
be specified based programming
on the nature of the problem. For example, x , means the electric
power consumptionMathematical
[63] of device n in the time period t.profit
Maximum Variables L, C, and
in the market andθreduction
in Equation (4)
in pollution
programming
represent the maximum power consumption in peak load, cost, and security limit, respec-
tively. However, ξ Monte-Carlo
[64]
, shows the security factor of device n at time t. k shows the priority
Optimum planning to increase profit by considering DR
of using
[65] any device. Equation
MINLP (5) also shows the non-negative limits. of
bi-level planning The LP has been
VPPs
used [66]
in [63] to optimize the electricity
Scenario-based PSO market considering electricity price, renewable
Reserve planning and VPP energy en-
ergy generation, and optimization
EV constraints.
The
[67] MILP Point
is alsoEstimation
used for (PE)
a wide rangePlanning
of optimization
resources inproblems. For instance,
the day-ahead market forthe
VPP
planning
[68] of VPPInterval
and MG has been formulated in [64] by
optimization a MILP
bi-level model. Moreover,
optimization of VPP devel-
oping a business framework [65] and modeling VPP considering
Multi-objective battery
optimization failureprogramming
stochastic cost [66],
[69] PSO
as well as profit maximization and GHG emission minimization [68], are a number of
for VPP
Combination of genetic
[70] and Monte Carlo Planning VPP uncertainties
algorithms
Sustainability 2022, 14, 12486 11 of 23

5.1. Classical Method in Optimal Planning of VPPs

The Linear Programming (LP) method is the simplest classical mathematical optimiza-
tion method that is applied when all objectives and constraints are linear or assumed to be
linear because the real relationships may be very complex [61]. The IP and MILP are linear
algorithms where all or some variables are integers. Based on the number of objectives and
constraints, and type of variables, classical optimization methods in VPPs are formulated
as follows [62]: 
 minPP, xn,t ∈ R
objective function = minCC, Rt ∈ R (3)
maxSS ∈ N, xn,t ∈ R


∑ xn,t ≤ L ∀t ∈ H, n ∈ Q


constraints = ∑ Pn × rate ≤ C ∀t ∈ H (4)

∑ ξn,t × k ≤ θ ∀t ∈ H, k ∈ N


xn,t , Pn , ξn,t ≥ 0 (5)

In (3), the variables p, c, and s stand for power consumption, cost, and safety, respec-
tively. x is a set of functions and n is the number of elements. Different time periods can be
specified based on the nature of the problem. For example, xn,t means the electric power
consumption of device n in the time period t. Variables L, C, and θ in Equation (4) represent
the maximum power consumption in peak load, cost, and security limit, respectively. How-
ever, ξn,t shows the security factor of device n at time t. k shows the priority of using any
device. Equation (5) also shows the non-negative limits. The LP has been used in [63] to
optimize the electricity market considering electricity price, renewable energy generation,
and EV constraints.
The MILP is also used for a wide range of optimization problems. For instance,
the planning of VPP and MG has been formulated in [64] by a MILP model. Moreover,
developing a business framework [65] and modeling VPP considering battery failure
cost [66], as well as profit maximization and GHG emission minimization [68], are a number
of proposed models based on MILP. Mixed integer nonlinear programming (MINLP)
is also used to solve nonlinear optimization problems. The MINLP model is used to
develop a planning model for VPPs in order to aggregate RESs and controllable loads [69].
Mathematical programming is one of the most basic approaches to planning and operating
VPPs. The allocation of limited resources is very important to achieve technical and
economic goals.

5.2. Heuristic and Meta-Heuristic Methods in VPP Planning

Heuristic methods aim to achieve a specific result for a problem that balances the
solution’s accuracy and the computational cost by performing various tests. Although
the final answer cannot be confirmed as the optimal solution in heuristic methods, the
computational load is reduced compared to mathematical methods [69]. Meta-heuristic
approaches are high-level procedures designed to solve various optimization problems
without requiring specific knowledge about the problems and combine multiple heuristics
to reach an optimal or near-optimal solution. The PEM, one of the heuristic methods, uses
random samples to discover the estimated value of the population factors, as discussed
in [70]. The interval optimization, in contrast to PEM has been used to optimize the
operation of a VPP, including wind, PV, and storage systems [71].
The PSO algorithm has been used in [72] to schedule the operation of a VPP as a single
objective optimization considering costs and GHG emissions. In this method, solutions are
particles that move to the best location in a search region (taking the last position), where
location refers to the optimal solution. When all the particles have been completely moved,
it means that one iteration has been completed to find the optimal location.
The authors in [73] used a combination of genetic algorithm (GA) with Monte Carlo
simulation for daily scheduling of VPP. Due to the uncertainties arising from the nature of
Sustainability 2022, 14, 12486 12 of 23

RESs, the stochastic formulation has been studied more than the deterministic formulation
in the last few years since this method can estimate probability distributions.

5.3. VPP Planning Methods Based on Learning

Artificial intelligence and machine learning are interesting and attractive methods
that are used in different branches of science because, unlike conventional optimization
methods, significant expertise is not required to use these approaches [74]. Due to many
information challenges in modern power systems, in addition to the complexity, speed, and
computational load of conventional optimization methods, learning-based optimization
approaches are widely used in scheduling and planning problems of VPP. Three main
categories of machine learning methods, so-called supervised, unsupervised, and reinforce-
ment learning (RL) can be formulated as optimization problems in the machine learning
method. The objective of supervised learning is to minimize the loss function by finding the
optimal mapping function f(x). Equation (6) shows the objective function of a supervised
learning optimization problem, where N is the number of available samples, xn and yn are
the feature vector and function of sample n, respectively. In addition, l represents the loss
function [75].
min 1 N
a N n∑
l(yn , f(xn , a)) (6)
=1
Unsupervised learning usually uses dividing samples into multiple groups for clus-
tering purposes and ensures that the difference between samples in a cluster is as small
as possible. For example, the k means optimization algorithm is formulated, as shown in
Equation (7) that minimizes the loss as the main objective function [76].

1 N
N n∑ ∑ x − C2n2
min (7)
β
=1 x∈β n

x is the feature vector of samples divided into N clusters. C2n2 and βn are the index of the
center and sample of cluster n. The RL technique aims to find an optimal function that
can control the output changes with any change in the environment and choose the best
response for each given state. The RL optimization formula is shown in Equation (8).
 max
 π Vπ ( s )
∞
RL = E( ∑ αp rt+p | St = s) (8)
 Vπ ( s ) =
p=1

In this equation, π(s) and Vπ (s) are the value function and policy function of state S,
respectively. Finally, a penalty coefficient is also defined so that a ∈ [0, 1]. Table 3 summa-
rizes the advantages and disadvantages of all the mentioned programming algorithms. In
general, the components of RL are [77]:
- Policy determines how to deal with each action and how to make decisions in different
situations.
- The reward function determines the goal of the learner function. The purpose of this
function is to give a reward for each action of the agent so that the reward increases as
the goal gets closer.
- The model of the RL problem is probabilistic and stochastic, and its states are non-
deterministic. For one action, it can go to all states but with one probability.
Deep learning and RL have recently become popular in VPP operation planning
optimization. Techniques based on deep learning, including CNN and RNN, have shown
significant capabilities in feature extraction, approximation, and learning [78]. The RL is a
subset of machine learning that provides a mathematical structure for experience-based
learning to achieve optimal control of an MDP in which each agent interacts with the
environment in a trial-and-error manner to learn optimally.
Sustainability 2022, 14, 12486 13 of 23

Table 3. Advantages and disadvantages of VPP scheduling algorithms.

