Multi-objective Economic Emission Dispatch Research Articles

A New PSO Technique Used for the Optimization of Multiobjective Economic Emission Dispatch

Most power is generated using fossil fuels like coal, natural gas, and diesel. The contribution of coal to power generation is very high compared to other sources. Almost all thermal power plants use coal as a fuel for power generation. Such sources of fossil fuels are limited and thus the cost of power generation increases. At the same time, the induced toxic gases due to these fossil fuels pollute the environment. The objective of this work is to solve the economic emission dispatch problem. Economic emission dispatch helps to find out how to operate power plants at the minimum cost and induce the minimum emissions at a thermal power plant. Economic emission dispatch with constraints is a nonlinear optimization problem. For the solution of such nonlinear economic emission load dispatch problems, this work considers a new particle swarm optimization technique. The proposed new PSO gives the best solution for economic emission load dispatch and handles the constraints. For the testing of the proposed new PSO algorithm, this work considered a case study of a system of six generating units, and it was tested for load demands of 700 MW, 800 MW, and 1000 MW. The results of the new PSO for the three load demands considered give the minimum generation cost, minimum emission, and minimum total cost compared to other optimization algorithms. The proposed techniques are effective, and they can help obtain the minimum generation cost and minimize emissions.

A modified artificial bee colony algorithm based on a non-dominated sorting genetic approach for combined economic-emission load dispatch problem

The multi-objective optimization algorithms (MOO) are used to obtain the best compromising solutions when two or more objective functions need to be optimized simultaneously. The convergence and diversity are critical factors to consider while solving the MOO problems because they determine the possibility of obtaining an evenly distributed Pareto front. This paper proposes a hybrid optimization algorithm to solve a multi-objective economic emission dispatch (MOEED) problem of electrical power systems. The electrical power generated by consuming fossil fuels is very costly and also burning of these fuels contributes to global warming. Hence, electrical power is generated at the least cost and emission. The MOEED problems have been solved in the past by using swarm intelligence and evolutionary algorithms. However, the solutions reported in the literature, are either inferior or the constraints are violated. The algorithm proposed in this paper is an integration of an Artificial Bee Colony optimization algorithm and a Non-Dominated Sorting Genetic Algorithm-II. The effectiveness of the proposed algorithm is evaluated by applying it to three test systems having 6, 10, and 40 coal-based generators. Additionally, various multi-criteria decision-making algorithms are used to identify the best non-dominated solutions obtained by the proposed algorithm and compared with previously reported results. The best fuel costs obtained by the proposed approach, for a 6-unit test system with and without transmission losses are found to be 605.9983 and 600.11140 $/h respectively. While the best emission values, for this test system, with and without transmission losses are found to be 0.1941 and 0.1942 tons/h. Moreover, the best fuel costs obtained by the proposed approach, for 10 and 40-unit test systems are found to be 111181.9871 and 121369.0838 $/h respectively. Furthermore, the best emission values for these test systems are found to be 3932.24322 and 176682.264 tons/h respectively. All these results are obtained without constraint violations and within 10–600 iterations.

Multi-objective economic emission dispatch of thermal power-electric vehicles considering user's revenue.

In recent years, the rapid development of electric vehicles has increased the load power system and brought new challenges to the safe and stable operation of the gird. Although the vehicle-to-grid technology can reduce the load that electric vehicles put on the grid, without any incentives, electric vehicle owners are more inclined not to use vehicle-to-grid services. In this paper, therefore, a new dynamic economic emission model based on electric vehicles (DEED_EV) is proposed to maximize the electric vehicle user’s revenue, as well as minimize the fuel cost and emission of the thermal power unit. In the DEED_EV model, the stochastic of electric vehicles user’s travel and wear of the battery, as well as some constraints such as electric vehicles charging/discharging rate and status, electric vehicles remain power, electric vehicles travel power capacity, ramp limits, up and down reserves, and the system balance are considered. To solve the DEED_EV model, a multi-objective evolutionary algorithm based on decomposition with a step-by-step constraint handling strategy is developed. Different test cases based on the 10-unit are simulated to verify the proposed model and method. The results show that the DEED_EV model not only encourages more electric vehicles to plug into the grid but also reduces the fuel cost and emission of the thermal power unit. Besides, the electric vehicles in the DEED_EV completely realizes the peak shaving and valley filling of the load.

