Combined Economic Emission Dispatch in a Micro Grid: A

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 24 (2017) pp. 15876-15901

© Research India Publications. http://www.ripublication.com

15876

Combined Economic Emission Dispatch in a Micro Grid: A Comprehensive

Survey

Archana Pathak

Research Scholar, Department of Electrical Engineering, MANIT Bhopal, Madhya Pradesh, India.

Orcid Id: 0000-0002-1920-501X

Manjaree Pandit

Professor and Dean Academic, Department of Electrical Engineering,

M.I.T.S. Gwalior, Madhya Pradesh, India.

Y. Kumar

Professor, Department of Electrical Engineering, M.A.N.I.T. Bhopal, Madhya Pradesh, India.

Abstract

The Micro Grid (MG) is a technique to work out myriad

number of problems linked with the incorporation of many

small generators in supply feeders. With ever-increasing

apprehension of ecological protection, renewable energy

resources are extensively applied as a means to achieve

minimum emission.. The facility of MG allows exploring in

huge scale, the use of the renewable energy resources for local

consumption. The Economic Dispatch is intended at

minimizing the cost function and emissions of the power

system though fulfilling all active constraints taking into

account both conventional and renewable energy resources In

this paper, Economic Dispatch (ED) problem in Micro Grid is

discussed with power security constraints by means of many

types of soft computing techniques.

Keywords : Micro Grid(MG); Economic Dispatch(ED).

INTRODUCTION

The heavy investment, inadequate reliability, increased

emissions of green house gases and increased line losses in

the conventional network made the utility to choose for

involving myriad renewable based micro sources, as per the

necessity and ecological constraints. To provide reliable,

quality and competent supply to its consumers the researchers

have recommended micro grid as a complete solution.

Restructured Electrical power utilities have formed extremely

energetic and viable market that changed many aspect of the

power production. Therefore, there is a need of optimal

economic dispatch due to shortage of energy resources,

increasing power generation cost, rising global warming

concern, and ever growing electricity demand. The key

purpose is to minimize the fuel cost besides emission level

simultaneously, though fulfilling the energy demand and

operational constraints. The Multi-objective problem is

converted into a single objective problem using the price

penalty factor approach. Weight factor is used to get the

optimal settlement solution as minimizing fuel cost and

emission simultaneously are rival to each other.

The conventional optimization methods are not suitable to

solve ED problems as due to local optimum solution

convergence as they are nonlinear, non-convex with severe

equality and inequality constraints. Metaheuristic optimization

has gained an unbelievable appreciation as the solution

algorithm for such problems in last few years.

[2] Analyzes the prospect to extend the simple micro-grid

model in optimizing the operation of limited renewable

energy for grid area which integrates the renewable energy

resources driven power plants. The said model is examined

using HOMER and MATLAB software. [3] says about the

resurgence of the history of power system and how and why

the micro grids have taken birth in this restructured and

deregulated electricity market with advantages of the micro

grid and this paper studies the reliability of 6-bus system with

a Fast de-coupled load flow (FDLF) method in Compaq visual

FORTRAN .Micro grid is presented as the best way to

comprehend the promising potential of distributed generation

in [4]. The purpose of the study is to extend micro grid

optimal dispatch through demand response (MOD-DR), which

fill in the gap by coordinating both the demand and supply

sides in a renewable-integrated, storage-augmented, DR-

enabled MG to attain economically viable and system-wide

resilient solutions [8]. The object of the study is to develop

micro grid optimal dispatch with demand response (MOD-

DR), which fills in the gap by coordinating both the demand

and supply sides in a renewable-integrated, storage-

augmented, DR-enabled MG to achieve economically viable

and system-wide resilient solutions. The key contribution of

this paper is the formulation of a Multi-objective optimization

with prevailing constraints and utility trade-off based on the

model of a large-scale MG with flexible loads, which leads to

the derivation of strategies that incorporate uncertainty. The

use of small cluster of generators of less than 2 MW,

efficiency reserves, heat and electrical storage sets, and

combined heat and power (CHP) appliances may be the

forerunners to widespread micro grid management [16]. [19]

discusses the planned management of the DERs in micro grid

and the detailed impact of this deployment of DERs in micro

grid is given in [18].



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In [23] the analysis of Distributed energy resources was done

for emission reduction findings and it is cleared that the DERs

have very bright future as the emission at 100 % load ratio is

much lesser than that at other load. [25] Presents a significant

literature review of different 𝜇G planning. This paper

discusses the benefits of grid-connected or isolated 𝜇G with

storage. A bi-level optimization model is developed in [27] in

which the upper level objective is to maximize the profit of

DISCOM and the lower level objective is to minimize the cost

of MGs. In [28], a DE algorithm is proposed that uses a novel

mechanism to dynamically choose the best performing blend

of parameter (, crossover rate, amplification factor and the

population size) for a problem in a single run. The

performance is evaluated by solving three (two constrained

and one unconstrained) well known sets of optimization test

problems . The results cleared that the proposed algorithm

saves the computational time, and also shows improved

performance over the other modern algorithms. In [31] a

hybrid method to optimize the capacity of DG and operational

strategy in micro grids is presented ,the hybrid optimization

method detailed here is a combination of the quadratic

programming (QP)and the particle swarm optimization (PSO)

algorithm. Hybrid Renewable Energy Systems (HRES) is

given in concise [34]. Here a myriad number of optimization

objectives that have been aimed in power system applications

has been incorporated with a clear outline.

An optimization model considering the economical and

environmental criteria is given to study the optimal power

allocation of DERs (distributed energy resources), ESS

(energy storage systems) and main grid in [33]. [35] gives the

survey of the various computational optimization techniques

which can be applied for micro grid planning. This survey

may be used for power system engineers and planners. To

assess the voltage stability margin of a two-bus system based

on both saddle node and partial induced bifurcations, a better

indicator is proposed in [38]. Reduced islanded MG network

(RIN) is introduced for simplification of the proposed

indicator to n-bus islanded MGs and a advance power flow

algorithm by dividing these networks. Here the islanded micro

grid reconfiguration IMGR is proposed. Reconfiguration is

applied as an optimization tool to get an improved voltage

stability indicator such as minimizing losses.[38] shows that

the presented algorithm is very helpful for power system

operators in future. Micro grids have been researched to

improve power reliability ,security, sustainability and

decrease power costs for the consumer. It has been found that

micro grids have significantly depends on the location,

components, and optimization goals and most common

barriers were identified as technical, regulatory, financial, and

stakeholder in [113].

In [39] the structure of micro grid is represented as virtual

energy storage system VSS to involve in the optimal

scheduling of micro grid for the purpose minimization of

micro grid operating cost. A method for hybrid renewable

energy systems best possible planning and design is given in

[40] . In this paper an evaluation of the electrical distribution

circuit is done to assess the electrical performance when the

DER-CAM model selects the size and type of the DERs and

their utility operating schedules. it also gives the economic

benefits of allocating the DERs optimally. . In [43] the

discussion about the scenario for future micro grid design,

policy and planning is given. An exhaustive survey on all

layers related to the micro grid is done in [44], a hierarchical

MG scheme with clearly defined Micro grid pico grid , and

nano grid is detailed here. Out of the four layer classification,

in first layer ,generators, electric vehicles (EV), converters,

and energy storage systems (DS) have been described,

Various communication protocols are described in second

layer classification, in the next layer an intelligent layer is

detailed in which decision making in operation and planning

is described, and in the last layer classification ,different

business models for the future micro grid are given in detail.

The detailed discussion related to the myriad number of

challenges of the research in micro grid is briefed in [45].This

paper gives the review of the recent research in the area of

micro grid. this paper also describes the micro grid with its

components, its benefits, future applications . To get the

integrated voltage profile and for the purpose to minimize the

losses in the micro grid, a specific use of combined

distributed optimization for DGs generated reactive power

optimal dispatch in micro grid is examined in [48] .

A micro grid optimal scheduling model based on recent

deregulated electricity market was proposed in [49] which is

compared with the generally used optimal scheduling model

which is price-based to find out the differences ,the pros and

cons . a new structure for smart energy management which is

based on the idea of quality-of-service in electricity (QoSE) is

introduced in [50]. The aim of micro grid control center

(MGCC) is to minimize the MG operating cost and to

maintain the outage probability of quality usage, i.e., QoSE,

below the pre specified value, by forecasting the electricity

from different energy resources. Lyapunov optimization

method is used for given problem. In [51] ,it is given that how

the multiple distributed generators share the active power in

micro grid using its different control modes . The feasibility of

the proposed (UPC) unit output power control to control DGs

active power is simulated by MATLAB/SIMULINK. The

prime objective of [88] is to develop micro grid optimal

dispatch with demand response (MOD-DR) which fills in the

gap by coordinating both the demand and supply sides in a

renewable-integrated, storage-augmented, DR-enabled MG to

achieve economically viable and system-wide resilient

solutions.

The detailed study over the power management policies

related to the micro grid is done in [54], it discusses about the

challenges of the power management planning of the micro

grid and also the solution impacts of the various analysis

methods IN AC, DC, hybrid AC/DC micro grid , FACTS and

storage devices, optimization techniques, multi agents. In

[115] author has presented a parametric programming based

approach for energy management in micro grids. The

parametric mixed-integer linear programming problem (p-



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MILP) formulation leads to significant improvements in

uncertainty handling, solution quality and computational ease

and problem is solved offline on a flexible time-scale basis.

Micro grid study of the University of California, San Diego

(UCSD) campus has been done in [114] which has diverse

distributed energy resources (DER) and the study reveals that

renewables energy integration strategies are technically

feasible and cost-effective for PLS, PV firming and grid

services. It was also suggested that participation in the AS

market is unlikely to provide sufficient incentives for UCSD

to offer integration services, highlighting the nature of a

‘‘missing money” problem from the perspective of a

microgrid.

[55] gives the detail description of the micro grid ,its

components like micro grid loads, power electronic

interface modules ,energy storage devices, ,distributed energy

resources (DER), and multiple micro grids interconnection

,its different modes,. This paper also discusses the advantages

of the distributed generation in micro grid and the application

of different renewable energy resources because of its

reliability and cleans an environmentally safe energy. [99]

gives a comprehensive description of micro grid technology.

[56] Discussed the micro grid technology with advancements

and most recent amendments. [94] presented an optimization

model to find out the optimal power allocating for DERs, ESS

and main grid and optimization problem of MGEMS is solved

by CPLEX solver based on AIMMS software in comparison

with GA solution.The results proved thatAIMMS is capable to

provide a fast, robust, and efficient solution compared to GA.

Distributed Energy Resources Customer Adoption Model

(DER-CAM) is proposed in [102] to determine the optimal

size and type of distributed energy resources (DERs). The

final simulation results show that hybrid renewable energy

systems are essential for efficient & best utilization of

renewable energy resources for micro grid applications.

Increasing environmental and energy shortage issues

necessitates the use of CCHP micro grid, A comprehensive

survey of the CCHP micro grid planning, modeling, and

power management is presented in [60].this energy

management planning is concised in terms of its control

strategies, noxious gas emission reduction ,cogeneration

decoupling methods. In [116] a multi-objective optimization

model is proposed for robust micro grid planning, on the basis

of economic robustness measure. The efficacy of the model is

successfully applied into Taichung Industrial Park in Taiwan,

where significant amount of greenhouse gases are emitted and

concluded that proposed model provides an efficient decision-

making tool for robust micro grid planning at the preliminary

stage. 109) In this paper an Incentive Based Demand

Response Program isincorporated and optimal dispatch

strategy is obtainedby minimizing generators fuel cost,

transaction costs of the transferablepower and maximizing the

microgrid operator's demand response. Results of the

proposed model show that the demand response program

curtails significant grid relieving amounts of energy and

optimality at both the supply and demand spectrum of the

grid.

[61] Proposed an optimal power flow controller, (the purpose

of which is to control the active and reactive power current

connecting micro grid to the main), for micro grid operation

which is utility connected. ,an intelligent search algorithm

PSO is used to execute the real time self tuning method

.Here execution of the self tuning method is to share the load

equally in varying load conditions. An original approach and

computational method to assess the impact of DG on

investments deferral in radial distribution networks in the

medium and long-term have been presented. This approach

models the stochastic nature of load demands and DG energy

productions. The impact of DG penetration levels and

different DG locations along the network have been analyzed

in [140] Results show that, once initial reinforcements to

accommodate DG connection have been implemented, DG

could defer network investments in the face of expected

natural load growths. In addition, DG technology impacts on

investments deferral are different. Results show that

photovoltaic and CHP plants have a more positive impact than

wind turbines, due mainly to a highest randomness of wind

energy production. Finally, DG concentration along the feeder

has also an important effect on investment deferrals. The more

dispersed DG units are located along the network the better is

their impact. This effect is explained because the

improvement of DG total availability and the complementary

energy production patterns from DG units. A micro-grid

model is proposed in [120] which integrates the grid

connected power plants driven by renewable energy sources

employing micro hydro (MHP) and photovoltaic system (PV)

and proposed model is analyzed using HOMER and

MATLAB software. Simulation results show that model has

lowest energy cost, greatest reduction of CO2 emission and

largest fraction of renewable energy and the PV power

generation was always recommended with a minimum

capacity.

MICRO GRID WITH WIND ENERGY CONVERSION

SYSTEM

A model which includes the wind energy conversion system

(WECS) generator is developed in economic dispatch (ED)

problem in [57]. In this paper the optimization is done on a

Weibull probability density function based stochastic wind

speed characterization which is numerically solved for a state

including the combined conventional and wind-power plants.

In [160] a model is developed for stochastic

economic/emission dispatch of thermal/wind/solar hybrid

generation system considering the cost of modern thermal

units with multiple valves point effect, polluting gases

emission and factors for both overestimation and

underestimation of available wind and photovoltaic power.

[117] deals with the impact of policy decisions on optimal

micro grid design. A micro grid, containing the solar photo

voltaic, , micro turbines, wind turbine, gas-fired boiler,

electric boiler, and a battery bank, is considered here. The



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analysis on the optimal design of the micro grid is done under

various policy scenarios like emission taxation and reduction,

and minimum system autonomy.

PROBLEM FORMULATION

With simultaneous optimization of all four objectives cost,

emission, loss and heat it is difficult to find the best solution

when multiple objectives may be considered at a time.

Therefore a fuzzy satisfaction degree may be computed for

each objective for every solution. The overall satisfaction

degree may be found based on the minimum satisfaction

degree for all objectives. For each objective may be

individually optimized using single objective optimization

technique. After that all four objectives may be combined into

a single function using an assigned weight and price penalty

factor approach.

Single Objective Optimization

A. Minimum Cost Dispatch

First, cost objective given in (1) may minimized individually

using single objective formulation.

)(2

11 i

N

i iiii PGcPGbaOf

[1]

B. Minimum Emission Dispatch

Then the emission objective given in (2) may be minimized

individually using single objective formulation.

)(2

12 i

N

i iiii PGPGOf

C. Minimum loss Dispatch

Here loss objective given in (3) may be minimized


)(2

13j

N

j jjjj PGcPGbaOf

D. Maximum Heat Dispatch

Here Heat objective given in (4) may be maximized


)(2

14j

N

j jjjj GPGOf

E. Multi-objective optimization

Then cost, emission, loss & Heat may be simultaneously

optimized using price penalty factor (PPF) approach to solve

multi-objective optimization problem given in equation (5).