Algorithm Advantages Disadvantages

- Simplicity
- Inability to deal with
- ease of use
uncertainties
IP and LP - Place of reassessment
- Necessary need for objective
- Improving the quality of
function and constraints
decision-making
- Difficult to understand
- Flexibility in model development - Hard implementation of the
MILP - Accurate modeling capability algorithm
- Convergence to the final solution - Slow solving speed—high cost
for large-scale issues
- Low accuracy for
multidimensional environments
- Low cost
- Recognizable pattern of
PEM - Proper accuracy
restriction
- Balance between speed and accuracy
- Weakness in handling samples
with high distribution
- Lack of a precise theoretical basis
- Easy implementation
- Instability in solving scattered
- Parallel computing capability
PSO problems
- Fast and easy convergence
- Convergence to local optimality
- Low computational cost
in complexity problems
- Fast and cheap generalization
Algorithms
- Data analysis - High knowledge is needed for
based on
- Production and extraction of problem implementation
machine learning
features

6. Participation of VPPs in Electricity Markets

VPP can enable intelligent energy consumption in a distributed environment through
optimal electricity generation and load management. This means that users produce and
consume their own energy, which leads to the consumer’s active participation in decision-
making. In addition, VPP is a useful tool for renewable energy integration to ensure grid
balance. These small-scale power plants better compensate for the possible generation
and demand fluctuations. In addition, they reduce the prediction errors and the penalty
of power fluctuations. The generation resources within the VPP have better access to
the electricity markets because it is difficult for them to enter the market alone. Another
important advantage of VPP is using the potential of EV management [79]. An important
point in the electricity market is the pricing strategy. There are different methods in pricing
strategy. Some pricing strategy methods are explained below [80–83].
• Penetration pricing or pricing to gain market share: Some power companies adopt
this pricing policy to penetrate the market and gain a part of the market for a short
period of time. These power companies offer some of their services for free or at a low
price for selling energy for a limited period of several months.
• Economic price or no low-frill price: The pricing strategies of these products are
considered as no low-frill prices where the cost of marketing a product is minimized.
Economical pricing is determined for a certain period when the company does not
spend more on advertising products and services.
• Using psychological pricing strategies: Psychological pricing strategies are an ap-
proach to elicit the consumer’s emotional response rather than their logical response.
For example, a company prices its product at $99 instead of $100. The product is
priced at $100, making the customer feel that the product is not too expensive. For
most consumers, price is a factor in whether or not to buy a product.
Sustainability 2022, 14, 12486 14 of 23

An important advantage of VPP is that it sells energy and increases its profits when
accessing wholesale and retail markets on behalf of DER owners. In this section, we
examine the performance of VPP in different electricity markets. The aim of this section is
to examine the characteristics and performance of VPP in different markets.

6.1. Bilateral Contracts

In electricity markets, bilateral contracts could be used to directly trade power between
the buyer and seller. The energy price, the amount of power, and the contract’s validity
period are agreed upon in this contract.
The growth of RESs in recent years has strengthened this type of contract that avoids
price uncertainty, and thus, ensures price stability in the long term. This contract offers a
strong opportunity to guarantee VPP income due to market price fluctuations and possible
limitations of the transmission system operator [84].

6.2. VPP in the Day-Ahead Market

The day-ahead market is designed to carry out power transactions on an hourly basis
for the next day through market agents’ buying and selling offers [85]. The VPP allows
power producers to access the electricity markets to sell their generated power directly.
Moreover, it allows consumers to produce their energy and sell their surplus in the market.
When the price in the market is high, the strategy of VPP is to produce with its maximum
capacity and sell its excess power at a higher price in the market. Many papers have used
this feature of VPP in futures markets [86,87]. The VPPs usually act as price takers in the
electricity markets [88,89]. However, they act as price makers due to the wide use of DERs
in the structure of VPPs.

6.3. VPP in the Ancillary Services Market

This market aims to increase the reliability and security of the power grid. This
market provides a balance between generation and consumption at each moment. From
the technical point of view, the gradual growth of RESs in current power systems can
weaken the system’s performance in terms of stability and worst case, lead to power system
collapse. Therefore, the authors in [90,91] have included the possibility of frequency control
through VPP to guarantee power quality and system security.

6.4. VPP in the Reserve Market

The reserve market is a mechanism that ensures system security. Usually, producers
who submit their offers for this market are subjected to incentive programs. A Reserve
market is necessary for locations where RESs exist to enhance grid reliability and security.
Based on the obtained results, reserve markets are highly important when the network
demand is maximum. Furthermore, when DERs generate at their full capacity, VPP can sell
the excess power in the day-ahead market or charge the energy storage devices so that they
can participate in the reserve market [92]. Table 4 summarizes the different characteristics
of the markets mentioned above.

6.5. Virtual Powerhouse in Daily Markets

Intra-day markets are designed to regulate the energy traded in the day-ahead market
more precisely because there is more information than the mechanism of the day-ahead
market. A smaller volume of power and energy is traded in this market compared to the
day-ahead market. Intra-day markets have become more important due to the increase in
RESs and their unpredictable nature. This market can also be very useful for the agents
participating in it. For example, suppose a power generator fails for any reason and cannot
generate power. In this case, the owners of the resources can repurchase the energy they
sold in the day-ahead market mechanism in the intra-day market [93].
Sustainability 2022, 14, 12486 15 of 23

Table 4. The main characteristics of market types.

Reference Market Type Characteristics

Offer to buy and sell energy for every hour of the
next day
[87,88] Day-ahead
Increasing the flexibility of the power
system—high operating profit
Increasing security and reliability of power
[90,91] Ancillary services market generation and transmission—the balance of
generation and demand at any time
Management of excess power generation to
[94] Reserve market ensure supply of demand and security of
power supply
- Adjusting the price of energy trading in the
[95] Daily market future market—reducing the cost of supply and
demand imbalance
- Management of power fluctuations between
[96] Real-time market
supply and demand and network security
Sustainability 2022, 14, x FOR PEER REVIEW 16 of 24

6.6. VPP in Real-Time Market

The
6.6. VPP real-timeMarket
in Real-Time market is the last chance to balance supply and demand. This market
usually operates between 5 to 30 min and closes the market during this period. In other
The real-time market is the last chance to balance supply and demand. This market
words,operates
usually in this market,
between 5the to time toand
30 min provide
closes electric power
the market services
during is between
this period. In other5 to 30 min and
words, in this power
the electric market, istheprovided
time to provide electric
according topower services is
the required between 5 Although
demands. to 30 min the intra-day
and the electric
markets allowpower
the is
VPPprovided according
to adjust to the required
its scheduled demands.
energy after Although the intra-
the day-ahead market, it is still
day markets
possible allow
that thethe
VPPVPPcould
to adjust
notits
bescheduled energy after
able to supply the day-ahead
the required powermarket, it
of consumers at the
ispower
still possible that the VPP could not be able to supply the required power of consumers
delivery time. Therefore, the real-time market compensates for the lack of power
at the power delivery time. Therefore, the real-time market compensates for the lack of
supply. Figure 6 shows different steps for market mechanisms.
power supply. Figure 6 shows different steps for market mechanisms.

