ABSTRACT-

Carbon emission reduction is a main goal of today’s world. Transportation and

electricity industries are considered as the major sources of greenhouse gases emission. A
large-scale integration of plug in electric vehicles (PEVs) with renewable energy sources
could subside the exponential increase of emission. This project gives a detailed approach of
combined economic emission dispatch (CEED) to ensure the effectiveness of using PEVs and
RERs from different scenarios. Applying Constriction Factor Based Particle Swarm
Optimization (CFBPSO) algorithm and considering 30000 PEVs three test cases (thermal,
thermal-PEV, thermal-PEV-wind) with two different practical operating conditions of Valve
Point Effect (VPE) and Ramp Rate Limit (RRL) are taken into consideration to investigate
the efficiency of different type of integration in the proposed model of 10 Thermal Power
Generating Units. The total fuel cost and the pollutant emission are taken as the optimization
objectives. The results determine the fact that the PEVs substituting conventional vehicles
does not reduce the net emission from the industries efficiently. The critical investigation
suggests the integration of both PEVs and wind power for desired performance in CEED.
Keywords:
INTRODUCTION:
Due to the lack of conventional energy resources and continuous deterioration of the
environment globally, protecting both environment and energy resources have become a
global concern. Most of the power plant have been operated by non-renewable energy. Usage
of fossil fuel in a vast amount exhaust hazardous gas which represent a major part of
environmental pollution. Emission for now one of the major problems in addition to reducing
fuel cost and economically operate power plants. The electricity demand is also stochastic.
The above two statements formulate dynamic economic emission dispatch (DEED) is a
fundamental problem of power system dispatching which aim at distributing available
electric power generation to meet the load demand with minimal possible cost and emission.
The swarm based optimization algorithms have exhibited dependable trait in finding the
solution of this multidimensional problem.
Also, there are various practical limitation in operation and control like ramp rate
limit (RRL), valve point effect (VPE), prohibited operating zone (POZ). To overcome this
global issue, governments and other agencies around the world have begun searching for
means to address this energy sustainability problem. Two technological solutions that have
gained particular interest are renewable electricity sources (RESs) and transportation
electrification. Due to the limited availability and increase in cost of fossil fuel today’s
generation headed toward plugged-in-electric vehicles (PEVs). Now a days PEV are
integrated with RESs and that supplement for PEVs charging. In terms of impacts on electric
power systems, PEV charging not only increases energy and power demand, calling for new
capacity investments but can also lead to synergies with renewable energy and improve the
utilization of assets in the electric power system. If PEVs are scheduled to recharge when
system demand is lowest, they can improve the overall capacity utilization factor of the
electric power system and generate cost savings for the user and the system. PEVs broadly
provide two types of services that can benefit electric power systems with renewable energy
generation: passive services, wherein the PEV is used as a load with optimizable
consumption, or active services, which rely on bidirectional power flow between the vehicle
and the charger. In the latter case the PEV provides power back to the grid. These services
can resolve some of the problems of integrating renewables on different time scales: real time
demand and supply balancing, unit commitment, and long-term system planning. The first
priority is given to the renewable energy resources to supply the demand and after the
utilization of whole renewable energy, thermal plants supply the remaining demand.
LITRETURE REVIEW-
In today’s world dynamic economic emission dispatch is an important factor due to
increase in environmental issue. Carbon dioxide (CO2) emission constitutes a major part in
environmental pollution [1],[2]. Considering the dynamic demand data of 10 PGs for 24hr the
EED become a multi objective optimization problem by adding operating cost function with
ED [3][4]. Many cities used PEV in a large amount to reduce emission [5]. However random
charging of EVs increase load demand and further burdens PGs [6]. US spent a large amount
for installation of EVs charging infrastructure [7]. It only considered the registered EVs and
the basic battery capacity rather than the user travel demand and the battery charging and
discharging characteristics. In [8], pointed out that with an increased number of EVs and
growing computational complexity, a decentralized approach could be potentially stable. The
implementation of PEVs cannot reduce operational cost and emission to a noticeable amount
unless they are integrated with RESs [9][10]. in Tasmania, the diesel consumption of King
Island has fallen to 50% after implementation of renewable energy and battery storage for the
conventional power plants [11]. Swapping of fossil fuel with new RESs technologies has
proved it efficiency in many cases. Constraints like RRL and VPE are applied to this
optimization for more precise and rational modelling of cost function [12][13]. Constriction
Factor based Particle Swarm Optimization (CFBPSO) algorithm is used for finding best
result and because of it less computational effort it gives more accurate solution than other
algorithms.
Applying CFBPSO algorithm and considering 30000 PEVs three test cases (thermal,
thermal-PEV, thermal-PEV-wind) with two different practical operating conditions of Valve
Point Effect (VPE) and Ramp Rate Limit (RRL) are taken into consideration to investigate
the efficiency of different type of integration in the proposed model of 10 Thermal Power
Generating Units. The total fuel cost and the pollutant emission are taken as the optimization
objectives. Fuzzy Decision Making (FDM) has been applied to find the best compromise
solution based on desired system operating conditions from the Pareto optimal solution [14].
Here the concept related to CFBPSO and FDM has not been explained as they are mentioned
in many papers.
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