)(32)(21)(1 *****.. iii HeatwppfemwppffcwFO

2^))((*** 1)(43 PDPWxsumlosswppf i

14321 wwww

F. Equality and inequality constraints

Combined economic emission dispatch with heat & loss

optimization may be carried out for a microgrid having CHP

units; optimal dispatch is computed for an assumed load

profile for dynamic ED problem. The objectives specified in

(1)-(6) are optimized subject to the subsequent limits.

1) Power Balance

N

i LDi PPPG1

0

Network Loss PL can be considered using Kron’s Loss

Method given below:

N

i iijiji

N

i

N

jL BPGBPGBPGP1 0001 1

(8)

Where Bij,,ij=1,………N are called loss co-efficient; their

units are MW-1

2) DERs Capacity Limits Constraint

Power produced by DERs shall be within the defined lower

limit PGimin and upper limit PGimax, so that

maxmin iii PGPGPG

3) Ramp Rate Limits Constraint

))(,(max min ldomn rrPPP

))(,(min max luomx rrPPP

mxmn PPPP maxmin ,

4) Heat Balance Inequality Constraint

Heat output (HO) of Diesel Generator(Dg) and Micro turbine

(Mt) are proportional to their respective electrical output,

N

i iio PGOutputHeatTotalH1

Heat Balance inequality constraint is as follows:

D

N

i ii HPG

1

i is proportionality constant

exithi

kWh

kJHeatRate

3600

)(

Earlier Many techniques were suggested to minimize the

emission for economic dispatch. In this paper many are



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discussed..

G. Price Penalty Factor Approach:

The multi-objective optimization problem is converted into

single objective one through price penalty factor (PPF) by the

formula described as:

N

i

iiiii

N

i

iiiii

PGPG

PGcPGba

iPPF

1

2

maxmax

1

2

maxmax

)(

)(

)(

(16)

Emission

CostiPPF )(

(17)

In order to find optimal economic dispatch cost, basic gaseous

emission, generated Heat and the energy loss may be

considered simultaneously. The complex multi-objective

problem can be formulated as:

)(..,)(..),(..),(.. 4321 iiii PFOPFOPFOPFOCMin

Above functions are correspond to economic fuel cost,

emission, Loss ,& Heat respectively. When weighing factor is

considered the MOF gets converted into SOF problem which

can be formulated as:

)(),(),(),( 4321 iofiofiofiof PSDPSDPSDPSDCMin

(19)

ECONOMIC DISPATCH

Static Load Dispatch

Static economic dispatch gives the economic dispatch

solutions for a static or fixed load conditions. In this type of

economic dispatch problem it is assumed that the load is fixed

for 24 hrs. Flower pollination algorithm (FPA) is applied to

optimize the generation schedule in [190] because of the

robustness of this algorithm for solving the non linear

problems, here the effects of the wind power integration in

power network for static load dispatch problem have been

analyzed. Stochastic nature of wind is solved by Weibull

distribution function. A static and a dynamic economic

dispatch problems are solved in [153] using the artificial

neural networks. Here this method has been tested on a

standard IEEE 30 bus system results obtained here shows that

the neural networks method give better results than the

classical method. A number of real life scenarios relating to

resource management in micro grids are modeled as multi-

objective optimization formulations. In [91] author has

investigated the various optimization objectives with respect

to designing, planning and operation of micro grids and Some

mathematical formulations for resource management in micro

grids have also been tabulated.

Dynamic Load Dispatch

In dynamic economic load dispatch problem varying load for

24 hrs of the day is considered.[11] Gives the multi objective

PSO technique for optimization dynamic economic load

dispatch problem. [30] Presents a review of the research of the

optimal power dynamic dispatch problem. The dynamic

economic dispatch problems differ from the static economic

dispatch problem by incorporating ramp rate constraints of

generators. A dynamic multi objective optimal dispatch

(DMOOD) for wind-thermal system with a hybrid flower

pollination algorithm (HFPA) is given in [5].In this paper the

cost ,emission and loss are simultaneously minimized with

intricate constraints like ramp limits ,valve point loadings,

spinning reserve, and prohibited zones together with the

inclusion of the cost of wind power uncertainty The projected

HFPA improves the exploration & exploitation strength of the

flower population which conducts the search. In the HFPA the

flower pollination algorithm (FPA) and differential evolution

(DE) algorithm are incorporated to maintain good solutions

and to prevent premature convergence. A 5-class, 3-step time

varying fuzzy selection mechanism (TVFSM) is included with

HFPA for solving multi-objective problems. The TVFSM

finds a fuzzy selection index (FSI) by aggregating different

conflicting objectives. The FSI is adopted as the worth

standard while updating the population. Gaussian membership

function is applied to compute FSI in such a way that extreme

solutions are filtered out and trade off solutions on the central

portion of the Pareto-front are obtained. The HFPA-TVFSM

approach successfully searches the optimal solution which

fulfils all the three objectives optimally.

Another Dynamic Economic and Emission Dispatch model is

again developed in [6], with security constraints, for a system

including wind, pv and non-convex thermal units. Weighted

sum method is applied to facilitate particle swarm

optimization (PSO) to resolve environmental/economic multi-

objective (MO) task. The optimization is meant at minimizing

the cost function and emissions of the system while fulfilling

all operational constraints in view of both conventional and

renewable energy generators. An overview of dynamic

economic dispatch problem is detailed in [30] ,an Artificial

Intelligence (AI) techniques and hybrid methods are used to

solve the complex problems. The DED problem in

deregulated electricity markets is also described here.

Formulations of the dynamic economic dispatch problem are

given in section II in this paper. The dynamic dispatch

problem differs from the static economic dispatch problem by

incorporating generator ramp rate constraints. [158] outlines

the two formulations (i) optimal control dynamic dispatch

(OCDD) (ii) dynamic economic dispatch (DED) is elaborated,

then presented an overview on the mathematical optimization

methods, Artificial Intelligence (AI) techniques and hybrid

methods used to solve the problem incorporating extended

and complex objective functions or constraints.

[203] paper presents dynamic environmental economic

dispatch (DEED) model in which the total fuel cost and

emission is optimized as conflicting objectives over a

dispatch period and nonlinear factors of the generators ‘ramp



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rate limits, valve point effect, power load variation and power

losses are taken into consideration. Simulation results show

that proposed method has higher performances and better

potential applications.

TYPES OF OPTIMIZATION

Single objective optimization

A linear programming model based cost minimization model

is designed to choose the optimal system operation schedule

for micro grid is presented in [17]. Optimal planning and

design for the micro grid based on renewable energy is given

in [53]. In this paper the cost minimization is the main

objective of the planning and analysis which is done on

HOMER software. this analysis confirms that the micro grid

is more favorable when it is connected to the external grid s

far as the cost is concerned also the micro grid based only on

renewable energy is the most environment friendly as

compared with other grid with any other traditional power

generators like diesel generators etc.. A generalized

formulation is presented to determine the optimal operating

strategy and cost optimization scheme for a Micro Grid and

Mesh Adaptive Direct Search (MADS) algorithm is used to

optimize the cost function of the system to meet the customer

demand and safety of the system [126].

An efficient hybrid heuristic search method is given in [166]

to solve the realistic economic dispatch problem which

considers several nonlinear characteristics of generators, and

also their operational constraints, like transmission losses,

multi-fuel options ,valve-point effects, , ramp rate limits ,

spinning reserve and prohibited operating zones,. The

comparison of results obtained confirm the efficacy of the

presented technique .[195] presents the design and application

of an efficient hybrid heuristic search method to solve the

practical economic dispatch problem considering many

nonlinear characteristics of power generators ,and their

operational constraints, such as transmission losses, valve-

point effects, multi-fuel options, prohibited operating zones,

ramp rate limits and spinning reserve. A hybrid DE algorithm

which is based on particle swarm optimization is presented to

solve the nonconvex economic dispatch problems. The

usefulness of the presented technique to obtain the precise and

reasonable optimal solutions for realistic ELD problems is

confirmed by the comparison of the results obtained. The

Direct search (DS) methods are used to solve optimization

problems. [172] presents a new approach based on a

constrained pattern search algorithm to solve economic

dispatch problem (SCED) with non-smooth cost function. The

outcome of the simulation results proves that pattern search

(PS) algorithm is effective and also shows the ability in

handling highly nonlinear discontinuous non-smooth cost

function of the SCED.

[181] proposed a flexible algorithm to solve the combined

heat and power (CHP) economic dispatch problem which can

be solved in two levels i.e. higher level and lower level.

Optimization of the alternate dual function for the fixed global

constraints in which the alternate sub gradient is applied to

update the Lagrangian multipliers, is the higher level

optimization Comprehensibly the optimizing of the sub

problems considering its local constraints is called as the

lower level optimization. Results show that proposed

algorithm is very reliable for much complex and nonconvex

EED problems. Modified Hopfield approach is investigated in

[188] to solve Economic dispatch (ED) problems with

transmission system representation through linear load flow

equations and constraints on active power flows. All the

internal parameters Modified Hopfield networks are

calculated using valid-subspace technique and internal

parameters guarantee the network convergence to feasible

equilibrium points results into the solution for the ED

problem. [192] aimed to explore the results & performance of

the various evolutionary algorithms (EAs) such as the Real-

coded Genetic Algorithm (RGA), Particle Swarm

Optimization (PSO), Differential Evolution (DE) and

Covariance Matrix Adapted Evolution Strategy (CMAES) to

solve the multi-area economic dispatch (MAED) problems.

The simulation results reveal that CMAES algorithm performs

well in terms of results, its quality and consistency. Karush–

Kuhn–Tucker (KKT) conditions are applied to the solutions

obtained using EAs to confirm the optimality. An optimal

scheduling model for a hybrid energy microgrid considering

the building based virtual energy storage system (VSS) is

presented in [101]. The Numerical studies results demonstrate

that the proposed model will provide effective and economical

scheduling scheme and reduction in operation costs for

microgrid.

[194] identify a new Hopfield neural network approaches to

solve economic dispatch problems and a new mapping

technique is identified for quadratic programming problems

with equality and inequality constraints. Quasi-Oppositional

Swine Influenza Model Based Optimization with Quarantine

(QOSIMBO-Q) is presented in [52] to minimize the operation

cost of micro grid and it is concluded here that the proposed

algorithm provides better results than other optimization

techniques. In [89] a methodology is presented that combines

an EA for sizing and MILP for scheduling. Simulation results

show that the operation strategy, initial conditions, time

resolution as well as uncertainty on input data influence the

sizing of the components, and consequently the total cost of

the micro grid. A compendium of optimization objectives,

constraints, tools and algorithms for energy management in

microgrids is provided in [90]. A brief review of Linear

optimization, non-linear optimization, integer and mixed

integer optimization and stochastic optimization is described

for Micro grid energy management. The proposed approaches

in this paper will provide bench mark for further research of

the cost effective energy management techniques for Smart

Micro grid Network (SMN).



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MULTI OBJECTIVE OPTIMIZATION

For wind included grid with reserve constraints Multi-period

multi-objective optimal dispatch (MPMOOD) is presented [1].

In this paper a new multi-objective Series PSO-DE (SPSO-

DE) hybrid algorithm is projected where the two paradigms,

particle swarm optimization (PSO) and differential evolution

(DE) allocate domain information and preserve a synergistic

collaboration to beat their individual flaws. Gaussian

membership function is employed to support convergence

towards the central part of the Pareto front and to promptly

segregate the boundary solutions,. The proposed technique

expedites the search for the finest solution, i.e. the result

which satisfies all the objectives of the MO problems.

A comprehensive formulation to establish the optimal

scheduling which includes the cost and emission minimization

in the micro gird is given in [58].Here the given formulation

considers the operation and maintenance cost and reduction

of emission of the noxious gases like NOx, SO2 and CO2 . In

this paper the Multi objective Mesh Adaptive Direct Search

(MOMADS) algorithm is used to solve the optimization

problem. At last the comparison of the proposed method of

optimization is given with Multi objective Sequential

Quadratic Programming (MOSQP) optimization method . An

improved economic dispatch (ED) model considering cost

and emission is developed in [65].this model also considers

the two concepts from the deregulated electricity market ,first

is the environmental protection and second one is the energy

conservation. [92] examined the annual

economic/environmentally optimal operation of a Low

Voltage Micro grid with DG capabilities. Recently,

Distributed Generation Technologies have gained more

attention because of the steady decrease of fossil fuel use and

increased load demand. The optimization model includes high

penetration of Fuel Cell units and Renewable Energy Sources.

In [122] quasi-oppositional teaching learning based

optimization (QOTLBO) is proposed for solving non-linear

multi-objective economic emission dispatch (EED) problem.

The simulation results demonstrate that the proposed

algorithm has much better operational fuel cost and emission

with computational time compared to other available

optimization techniques. A generalized formulation is

investigated to find out the optimal operating strategy in

[125]. The typical MG consists of a wind turbine, a micro

turbine, a diesel generator, a photovoltaic array, a fuel cell,

and battery storage. The Multi objective Mesh Adaptive

Direct Search (MOMADS) is applied to minimize the cost

function of the system while constraining it to meet the

customer demand and safety of the system and simulation

results demonstrate the efficiency of the MOMADS approach

to meeting load with reduction in cost and the emissions.

Hopfield neural networks approach is used for solving fuel

constrained economic emission load dispatch problems of

thermal generating units in [130]. The comparison of results

shows that fuel consumption is controlled by adjusting the

power output of various generating units so that power system

operates within its fuel limitations and contractual constraints.

In [141], a novel approach to the environmental economic

dispatch using a standard nonlinear multi objective

programming technique has been introduced. The simulation

results are compared with the genetic algorithm with

arithmetic crossover and neural network approach based on

improved Hopfield neural networks and finds that

performances of the proposed approach is much better than

GAAC and NNA. A comparative analysis has been carried

out on the solutions obtained using combined economic

emission dispatch(CEED) problem considering line flow

constraints with a objective is to minimize the cost of power

generation [145]. Intelligent techniques such as genetic

algorithm (GA), evolutionary programming (EP), particle

swarm optimization (PSO), and differential evolution (DE)

are applied to obtain CEED solutions. The final simulation

results of proposed approach are compared in terms of

solution time, total production cost and convergence criteria.

The basic idea behind environmentally constrained economic

dispatch is to estimate the optimal generation schedule of

generating units so the fuel cost and harmful emission levels

are minimized for a given load demand. Therefore a multi-

output modified neo-fuzzy neuron (NFN) approach is

proposed in [163] for economic and environmental power

generation allocation. This approach is tested on medium-

sized sample power systems and found suitable for on-line

combined environmental economic dispatch (CEED). In [167]

multi- objective optimization based solution is used for

combined economic-environmental dispatch issue which is

multidimensional, non-linear, non-convex and highly

constrained problem. Main objective of proposed method is

search an optimal solution for the dynamic combined

economic-environmental dispatch problem associated to real

power systems. In [168] an adaptive Hopfield neural network

(AHNN) based methodology is used where slope of the

activation function is adjusted for Pareto solutions for the

multi-objective optimization problem of emission and

economic load dispatch (EELD). Convergence characteristic

of AHNN is nearly 50% faster than the non-adaptive version.

[201] presents modified neo-fuzzy neuron (MNFN)approach

for on-line environmental and economic dispatch of

generating units in a power system and simulation results

demonstrate MNFN approach is capable of producing global

optimum results within a very short time.