Figure
Figure6. 6.
Stages of various
Stages energyenergy
of various markets.
markets.
7.7.Challenges
Challengesof Using VPPsVPPs
of Using
VPP enables each DER unit to participate in power system operation and wholesale
VPP enables each DER unit to participate in power system operation and wholesale
markets. However, several challenges in resource operation, control, communication tech-
nologies, andHowever,
markets. severalwithin
power transactions challenges
VPP needintoresource operation,
be addressed. Therefore,control, communication
in this sec-
technologies, and power transactions within VPP need
tion, the challenges of VPP in the power system are investigated. to be addressed. Therefore, in this
section, the challenges of VPP in the power system are investigated.
7.1. Challenges of Control and Operation System
Due to different adjustment factors, range, and stable time of the energy resources
within VPP, the actual control results cannot be exactly what is expected. In order to ac-
curately control DERs in VPP and obtain detailed information about the operation of these
resources, it is essential to provide a variety of ancillary services in multiple time frames
that need some requirements and control elements. Therefore, the system cannot be con-
Sustainability 2022, 14, 12486 16 of 23

7.1. Challenges of Control and Operation System

Due to different adjustment factors, range, and stable time of the energy resources
within VPP, the actual control results cannot be exactly what is expected. In order to
accurately control DERs in VPP and obtain detailed information about the operation of
these resources, it is essential to provide a variety of ancillary services in multiple time
frames that need some requirements and control elements. Therefore, the system cannot be
controlled by a unique method. The control system needs to use different features of DERs
and interacts with the power grid. Therefore, using different algorithms to control each
element (resource) in VPP may negatively affect other resources, and the desired results
may not be achieved [97].
Sustainability 2022, 14, x FOR PEER REVIEW 17 of 24

7.2. Communication and Information Challenges of VPPs

developed. In general,
Based for communication
on modern and telecommunication
smart technologies, information
4G/5G wireless exchange, and other tech-
networks,
thenologies,
presence ofsuch
four as
security and communication
information compression,layerscommunication,
could be included in VPP. Figurecomputing, mea-
intelligent
7 shows
surement,the basic
and layers of thetechnologies,
control communicationmore mechanism in VPP.platforms
advanced The communication
supporting VPP can be
layer is the basic infrastructure for the integration of DERs in VPP. The
developed. In general, for communication and telecommunication information exchange, communication
technologies and hardware outputs ensure the access of the power system to the VPP re-
the presence of four security and communication layers could be included in VPP. Figure 7
sources. The performance layer in VPP is a module that considers all basic functions and
shows theincluding
components, basic layers of the communication
load forecasting, mechanism
information monitoring, in VPP. Theetc.
risk assessment, communication
An- layer
is the basic infrastructure for the integration of DERs in VPP.
other layer of VPP is the service layer. This layer is developed based on the performanceThe communication technolo-
layer
giesandandthehardware
decisions made by that
outputs layer.the
ensure It complements
access of thethepower
functions of theto
system VPP
thesuite
VPP resources. The
byperformance
coordinating the elements
layer in VPPof theis function
a module layer.
thatThis service can
considers allinclude
basic auxiliary
functions ser-
and components,
vices such as peak
including loadload reduction, information
forecasting, energy arbitrage, DR, marketrisk
monitoring, trading, etc. The service
assessment, etc. Another layer of
layer modifies these things and cannot limit them.
VPP is the service layer. This layer is developed based on the performance layer and the
Another important layer in intelligent communication with VPP is the business layer.
decisions made by that layer. It complements the functions of the VPP suite by coordinating
The business layer can provide various business programs for consumers in VPP, such as
the elements
energy generationof the function
management, layer. This
generation service
safety can include
management, auxiliary
electricity market services
trans- such as peak
actions,
load optimal
reduction,energy utilization,
energy optimal
arbitrage, DR, energy
marketresource integration
trading, etc. The services,
serviceetc. In modifies these
layer
addition,
thingsitand provides
cannota smart
limitwebthem.page and mobile app for VPP users.

Figure 7. Communication mechanism for different layers of VPP.

Figure 7. Communication mechanism for different layers of VPP.
A new concept of IoT in the VPP is presented in [96]. The main goal of providing the
Another important layer in intelligent communication with VPP is the business layer.
IoT in VPP is the intelligent control of power transmission in high voltage lines at a low
The
cost. business
Although layer
smart canhave
devices provide various
been used in thebusiness programs
telecommunication for
and consumers in VPP, such
communica-
tion
asstructure
energyofgeneration
VPP; however, the main thinggeneration
management, in using these devices
safety is security checks.
management, electricity market
Therefore, it is necessary
transactions, optimalto use the latest
energy technologies
utilization, to enhance
optimal the resource
energy security ofintegration
VPP. services, etc.
In addition, it provides a smart web page and mobile app for VPP users.
7.3. Power Exchange Challenges
A new concept of IoT in the VPP is presented in [96]. The main goal of providing the
One
IoT inofVPP
the basic challenges
is the of VPP
intelligent is related
control to the uncertainty
of power of DERs.
transmission in On thisvoltage
high basis, lines at a low
the power balance should be met in the VPP. Therefore, power exchanges in VPPs with
more wind turbines and photovoltaic resources may create serious challenges for the grid-
connected mode. In other words, due to the uncertainty in the output power of WT and
PV resources, the best idea is to trade in the electricity market using the potential of these
resources or to have large batteries in the VPP structure. The most important challenge of
VPPs is the uncertainty that was fully examined in the previous sections. However, other
Sustainability 2022, 14, 12486 17 of 23

cost. Although smart devices have been used in the telecommunication and communica-
tion structure of VPP; however, the main thing in using these devices is security checks.
Therefore, it is necessary to use the latest technologies to enhance the security of VPP.

7.3. Power Exchange Challenges

One of the basic challenges of VPP is related to the uncertainty of DERs. On this
basis, the power balance should be met in the VPP. Therefore, power exchanges in VPPs
with more wind turbines and photovoltaic resources may create serious challenges for
the grid-connected mode. In other words, due to the uncertainty in the output power of
WT and PV resources, the best idea is to trade in the electricity market using the potential
of these resources or to have large batteries in the VPP structure. The most important
challenge of VPPs is the uncertainty that was fully examined in the previous sections.
However, other issues such as maintenance, resource failure, and resource lifetime can
also be other important challenges for the energy exchange issue. It is very important to
consider this issue in order to increase reliability, as well as to supply the critical and large
loads that are connected to VPP.