A knee-guided algorithm to solve multi-objective economic emission dispatch problem

Environmental protection and climate change have addressed tremendous pressure on thermal plants. So, the Economic Emission Dispatch (EED) problem has to consider bi-objective: the fuel cost and emission dispatch, which can be solved by the conventional Multi-Objective Evolutionary Algorithms (MOEAs). However, these MOEAs often provide well-distributed Pareto Optimal Front (POF), which may be a burden to thermal plants policymakers to select an optimal solution from a lot of candidate solutions. We develop a Knee-Guided Algorithm (KGA) to handle the EED problem, in which the knee solution is defined as the optimal by using the minimum Manhattan distance approach. The proposed KGA searches around the knee solution to boost the convergence and outputs the knee solution instead of the whole POF, which is convenient to thermal plant policymakers. Through four test cases, including six-unit, ten-unit, eleven-unit, and fourteen-unit, the proposed KGA is compared with some latest algorithms. The results have demonstrated that the KGA is superior.

Oppositional GOA Applied to Renewable Energy-Based Multi-Objective Economic Emission Dispatch

The renewable economic emission transmit is a significant and new assignment in the modern power system. This article develops oppositional grasshopper optimization algorithm (OGOA) which depends on the social dealings of the grasshopper in nature, to solve renewable energy based economic emission dispatch (EED) considering uncertainty in wind power availability and a carbon tax on emission from the thermal unit. To speed up the convergence speed and advance the simulation results, opposition based learning (OBL) is integrated with the fundamental GOA in OGOA algorithm. To show the nonlinearity of wind power availability the Weibull distribution is used. A standard system, containing of two wind farms and six thermal units is used for testing the dispatch model for three different loads. The statistical outcomes of the applied OGOA technique are compared with basic GOA and quantum-inspired particle swarm optimization (QPSO) optimization. It is observed OGOA is more skillful than basic GOA technique for significantly reducing the computation time and developing hopeful outcomes.

Multi-objective economic emission dispatch based on an extended crisscross search optimization algorithm

In recent years, a novel algorithm named crisscross search optimization (CSO) algorithm has been successfully applied in the conventional energy economic emission dispatch (EED) problems of pure thermal power system (PTPS) and hydrothermal generation system (HTGS). However, there still have some problems, such as slow convergence speed and low stability. To address these issues, an extended crisscross search optimization (ECSO) algorithm is proposed in this paper. The performances of the CSO algorithm are improved by an adaptive choice procedure of the extension coefficient. And a weakening equality constraint method is used in the MOEED problems for ECSO. To test the performance of the proposed algorithm, the IEEE-30 bus System (Test System-Ⅰ), the 40 generators System (Test System-Ⅱ) and the hydrothermal generation system (HTGS) (Test System-Ⅲ) are adopted. Experimental results show that the cost of economic operation and the pollutant emission with the proposed ECSO are minimum in these test systems. Further, the simulation and comparison results show the robustness of the ECSO is superior to the CSO and the other algorithms.

Solar‐wind‐hydro‐thermal scheduling using moth flame optimization

AbstractEver increasing concern of environmental safeguard makes renewable energy sources (RES) useful for emission reduction as well as for production cost minimization. In this article, the multiobjective economic emission dispatch (EED) model with security constraints incorporating photovoltaic, nonconvex thermal, and wind units is introduced for hydro‐thermal‐solar‐wind power scheduling arrangement. However, the significant reduction of emission is the foremost perspective for environmental sustainability and penetration of RESs into the electrical grid is being encouraged tremendously. To diminish the power generation expenditure and pollution generated by fossil fuels, renewable solar PV and wind power‐oriented hydro‐thermal scheduling have significant worth. Existing algorithms do not perform satisfactorily for unpredicted solar and wind‐based nonlinear hydro‐thermal‐wind‐solar scheduling problems and it may give local optimal solutions instead of global optimal solution. To overcome the shortcomings of the existing algorithms, an effective, and an intelligent robust algorithm, named moth flame optimization (MFO) has been proposed for solving the said nonlinear optimization problem. This article describes a scientific review on the application of the proposed method to obtain the scheduling of optimal generation for hydrothermal systems by incorporating RESs like solar PV and wind plant. Optimal solutions gained by the employment of different optimization methods for a variety of test instances are demonstrated and the projected methods are compared in terms of attained optimal solutions and convergence speed. The proposed MFO algorithm is competent for potential/hopeful outcomes, and it reduces the electrical power generation cost and emission significantly. The simulation outcomes reveal the usefulness and feasibility of the proposed method.

Cross entropy optimization based on decomposition for multi-objective economic emission dispatch considering renewable energy generation uncertainties

Due to the increasing deterioration of environmental problem, combined economic emission dispatch (CEED) problem has become one of the active research areas in recent years. However, with sustained growth of intermittent power supplies connected to power system, their randomness and volatility will pose new challenges to power system optimization dispatch. For dealing with this problem, in this study, a novel Pareto optimization algorithm, called multi-objective cross entropy algorithm based on decomposition (MOCE/D), is proposed to solve a multi-objective optimization model for wind/hydro/thermal/photovoltaic power system by considering the uncertainties of intermittent power supplies and various practical constraints. Then, a hyper-plane-based decision-making strategy is introduced to identify the best compromise solution for the obtained Pareto frontiers. The overall performance of the proposed MOCE/D algorithm have been comprehensively investigated on the modified IEEE 30-bus and 118-bus systems. The statistical simulation results demonstrated that the proposed power system structure effectively reduces the operational cost as well as hazardous emissions; the proposed MOCE/D exhibits more competitive performance than the other state-of-the-art optimization algorithms, and therefore the obtained optimized operation strategy can provide a better trade-off between all objectives considered in this study.