A mixed integer linear programming (MILP) model is

proposed in [119] to schedule the energy consumption within

smart homes using a micro grid system. Here the daily power

consumption is planned in a multi-objective optimization with

e-constraint method by coupling environmental and economic

sustainability. Here the objective is to minimize the two

conflicting objectives i.e. the daily energy cost and CO2

emissions. The micro grids are used to provide local energy

with less expenses and gas emissions by utilizing distributed

energy resources(DER) and optimal design of DERs within

CHP-based micro grids plays an important role in promoting

the penetration of micro grid systems. The simulation results



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show that the installation of multiple CHP technologies has a

lower cost with higher environmental saving compared to

other available technologies and micro grid works more

efficient way when applies multiple technologies [118] . [111]

presents an effective multi-objective model based

optimization approach for the optimal sizing of components.

The k-means algorithm itself is embedded in an optimization

problem for the calculation of the optimal number of clusters.

A new multi-objective optimal planning model is presented in

[98] for medium voltage stand-alone micro grid(SAMG) and

to avoid the power constraint of distributed generation (DG) a

novel two-step power dispatch control method is proposed in

which the absorption of distributed power by energy storage

system (ESS)and the reactive adjustment are used to regulate

voltage. The goal of this paper is to search the Pareto-optimal

front of the site and capacity of DG.

[104] presents a approach based on modified neo-fuzzy

neuron (MNFN) for on-line environmental and economic

dispatch of generating units in a power system. Being a hybrid

fuzzy neural network, MNFN outperforms both fuzzy logic

system (FLS) as well as ANN-based system. The results

shows that the MNFN approach is able to the produce global

optimum solution within a short time. A multi-objective

optimization based method was presented in [106] for solution

of combined economic-environmental power dispatch

problem. The proposed method was applied on different

representative thermal and hydrothermal power systems and

application of method will result in real-time solutions for the

combined economic- environmental dispatch.

SOLUTION OF ECONOMIC EMISSION DISPATCH

PROBLEM USING DIFFERENT ALGORITHMS

Genetic algorithm

Genetic algorithm (GA) is a stochastic optimization soft

computing technique which is based on the biological

concepts. A principle of Charles Darwin’s theory of ‘survival

of the fittest’ is followed by this evolutionary algorithm. In

this first the solution population i.e. chromosomes is

initialized which is then presented as vector of ‘n’ number of

bits. An appropriate fitness function is applied to assess the

fitness of each chromosome based on which, the best is

selected and then it undergo through crossover and selection

,then finally get the final solution .

Multi-objective environmental/economic dispatch problem is

solved by the use of niched Pareto genetic algorithm (NPGA),

the advantage of which is that there is no limit on the number

of optimized objective and it has a diversity-preserving

mechanism to overcome the premature convergence problem

[12]. Design and operation of ADNs using the multi micro

grids concept is given in two stages in [37] ,here the problem

is implemented in IEEE standard 33-bus distribution system

and solved by NSGA-II algorithm. ESDs were added to the

test ADN to minimize the cost of energy in first stage, . In the

next stage, the projected approach was concluded by testing

the chances of MG structure in the customized AND, The

operation of ADNs was carried out by integrated management

of all distributed energy resources(DERs) using probabilistic

forward-backward load flow using Monte Carlo simulation

(MCS) algorithm. The results of the proposed model are

compared with conventional operation method in different

scenarios. A hierarchical clustering algorithm, a novel

approach based on the NSGA is presented in [13] to solve

economic emission dispatch problem with competing and

noncommensurable objectives and a fuzzy based mechanism

is used to get the best compromise solution, also a diversity

preserving mechanism is used to get the Pareto optimal set of

solutions.

In [66] the multi objective optimization problem of combined

economic environmental dispatch is solved by the Classical

genetic algorithm, the optimal solution is derived from the

weighted sum method. The results obtained here are analyzed

and compared with different other optimization techniques

presented in this paper. A model is designed for stochastic

economic/emission dispatch of thermal/wind/solar hybrid

generation system considering multiple valves point effect,

polluting gases emission and factors for both over estimation

and under estimation of available wind and photovoltaic

power [123]. A multi-objective controlled elitist NSGA-II

algorithm is proposed to derive a set of Pareto-optimal hybrid

system and best solution are obtained using Fuzzy cardinal

priority ranking. With the depletion of fossil fuel resources

and increase in demand, Distributed Generation (DG) have

received more attention in Smart Micro Grid (SMG) systems

that integrate new and renewable energy generation systems

connected to the standard main grid. In [128] quantum genetic

algorithm approach confirms the accuracy and validity of a

mathematic model using practical examples and the same was

used to solve the economic dispatch problem. Quantum

Genetic Algorithm (QGA) is used to solve dynamic economic

dispatch problem in [211] and quantum bit coding is adopted

for coding of the problem itself. QGA algorithm is best to

minimize the cost and very beneficial to conserve energy.

In [135] fuel cost is calculated by optimizing the generated

power by applying particle swarm optimization (PSO),

Genetic Algorithm (GA), and integration of GA and PSO

fuzzy optimization ,along with condition of satisfying the

power system constraints which will improve the performance

of average fitness obtained with the help of optimal power

flow method. An ε-dominance-based multi objective genetic

algorithm approach is presented in [157] for solution of

economic emission load dispatch (EELD) optimization

problem. The EELD problem is formulated as a non-linear

constrained multi objective optimization problem with both

equality and inequality constraints. The simulation result

indicates the capability of the proposed strategy to generate

true and well-distributed Pareto optimal non-dominated

solutions of the multi objective EELD problem in one single

run. [159] gives the non dominated sorting genetic algorithm

II (NSGA-II) which lessen the problems like computational

complexity , non elitism approach, and the need for specifying



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a sharing parameter that are faced in Multi objective

evolutionary algorithms (EAs) . Simulation results found here

show that the proposed NSGA-II, is able to get far better

extend of solutions and improved convergence near the exact

Pareto-optimal front compared to two other elitist MOEAs i.e.

Pareto-archived evolution strategy and strength-Pareto EA

that pay particular consideration to generate a diverse Pareto-

optimal front. Simulation results of the constrained NSGA-II

are compared with another constrained multi objective

optimizer and found much improved performance of NSGA-

II.

Genetic Algorithms (GA) technique is popular in academia

and industry because of its intuitiveness, ease of

implementation and ability to effectively solve highly non-

linear, mixed integer optimization problems whereas particle

swarm optimization (PSO) technique is very new heuristic

search method whose mechanics are inspired by the swarming

behavior of biological populations. In [162] the application

and performance comparison of PSO and GA optimization

techniques are used for flexible ac transmission system

(FACTS) based controller design with the objective to

improve power system stability. The performance of both

optimization techniques are compared based on computational

effort, computational time and convergence rate. An efficient

procedure based on real-coded genetic algorithm (RCGA) is

proposed in [164] for solving the economic load dispatch

(ELD) problem with continuous and no smooth/ nonconvex

cost function and with various constraints. From the

simulation test results it has been observed that proposed

RCGA method is more efficient looking to the thermal cost

minimization and convergence time for solving the ELD

problem.

[161 give a new version of MOEA/D based on differential

evolution (DE), i.e., MOEA/D-DE, and compares the

proposed algorithm with NSGA-II with the same reproduction

operators. The practical results obtained show that the

MOEA/D technique can give significantly better results than

NSGA-II. [178] presents a novel algorithm-Isolation Niche

Immune Genetic Algorithm (INIGA) based on Bid-Based

Dynamic Economic Dispatch (INIGA–BDED). This model is

proposed to maximize the social profit under a competitive

electricity market environment. The proposed approach

ensures diverse group solutions having strong ability to guide

evolution. [184] presents nondominated sorting genetic

algorithm-II for solving the dynamic economic emission

dispatch problem formulated as a nonlinear constrained multi-

objective optimization problem. The approach has very good

performance in finding the diverse set of solutions and in

converging near the true pareto-optimal set.

[189] addresses a novel method elitist evolutionary multi

objective genetic algorithm variable (ev-MOGA)for solving

the multi-objective economic load dispatch (ELD) problem. In

this study, the results of the proposed ev-MOGA algorithm is

compared with other classic and intelligent algorithms and

found that the results obtained by ev-MOGA have good

computationally efficient having stable convergence

characteristics with superior quality& accuracy. In [197] a

hybrid approach combination of Genetic Algorithm (GA),

Pattern Search (PS) and Sequential Quadratic Programming

optimization (SQP) is proposed to study economic dispatch

problems considering the valve point effect. The results shows

that proposed method outperforms in terms of better optimal

solutions and significant reduction of computing times for

more complicated problems with larger number of units. An

efficient hybrid genetic algorithm (HGA) method is used for

solving the economic dispatch problem (EDP) with valve-

point effect is presented in [198]. In order to improve the

performance of algorithm, proposed HGA method combines

the GA algorithm as main optimizer with the differential

evolution (DE) and sequential quadratic programming (SQP)

technique to fine tune the solution of the GA run. The final

solution reveals the superiority and accuracy of the proposed

hybrid methodology to solve the EDP with value-point

effects. [199] presents an efficient method for solving the

economic dispatch problem (EDP) through combination of

genetic algorithm (GA), the sequential quadratic programming

(SQP) technique, uniform design technique, the maximum

entropy principle, simplex crossover and non-uniform

mutation. This hybrid approach uses GA as the main

optimizer, SQP to fine tune in the solution of the GA run and

result shows the improvement in solution, accuracy and

reliability compared to other method used for solving EDP

considering valve-point effects. [202] proposes a hybrid

approach integrating shuffled frog leaping algorithm (SFLA)

with Genetic Algorithm (GA) as modified shuffled frog

leaping algorithm (MSFLA) for solving constraint economic

dispatch problems considering valve point non-linearities of

generators. Simulation results shows MSFLA approach is well

suited for non-smooth and non-differentiable test systems. In

future, MSFLA method can be applied in power system

optimization problems such as optimal power flow, voltage

control, optimum capacitor placement, feeder balancing and

soon. [206] This paper proposes a hybrid method using

Genetic Algorithm (GA) with Active Power Optimization

(APO) algorithm based on Newton’s Second Order (NSO) for

solution of nonconvex economic dispatch problem. The

simulation result is compared with GA approach, show a

significant reduction in generation cost and is exponential

with the increase in system size. Non dominated sorting

genetic algorithm-II approach is used to solve combined heat

and power economic emission dispatch problem in [207].

Here the simulation results are compared with result obtained

from strength pareto evolutionary algorithm 2 and found that

used approach has competitive performance in terms of

solution quality and better CPU time. In [107] a mutli-

objective HS-GA algorithm method was proposed which

includes minimizing DG’s costs ,improving voltage profile

and increasing voltage stability of MG .In the proposed HS-

GA method mutation and crossover operators is used to

enhance the local search ability and develop the convergence

rate and simulation results show effective improvement in

MG operation and reducing its cost.



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EP and ESs

Evolution strategy (ESs) is an optimization algorithm which is

based on evolution and adaptation theory , is stimulated by

macro-level process of evolution (phenotype, variation,

hereditary) .This is very similar to GA in which it deals with

micro level process of evolution (chromosomes ,genome, ,

genes, alleles). The basic feature of ES is that the mutation

process is controlled by self-adaptive mechanisms.

Evolutionary programming (EP) uses probabilistic tournament

method and therefore it presents variety of solution. Here

there is a chance of survival of individual with low fitness

which is based on probability of their contender have fitness

which is even lower than that.

The assessment of available transfer capability (ATC) with

capacity benefit margin (CBM) and transmission reliability

margins (TRM) of power systems with combined economic

emission dispatch (CEED) is done in [152]. ATC is a measure

of unutilized capability of transmission system subjected to

wheeling transactions without violating transmission

constraints at given time and load. The Newton Raphson

power flow method and Evolutionary Programming (EP)

algorithm has been combined for ATC calculation considering

CBM,TRM and economic emission dispatch. The simulation

results are quite better and useful for present deregulated

environment. It was also find that the chemo-tactic step size

taken in this approach leads the solution to reach the global

optimum satisfying all the constraints. A hybrid methodology

integrating bee colony optimization with sequential quadratic

programming is used to solve the dynamic economic dispatch

problem of generating units having valve-point effects.

Simulation results are compared from hybrid of particle

swarm optimization and sequential quadratic programming

and hybrid of evolutionary programming and sequential

quadratic programming [177].

In [191] an integrated algorithm based on Evolutionary

Programming (EP) and Simulated Annealing (SA) is used to

solve the Economic Load Dispatch (ELD) problems. The

classical methods used for solving ELD are calculus-based we

may be have difficulties in the finding the global optimum

solution for non-differentiable fuel cost functions. By using

the proposed approach we are capable of determining the

global or near global optimum dispatch accurate solutions

within reasonable time for any type of fuel cost functions.

[209] Describes Particle Swarm Optimization (PSO) and

Classical Evolutionary Programming (CEP) approach to solve

economic dispatch (ED) problems having multi-area ED with

tie line limits, ED with multiple fuel options, combined

environmental economic dispatch and ED of generators with

prohibited operating zones. The test results shows PSO

method is having only one population in each iteration

whereas in the CEP method has two populations in each

iteration i.e. the parents and the children. It makes PSO has

faster computational capability and convergence characteristic

than CEP method therefore it can be applied to a wide range

of optimization problems.

Differential evolution

Differential evolution (DE) is a stochastic search process

based on population that works on the general outline of EAs.

The DE algorithm was first presented by Storn and Price in

1995 [64]. DE operates on three parameters; crossover,

mutation, and selection to get the final solution from initial

population. Three main control parameters are in Differential

evolution algorithm i.e. size of the initial population (NP),

crossover (Cr) and the mutation factor (F),. This algorithm has

simple framework, faster convergence. Modified DE which is

based on sequential quadratic programming method can find

the search space rapidly with a gradient direction but its

approach to search local gradient is more sensitive to the

initial points and generally gets trapped at a local best

solution.

In [9] the Modified Differential Evolution (MDE) technique is

presented to solve the non-convex Economic dispatch

Dispatch problem. MDE is the new efficient stochastic search

technique in which the positive characteristics of DE, PSO,

GA, and SA.In this paper a new mutation operator inspired

from PSO and GA and a new selection mechanism inspired

from SA are introduced. And the paper shows that the MDE

technique is having more capabilities and the robustness than

the individual technique. [20] gives in detail the differential

evolution theory is a new heuristic simple, and efficient

adaptive approach to minimize the nonlinear and non

differentiable functions over continuous spaces. This paper

compares the DE with Adaptive Simulated Annealing (ASA)

and Annealed Nelder and Mead (ANM) approach and shows

that the DE is more superior than both of them Also the same

combined economic emission dispatch problem for a large

scale system is solved by ANN approach in [22].

A novel comprehensive operation model for micro-grids

(MG) operating in the islanded mode is presented in [29] in

which a new multi-cross learning-based chaotic differential

evolution (MLCDE) algorithm is presented to solve the

optimization problem of MG operation. Here the numerical

results obtained in the paper are compared with the results of

several other recent optimization methods to check the

validity of the given approach. The results and comparisons of

this approach for three different MG cases authenticate the

algorithm.