7.4. Blockchain Applications in VPPs

Blockchain is basically an online ledger that securely stores information and makes
it available to everyone as an immutable source of information. This technology is not
controlled by one particular entity but is distributed among many computers and uses a
specific and secure format to record data so that other people cannot change or tamper with
the information. Using methods such as data decentralization and protecting them against
manipulation has facilitated transparency and information exchange in the blockchain.
Thus, this particular data recording method has provided a great capacity to shape the
new wave of technology and the ability to interact with it. Many start-up companies are
trying to use blockchain as a tool to develop accessible and sustainable energy networks
by developing instant information-sharing methods. The idea behind the creation of
blockchain-based energy networks is relatively simple. As long as consumers have complete
control over how to supply their consumed energy and complete information based on
how it is produced, effective competition and sustainable energy development will be
possible [98,99]. Using a blockchain-based smart grid, consumers will be able to compare
their energy suppliers better and purchase directly from them. In Estonia, a blockchain-
based smart grid called WePower has been piloted, which is actually a kind of customer-
choice energy market that operates based on data provided by a set of energy generators.
With the help of how to manage this program, consumers can enjoy more freedom of
action in choosing their energy supply source (according to the exact requirements of
instantaneous production and its price). Decentralization of energy systems will help
the process of democratizing information and empower people to make better decisions.
Blockchain-based smart grids, as a powerful tool, can reduce inequality and provide
cheaper and cleaner energy in all areas (both developed power grids and energy-deprived
areas) [100]. Therefore, blockchain can be one of the most important solutions to reduce
carbon emissions in the long term and contribute to sustainable development worldwide.
The growth of the use of VPP in recent years is due to the need for higher reliability,
lower cost, and participation of small generators in the electricity market. In the previous
section, the challenges of using VPPs were explained. The most important challenge is
assigned to communication and telecommunication issues within VPP. The idea in this
paper to improve this case is to use blockchain in a decentralized manner. In other words,
a decentralized blockchain-based communication solution should be provided for each
DER. Today, the exploitation of VPP requires coordination among the different small-scale
generation units. Blockchain is one of the most important topics related to every source
in VPP.
Blockchain technology can be a suitable method to ensure transparency between
integrated DERs in the VPP. The basic concept of blockchain is to improve information
Sustainability 2022, 14, 12486 18 of 23

transparency between interacting parties [101]. In the electric energy sector, blockchain
technology has played an essential role in solving communication challenges in the context
of VPP and improving the efficiency of current energy control processes. There are various
sets of papers regarding utilizing blockchain in power systems for a wide range of applica-
tions such as peer-to-peer (P2P) energy trading, electrical dynamics, network operation
and management, monitoring of RESs, and demand response.
When VPP operators apply a control command to each DER, this control command is
completely recorded in the blockchain. The recorded data in the blockchain are the traded
power, the time intervals, fluctuations, and all other information related to the contract
between DER and VPP. Finally, after the end of the event and the control command, the
smart contract that has been set up is published, and based on the recorded data in the
blockchain, payment is made to the DER owners [102].
Three types of transactions are provided in blockchain for energy management. The
first type of blockchain transaction for VPP is network services, which include Feed-in
tariffs (FIT) information or guaranteed electricity purchase policy, information related
to ancillary services, and information related to the demand response. The FIT service
allows VPP users to sell their generated energy to the grid and receive a guaranteed tariff
for their electricity production. The FIT service is the fee that the owners of electricity
generators (solar and wind) receive for selling their electricity to the main grid. This cost
is in addition to the cost of power sales and is considered an incentive to encourage these
owners to sell their generated power to the government or large private sectors. Note that
the amount paid as FIT varies between different retailers. This type of transaction provided
by blockchain is between VPP users and the network.
Another type of transaction that blockchain has provided is the P2P strategy that is
between VPP users. This strategy allows the VPP user to communicate with each other and
buy/sell energy directly. The third type is the token-based transaction since the blockchain
offers the token as a digital currency to facilitate online payment [103].
There are many potential applications for blockchain technology in the energy field,
and most of them target P2P energy trading. However, in power systems, the performance
of both sides may be different, and this difference is also due to the presence of EVs or
prosumers. Other applications of blockchain in the electrical energy sector can be classified
into the following two categories:
- Exchange of electrical energy
- Effectiveness in responding to the load and checking RESs
The first category includes all the programs that two different users do in order to
exchange power. The P2P strategy is included in this category. These transactions can
be managed centrally under the supervision of the network operator (TSO or DSO) to
obtain the maximum benefit for both network and users. The second category includes
renewable and loads response units. The reference [104] has investigated the connection
between VPP and MG using blockchain. This reference stated that blockchain could be
useful in the connection between the MG and the VPP. The authors in [105] also presented
the blockchain application of a distributed algorithm. The blockchain energy exchange
plan among VPP users has been addressed in [106].

8. Conclusions and Future Directions

This paper presented a relatively comprehensive review of the VPP concept. The paper
mainly discussed existing definitions, general components, basic challenges, operational
planning methods, market participation, energy management tools, and energy transac-
tions in the context of VPP. As a novel aspect, the paper has concentrated on blockchain
applications in VPP. In this paper, various uncertainties of VPP, including load, renewable
generation, and market price uncertainty, were fully investigated. The energy management
of VPP is another important topic in this review paper. This paper explains that each VPP
allows the resources within its structure to exchange freely with the energy markets. Classi-
cal and heuristic methods were also investigated to optimize VPP operation in the power
Sustainability 2022, 14, 12486 19 of 23

system. Moreover, different markets were discussed, and the impacts of these markets on
VPP have been evaluated. The last part of the review paper is about the challenges of VPP.
Communication challenges are among the basic challenges in VPPs. Another challenge is
system control and operation, which must be solved using different algorithms to facilitate
coordination between VPP control and operation. Despite examining the advantages and
disadvantages of VPP, there are still fundamental challenges to the wide use of VPP, so
appropriate infrastructures and solutions must be provided. Therefore, the following
suggestions can be made for future research.
• To use neural networks for VPP in the electricity markets and the overall modeling of
these power plants in the power system.
• To propose novel control methods in the context of VPP.
• To present an appropriate protection scheme for VPP.

Author Contributions: Conceptualization, M.M.R. and E.H.-F.; methodology, M.M.R.; supervision,

E.H.-F. and S.H.; writing—original draft, E.H.-F., S.H. and S.B.E.; writing—review and editing, E.H.-F.,
S.H. and S.B.E. All authors have read and agreed to the published version of the manuscript.
Funding: This work has been carried out in the framework of the European Union’s Horizon
2020 research and innovation program under grant agreement No. 957852 (Virtual Power Plant
forInteroperable and Smart is LANDS’-VPP4ISLANDS).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations

ADN Active Distribution Network

CNN Convolutional Neural Network
DR Demand Response
DERs Distributed Energy Resources
DG Distributed Generation
DSO Distribution System Operator
EV Electric Vehicle
EIA Energy Information Administration
EMS Energy Management System
EN Energy Nodes
FIT Feed-In Tariff
IP Integer Programming
IoT Internet of Things
LP Linear Programming
MDP Markov Decision Process
MF Membership Function
MG Micro-grid
MINLP Mixed Integer Non-Linear Programming
PSO Particle Swarm Optimization
PV Photovoltaic
PEM Point-estimate Method
PDF Probability Distribution Function
RL Reinforcement Learning
RES Renewable Energy Source
RNN Recurrent Neural Networks
TSO Transmission System Operator
VPP Virtual Power Plant
WT Wind Turbine
Sustainability 2022, 14, 12486 20 of 23