Multi-objective differential evolution (MODE) algorithm is

presented in [121] for environmental/ economic power

dispatch (EED) problem which is formulated as nonlinear

constrained multi-objective problem with competing and non-

commensurable objectives of fuel cost, emission and system

loss. Simulation results demonstrate that MODE algorithm is

capable to generate well-distributed Pareto optimal non-

dominated solutions of multi-objective EED problem.

Planning of optimum fuel energy management of a CHP-

based micro grid requires strategic deployment of DERs based

on their optimal locations, sizes & technologies which are

selected, by loss sensitivity index (LSI) and differential

evolution (DE) algorithm. The optimal fuel cost is obtained



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from the proposed approach and to decide better investment

option economic comparison is done based on payback period

(PP), internal rate of return (IRR) and net present value (NPV)

of the micro grid and simulation results of DE are confirmed

and compared with particle swarm optimization (PSO)

technique [129]. An estimation of distribution and differential

evolution cooperation (ED-DE) algorithm is proposed for

solving the Economic Load Dispatch (ELD) optimization. The

ED-DE algorithm provides robust and accurate solutions even

for difficult large scale ELD problems and also hope to

stimulate and find more research on serial cooperative

framework-based algorithms for practical engineering

applications [136].

[130] provides an comprehensive analysis of that how the

population initialization affects on Differential Evolution

(DE) for large level optimization tasks. These analyses specify

that the role of population initialization is more crucial in DE

with a smaller size of population. However, this observation

may be the consequence of insufficient convergence for large

size of population, it needs more research. [213] proposes a

novel hybrid shuffled differential evolution (SDE) algorithm

for nonconvex ED problems solution. Results showed

accurate and fast convergence performances with reduced

generation costs. Hybrid model of DE & BBO (DE/BBO)

approach for convergence speed of algorithm and improve

solution quality to solve EELD problems is proposed in [214].

It has been observed that when complex fuel cost

characteristic is taken for consideration than computational

efficiency& quality is much better than other methods. A

hybrid multi-objective optimization algorithm based on

particle swarm optimization (PSO) and differential evolution

(DE) approach is proposed in [146] to solve the highly

constrained environmental/economic dispatch problem.

Simulation results demonstrate the accuracy of the proposed

approach which in turn confirmed its potential to solve the

multi-objective EED problem.

In [131] Differential Evolution (DE) algorithm was studied &

applied for solving economic load dispatch (ELD) problems

in power systems. DE has proven to be very effective in

solving optimization problems in different domains and

results was found much better in terms of quality of the

solution obtained from other available methods. A differential

evolution (DE) algorithm was proposed for solving economic

load dispatch (ELD) complex and nonlinear problems having

equality and inequality constraints in power systems [131].

DE has established itself to be the successful in solving many

practical constrained optimization problems in different

sphere of influences. Comparing with other approach, the

proposed DE method was found much better with quality

solution [171]. Adaptive hybrid differential Evolution

(AHDE) algorithm has been proposed in [169] to solve

dynamic economic dispatch (DED) problem with valve-point

effects. In AHDE method, adaptive dynamic parameter

control mechanism is applied to find out the value of the

parameter CR of differential evolution (DE).Simulation

results show that AHDE algorithm obtain good dispatch

results with lower fuel cost in faster computational time with

better solution quality and convergence property as compared

to other methods. Dynamic economic dispatch (DED)with

valve-point effects is a complicated non-linear constrained

optimization problem with non-smooth and non-convex

characteristics which can be solved through proposed three

chaotic differential evolution (CDE) methods based on the

Tent equation. The simulation results are compared with DE

and other methods reveals that proposed approach in [179] is

capable of obtaining better quality solutions with higher

efficiency. [213] proposes a novel hybrid shuffled differential

evolution (SDE) algorithm for nonconvex ED problems

solution. Results showed accurate and fast convergence

performances with reduced generation costs. A differential

evolution (DE) algorithm is proposed in [182] to solve

emission constrained economic power dispatch (ECEPD)

problem. The DE algorithm attempts to reduce the production

of atmospheric emissions such as sulfur oxides and nitrogen

oxides, caused by the operation of fossil-fuelled thermal

generation and simulation results shows the effectiveness of

the proposed DE algorithm. [186] presents a multi-objective

differential evolution approach for solving economic

environmental dispatch problem with competing fuel cost and

emission objectives. Results are compared with pareto

differential evolution, strength pareto evolutionary algorithm

2 and nondominated sorting genetic algorithm-II and it has

been found that multi-objective differential evolution

approach provides a competitive performance in terms of

solution as well as computation time. The approach is very

simple, robust and efficient and does not impose any

limitation on the number of objectives.

Swarm intelligence-based algorithms

Swarm Intelligence based Algorithm is defined as a

distributed problem-solving strategy which is motivated by

the combined behavior of animal societies and social insect

colonies . There are many such techniques which have been

used by recent researchers to solve the myriad number of

optimization problems in modern power system like particle

swarm optimization (PSO), ant colony optimization (ACO),

bacterial foraging (BFA), artificial immune systems (AIS),

artificial bee colony (ABC) algorithm, group search optimizer

(GSO), firefly algorithm (FFA)

Particle swarm optimization

An improved coordinated aggregation-based particle swarm

optimization (ICA-PSO) algorithm is introduced in paper [7]

to solve the optimal economic load dispatch (ELD) problem in

power systems. [10] Gives the multi objective particle swarm

optimization (MOPSO) techniques for optimization. In this

paper the ICA-PSO algorithm is tested on power systems with

6, 13, 15, and 40 generating units and is compared with new

state-of-the-art heuristic optimization techniques (HOTs),

indicating better performance over them. In this algorithm



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every particle in the swarm retain a memory of its finest

position still encountered, and is involved only by other

particles with improved success than its individual with the

exclusion of the particle with the best achievement, which

shift randomly.

Furthermore, the population size is increased adaptively, the

number of search intervals for the particles is chosen

adaptively and the particles search the decision space with

accuracy up to two digit points resulting in the improved

convergence of the process. A fuzzified multi-objective

particle swarm optimization (FMOPSO) is introduced in [14].

[15] Mentions the improved PSO technique for emission

dispatch problem. To optimize the capacity of DG and

operational strategy in micro grids, a hybrid method is

presented in [31] ,this hybrid optimization method detailed

here is a combination of the particle swarm optimization

(PSO) algorithm and the quadratic programming (QP). [32]

gives the summary of the economic dispatch problem using

the particle swarm optimization techniques and it also

suggests the further improvement scope in PSO in this field.

[41] presented the multi-objective optimization for cost and

emission objectives simultaneously solved with PSO and DE

and then both are compared.

Planning for Optimal allocation of DERs in 14 bus radial

micro grid is given in [59], here the cost and emission

optimization is done by PSO and the results are then

compared with the results obtained by DE.. An autonomous

micro grid is designed in [62] ,the task to design this grid is

here given as an optimization problem, and solved by PSO

algorithm by searching the optimal setting of the parameters

which are to be optimized in micro grid. Also here in this

paper the micro grid containing distributed generation (DG)

units, lines, loads, voltage, current and power controllers, and

LC Filters , coupling inductances, has been scrutinized using

RTDS (Real time digital simulator) . Results came out in [62]

confirms the micro grid stability and its effectiveness. The

Conventional economic load dispatch problem uses

deterministic models whereas Stochastic models are used for

investigation of power dispatch problems. In [127] both

deterministic and stochastic models are formulated and then

improved particle swarm optimization (PSO) method is

applied to deal with the economic load dispatch problem

considering the environmental impact. In [133] a novel

(Fuzzy adaptive improved particle swarm optimization)

FAIPSO algorithm is proposed to cope with multi-objective

dynamic economic emission dispatch (DEED) problem by

integrating wind power output of wind turbines. The

stochastic DEED (SDEED) problem is also transformed into

an equivalent deterministic scenario-based DEED and

enhanced particle swarm optimization (PSO) algorithm is

applied to get the best solution for the corresponding

scenarios.

Wind power is a source of clean energy and availability of

wind power is highly dependent on the weather conditions

therefore penetration of wind power into the grids may have

some security implications. For economic power dispatch

considering wind power penetration is a reasonable trade-off

between system risk and operational cost. [154] presents a

modified multi-objective particle swarm optimization

(MOPSO) algorithm to develop a power dispatch scheme

which can be able to reach a compromise between economic

and security requirements. Quantum-behaved particle swarm

optimization algorithm is used to solve the economic load

dispatch problem of power system in [138]. In this algorithm

superposition characteristic and probability representation of

quantum methodology are combined. The simulation results

validate that it can effectively solve economic load dispatch

problem and solution is superior than other optimization

algorithms. Non-convex economic power dispatch problem is

solved using Particle Swarm Optimization (PSO) and Time

Varying Acceleration Coefficients (TVAC) approach in [210].

In this approach, TVAC effectively handled the problem of

premature convergence and rampant in PSO and results shows

the PSO_TVAC approach has stable dynamic convergence

characteristics, robustness and stability compared to other

strategies for solving non-convex cases.

The author has introduced CRAZYPSO algorithm in [142] to

solve the economic load dispatch (ELD) problems with

different types of non-smooth cost coefficients associated

valve point loadings and up rate and down rate limits and

other constraints for 40 generating units. The proposed

CRAZYPSO algorithm out performs and provides true global

optimal solutions comparing other techniques for economic

load dispatch. Global optimization approaches inspired by

swarm intelligence and evolutionary computation approaches

have been used to solve the difficult Economic Dispatch

Problems (EDPs). [143] proposed improved PSO approaches

for solving EDPs that takes into account nonlinear generator

features such as ramp-rate limits and prohibited operating

zones in the power system operation. The main objective of

[144] is to search a simple and effective method for the

economic load dispatch (ELD) problem with security

constraints in thermal units. Improved particle swarm

optimization (IPSO) method is proposed for a new velocity

strategy equation which is suitable for a large scale system

and the features of constriction factor approach (CFA). CFA

generates higher quality solutions than PSO approach. The

IPSO approach takes care of security constraints and results

show that the IPSO method is very effective in solving ELD

problem. An efficient evolutionary economic load dispatch

method is presented in [148]. Here the allocation of online

power generators is done to minimize the cost of operation.

Here the ‘‘crazy particles’’ are introduced to tackle the

problem of premature local optima for irregular search space

in GA and PSO technique and the velocities of those crazy

particles are randomized to uphold the impetus in the search

and to evade saturation. The results of the PSO with the

‘crazy particles’ found superior than GA and classical PSO.

Particle swarm optimization (PSO) and differential evolution

(DE) are the renowned recent optimization algorithms. [149]

gives the comparative analysis for optimization with PSO and

DE and it is concluded that the DE gives better results as far



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as the quality of the solutions and the repeatability are

concerned. In [150] h-particle swarm optimization (h-PSO)

based algorithm is applied to solve constrained economic load

dispatch (ELD) problems of thermal plants. h-PSO algorithm

takes care of constraints like transmission losses, ramp rate

limits and prohibited operating zones and deals with non-

smoothness of cost function arising due to the use of valve

point effects. The results confirm that proposed approach

outperforms in terms of solution quality and computational

efficiency. In [155] deterministic and stochastic models are

formulated and thereafter modified particle swarm

optimization (PSO) method is developed to deal with the

economic load dispatch problem considering the

environmental impact. The reliability indices can be

introduced for ensuring power system adequacies which

includes loss of load expectation (LOLE) and loss of energy

expectation (LOEE) and so on.

[156] presents a modified particle swarm optimization

(MPSO) for constrained economic load dispatch (ELD)

problem. Real cost functions are more complex than

conventional second order cost functions when multi-fuel

operations, valve-point effects, accurate curve fitting, etc., are

considering in deregulated changing market. MPSO

accurately tracks the continuous change in solution of the

complex cost function and no extra concentration/effort is

needed for the complex higher order cost polynomials in

ELD. A novel method combination of cultural algorithm (CA)

and particle swarm algorithm (PSO) is proposed in [170] to

solve the economic dispatch problems with valve-point effect.

The comparison of simulation results with approaches reveals

the robustness and proficiency of the proposed approach. The

author proposes artificial immune system in [175] which is

based on the clonal selection principle to solve the dynamic

economic dispatch problem. The simulation result shows that

the proposed approach capable to provide good solution than

particle swarm optimization and evolutionary programming

having minimum cost and computation time. Integrating a

Battery Energy Storage System (BESS) into a micro grid is a

challenge because it is difficult to assess an optimum size of

BESS to prevent the micro grid from instability and system

collapse. [103] presented a new method particle swarm

optimization (PSO)to decide the optimum size of BESS at

minimal cost. Simulation results show that the optimum size

of BESS-based PSO method can achieve higher dynamic

performance of the system and we can significantly improve

power system stability, grid security, and planning flexibility

for the micro grid system.

In [185], a new method called enhanced multi-objective

cultural algorithm (EMOCA) is presented to solve economic

environmental dispatch (EED) problem. EMOCA method

combines the particle swarm optimization with cultural

algorithm framework to implement population space

evolution and two knowledge structures of the belief space are

redefined to improve the convergence. The comparison of

simulation results shows that EMOCA provides a competitive

performance in terms of optimal solutions with efficient

convergence efficiency while dealing the EED problem. It

was demonstrated the employing modified PSO approaches

for efficient solving of EDPs with generator constraints. A

chaotic sequence based on logistic map is incorporated as a

randomizer instead of traditional uniform random function

into the PSO approach to enrich the searching behavior and to

avoid being trapped into local optimum and combining PSO

with Gaussian and chaotic signals is very effective in solving

EDPs [187].

[200] presents a novel method for solving the large-scale

complex economic dispatch problem (EDP) by integrating the

particle swarm optimization (PSO) technique with the

sequential quadratic programming (SQP) technique. PSO is a

derivative free optimization technique for solving EDP

without considering incremental fuel cost function and SQP is

a nonlinear programming method which finds solution using

the gradient information. PSO–SQP method has the ability to

find highly accurate & quality results at fast converging

characteristics. [210] Non-convex economic power dispatch

problem is solved using Particle Swarm Optimization (PSO)

and Time Varying Acceleration Coefficients (TVAC)

approach. In this approach, TVAC effectively handled the

problem of premature convergence and rampant in PSO and

results shows the PSO_TVAC approach has stable dynamic

convergence characteristics, robustness and stability

compared to other strategies for solving non-convex cases.

[212] Researches on economy dispatch considering wind

system aiming lowest generating cost of complete power

system as objective function and simulation has been carried

out using particle swarm optimization algorithm. Particle

Swarm Optimization (PSO) method has been introduced in

[112] as a global optimizer and incorporated within the

problem context. The simulation result illustrate that the PSO

method is efficient technique which determine a high quality

solution for micro grid at a relatively low computational cost.

Ant colony optimization

ACO contains two foremost steps: Ant solutions creation: In

this step, according to a transition rule, the artificial ants go

through the neighboring states of a optimization problem, then

build a solution iteratively .Pheromone update: In this step the

pheromone is updated in each iteration.