References
1. Amuta, E.O.; Wara, S.T.; Agbetuyi, A.F.; Sawyerr, B.A. Weibull Distribution-Based Analysis for Reliability Assessment of an
Isolated Power Micro-Grid System. Mater. Today Proc. 2022, 65, 2215–2220. [CrossRef]
2. Alagappan, A.; Venkatachary, S.K.; Andrews, L.J.B. Augmenting Zero Trust Network Architecture to Enhance Security in VPPs.
Energy Rep. 2022, 8, 1309–1320. [CrossRef]
3. International Energy Agency. Key World Energy Statistics 2015; IEA: Paris, France, 2015.
4. Delft, C.E.; Directorate-General for Energy (European Commission); Hinicio; ICF International. Financing the Energy Renovation of
Buildings with Cohesion Policy Funding; Technical Guidance; Publications Office of the European Union: Luxembourg, 2015.
5. Lin, W.-T.; Chen, G.; Li, C. Risk-Averse Energy Trading among Peer-to-Peer Based VPPs: A Stochastic Game Approach. Int. J.
Electr. Power Energy Syst. 2021, 132, 107145. [CrossRef]
6. Li, Z.; Liu, M.; Xie, M.; Zhu, J. Robust Optimization Approach with Acceleration Strategies to Aggregate an Active Distribution
System as a Virtual Power Plant. Int. J. Electr. Power Energy Syst. 2022, 142, 108316. [CrossRef]
7. Wilkens, J.; Thulesius, H.; Schmidt, I.; Carlsson, C. The 2015 National Cancer Program in Sweden: Introducing Standardized Care
Pathways in a Decentralized System. Health Policy 2016, 120, 1378–1382. [CrossRef]
8. Magdy, F.E.Z.; Ibrahim, D.K.; SABRY, W. Virtual Power Plants Modeling and Simulation Using Innovative Electro-Economical
Concept. In Proceedings of the 2019 16th Conference on Electrical Machines, Drives and Power Systems (ELMA), Varna, Bulgaria,
6–8 June 2019; pp. 1–5.
9. Pudjianto, D.; Ramsay, C.; Strbac, G. Virtual Power Plant and System Integration of Distributed Energy Resources. IET Renew.
Power Gener. 2007, 1, 10–16. [CrossRef]
10. Xin, H.; Gan, D.; Li, N.; Li, H.; Dai, C. Virtual Power Plant-Based Distributed Control Strategy for Multiple Distributed Generators.
IET Control Theory Appl. 2013, 7, 90–98. [CrossRef]
11. Bagchi, A.; Goel, L.; Wang, P. Adequacy Assessment of Generating Systems Incorporating Storage Integrated Virtual Power
Plants. IEEE Trans. Smart Grid 2018, 10, 3440–3451. [CrossRef]
12. Zubov, D. An Iot Concept of the Small Virtual Power Plant Based on Arduino Platform and Mqtt Protocol. In Proceedings of the
2016 International Conference on Applied Internet and Information Technologies, Bitola, Macedonia, 3–4 June 2016; pp. 95–103.
13. Sierla, S.; Pourakbari-Kasmaei, M.; Vyatkin, V. A Taxonomy of Machine Learning Applications for Virtual Power Plants and
Home/Building Energy Management Systems. Autom. Constr. 2022, 136, 104174. [CrossRef]
14. Cabrane, Z.; Kim, J.; Yoo, K.; Lee, S.H. Fuzzy Logic Supervisor-Based Novel Energy Management Strategy Reflecting Different
Virtual Power Plants. Electr. Power Syst. Res. 2022, 205, 107731. [CrossRef]
15. Azimi, Z.; Hooshmand, R.-A.; Soleymani, S. Optimal Integration of Demand Response Programs and Electric Vehicles in
Coordinated Energy Management of Industrial Virtual Power Plants. J. Energy Storage 2021, 41, 102951. [CrossRef]
16. Aguilar, J.; Bordons, C.; Arce, A. Chance Constraints and Machine Learning Integration for Uncertainty Management in Virtual
Power Plants Operating in Simultaneous Energy Markets. Int. J. Electr. Power Energy Syst. 2021, 133, 107304. [CrossRef]
17. Giuntoli, M.; Poli, D. Optimized Thermal and Electrical Scheduling of a Large Scale Virtual Power Plant in the Presence of Energy
Storages. IEEE Trans. Smart Grid 2013, 4, 942–955. [CrossRef]
18. Mashhour, E.; Moghaddas-Tafreshi, S.M. Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning
Reserve Markets—Part I: Problem Formulation. IEEE Trans. Power Syst. 2010, 26, 949–956. [CrossRef]
19. Van Summeren, L.F.M.; Wieczorek, A.J.; Bombaerts, G.J.T.; Verbong, G.P.J. Community Energy Meets Smart Grids: Reviewing
Goals, Structure, and Roles in Virtual Power Plants in Ireland, Belgium and the Netherlands. Energy Res. Soc. Sci. 2020, 63, 101415.
[CrossRef]
20. Pandžić, H.; Morales, J.M.; Conejo, A.J.; Kuzle, I. Offering Model for a Virtual Power Plant Based on Stochastic Programming.
Appl. Energy 2013, 105, 282–292. [CrossRef]
21. Dong, L.; Fan, S.; Wang, Z.; Xiao, J.; Zhou, H.; Li, Z.; He, G. An Adaptive Decentralized Economic Dispatch Method for Virtual
Power Plant. Appl. Energy 2021, 300, 117347. [CrossRef]
22. Sakr, W.S.; EL-Sehiemy, R.A.; Azmy, A.M.; Abd el-Ghany, H.A. Identifying Optimal Border of Virtual Power Plants Considering
Uncertainties and Demand Response. Alex. Eng. J. 2022, 61, 9673–9713. [CrossRef]
23. Tan, C.; Tan, Z.; Wang, G.; Du, Y.; Pu, L.; Zhang, R. Business Model of Virtual Power Plant Considering Uncertainty and Different
Levels of Market Maturity. J. Clean. Prod. 2022, 362, 131433. [CrossRef]
24. Ju, L.; Yin, Z.; Zhou, Q.; Li, Q.; Wang, P.; Tian, W.; Li, P.; Tan, Z. Nearly-Zero Carbon Optimal Operation Model and Benefit
Allocation Strategy for a Novel Virtual Power Plant Using Carbon Capture, Power-to-Gas, and Waste Incineration Power in Rural
Areas. Appl. Energy 2022, 310, 118618. [CrossRef]
25. Jordehi, A.R. A Stochastic Model for Participation of Virtual Power Plants in Futures Markets, Pool Markets and Contracts with
Withdrawal Penalty. J. Energy Storage 2022, 50, 104334. [CrossRef]
26. Tan, C.; Wang, J.; Geng, S.; Pu, L.; Tan, Z. Three-Level Market Optimization Model of Virtual Power Plant with Carbon Capture
Equipment Considering Copula—CVaR Theory. Energy 2021, 237, 121620. [CrossRef]
27. Lombardi, P.; Powalko, M.; Rudion, K. Optimal Operation of a Virtual Power Plant. In Proceedings of the 2009 IEEE Power &
Energy Society General Meeting, Calgary, AB, Canada, 26–30 July 2009; pp. 1–6.
Sustainability 2022, 14, 12486 21 of 23

28. Tarazona, C.; Muscholl, M.; Lopez, R.; Passelergue, J.C. Integration of Distributed Energy Resources in the Operation of Energy
Management Systems. In Proceedings of the 2009 IEEE PES/IAS Conference on Sustainable Alternative Energy (SAE), Valencia,
Spain, 28–30 September 2009; pp. 1–5.
29. Bhuiyan, E.A.; Hossain, M.Z.; Muyeen, S.M.; Fahim, S.R.; Sarker, S.K.; Das, S.K. Towards next Generation Virtual Power Plant:
Technology Review and Frameworks. Renew. Sustain. Energy Rev. 2021, 150, 111358. [CrossRef]
30. Lima, R.M.; Conejo, A.J.; Giraldi, L.; Le Maitre, O.; Hoteit, I.; Knio, O.M. Sample Average Approximation for Risk-Averse
Problems: A Virtual Power Plant Scheduling Application. EURO J. Comput. Optim. 2021, 9, 100005. [CrossRef]
31. Elgamal, A.H.; Kocher-Oberlehner, G.; Robu, V.; Andoni, M. Optimization of a Multiple-Scale Renewable Energy-Based Virtual
Power Plant in the UK. Appl. Energy 2019, 256, 113973. [CrossRef]
32. El Bakari, K.; Kling, W.L. Virtual Power Plants: An Answer to Increasing Distributed Generation. In Proceedings of the 2010 IEEE
PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), Gothenburg, Sweden, 11–13 October 2010; pp. 1–6.
33. Sikorski, T.; Jasiński, M.; Ropuszyńska-Surma, E.; Weglarz, M.; Kaczorowska, D.; Kostyła, P.; Leonowicz, Z.; Lis, R.; Rezmer, J.;
Rojewski, W.; et al. A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept:
Economic Aspects. Energies 2019, 12, 4447. [CrossRef]
34. Arif, M.S.B.; Hasan, M.A. Microgrid Architecture, Control, and Operation. In Hybrid-Renewable Energy Systems in Microgrids;
Elsevier: Amsterdam, The Netherlands, 2018; pp. 23–37.
35. Hashmi, M.; Hänninen, S.; Mäki, K. Survey of Smart Grid Concepts, Architectures, and Technological Demonstrations Worldwide.
In Proceedings of the 2011 IEEE PES Conference on Innovative Smart Grid Technologies Latin America (ISGT LA), Medellin,
Colombia, 19–21 October 2011; pp. 1–7.
36. Binding, C.; Gantenbein, D.; Jansen, B.; Sundström, O.; Andersen, P.B.; Marra, F.; Poulsen, B.; Træholt, C. Electric Vehicle Fleet
Integration in the Danish EDISON Project-a Virtual Power Plant on the Island of Bornholm. In Proceedings of the IEEE PES
General Meeting, Minneapolis, MN, USA, 25–29 July 2010; pp. 1–8.
37. Yu, S.; Fang, F.; Liu, Y.; Liu, J. Uncertainties of Virtual Power Plant: Problems and Countermeasures. Appl. Energy 2019, 239,
454–470. [CrossRef]
38. Liu, C.; Yang, R.J.; Yu, X.; Sun, C.; Wong, P.S.P.; Zhao, H. Virtual Power Plants for a Sustainable Urban Future. Sustain. Cities Soc.
2021, 65, 102640. [CrossRef]
39. Urcan, D.-C.; BicẶ, D. Simulation Concept of a Virtual Power Plant Based on Real-Time Data Acquisition. In Proceedings of the
2019 54th International Universities Power Engineering Conference (UPEC), Bucharest, Romania, 3–6 September 2019; pp. 1–4.
40. Thavlov, A.; Bindner, H.W. An Aggregation Model for Households Connected in the Low-Voltage Grid Using a VPP Interface. In
Proceedings of the IEEE PES ISGT Europe 2013, Lyngby, Denmark, 6–9 October 2013; pp. 1–5.
41. Nosratabadi, S.M.; Hooshmand, R.-A.; Gholipour, E. A Comprehensive Review on Microgrid and Virtual Power Plant Concepts
Employed for Distributed Energy Resources Scheduling in Power Systems. Renew. Sustain. Energy Rev. 2017, 67, 341–363.
[CrossRef]
42. Aien, M.; Hajebrahimi, A.; Fotuhi-Firuzabad, M. A Comprehensive Review on Uncertainty Modeling Techniques in Power
System Studies. Renew. Sustain. Energy Rev. 2016, 57, 1077–1089. [CrossRef]
43. Shabanzadeh, M.; Sheikh-El-Eslami, M.-K.; Haghifam, M.-R. A Medium-Term Coalition-Forming Model of Heterogeneous DERs
for a Commercial Virtual Power Plant. Appl. Energy 2016, 169, 663–681. [CrossRef]
44. Zamani, A.G.; Zakariazadeh, A.; Jadid, S. Day-Ahead Resource Scheduling of a Renewable Energy Based Virtual Power Plant.
Appl. Energy 2016, 169, 324–340. [CrossRef]
45. Tan, Z.; Zhong, H.; Xia, Q.; Kang, C.; Wang, X.S.; Tang, H. Estimating the Robust PQ Capability of a Technical Virtual Power Plant
under Uncertainties. IEEE Trans. Power Syst. 2020, 35, 4285–4296. [CrossRef]
46. Peik-Herfeh, M.; Seifi, H.; Sheikh-El-Eslami, M.K. Decision Making of a Virtual Power Plant under Uncertainties for Bidding in a
Day-Ahead Market Using Point Estimate Method. Int. J. Electr. Power Energy Syst. 2013, 44, 88–98. [CrossRef]
47. Pourbehzadi, M.; Niknam, T.; Aghaei, J.; Mokryani, G.; Shafie-khah, M.; Catalão, J.P.S. Optimal Operation of Hybrid AC/DC
Microgrids under Uncertainty of Renewable Energy Resources: A Comprehensive Review. Int. J. Electr. Power Energy Syst. 2019,
109, 139–159. [CrossRef]
48. Nosratabadi, S.M.; Hooshmand, R.-A.; Gholipour, E. Stochastic Profit-Based Scheduling of Industrial Virtual Power Plant Using
the Best Demand Response Strategy. Appl. Energy 2016, 164, 590–606. [CrossRef]
49. Luo, X.; Wang, J.; Dooner, M.; Clarke, J. Overview of Current Development in Electrical Energy Storage Technologies and the
Application Potential in Power System Operation. Appl. Energy 2015, 137, 511–536. [CrossRef]
50. Su, Y.-W. Residential Electricity Demand in Taiwan: Consumption Behavior and Rebound Effect. Energy Policy 2019, 124, 36–45.
[CrossRef]
51. Sinsel, S.R.; Riemke, R.L.; Hoffmann, V.H. Challenges and Solution Technologies for the Integration of Variable Renewable Energy
Sources—A Review. Renew. Energy 2020, 145, 2271–2285. [CrossRef]
52. Prabatha, T.; Hager, J.; Carneiro, B.; Hewage, K.; Sadiq, R. Analyzing Energy Options for Small-Scale off-Grid Communities: A
Canadian Case Study. J. Clean. Prod. 2020, 249, 119320. [CrossRef]
53. Adu-Kankam, K.O.; Camarinha-Matos, L.M. Towards Collaborative Virtual Power Plants: Trends and Convergence. Sustain.
Energy Grids Netw. 2018, 16, 217–230. [CrossRef]
Sustainability 2022, 14, 12486 22 of 23