Bee colony optimization

In [173] artificial bee colony optimization approach is

proposed for solving multi-area economic dispatch (MAED)

problem with tie line constraints considering transmission

losses, multiple fuels, valve-point loading and prohibited

operating zones. The proposed algorithm is effective

compared to differential evolution, evolutionary programming

and real coded genetic algorithm, considering the quality&

accuracy of simulation results. Also in [174] , Incremental

artificial bee colony (IABC) and incremental artificial bee

colony with local search (IABC-LS)algorithm approach is



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proposed to solve the non-convex valve point effect economic

power dispatch problem,. The IABC and IABC-LS algorithms

will also be applied to other energy problems like economic

power dispatch problems with prohibited operation zone,

environmental economic power dispatch problems and short-

term hydrothermal power dispatch problems, and also other

engineering optimization problems. In [95] A hybrid method

by combining BCO with SQP for solving dynamic economic

dispatch problem considering valve point effect is presented

by the author. It is evident from the comparison of results that

the proposed BCO-SQP based approach provides better

results than PSO-SQP and EP-SQP methods in terms of

minimum production cost and computation time.

BFO algorithm

Assembling of the bacteria into groups and moving in

concentric patterns with high bacterial density to search for

nutrients is called as chemotax and is enabled by the

swarming .Survival of the fittest bacteria is called as the

reproduction and that bacteria pass on the genetic characters

to subsequent generations. Because of the change in nutrients

or any other factor a particular location may be altered slowly

or rapidly which may further cause removal/disperse of a set

of bacteria in the different atmosphere .this is called as the

Elimination-dispersal and this step prevent from getting

trapped to local optima in the search space this improves the

global search ability of the algorithm.

The price penalty factor based fuzzy ranking approach is

applied in [93] for simultaneous minimization of cost and

emission. Author has proposed the improved bacterial

foraging algorithm (IBFA) automation strategy and crossover

operation is used in micro BFA to improve computational

efficiency. Economic dispatch is carried out at the energy

control center to find out the optimal output of thermal

generating units. In [147] modified bacterial foraging

algorithm (MBFA) approach is applied for solving the

economic and emission load dispatch (EELD) problem. The

natural selection of universal optimal bacterium is utilized by

MBFA technique which is having successful foraging

strategies in the fitness function. Simulation results validates

the effectiveness of the proposed MBFA algorithm as

compared to various other methods such as, linear

programming (LP), multi-objective stochastic search

technique (MOSST), differential evolution (DE), non-

dominated sorting genetic algorithm (NSGA), niched pareto

genetic algorithm (NPGA), strength pareto evolutionary

algorithm (SPEA) and fuzzy clustering based particle swarm

optimization (FCPSO) performed in different central load

dispatch centers to solve EELD problems. An improved

bacterial foraging algorithm (IBFA) is presented in [134] in

which crossover operation and a parameter automation

strategy is used to get the better computational efficiency in

micro BFA. The performance of IBFA is compared with

classical BFA and found to be better.

In [137] Power generation, spinning reserve and emission

costs are considered while solving the ELD problem. A hybrid

method combination of bacterial foraging (BF) algorithm with

Nelder–Mead (NM) method (BF–NM algorithm) is presented

to solve the ELD problem and study results are compared with

the results of other classic and intelligent algorithms like

particle swarm optimization (PSO), genetic algorithm (GA),

differential evolution (DE) and BF algorithms and simulation

results reveals that proposed method reduced the total cost of

the system. Economic dispatch is carried out at the energy

control center to find out the optimal output of thermal

generating units. In [176] author presents a heuristic

optimization methodology viz Bacterial foraging PSO-DE

(BPSO-DE)algorithm by integrating Bacterial Foraging

Optimization Algorithm (BFOA), Particle Swarm

Optimization (PSO) and Differential Evolution (DE) for

solving non-smooth, non-convex Dynamic Economic

Dispatch (DED) problem. The proposed hybrid approach

completely eliminates the problem of stagnation of solution.

In [193] a new optimization approach using modified bacterial

foraging algorithm (MBFA) is applied for solving economic

and emission load dispatch (EELD) problems. MBFA

employs the natural selection of global best bacterium which

is having successful foraging approach in the fitness function.

The final results certify the effectiveness of the MBFA

algorithm compared to various other methods viz linear

programming (LP), multi-objective stochastic search

technique (MOSST), differential evolution(DE), non-

dominated sorting genetic algorithm (NSGA), niched pareto

genetic algorithm (NPGA), strength pareto evolutionary

algorithm (SPEA) and fuzzy clustering based particle swarm

optimization (FCPSO) to solve EELD problems.

Group search optimizer

In GSO, a group is correlated with three kinds of components

i.e. scroungers, dispersed members called rangers and

producers.

A dynamic economic emission dispatch problem is solved by

applying group search optimizer with multiple producers

(GSOMP) in [205].The analysis of the results obtained here

shows that the DEED problem is very well solved as widely

spread Pareto-optimal set of solutions and GSOMP obtain

improved objective values for both the fuel cost and the

emission, and it converges faster to a better Pareto front

Biogeography-based optimization

Study of the distribution of the various types of species in

nature over time and space or the immigration and emigration

of species, is called as the Biogeography which is a heuristic

optimization technique in which each solution is equivalent to

an island and their features represented by the new term i.e.

the suitability index variables (SIV). habitat suitability index



15890

(HSI) is the fitness of each result which depends on various

features of the environment. Migration and mutation are the

two main operators of the BBO. Diversity of the algorithm is

decreased by the first operator which creates like habitats, and

the exploration ability of the algorithm is increased by the

second operator i.e. the mutation 105] presents the

combination of differential evolution (DE) and Biogeography-

based Optimization (BBO) algorithm which accelerate the

convergence speed and to improve solution quality and

algorithm is applied to solve complex economic emission load

dispatch (EELD) problems of thermal generators of power

systems

[21] gives the Oppositional biogeography based algorithm to

optimize the cost and emission objectives simultaneously,

which combines the opposition based learning scheme along

with migration concept of BBO. [165] presents the

combination of differential evolution (DE) and Biogeography-

based Optimization (BBO) algorithm to solve complex

economic emission load dispatch (EELD) problems of thermal

generators of power systems. Differential evolution (DE) is

very accurate evolutionary algorithms for global optimization

and solution for EELD problems and Biogeography-based

Optimization (BBO) is another algorithm which deals with the

geographical distribution of different biological species. It is

found the proposed method is much better in terms of quality

of the compromising and individual solution obtained.

[196] presents a combination of differential evolution (DE)

and biogeography-based optimization (BBO) algorithm to

solve complex economic emission load dispatch (EELD)

problems of thermal generators of power systems. Differential

evolution (DE) is one of the very fast, robust, accurate

algorithms for solving of EELD problems. Biogeography-

based optimization (BBO) is another new biogeography

inspired algorithm. The combination of DE and BBO

(DE/BBO) is used to accelerate the convergence speed of both

the algorithm and to improve solution quality. [214] proposed

hybrid model of DE & BBO (DE/BBO) approach for

convergence speed of algorithm and improve solution quality

to solve EELD problems. It has been observed that when

complex fuel cost characteristic is taken than computational

efficiency& quality is much better than other methods.

Invasive weed optimization

It is a stochastic search algorithm ,inspired by the biological

process of distribution and weed colonization. Invasive

weeds settle in the environment by covering the opportunity

spaces leaving behind an improper tillage which is then

followed by enduring living in the field. Behaviors of the

weeds vary with time , and there is less opportunity of life of

those having less fitness as the colony become more dense .

Invasive weed optimization algorithm is applied to 6 ,10 and

40 bus system for optimization of economic emission dispatch

problem in [36] which results in better solution .

Other bio-inspired algorithms

Clonal selection algorithm

Artificial immune algorithm is is a population-based

algorithm which is based on the principle of clonal selection.

AIS algorithm copy the human immune system therefore it is

a well advanced adaptive system. AIS have been gaining

significant consideration because of its influential adaptive

learning and memory capabilities. In AIS, potential solutions

of problems are referred by antibodies, the value of the

objective function which is to be optimized is referred by the

antigens. Evaluation process of antibody is called as the

cloning. In [183] the author has applied the artificial immune

system (AIS) algorithm to solve the constrained Dynamic

economic dispatch (DED) problem. Comparing the results

with other methods shows the superiority of AIS method and

can be concluded that clonal selection based AIS is a best

technique for solving complex non-smooth optimization

problems in power system.

Flower pollination algorithm

A dynamic multi objective optimal dispatch (DMOOD) for

wind-thermal system with a hybrid flower pollination

algorithm (HFPA) is given in [5].In this paper the cost,

emission and loss are simultaneously minimized with

complicated constraints like ramp limits ,valve point loadings,

spinning reserve, and prohibited zones together with the

inclusion of the cost of wind power uncertainty The projected

HFPA improves the exploration & exploitation strength of the

flower population which conducts the search. In the HFPA the

flower pollination algorithm (FPA) and differential evolution

(DE) algorithm are incorporated to maintain good solutions

and to prevent premature convergence. A 5-class, 3-step time

varying fuzzy selection mechanism (TVFSM) is included with

HFPA for solving multi-objective problems. The TVFSM

finds a fuzzy selection index (FSI) by aggregating different

conflicting objectives. The FSI is adopted as the worth

standard while updating the population. Gaussian membership

function is applied to compute FSI in such a way that extreme

solutions are filtered out and trade off solutions on the central

portion of the Pareto-front are obtained. The HFPA-TVFSM

approach successfully searches the optimal solution which

fulfills all the three objectives optimally. The Flower

pollination method is tested ,validated and applied on IEEE 30

bus standard system in [190] for static load dispatch problem .

In [24], a modified flower pollination algorithm (MFPA) is

developed by (1) making the local pollination operation of

FPA more effective through a user-controlled scaling factor

and (2) adding an extra intensive exploitation phase to

perform exhaustive search through the solution domain to

attain the optimal solution.

Harmony Search algorithm

An annual economic/environmental optimal operation of a



15891

Low Voltage Micro grid with DG capabilities (FC, MT, PV

units) is scrutinized in [26] with the help of Harmony Search

algorithm so as to perform hourly optimizations on a Low

Voltage Micro grid. [124] presents the hybrid harmony

search (HHS) algorithm with swarm intelligence to solve the

dynamic economic load dispatch problem. Harmony Search

(HS) is a recently developed derivative-free, meta-heuristic

optimization algorithm. The HHS algorithm takes care of

different constraints like power balance, ramp rate limits and

generation limits by using penalty function method and

simulation results affirmed the robustness and proficiency of

the algorithm over the other available existing techniques.

In[100] a new concept called reduced islanded MG network

(RIN) is used for generalization of the proposed index to n-

bus islanded MGs. The increasing loadability index of micro

grids in the islanding mode is more important and islanded

micro grid reconfiguration (IMGR) is proposed in this paper

as an operational tool for improving loadability as well as

decreasing power losses using an Adaptive Multi Objective

Harmony Search Algorithm.

Gravitational search algorithm

Gravitational search algorithm is a new heuristic optimization

algorithm which is based on the law of gravity and mass

interactions. In this algorithm, the search agent are a set of

masses which relate with each other based on gravity the laws

of motion and the Newtonian. It is compared with some

renowned heuristic search methods and results of that confirm

the high performance of the GSA for nonlinear functions.

Gravitational Search Algorithm (GSA) approach has been

applied to find the best compromising emission and cost in an

economic emission load dispatch (EELD) problem [132].

Simulation results found here shows that GSA has better

solution quality and computational efficiency. [180] proposed

the Gravitational Search Algorithm (GSA) to solve the

optimal solution for Combined Economic and Emission

Dispatch (CEED) problems. A price penalty factor approach

is used to solve the Combined economic emission dispatch

(CEED problem which is to optimize the two objectives ,the

fuel cost and the emission of generating units with GSA.

Teaching Learning Based Optimization (TLBO)

In [204], teaching learning based optimization (TLBO)

algorithm is proposed to solve multi-objective economic

emission dispatch (EED) problem and to improve the

efficiency opposition based learning (OBL) concept is

integrated with TLBO algorithm. Numerical simulation results

shows that the proposed approach is much efficient and better

computational time compared to the other methods.

Novel Stochastic Search (NSS) method

[208] has proposed a Novel Stochastic Search (NSS) method

to solve economic dispatch problems with non-linear

characteristics of the cost functions with valve-point effects.

Simulation results reflect that NSS has consistently converges

to a optimal solution to solve the ED problem. Comparing to

Genetic Algorithm (GA), Evolutionary Programming (EP),

Particle Swarm Optimization (PSO) and Simulated Annealing

(SA),NSS method has three key advantages viz. (i) converges

to optimal solution in a shortest possible time (less than 5 s for

the three test cases) (ii) with parameter restrictions NSS has

better results than other evolutionary algorithms. (iii) NSS

provides a candidate solution of optimal solution in each

search process.

New gumption approach

Economic dispatch (ED) with convex problem can be solved

using Tabu search (TS), evolutionary programming (EP) and

genetic algorithm (GA) but these methods are heuristic and do

not guarantee for optimal solutions in finite time. [215] paper

gives another approach to solve the problem by use of

optimization algorithm with valve-point and losses effect. The

proposed optimization algorithm which is based on the valve

point of power generators minimizes the operation cost of the

plants and provides the optimum generation schedule

satisfying the system operating constrains. Results are

obtained on the IEEE 6-mchines 30-bus systems , IEEE 5-

machines 14-bus, and Three machines 6-bus system,. Results

found by using this algorithm compared with results obtained

from the other algorithm such as the hybrid genetic algorithm

(HGA), genetic algorithm (GA), and hybrid (H) method give

noteworthy improvements in the operation cost.

CONCLUSION

During the past decade myriad numbers of metaheuristic

techniques are being used so far in solving the problems of

economic load dispatch. In this paper detailed comparative

study is done on various techniques used for solving the

economic emission dispatch multi objective optimization

problem. This may give an idea to researchers about various

state of the art techniques they may take to use for solving the

multiobjective optimization problem. After the detailed study

done here it can be concluded that some techniques perform

well for specific problem while at the same time some may

not give better outcome.So we can say that the metaheuristic

are problem specific.

REFERENCES

[1] Manjaree Pandit, Vishal Chaudhary,Hari Mohan Dubey

and B.K. Panigrahi, “Multi-period wind integrated

optimal dispatch using series PSO-DE with time-

varying Gaussian membership function based fuzzy

selection,” Electrical Power and Energy Systems 73

(2015) 259–272



15892

[2] R. Nazira, H.D.Laksonoa,E.P.Waldia,E.Ekaputrab and

P. Coveriaa, “Renewable Energy Sources

Optimization: A Micro-Grid,” Model Design, Energy

Procedia 52 ( 2014 ) 316 – 327

[3] A.K.BASU, S.P.Chowdhury, S.Chowdhury, D.Ray and

P.A.Crossley,” Reliability Study of A Micro-Grid

Power System”,

[4] Robert H. Lasseter and Paolo Piagi, “Microgrid: A

Conceptual Solution,” PESC’04 Aachen, Germany 20-

25 June 2004

[5] Hari Mohan Dubey,Manjaree Pandit and B.K.

Panigrahi, “Hybrid flower pollination algorithm with

time-varying fuzzy selectionmechanism for wind

integrated multi-objective dynamic economic

dispatch,” Renewable Energy 83 (2015) 188 - 202

[6] Azza A. ElDesouky,“ Security and Stochastic

Economic Dispatch of Power System Including Wind

and Solar Resources with Environmental

Consideration,” INTERNATIONAL JOURNAL of

RENEWABLE ENERGY RESEARCH Azza A.