54. Gharaibeh, A.; Salahuddin, M.A.; Hussini, S.J.; Khreishah, A.; Khalil, I.; Guizani, M.; Al-Fuqaha, A. Smart Cities: A Survey on
Data Management, Security, and Enabling Technologies. IEEE Commun. Surv. Tutor. 2017, 19, 2456–2501. [CrossRef]
55. Steffen, B. Estimating the Cost of Capital for Renewable Energy Projects. Energy Econ. 2020, 88, 104783. [CrossRef]
56. Ramos, C.; Garcia, A.S.; Moreno, B.; Diaz, G. Small-Scale Renewable Power Technologies Are an Alternative to Reach a Sustainable
Economic Growth: Evidence from Spain. Energy 2019, 167, 13–25. [CrossRef]
57. Zhang, G.; Jiang, C.; Wang, X. Comprehensive Review on Structure and Operation of Virtual Power Plant in Electrical System.
IET Gener. Transm. Distrib. 2019, 13, 145–156. [CrossRef]
58. Mancarella, P. MES (Multi-Energy Systems): An Overview of Concepts and Evaluation Models. Energy 2014, 65, 1–17. [CrossRef]
59. Robu, V.; Chalkiadakis, G.; Kota, R.; Rogers, A.; Jennings, N.R. Rewarding Cooperative Virtual Power Plant Formation Using
Scoring Rules. Energy 2016, 117, 19–28. [CrossRef]
60. Pedrasa, M.A.A.; Spooner, T.D.; MacGill, I.F. A Novel Energy Service Model and Optimal Scheduling Algorithm for Residential
Distributed Energy Resources. Electr. Power Syst. Res. 2011, 81, 2155–2163. [CrossRef]
61. Nikonowicz, Ł.; Milewski, J. Virtual Power Plants-General Review: Structure, Application and Optimization. J. Power Technol.
2012, 92, 135–149.
62. Alahyari, A.; Ehsan, M.; Mousavizadeh, M. A Hybrid Storage-Wind Virtual Power Plant (VPP) Participation in the Electricity
Markets: A Self-Scheduling Optimization Considering Price, Renewable Generation, and Electric Vehicles Uncertainties. J. Energy
Storage 2019, 25, 100812. [CrossRef]
63. Ju, L.; Tan, Z.; Yuan, J.; Tan, Q.; Li, H.; Dong, F. A Bi-Level Stochastic Scheduling Optimization Model for a Virtual Power Plant
Connected to a Wind-Photovoltaic-Energy Storage System Considering the Uncertainty and Demand Response. Appl. Energy
2016, 171, 184–199. [CrossRef]
64. Akkacs, Ö.P.; Çam, E. Optimal Operational Scheduling of a Virtual Power Plant Participating in Day-Ahead Market with
Consideration of Emission and Battery Degradation Cost. Int. Trans. Electr. Energy Syst. 2020, 30, e12418.
65. Sakr, W.S.; Abd el-Ghany, H.A.; EL-Sehiemy, R.A.; Azmy, A.M. Techno-Economic Assessment of Consumers’ Participation in
the Demand Response Program for Optimal Day-Ahead Scheduling of Virtual Power Plants. Alex. Eng. J. 2020, 59, 399–415.
[CrossRef]
66. Kong, X.; Xiao, J.; Wang, C.; Cui, K.; Jin, Q.; Kong, D. Bi-Level Multi-Time Scale Scheduling Method Based on Bidding for
Multi-Operator Virtual Power Plant. Appl. Energy 2019, 249, 178–189. [CrossRef]
67. Qiu, J.; Meng, K.; Zheng, Y.; Dong, Z.Y. Optimal Scheduling of Distributed Energy Resources as a Virtual Power Plant in a
Transactive Energy Framework. IET Gener. Transm. Distrib. 2017, 11, 3417–3427. [CrossRef]
68. Hooshmand, R.-A.; Nosratabadi, S.M.; Gholipour, E. Event-Based Scheduling of Industrial Technical Virtual Power Plant
Considering Wind and Market Prices Stochastic Behaviors—A Case Study in Iran. J. Clean. Prod. 2018, 172, 1748–1764. [CrossRef]
69. Wei, C.; Xu, J.; Liao, S.; Sun, Y.; Jiang, Y.; Ke, D.; Zhang, Z.; Wang, J. A Bi-Level Scheduling Model for Virtual Power Plants with
Aggregated Thermostatically Controlled Loads and Renewable Energy. Appl. Energy 2018, 224, 659–670. [CrossRef]
70. Zamani, A.G.; Zakariazadeh, A.; Jadid, S.; Kazemi, A. Stochastic Operational Scheduling of Distributed Energy Resources in a
Large Scale Virtual Power Plant. Int. J. Electr. Power Energy Syst. 2016, 82, 608–620. [CrossRef]
71. Hadayeghparast, S.; Farsangi, A.S.; Shayanfar, H. Day-Ahead Stochastic Multi-Objective Economic/Emission Operational
Scheduling of a Large Scale Virtual Power Plant. Energy 2019, 172, 630–646. [CrossRef]
72. Ju, L.; Li, H.; Zhao, J.; Chen, K.; Tan, Q.; Tan, Z. Multi-Objective Stochastic Scheduling Optimization Model for Connecting a
Virtual Power Plant to Wind-Photovoltaic-Electric Vehicles Considering Uncertainties and Demand Response. Energy Convers.
Manag. 2016, 128, 160–177. [CrossRef]
73. Fan, S.; Ai, Q.; Piao, L. Fuzzy Day-Ahead Scheduling of Virtual Power Plant with Optimal Confidence Level. IET Gener. Transm.
Distrib. 2016, 10, 205–212. [CrossRef]
74. Shayegan-Rad, A.; Badri, A.; Zangeneh, A. Day-Ahead Scheduling of Virtual Power Plant in Joint Energy and Regulation Reserve
Markets under Uncertainties. Energy 2017, 121, 114–125. [CrossRef]
75. Ball, M.O. Heuristics Based on Mathematical Programming. Surv. Oper. Res. Manag. Sci. 2011, 16, 21–38. [CrossRef]
76. Rouzbahani, H.M.; Karimipour, H.; Lei, L. A Review on Virtual Power Plant for Energy Management. Sustain. Energy Technol.
Assess. 2021, 47, 101370. [CrossRef]
77. Malhotra, Y. AI, Model Risk Management in AI, Machine Learning & Deep Learning: Princeton Presentations in AI-ML Risk
Management & Control Systems (Presentation Slides). In Proceedings of the Machine Learning and Deep Learning Conference,
Princeton University, Princeton, NJ, USA, 21 April 2018.
78. Li, S.; Liu, G.; Tang, X.; Lu, J.; Hu, J. An Ensemble Deep Convolutional Neural Network Model with Improved DS Evidence
Fusion for Bearing Fault Diagnosis. Sensors 2017, 17, 1729. [CrossRef]
79. Lei, L.; Tan, Y.; Zheng, K.; Liu, S.; Zhang, K.; Shen, X. Deep Reinforcement Learning for Autonomous Internet of Things: Model,
Applications and Challenges. IEEE Commun. Surv. Tutor. 2020, 22, 1722–1760. [CrossRef]
80. Wu, W.; Huang, X.; Wu, C.-H.; Tsai, S.-B. Pricing Strategy and Performance Investment Decisions in Competitive Crowdfunding
Markets. J. Bus. Res. 2022, 140, 491–497. [CrossRef]
81. Shojaabadi, S.; Galvani, S.; Talavat, V. Wind Power Offer Strategy in Day-Ahead Market Considering Price Bidding Strategy for
Electric Vehicle Aggregators. J. Energy Storage 2022, 51, 104339. [CrossRef]
Sustainability 2022, 14, 12486 23 of 23