ElDesouky, Vol.3, No.4, 2013

[7] John G. Vlachogiannis and Kwang Y. Lee,” Economic

Load Dispatch—A Comparative Study on Heuristic

Optimization Techniques With an Improved

Coordinated Aggregation-Based PSO,” IEEE

TRANSACTIONS ON POWER SYSTEMS, VOL. 24,

NO. 2, MAY 2009

[8] Ming Jin,Wei Feng, Ping Liu,Chris Marnay and Costas

Spanos, “MOD-DR: Microgrid optimal dispatch with

demand response,” Applied Energy 187 (2017) 758–

776

[9] Nima Amjady and Hossein Sharifzadeh,” Solution of

non-convex economic dispatch problem considering

valve loading effect by a new Modified Differential

Evolution algorithm,” Electrical Power and Energy

Systems 32 (2010) 893–903

[10] Aftab Ahmad Khan, Muhammad Naeem, Muhammad

Iqbal, Saad Qaisar and Alagan Anpalagan, “A

compendium of optimization objectives, constraints,

tools and algorithms for energy management in

microgrids, “ Renewable and Sustainable Energy

Reviews 58 (2016) 1664–1683

[11] Muhammad Naveed Naz, Muhammad Irfan Mushtaq,

Muhammad Naeema, Muhammad Iqbal, Muhammad

Waseem Altaf and Muhammad Haneef,“Multicriteria

decision making for resource management in renewable

energy assisted microgrids,” Renewable and

Sustainable Energy Reviews (xxxx) xxxx–xxxx

[12] Carlos A. Hernandez-Aramburo, Tim C. Green and

Nicolas Mugniot, “Fuel Consumption Minimization of

a Microgrid,” IEEE TRANSACTIONS ON

INDUSTRY APPLICATIONS, VOL. 41, NO. 3,

MAY/JUNE 2005

[13] Joydeep Mitra, Mallikarjuna R. Vallem and Shashi B.

Patra,”A Probabilistic Search Method for Optimal

Resource Deployment in a Microgrid,” 9th

International Conference on Probabilistic Methods

Applied to Power Systemsn KTH, Stockholm, Sweden

- June 11-15, 2006

[14] N. D. Hatziargyriou, A.G.Anastasiadis, J.Vasiljevska

and A.G. Tsikalakis,”Quantification of Economic,

Environmental and Operational Benefits of

Microgrids,” Paper accepted for presentation at 2009

IEEE Bucharest Power Tech Conference, June 28th -

July 2nd, Bucharest, Romania

[15] Huiqiu Cao, Jian Xu, Deping Ke, Chengxu Jin,

Shengchu Deng, Chenghui Tang, Mingjian Cui and Ji

Liu,” Economic Dispatch of Micro-Grid Based on

Improved Particle-Swarm Optimization Algorithm,”

[16] C. Marnay, G. Venkataramanan, M. Stadler, A.

Siddiqui, R. Firestone, and B. Chandran, “Optimal

Technology Selection and Operation of Microgrids in

Commercial Buildings,”

[17] A.D. Hawkes ana M.A. Leach, “Modelling high level

system design and unit commitment for a microgrid,”

Applied Energy 86 (2009) 1253–1265

[18] Ashoke Kumar Basu, Sunetra Chowdhury and S. P.

Chowdhury, “Impact of Strategic Deployment of CHP-

Based DERs on Microgrid Reliability,” IEEE

TRANSACTIONS ON POWER DELIVERY, VOL.

25, NO. 3, JULY 2010

[19] Ashoke Kumar Basu, Sunetra Chowdhury and S. P.

Chowdhury, “Strategic Deployment of CHP-based

Distributed Energy Resources in Microgrids,”

[20] Rainer Storn and Kenneth Price,“Differential Evolution

- A simple and efficient adaptive scheme for global

optimization over continuous spaces,” TR-95-012

March 1995

[21] Aniruddha Bhattacharya and Pranab Kumar

Chattopadhyay,” Oppositional Biogeography-Based

Optimization for Multi-objective Economic Emission

Load Dispatch,”

[22] N.Kumarappan, M.R.Mohan and S.Murugappan,

“ANN Approach Applied to Combined Economic and

Emission Dispatch for Large- scale system,” 0-7803-

7278-6/02/$10.002 ,2002 IEEE

[23] YAN Qin, XU Er-shu and YANG Yong-ping,

“Pollutant Emission Reduction Analysis of Distributed

Energy Resource,” 978-1-4244-1748-3/08/$25.00 ©

2008 IEEE

[24] Hari Mohan Dubey, Manjaree Pandit and B. K.

Panigrahi, “A Biologically Inspired Modified Flower

Pollination Algorithm for Solving Economic Dispatch



15893

Problems in Modern Power Systems, “Cogn Comput

DOI 10.1007/s12559-015-9324-1

[25] LubnaMariam, Malabika Basu and Michael F.

Conlon,”A Review of Existing Microgrid

Architectures,” Hindawi Publishing Corporation

Journal of Engineering Volume 2013, Article ID

937614, 8 pages

[26] Anestis G. Anastasiadis, Stavros A. Konstantinopoulos,

Georgios P. Kondylis, Georgios A. Vokas and

Panagiotis Papageorgas,“Effect of fuel cell units in

economic and environmental dispatch of a Microgrid

with penetration of photovoltaic and micro turbine

units,” international journal of hydrogen energy xxx

(2016) 1-8

[27] S. Bahramara, M. Parsa Moghaddam and M.R.

Haghifam,“A bi-level optimization model for operation

of distribution networks with micro-grids,” Electrical

Power and Energy Systems 82 (2016) 169–178

[28] Ruhul A. Sarker, Member, IEEE, Saber M. Elsayed,

and Tapabrata Ray,” Differential Evolution With

Dynamic Parameters Selection for Optimization

Problems,” IEEE TRANSACTIONS ON

EVOLUTIONARY COMPUTATION, VOL. 18, NO.

5, OCTOBER 2014

[29] Meisam Hemmati, Nima Amjady and Mehdi Ehsan,”

System modeling and optimization for islanded micro-

grid using multi-cross learning-based chaotic

differential evolution algorithm,” Electrical Power and

Energy Systems 56 (2014) 349–360

[30] X. Xia and A.M. Elaiw ,”Optimal dynamic economic

dispatch of generation: A review,” Electric Power

Systems Research 80 (2010) 975–986

[31] Mohammad H. Moradi, Mohsen Eskandari and Hemen

Showkati ,” A hybrid method for simultaneous

optimization of DG capacity and operational strategy in

microgrids utilizing renewable energy resources,”

Electrical Power and Energy Systems 56 (2014) 241–

258

[32] Amita Mahor, Vishnu Prasad and Saroj Rangnekar,”

Economic dispatch using particle swarm optimization:

A review,” Renewable and Sustainable Energy

Reviews 13 (2009) 2134–2141

[33] Moataz Elsied, Amrane Oukaour, Hamid Gualous and

Radwan Hassan,” Energy management and

optimization in microgrid system based on green

energy,” Energy 84 (2015) 139-151

[34] A.Hina Fathima and K. Palanisamy,” Optimization in

micro grids with hybrid energy systems – A review,”

Renewable and Sustainable Energy Reviews 45 (2015)

431–446

[35] Carlos Gamarra and Josep M. Guerrero,”

Computational optimization techniques applied to

microgrids planning: A review,” Renewable and

Sustainable Energy Reviews 48 (2015) 413–424

[36] B.K. Panigrahi, Manjaree Pandit, Hari Mohan Dubey,

Ashish Agarwal and Wei-Chiang Hong,”Invasive

Weed Optimization for Combined Economic and

Emission Dispatch Problems,”International Journal of

Applied Evolutionary Computation, 5(1), 1-18,

January-March 2014

[37] Hossein Haddadian and Reza Noroozian,” Multi-

microgrids approach for design and operation of future

distribution networks based on novel technical

indices,” Applied Energy xxx (2016) xxx–xxx

[38] Mohammad Hasan Hemmatpour, Mohsen

Mohammadian and Ali Akbar Gharaveisi,” Optimum

islanded microgrid reconfiguration based on

maximization of system loadability and minimization

of power losses,” Electrical Power and Energy Systems

78 (2016) 343–355

[39] Xiaolong Jina, Yunfei Mua, Hongjie Jiaa, Tao Jiangb,

Houhe Chenb and Rufeng Zhangb,” An Optimal

Scheduling Model for a Hybrid Energy Microgrid

Considering Building Based Virtual Energy Storage

System,” Energy Procedia 88 ( 2016 ) 375 – 381

[40] Jaesung Junga and Michael Villaranb,”Optimal

planning and design of hybrid renewable energy

systems for Microgrids,” Renewable and Sustainable

Energy Reviews xx (xxxx) xxxx–xxxx

[41] Ashoke Kumar Basu, Aniruddha Bhattacharya,Sunetra

Chowdhury and S. P. Chowdhury,” Planned

Scheduling for Economic Power Sharing in a CHP-

Based Micro-Grid,” IEEE TRANSACTIONS ON

POWER SYSTEMS, VOL. 27, NO. 1, FEBRUARY

2012

[42] Christos D. Korkas, Simone Baldi, Iakovos Michailidis

and Elias B. Kosmatopoulos,” Occupancy-based

demand response and thermal comfort optimization in

microgrids with renewable energy sources and energy

storage,” Applied Energy 163 (2016) 93–104

[43] Lubna Mariam, Malabika Basu and Michael F. Conlon,

”Microgrid: Architecture, policy and future trends,”

Renewable and Sustainable Energy Reviews

64(2016)477–489

[44] F.Martin-Martínez,A.Sánchez-Miralles and M.Rivier,”

A literature review of Microgrids:A functional layer

based classification,” Renewable and Sustainable

Energy Reviews 62(2016)1133–1153

[45] SINA PARHIZI, HOSSEIN LOTFI, AMIN KHODAEI

AND SHAY BAHRAMIRAD ,” State of the Art in

Research on Microgrids: A Review,” Digital Object

Identifier 10.1109/ACCESS.2015.2443119

[46] Josep M. Guerrero, Frede Blaabjerg, Yun Wei Li

,Yasser Abdel-Rady I. Mohamed and Magdy M. A.



15894

Salama,” Introduction to the Special Section on

Distributed Generation and Microgrids,” IEEE

TRANSACTIONS ON INDUSTRIAL

ELECTRONICS, VOL. 60, NO. 4, APRIL 2013

[47] Emiliano Dall’Anese, Hao Zhu and Georgios B.

Giannakis,” Distributed Optimal Power Flow for Smart

Microgrids,” IEEE TRANSACTIONS ON SMART

GRID, VOL. 4, NO. 3, SEPTEMBER 2013

[48] Ali Maknouninejad and Zhihua Qu,”Realizing Unified

Microgrid Voltage Profile and Loss Minimization: A

Cooperative Distributed Optimization and Control

Approach,” IEEE TRANSACTIONS ON SMART

GRID, VOL. 5, NO. 4, JULY 2014

[49] Sina Parhizi, Amin Khodaei and Mohammad

Shahidehpour,” Market-based vs. Price-based

Microgrid Optimal Scheduling,”

[50] Yingsong Huang, Shiwen Mao and R.M. Nelms,”

Adaptive Electricity Scheduling in Microgrids,” IEEE

TRANSACTIONS ON SMART GRID, VOL. 5, NO.

1, JANUARY 2014

[51] V.Logeshwaria, N.Chitrab, A.Senthil Kumarc and

Josiah Munda,” Optimal Power Sharing for Microgrid

with Multiple Distributed Generators,” Procedia

Engineering 64 ( 2013 ) 546 – 551

[52] Sharmistha Sharma, Subhadeep Bhattacharjee and

Aniruddha Bhattacharya,”Operation cost minimization

of a Micro-Grid using Quasi-Oppositional Swine

Influenza Model Based Optimization with Quarantine,”

Ain Shams Engineering Journal (2015) xxx, xxx–xxx

[53] Omar Hafez and Kankar Bhattacharya,” Optimal

planning and design of a renewable energy based

supply system for microgrids,” Renewable Energy 45

(2012) 7-15

[54] Chitra Natesan, Senthil Kumar Ajithan, Swathi

Chozhavendhan and Anitha Devendhiran,” Power

Management Strategies in Microgrid: A Survey,”

INTERNATIONAL JOURNAL of RENEWABLE

ENERGY RESEARCHC. Natesan et al., Vol.5, No.2,

2015

[55] Eklas Hossain, Ersan Kabalci, Ramazan Bayindir and

Ronald Perez,”A Comprehensive Study on Microgrid

Technology,” INTERNATIONAL JOURNAL OF

RENEWABLE ENERGY RESEARCH Ramazan

Bayindir et al. ,Vol. 4, No. 4, 2014

[56] Er.Vikas Kumar, Er.Navdeep Kaur and Er.Sandeep

Kumar,” Advancements in Smart Micro Grid

Technology,” SSRG International Journal of Electrical

and Electronics Engineering (SSRG-IJEEE) – EFES

April 2015

[57] John Hetzer, David C. Yu and Kalu Bhattarai ,” An

Economic Dispatch Model Incorporating Wind Power,”

IEEE TRANSACTIONS ON ENERGY

CONVERSION, VOL. 23, NO. 2, JUNE 2008

[58] Faisal A. Mohamed and Heikki N. Koivo,”

Multiobjective optimization using Mesh Adaptive

Direct Search for power dispatch problem of

microgrid,” Electrical Power and Energy Systems 42

(2012) 728–735

[59] Ashoke Kumar Basu,” Microgrids: Planning of fuel

energy management by strategic deployment of CHP-

based DERs – An evolutionary algorithm approach,”


336

[60] Wei Gu , Zhi Wu, Rui Bo, Wei Liu, Gan Zhou, Wu

Chen and Zaijun Wua,”Modeling, planning and optimal

energy management of combined cooling, heating and

power microgrid: A review,” Electrical Power and


[61] Waleed Al-Saedi, Stefan W. Lachowicz, Daryoush

Habibi and Octavian Bass,” Power flow control in grid-

connected microgrid operation using Particle Swarm

Optimization under variable load conditions,”

Electrical Power and Energy Systems 49 (2013) 76–85

[62] M.A. Hassan and M.A. Abido,” Real time

implementation and optimal design of autonomous

microgrids,” Electric Power Systems Research 109

(2014) 118– 127

[63] M. Basu,” Dynamic economic emission dispatch using

nondominated sorting genetic algorithm-II,” Electrical


[64] Meisam Hemmati, Nima Amjady and Mehdi Ehsan,”





[65] Yantao Wang, Jing Yan, Jian Li, Zhenxin Li and Wuyi

Zhang,” A New Model of Economic Dispatch

Considering Energy Conservation and Environmental

Protection in Electricity Market,” Energy Procedia 17 (

2012 ) 1769 – 1777

[66] Blaze Gjorgiev and Marko Cepin,”A multi-objective

optimization based solution for the combined

economic-environmental power dispatch problem,”

Engineering Applications of Artificial Intelligence 26

(2013) 417–429

[67] Juan P. Fossati, Ainhoa Galarza, Ander Martín-Villate,

José M. Echeverría and Luis Fontán,” Optimal

scheduling of a microgrid with a fuzzy logic controlled

storage System,” Electrical Power and Energy Systems

68 (2015) 61–70

[68] Neha Mishra and Manjaree Pandit,” Economic

Emission Dispatch Using Weighted Sum Based PSO

with Fuzzy Decesion Making,” International

Conference on Microelectronics, Communication and



15895

Renewable Energy (ICMiCR-2013)

[69] Wanxing Sheng, Ke-yan Liu, Xiaoli Meng, Xueshun

Ye and Yongmei Liu,”Research and practice on typical

modes and optimal allocationmethod for PV-Wind-ES

in Microgrid,”Electric Power Systems Research 120

(2015) 242–255

[70] Leandro dos Santos Coelho and Viviana Cocco

Mariani,” Combining of Chaotic Differential Evolution

and Quadratic Programming for Economic Dispatch

Optimization With Valve-Point Effect,” IEEE

TRANSACTIONS ON POWER SYSTEMS, VOL. 21,

NO. 2, MAY 2006

[71] Elena Provata, Dionysia Kolokotsa, Sotiris

Papantoniou , Maila Pietrini, Antonio Giovannelli and

Gino Romiti,” Development of optimization algorithms

for the Leaf Community Microgrid,” Renewable

Energy 74 (2015) 782-795

[72] Di Zhang, Sara Evangelisti, Paola Lettieri and Lazaros

G. Papageorgiou ,” Optimal design of CHP-based

microgrids: Multiobjective optimisation and life cycle

assessment,” Energy 85 (2015) 181e193

[73] R.A.Gupta and Nand Kishor Gupta,”A robust

optimization based approach for microgrid operation in

deregulated environment,” Energy Conversion and

Management 93 (2015) 121–131

[74] Michael Zachar, Milana Trifkovic and Prodromos

Daoutidis,” Policy effects on microgrid economics,

technology selection, andenvironmental impact,”

Computers and Chemical Engineering xxx (2015) xxx–

xxx

[75] Juan P. Fossati,Ainhoa Galarza,Ander Martín-Villate,

José M. Echeverría and Luis Fontán,”Optimal

scheduling of a microgrid with a fuzzy logic controlled

storage System,” Electrical Power and Energy Systems

68 (2015) 61–70

[76] Manisha Sharma, Manjaree Pandit and Laxmi

Srivastava,”Reserve constrained multi-area economic

dispatch employing differential evolution with time-

varying mutation,” Electrical Power and Energy

Systems 33 (2011) 753–766

[77] E.E. Sfikas, Y.A. Katsigiannis and P.S.