82. Xia, Y.; Xie, J.; Zhu, W.; Liang, L. Pricing Strategy in the Product and Service Market. J. Manag. Sci. Eng. 2021, 6, 211–234.
[CrossRef]
83. Adhikari, A.; Sharma, M.; Basu, S.; Jha, A.K. Uniform or Spatially Differentiated? Pricing Strategies for Information Goods under
Simultaneous and Sequential Decision-Making in Multi-Market Context. J. Retail. Consum. Serv. 2022, 64, 102832. [CrossRef]
84. Saboori, H.; Mohammadi, M.; Taghe, R. Virtual Power Plant (VPP), Definition, Concept, Components and Types. In Proceedings
of the 2011 Asia-Pacific Power and Energy Engineering Conference, Washington, DC, USA, 25–28 March 2011; pp. 1–4.
85. Liu, Z.; Zheng, W.; Qi, F.; Wang, L.; Zou, B.; Wen, F.; Xue, Y. Optimal Dispatch of a Virtual Power Plant Considering Demand
Response and Carbon Trading. Energies 2018, 11, 1488. [CrossRef]
86. Rahmani-Dabbagh, S.; Sheikh-El-Eslami, M.K. A Profit Sharing Scheme for Distributed Energy Resources Integrated into a Virtual
Power Plant. Appl. Energy 2016, 184, 313–328. [CrossRef]
87. Baringo, L.; Freire, M.; García-Bertrand, R.; Rahimiyan, M. Offering Strategy of a Price-Maker Virtual Power Plant in Energy and
Reserve Markets. Sustain. Energy Grids Netw. 2021, 28, 100558. [CrossRef]
88. Toubeau, J.-F.; De Grève, Z.; Vallée, F. Medium-Term Multimarket Optimization for Virtual Power Plants: A Stochastic-Based
Decision Environment. IEEE Trans. Power Syst. 2017, 33, 1399–1410. [CrossRef]
89. Pandžić, H.; Kuzle, I.; Capuder, T. Virtual Power Plant Mid-Term Dispatch Optimization. Appl. Energy 2013, 101, 134–141.
[CrossRef]
90. Yang, D.; He, S.; Wang, M.; Pandžić, H. Bidding Strategy for Virtual Power Plant Considering the Large-Scale Integrations of
Electric Vehicles. IEEE Trans. Ind. Appl. 2020, 56, 5890–5900. [CrossRef]
91. Tang, W.; Yang, H.-T. Optimal Operation and Bidding Strategy of a Virtual Power Plant Integrated with Energy Storage Systems
and Elasticity Demand Response. IEEE Access 2019, 7, 79798–79809. [CrossRef]
92. Tajeddini, M.A.; Rahimi-Kian, A.; Soroudi, A. Risk Averse Optimal Operation of a Virtual Power Plant Using Two Stage Stochastic
Programming. Energy 2014, 73, 958–967. [CrossRef]
93. Wang, H.; Riaz, S.; Mancarella, P. Integrated Techno-Economic Modeling, Flexibility Analysis, and Business Case Assessment of
an Urban Virtual Power Plant with Multi-Market Co-Optimization. Appl. Energy 2020, 259, 114142. [CrossRef]
94. Soares, J.; Ghazvini, M.A.F.; Vale, Z.; de Moura Oliveira, P.B. A Multi-Objective Model for the Day-Ahead Energy Resource
Scheduling of a Smart Grid with High Penetration of Sensitive Loads. Appl. Energy 2016, 162, 1074–1088. [CrossRef]
95. Ullah, Z.; Mokryani, G.; Campean, F.; Hu, Y.F. Comprehensive Review of VPPs Planning, Operation and Scheduling Considering
the Uncertainties Related to Renewable Energy Sources. IET Energy Syst. Integr. 2019, 1, 147–157. [CrossRef]
96. Ramli, M.A.M.; Bouchekara, H.R.E.H. Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources
in Smart Grids Using an Improved Variable Neighborhood Search. IEEE Access 2020, 8, 77321–77335. [CrossRef]
97. Lv, M.; Lou, S.; Liu, B.; Fan, Z.; Wu, Z. Review on Power Generation and Bidding Optimization of Virtual Power Plant. In
Proceedings of the 2017 International Conference on Electrical Engineering and Informatics (ICELTICs), Banda Aceh, Indonesia,
18–20 October 2017; pp. 66–71.
98. Afzal, M.; Li, J.; Amin, W.; Huang, Q.; Umer, K.; Ahmad, S.A.; Ahmad, F.; Raza, A. Role of Blockchain Technology in Transactive
Energy Market: A Review. Sustain. Energy Technol. Assess. 2022, 53, 102646. [CrossRef]
99. Dinesha, D.L.; Balachandra, P. Conceptualization of Blockchain Enabled Interconnected Smart Microgrids. Renew. Sustain. Energy
Rev. 2022, 168, 112848. [CrossRef]
100. Gawusu, S.; Zhang, X.; Ahmed, A.; Jamatutu, S.A.; Miensah, E.D.; Amadu, A.A.; Osei, F.A.J. Renewable Energy Sources from the
Perspective of Blockchain Integration: From Theory to Application. Sustain. Energy Technol. Assess. 2022, 52, 102108. [CrossRef]
101. Mao, T.; Guo, X.; Xie, P.; Zhou, J.; Zhou, B.; Han, S.; Wu, W.; Sun, L. Virtual Power Plant Platforms and Their Applications in
Practice: A Brief Review. In Proceedings of the 2020 IEEE Sustainable Power and Energy Conference (iSPEC), Chengdu, China,
23–25 November 2020; pp. 2071–2076.
102. Andoni, M.; Robu, V.; Flynn, D.; Abram, S.; Geach, D.; Jenkins, D.; McCallum, P.; Peacock, A. Blockchain Technology in the
Energy Sector: A Systematic Review of Challenges and Opportunities. Renew. Sustain. Energy Rev. 2019, 100, 143–174. [CrossRef]
103. Siano, P.; De Marco, G.; Rolán, A.; Loia, V. A Survey and Evaluation of the Potentials of Distributed Ledger Technology for
Peer-to-Peer Transactive Energy Exchanges in Local Energy Markets. IEEE Syst. J. 2019, 13, 3454–3466. [CrossRef]
104. Yang, Q.; Wang, H.; Wang, T.; Zhang, S.; Wu, X.; Wang, H. Blockchain-Based Decentralized Energy Management Platform for
Residential Distributed Energy Resources in a Virtual Power Plant. Appl. Energy 2021, 294, 117026. [CrossRef]
105. Mathew, R.; Mehbodniya, A.; Ambalgi, A.P.; Murali, M.; Sahay, K.B.; Babu, D.V. In a virtual power plant, a blockchain-based
decentralized power management solution for home distributed generation. Sustain. Energy Technol. Assess. 2022, 49, 101731.
[CrossRef]
106. Guan, Z.; Lu, X.; Wang, N.; Wu, J.; Du, X.; Guizani, M. Towards Secure and Efficient Energy Trading in IIoT-Enabled Energy
Internet: A Blockchain Approach. Future Gener. Comput. Syst. 2020, 110, 686–695. [CrossRef]
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