Georgilakis,”Simultaneous capacity optimization of

distributed generation and storage in medium voltage

microgrids,” Electrical Power and Energy Systems 67

(2015) 101–113

[78] Elizaveta Kuznetsova, Carlos Ruiz, Yan-Fu Li and

Enrico Zio,” Analysis of robust optimization for

decentralized microgrid energy management under

uncertainty,” Electrical Power and Energy Systems 64

(2015) 815–832

[79] Hongbin Wu, Xingyue Liu and Ming Ding,”Dynamic

economic dispatch of a microgrid: Mathematical

models and solution algorithm,” Electrical Power and


[80] Abdorreza Rabiee, Mohammad Sadeghi, Jamshid

Aghaeic and Alireza Heidari,” Optimal operation of

microgrids through simultaneous scheduling of

electrical vehicles and responsive loads considering

wind and PV units uncertainties,´ Renewable and


[81] Sandip Chanda and Abhinandan De,”A multi-objective

solution algorithm for optimum utilization of Smart

Grid infrastructure towards social welfare,” Electrical


[82] Alireza Zakariazadeh, Shahram Jadid and Pierluigi

Siano,” Economic-environmental energy and reserve

scheduling of smart distribution systems: A

multiobjective mathematical programming approach,”

Energy Conversion and Management 78 (2014) 151–

164

[83] Ashoke Kumar Basua, S.P.Chowdhury, S.Chowdhury

and S. Paul,” Microgrids: Energy management by

strategic deployment of DERs—A comprehensive

survey,” Renewable and Sustainable Energy Reviews

15 (2011) 4348– 4356

[84] A.K.Basu,S.Chowdhury and S.P.Chowdhury,” Value-

based operational strategy at the planning of CHPbased

micro-grid,” INTERNATIONAL JOURNAL OF

ENERGY AND ENVIRONMENT Volume 1, Issue 5,

2010 pp.871-882

[85] Ahmed Saif, V. Ravikumar Pandi, H.H. Zeineldin and

Scott Kennedy,” Optimal allocation of distributed

energy resources through simulation-based

optimization,” Electric Power Systems Research 104

(2013) 1– 8

[86] Mohammad H. Moradi, Mohsen Eskandari and Hemen

Showkati,”A hybrid method for simultaneous

optimization of DG capacity and operational strategy in

microgrids utilizing renewable energy resources,”


258

[87] Xingguo Tan, Qingmin Li and Hui Wang,”Advances

and trends of energy storage technology in Microgrid,”


191

[88] Ming Jin, Wei Feng, Ping Liu , Chris Marnay and

Costas Spanos,” MOD-DR: Microgrid optimal dispatch

with demand response,” Applied Energy 187 (2017)

758–776

[89] Bei Li, Robin Roche and Abdellatif Miraoui,”

Microgrid sizing with combined evolutionary algorithm

and MILP unit Commitment,” Applied Energy 188

(2017) 547–562

[90] Aftab Ahmad Khan, Muhammad Naeem, Muhammad



15896

Iqbal, Saad Qaisar and Alagan Anpalagan,”A

compendium of optimization objectives, constraints,

tools and algorithms for energy management in

microgrids,” Renewable and Sustainable Energy

Reviews 58(2016)1664–1683

[91] Muhammad Naveed Naz, Muhammad Irfan Mushtaq,

Muhammad Naeem,Muhammad Iqbal, Muhammad

Waseem Altaf and Muhammad Haneef,”Multicriteria

decision making for resource management in renewable

energy assisted microgrids,”Renewable and Sustainable

Energy Reviews (xxxx) xxxx–xxxx

[92] Anestis G. Anastasiadis, Stavros A. Konstantinopoulos

, Georgios P. Kondylis, Georgios A. Vokas and

Panagiotis Papageorgas,” Effect of fuel cell units in

economic and environmental dispatch of a Microgrid

with penetration of photovoltaic and micro turbine

units,” inte rnational journal of hydrogen energy xxx (

2 0 1 6 ) 1 - 8

[93] Nicole Pandit, Anshul Tripathi, Shashikala Tapaswi

and Manjaree Pandit,” An improved bacterial foraging

algorithm for combined static/dynamic environmental

economic dispatch,” Applied Soft Computing 12

(2012) 3500–3513

[94] Moataz Elsied, Amrane Oukaour, Hamid Gualous and

Radwan Hassan,” Energy management and

optimization in microgrid system based on green

energy,” Energy 84 (2015) 139e151

[95] M. Basu,” Hybridization of bee colony optimization

and sequential quadratic programming for dynamic

economic dispatch,” Electrical Power and Energy

Systems 44 (2013) 591–596

[96] A. Hina Fathima and K.Palanisamy,” Optimization in

micro grids with hybrid energy systems – A review,”

Renewable and Sustainable Energy Reviews

45(2015)431–446

[97] Carlos Gamarra and Josep M. Guerrero,”

Computational optimization techniques applied to

microgrids planning: A review,” Renewable and

Sustainable Energy Reviews 48(2015)413–424

[98] Li Guo, Nan Wang, Hai Lu, Xialin Li and Chengshan

Wang,”Multi-objective optimal planning of the stand-

alone microgrid system based on different benefit

subjects,”Energy 116 (2016) 353e363

[99] Eklas Hossein ,Ersan kabalci ,RamazanBayinder and

Ronald perez,” A comprehensive study on microgrid

technology,’’ IJRER (2014)

[100] Mohammad Hasan Hemmatpour, Mohsen

Mohammadian and Ali Akbar Gharaveisi,” Optimum

islanded microgrid reconfiguration based on

maximization of system loadability and minimization

of power losses,” Electrical Power and Energy Systems

78 (2016) 343–355

[101] Xiaolong Jin, Yunfei Mu, Hongjie Jia, Tao Jiang,

Houhe Chen and Rufeng Zhang,” An Optimal

Scheduling Model for a Hybrid Energy Microgrid

Considering Building Based Virtual Energy Storage

System,” Energy Procedia 88 ( 2016 ) 375 – 381

[102] Jaesung Jung and Michael Villaran,” Optimal planning

and design of hybrid renewable energy systems for

Microgrids,” Renewable and Sustainable Energy

Reviews xx (xxxx) xxxx–xxxx

[103] Thongchart Kerdphol, Kiyotaka Fuji, Yasunori Mitani,

Masayuki Watanabe and Yaser Qudaih,”Optimization

of a battery energy storage system using particle swarm

optimization for stand-alone microgrids,” Electrical


[104] Krishna Teerth Chaturved, Manjaree Pandit and Laxmi

Srivastava,” Modified neo-fuzzy neuron-based

approach for economic and environmental optimal

power dispatch,” Applied Soft Computing 8 (2008)

1428–1438


Chattopadhyay,”Solving economic emission load

dispatch problems using hybrid differential evolution,”

Applied Soft Computing 11 (2011) 2526–2537



economic-environmental power dispatch problem,”

Engineering Applications of Artificial Intelligence 26

(2013) 417–429

[107] Mohammad H. Moradi, Mohammad Abedini and S.

Mahdi Hosseinian,” Optimal operation of autonomous

microgrid using HS–GA,” Electrical Power and Energy

Systems 77 (2016) 210–220

[108] Seyyed Mostafa Nosratabadi, Rahmat-Allah

Hooshmand and Eskandar Gholipour,” A

comprehensive review on microgrid and virtual power

plant concepts employed for distributed energy

resources scheduling in power systems,”Renewable and


[109] Nnamdi I. Nwulu and Xiaohua Xia,”Optimal dispatch

for a microgrid incorporating renewables and demand

response,” Renewable Energy 101 (2017) 16e28

[110] Abdorreza Rabiee, Mohammad Sadeghi , Jamshid

Aghaeic and Alireza Heidari,” Optimal operation of

microgrids through simultaneous scheduling of

electrical vehicles and responsive loads considering

wind and PV units uncertainties,” Renewable and


[111] Julia Sachs and Oliver Sawodny,” Multi-objective three

stage design optimization for island microgrids,”

Applied Energy 165 (2016) 789–800

[112] Sara Ghaem Sigarchian, Matthew S. Orosz , Harry F.

Hemond and Anders Malmquist,” Optimum Design of



15897

a hybrid PV-CSP-LPG Microgrid with Particle Swarm

Optimization Technique,” ATE 8334 S1359-

4311(16)30797-9

[113] Mariya Soshinskaya, Wina H.J. Crijns - Graus, Josep

M. Guerrero and Juan C. Vasquez,” Microgrids:

Experiences, barriers and success factors,” Renewable

and Sustainable Energy Reviews 40(2014)659–672

[114] P. Sreedharan, J. Farbes, E. Cutter, C.K. Woo and J.

Wang,” Microgrid and renewable generation

integration: University of California, San Diego,”

Applied Energy 169 (2016) 709–720

[115] Evar Chinedu Umeozor and Milana Trifkovic,”

Operational scheduling of microgrids via parametric

programming,” Applied Energy 180 (2016) 672–681

[116] Nan Yu, Jin-Su Kang, Chung-Chuan Chang, Tai-Yong

Lee and Dong-Yup Lee,” Robust economic

optimization and environmental policy analysis for

microgrid planning: An application to Taichung

Industrial Park, Taiwan,” Energy 113 (2016) 671 - 682

[117] Michael Zachar, Milana Trifkovic and Prodromos

Daoutidis,”Policy effects on microgrid economics,

technology selection, andenvironmental impact,”

Computers and Chemical Engineering xxx (2015) xxx–

xxx


G. Papageorgiou,”Optimal design of CHP-based

microgrids: Multiobjective optimisation and life cycle

assessment,” Energy 85 (2015) 181 - 193


G. Papageorgiou ,” Economic and environmental

scheduling of smart homes with microgrid: DER

operation and electrical tasks,” Energy Conversion and

Management 110 (2016) 113–124

[120] R. Nazir, H. D. Laksono, E. P. Waldi, E. Ekaputra and

P. Coveria,” Renewable Energy Sources Optimization:

A Micro-Grid Model Design,” Energy Procedia 52 (

2014 ) 316 – 327

[121] L.H.Wu, Y.N.Wang, X.F.Yuan and S.W. Zhou,”

Environmental/ economic power dispatch problem

using multi-objective differential evolution algorithm,”

Electric Power Systems Research 80 (2010) 1171–1181

[122] Provas Kumar Roy and Sudipta Bhui,” Multi-objective

quasi-oppositional teaching learning based optimization

for economic emission load dispatch problem,”


948

[123] Ahmed R. Abul’Wafa,” Optimization of

economic/emission load dispatch for hybridgenerating

systems using controlled Elitist NSGA-II,” Electric

Power Systems Research 105 (2013) 142 – 151

[124] V. Ravikumar Pandi and Bijaya Ketan Panigrahi,”

Dynamic economic load dispatch using hybrid swarm

intelligence based harmony search algorithm,” Expert

Systems with Applications 38 (2011) 8509–8514

[125] Faisal A. Mohamed and Heikki N.

Koivo,”Multiobjective optimization using Mesh

Adaptive Direct Search for power dispatch problem of

microgrid,” Electrical Power and Energy Systems 42

(2012) 728–735

[126] Faisal A. Mohamed and Heikki N. Koivo,”System

modelling and online optimal management of

MicroGrid using Mesh Adaptive Direct

Search,”Electrical Power and Energy Systems 32

(2010) 398–407

[127] Lingfeng Wang and Chanan Singh,” Stochastic

economic emission load dispatch through a modified

particle swarm optimization algorithm,” Electric Power


[128] Gwo-Ching Liao,”Solve environmental economic

dispatch of Smart MicroGrid containing distributed

generation system – Using chaotic quantum genetic

algorithm,”Electrical Power and Energy Systems 43

(2012) 779–787

[129] Ashoke Kumar Basu,” Microgrids: Planning of fuel

energy management by strategic deployment of CHP-

based DERs – An evolutionary algorithm approach,”


336

[130] M. Basu,”Fuel constrained economic emission load

dispatch using hopfield neural networks,” Electric

Power Systems Research 63 (2002) 51-57

[131] Nasimul Noman and Hitoshi Iba,” Differential

evolution for economic load dispatch problems,”


[132] Soumitra Mondal, Aniruddha Bhattacharya and Sunita

Halder nee Dey,” Multi-objective economic emission

load dispatch solution using gravitational search

algorithm and considering wind power penetration,”


292

[133] Jamshid Aghaei, Taher Niknam, Rasoul Azizipanah-

Abarghooee and José M. Arroyo,” Scenario-based

dynamic economic emission dispatch considering load

and wind power uncertainties,” Electrical Power and


[134] Meisam Hemmati , Nima Amjady and Mehdi Ehsan,”





[135] Sanjeev Kumar and D.K. Chaturvedi,”Optimal power

flow solution using fuzzy evolutionary and swarm

optimization,” Electrical Power and Energy Systems 47



15898

(2013) 416–423

[136] Yu Wang, Bin Li and Thomas Weise,” Estimation of

distribution and differential evolution cooperation for

large scale economic load dispatch optimization of

power systems,” Information Sciences 180 (2010)

2405–2420

[137] Rahmat-Allah Hooshmand, Moein Parastegari and

Mohammad Javad Morshed ,” Emission, reserve and

economic load dispatch problem with non-smooth and

non-convex cost functions using the hybrid bacterial

foraging-Nelder–Mead algorithm,” Applied Energy 89

(2012) 443–453

[138] Zhang Zhisheng,”Quantum-behaved particle swarm

optimization algorithm for economic load dispatch of

power system,” Expert Systems with Applications 37

(2010) 1800–1803

[139] J.Nanda and R.Badri Narayan,” Application of genetic

algorithm to economic load dispatch with Lineflow

constraints,” Electrical Power and Energy System 24

(2002) 723 -729

[140] V.H. Mendez, J. Rivier, J.I. de la Fuente, T. Gomez, J.

Arceluz, J. Marin and A. Madurga,” Impact of

distributed generation on distribution investment

deferral,” Electrical Power and Energy Systems 28

(2006) 244–252

[141] Tankut Yalcinoz and Onur Koksoy,”A multiobjective

optimization method to environmental economic

dispatch,” Electrical Power and Energy Systems 29

(2007) 42–50

[142] Ranjit Roy and S.P. Ghoshal,”A novel crazy swarm

optimized economic load dispatch for various types of

cost functions,” Electrical Power and Energy Systems

30 (2008) 242–253

[143] Leandro dos Santos Coelho and Chu-Sheng

Lee,”Solving economic load dispatch problems in

power systems using chaotic and Gaussian particle

swarm optimization approaches,” Electrical Power and


[144] G. Baskar and M.R. Mohan,”Security constrained

economic load dispatch using improved particle swarm

optimization suitable for utility system,” Electrical


[145] I. Jacob Raglend, Sowjanya Veeravalli, Kasanur

Sailaja, B. Sudheera and D.P. Kothari,”Comparison of

AI techniques to solve combined economic emission

dispatch problem with line flow constraints,” Electrical


[146] Dun-wei Gong, Yong Zhang and Cheng-liang Qi,”

Environmental/ economic power dispatch using a

hybrid multi-objective optimization

algorithm,”Electrical Power and Energy Systems 32

(2010) 607–614

[147] P.K. Hota, A.K. Barisal and R. Chakrabarti,”Economic

emission load dispatch through fuzzy based bacterial

foraging algorithm,”Electrical Power and Energy

Systems 32 (2010) 794–803

[148] Soumitra Mondal, Aniruddha Bhattacharya and Sunita

Halder nee Dey,” Multi-objective economic emission

load dispatch solution using gravitational search

algorithm and considering wind power penetration,”


292

[149] Jamshid Aghaei, Taher Niknam, Rasoul Azizipanah-

Abarghooee and José M. Arroyo,” Scenario-based

dynamic economic emission dispatch considering load

and wind power uncertainties,” Electrical Power and


[150] Vahid Hosseinnezhad and Ebrahim Babaei,”Economic

load dispatch using -PSO,” Electrical Power and


[151] M. Basu,”Fuel constrained economic emission load

dispatch using hopfield neural networks,”Electric

Power Systems Research 63 (2002) 51-57

[152] R. Gnanadass, Narayana Prasad Padhy and K.

Manivannan,” Assessment of available transfer

capability for practical power systems with combined

economic emission dispatch,” Electric Power Systems

Research 69 (2004) 267–276

[153] Nasimul Nomanand and Hitoshi Iba,”Differential

evolution for economic load dispatch problems,”


[154] Lingfeng Wang and Chanan Singh,” Balancing risk and

cost in fuzzy economic dispatch including wind power

penetration based on particle swarm optimization,”


[155] Lingfeng Wang and Chanan Singh,” Stochastic

economic emission load dispatch through a modified

particle swarm optimization algorithm,” Electric Power


[156] Ahmed Yousuf Saber, Shantanu Chakraborty,S.M.

Abdur Razzak and Tomonobu Senjyu,” Optimization

of economic load dispatch of higher order general cost

polynomials and its sensitivity using modified particle

swarm optimization,” Electric Power Systems Research

79 (2009) 98–106

[157] M.S. Osman, M.A. Abo-Sinna and A.A. Mousa,” An ε-

dominance-based multiobjective genetic algorithm for

economic emission load dispatch optimization

problem,” Electric Power Systems Research 79 (2009)

1561–1567

[158] X.Xia and A.M. Elaiw,”Optimal dynamic economic

dispatch of generation: A review,” Electric Power




15899

[159] L.H.Wu,Y.N.Wang,X.F.Yuan and S.W. Zhou,”

Environmental/ economic power dispatch problem

using multi-objective differential evolution algorithm,”


[160] Ahmed R. Abul’Wafa,”Optimization of

economic/emission load dispatch for hybridgenerating

systems using controlled Elitist NSGA-II,” Electric

Power Systems Research 105 (2013) 142– 151

[161] V. Ravikumar Pandi and Bijaya Ketan Panigrahi,”

Dynamic economic load dispatch using hybrid swarm

intelligence based harmony search algorithm,” Expert

Systems with Applications 38 (2011) 8509–8514

[162] Sidhartha Panda and Narayana Prasad

Padhy,”Comparison of particle swarm optimization and

genetic algorithm for FACTS-based controller

design,”Applied Soft Computing 8 (2008) 1418–1427

[163] Krishna Teerth Chaturvedi, Manjaree Pandit and Laxmi

Srivastava,” Modified neo-fuzzy neuron-based

approach for economic and environmental optimal

power dispatch,”Applied Soft Computing 8 (2008)

1428–1438

[164] Sushil Kumar and R. Naresh,” Nonconvex economic

load dispatch using an efficient real-coded genetic

Algorithm,” Applied Soft Computing 9 (2009) 321–

329


Chattopadhyay,”Solving economic emission load



[166] Samir Sayah and Abdellatif Hamouda,”A hybrid

differential evolution algorithm based on particle

swarm optimization for nonconvex economic dispatch

problems,” Applied Soft Computing 13 (2013) 1608–

1619



economic-environmental power dispatch

problem,”Engineering Applications of Artificial

Intelligence 26 (2013) 417–429

[168] S. Balakrishnan, P.S. Kannanb, C. Aravindan and P.

Subathra,” On-line emission and economic load

dispatch using adaptive Hopfield neural network,”


[169] Youlin Lu, Jianzhong Zhou, Hui Qin, Yinghai Li and

Yongchuan Zhang,” An adaptive hybrid differential

evolution algorithm for dynamic economic dispatch

with valve-point effects,”Expert Systems with

Applications 37 (2010) 4842–4849

[170] Qun Niu, Xiaohai Wang and Zhuo Zhou,”An Efficient

Cultural Particle Swarm Optimization for Economic

Load Dispatch with Valve-point Effect,” Procedia

Engineering 23 (2011) 828 – 834a

[171] M.Swethasai Kumari and Dr. K.R.Sudha ,” Static

economic load dispatch of generators including

transmission line losses using differential

evolution,”International advance research journal in

science ,engineering and technology, ISSN 2394–1588

[172] A.K. Al-Othman and K.M. El-Naggar,”Application of

pattern search method to power system security

constrained economic dispatch with non-smooth cost

function,” Electric Power Systems Research 78 (2008)

667–675

[173] M. Basu,”Artificial bee colony optimization for multi-

area economic dispatch,” Electrical Power and Energy

Systems 49 (2013) 181–187

[174] Dogan Aydına,Serdar Ozyonb,”Solution to non-convex

economic dispatch problem with valve point effects by

incremental artificial bee colony with local

search,”Applied Soft Computing 13 (2013) 2456–2466

[175] M. Basu,”Artificial immune system for dynamic


Systems 33 (2011) 131–136

[176] K. Vaisakh, P. Praveena, S. Rama Mohana Rao and

Kala Meah,” Solving dynamic economic dispatch

problem with security constraints using bacterial

foraging PSO-DE algorithm,” Electrical Power and


[177] M. Basu,”Hybridization of bee colony optimization and

sequential quadratic programming for dynamic

economic dispatch,”Electrical Power and Energy

Systems 44 (2013) 591–596

[178] Gwo-Ching Liao,”Integrated Isolation Niche and

Immune Genetic Algorithm for solving Bid-Based

Dynamic Economic Dispatch,” Electrical Power and


[179] Youlin Lu, Jianzhong Zhou, Hui Qin,Ying Wang and

Yongchuan Zhang,”Chaotic differential evolution

methods for dynamic economic dispatch with valve-

point effects,”Engineering Applications of Artificial

Intelligence 24 (2011) 378–387

[180] U. Güvenç, Y. Sönmez, S. Duman and N.

Yörükeren,”Combined economic and emission dispatch

solution using gravitational search algorithm,” Scientia

Iranica D (2012) 19 (6), 1754–1762

[181] A. Sashirekha, J. Pasupuleti, N.H. Moin and C.S.

Tan,”Combined heat and power (CHP) economic

dispatch solved using Lagrangian relaxation with

surrogate subgradient multiplier updates,” Electrical


[182] A.A. Abou El Ela, M.A. Abido and S.R. Spea,”

Differential evolution algorithm for emission

constrained economic power dispatch problem,”




15900

[183] S. Hemamalini and Sishaj P. Simon,” Dynamic

economic dispatch using artificial immune system for

units with valve-point effect,” Electrical Power and


[184] M. Basu,”Dynamic economic emission dispatch using

nondominated sorting genetic algorithm-II,”Electrical


[185] Rui Zhang, Jianzhong Zhou, Li Mo, Shuo Ouyang and

Xiang Liao,” Economic environmental dispatch using

an enhanced multi-objective cultural Algorithm,”

Electric Power Systems Research 99 (2013) 18– 29

[186] M. Basu,” Economic environmental dispatch using

multi-objective differential evolution,” Applied Soft

Computing 11 (2011) 2845–2853

[187] Leandro dos Santos Coelho and Chu-Sheng

Lee,”Solving economic load dispatch problems in

power systems using chaotic and Gaussian particle

swarm optimization approaches,” Electrical Power and


[188] Ivan N. da Silva, Leonardo Nepomuceno and Thiago

M. Bastos,” An efficient Hopfield network to solve

economic dispatch problems with transmission system

representation,” Electrical Power and Energy Systems

26 (2004) 733–738

[189] Ehsan Afzalan and Mahmood Joorabian,” Emission,

reserve and economic load dispatch problem with non-

smooth and non-convex cost functions using epsilon-

multi-objective genetic algorithm variable,” Electrical


[190] Lingfeng Wang and Chanan

Singh,”Environmental/economic power dispatch using

a fuzzified multi-objective particle swarm optimization

algorithm,” Electric Power Systems Research 77

(2007) 1654–1664

[191] C. Christober Asir Rajan,” A solution to the economic

dispatch using EP based SA algorithm on large scale

power system,” Electrical Power and Energy Systems

32 (2010) 583–591

[192] P.S.Manoharan,P.S.Kannan,S.Baskar and M. Willjuice

Iruthayarajan,” Evolutionary algorithm solution and

KKT based optimality verification to multi-area


Systems 31 (2009) 365–373

[193] P.K. Hota, A.K. Barisal and R. Chakrabarti,”Economic

emission load dispatch through fuzzy based bacterial

foraging algorithm,”Electrical Power and Energy

Systems 32 (2010) 794–803

[194] T.Yalcinoz, B.J.Cory and M.J.Short,”Hopfiled neural

network approachesto economic dispatch problems,

Electrical Power and Energy System 23 (2001) 435 -

442

[195] Samir Sayah and Abdellatif Hamouda,”A hybrid

differential evolution algorithm based on particle

swarm optimization for nonconvex economic dispatch

problems,” Applied Soft Computing 13 (2013) 1608–

1619

[196] Aniruddha Bhattacharya and P.K.

Chattopadhyay,”Hybrid differential evolution with

biogeography-based optimization algorithm for

solution of economic emission load dispatch

problem,”Expert Systems with Applications 38 (2011)

14001–14010

[197] J.S. Alsumait, J.K. Sykulski and A.K. Al-Othman,”A

hybrid GA–PS–SQP method to solve power system

valve-point economic dispatch problems,” Applied

Energy 87 (2010) 1773–1781

[198] Dakuo He, Fuli Wang and Zhizhong Mao,”A hybrid

genetic algorithm approach based on differential

evolution for economic dispatch with valve-point

effect,” Electrical Power and Energy Systems 30

(2008) 31–38

[199] He Da-kuo, Wang Fu-li and Mao Zhic and

environmental optimal power dispatch,” Applied Soft

Computing 8 (2008) 1428–1438

[200] Priyanka Roy, Pritam Roy and Abhijit Chakrabarti,”

Modified shuffled frog leaping algorithm with genetic

algorithm crossover for solving economic load dispatch

problem with valve-point effect,” Applied Soft

Computing 13 (2013) 4244–4252

[201] Xingwen Jiang, Jianzhong Zhou, Hao Wang and

Yongchuan Zhang,” Dynamic environmental economic

dispatch using multiobjective differential evolution

algorithm with expanded double selection and adaptive

random restart,” Electrical Power and Energy Systems

49 (2013) 399–407

[202] Provas Kumar Roy and Sudipta Bhui,”Multi-objective

quasi-oppositional teaching learning based optimization

for economic emission load dispatch problem,”


948

[203] C.X. Guo, J.P. Zhan and Q.H. Wu,”Dynamic economic

emission dispatch based on group search optimizer

with multiple producers,” Electric Power Systems

Research 86 (2012) 8 – 16

[204] Tahir Nadeem Malik, Azzam ul Asar, Mudasser F.

Wyne and Shakil Akhtar,” A new hybrid approach for

the solution of nonconvex economic dispatch problem

with valve-point effects,” Electric Power Systems

Research 80 (2010) 1128–1136

[205] M. Basu,”Combined heat and power economic

emission dispatch using nondominated sorting genetic

algorithm-II,”Electrical Power and Energy Systems 53

(2013) 135–141



15901

[206] Ming-Tang Tsai, Hong-Jey Gow and Whei-Min Lin,”A

novel stochastic search method for the solution of

economic dispatch problems with non-convex fuel cost

functions,” Electrical Power and Energy Systems 33

(2011) 1070–1076

[207] D.N. Jeyakumar, T. Jayabarathi, T. Raghunathan ,”

Particle swarm optimization for various types of

economic dispatch problems,” Electrical Power and


[208] Krishna Teerth Chaturvedi, Manjaree Pandit and Laxmi

Sriv,”Particle swarm optimization with time varying

acceleration coefficients for non-convex economic

power dispatch,” Electrical Power and Energy Systems

31 (2009) 249–257

[209] Jia-Chu Lee, Whei-Min Lin,Gwo-Ching Liao and Ta-

Peng Tsao,” Quantum genetic algorithm for dynamic

economic dispatch with valve-point effects and

including wind power system,”Electrical Power and


[210] Zhong Jia-qing,”Research on Environmental Economic

Dispatch of Power System Including Wind Farm,”

Physics Procedia 24 (2012) 107 – 113

[211] A. Srinivasa Reddy and K. Vaisakh,”Shuffled

differential evolution for economic dispatch with valve

point loading effects,”Electrical Power and Energy

Systems 46 (2013) 342–352


Chattopadhyay,” Solving economic emission load



[213] Sayyid Mohssen Sajjadi, Ahmad Sadeghi Yazdankhah

and Farzad Ferdowsi,”A new gumption approach for

economic dispatch problem with losses effect based on

valve-point active power,” Electric Power Systems

Research 92 (2012) 81– 86

Documents

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