Multi-objective economic emission dispatch of thermal power plants based on grey relational analysis and analytic hierarchy process

In this paper, mathematical models for a synthesized evaluation were established according to grey relational analysis and analytic hierarchy process. The models were used to select a power dispatch scheme considering hierarchies of material consumption, electrical efficiency, exergy efficiency, and environmental benefit. Four unit dispatch schemes, i.e., proportional fair allocation dispatch, conventional economic dispatch, economic emission dispatch, and economic emission dispatch with varied weights were investigated and compared. Analytic hierarchy process decision-making approach has been employed to find the optimal Pareto solution as the best tradeoff between cost and pollutant emission. The model indicated that the economic emission dispatch was preferred as the best option and could further reduce fuel consumption and pollutant emission, followed by the economic emission dispatch with varied weights. The assessment performed serves decision makers a valuable reference for policy making in the power dispatch sector.

A review of combined economic emission dispatch for optimal power dispatch with renewable energy

This paper provides comprehensive review of recent formulation and solution of combined economic emission dispatch problem considering renewable energy. Generally, power dispatch plays an important role in power generation system that mainly depends on the fossil fuels. Rapid depletion of these sources cause a limitation and increase the fuel price. At the same time, the poisonous gases emit by the thermal generator causes a long term environmental pollution. An extensive researches have been conducted for last few decades by considering Renewable Energy (RE) in power generation system as an alternative source. Therefore, Combined Economic Emission Dispatch (CEED) of thermal and renewable energy resources currently formulated in power dispatch problem. The optimization methods that commonly used to solve the CEED problem are categorized as traditional method, non-conventional method and hybrid method. It can be concluded that non-conventional and hybrid method have great and superior solution for solving multiobjective economic emission dispatch problem. The recent research related to CEED with RE are summarized in terms of methods, test cases, conditions, types of RE and constraints. Thus, it can be useful references for the research in this area.

Solving the multi-objective economic emission dispatch problems using Fast Non-Dominated Sorting TVAC-PSO combined with EMA

In this study, the Fast Non-Dominated Time-Varying Acceleration Coefficient-Particle Swarm Optimization (TVAC-PSO) combined with Exchange Market Algorithm (EMA) is proposed to solve the economic emission dispatch problems consisting of Combined Heat and Power Economic Emission Dispatch (CHPEED) and Dynamic Economic Emission Dispatch (DEED) multi-objective optimization problems considering operational constraints. A two-stage approach has been used to select the Best Compromise Solutions (BCSs), as the best solution which minimizes the operational cost and emission, simultaneously. For this purpose, at the first stage, applying Fuzzy Clustering Mean (FCM), the obtained Pareto Optimal Front (POF) is divided into several separated clusters. Then, using the Technique for Order of Performance by Similarity to Ideal Solution (TOPSIS), a single BCS is selected among each cluster. At first, the superiority of the proposed algorithm is evaluated on a number of benchmark functions, as well as the 48-unit CHPED test case. Then, to demonstrate the ability of the proposed algorithm in solving the multi-objective problem by finding the POF, the presented method has been applied to three case studies, and the results are compared with other algorithms in this field. Furthermore, a new test case is presented to confirm the proposed algorithm’s performance. The results verify the proposed method’s superiority over other available techniques in the literature. One of the most important novelty of this study is solving a multi-objective DEED problem considering the Ramp Rate Limits (RRLs), Valve Point Loading Effect (VPLE), power transmission loss impact, Spinning Reserve Requirements (SRRs), Prohibited Operating Zones (POZs) and Multiple Fuel Units (MFUs) simultaneously, for the first time.

Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms

The concept of a microgrid system, when put in simple words, is a small scale generation and deployment of power to a small geographical area in order to avoid transmission losses and maintain an uninterrupted power supply. It has been a mandatory protocol to implement the available renewable energy sources (RES) in order to minimize the emission of harmful pollutants to the atmosphere from the combustion of the fossil fuels. Economic load dispatch (ELD) deals with the optimal sizing of the distributed energy resources (DERs) by minimizing the fuel costs. Emission dispatch does the optimal sizing of the DERs sources by minimizing the amount of pollutants released in the atmosphere. A multi-objective Combined Economic-Emission Dispatch (CEED) does the optimal DER sizing providing a compromised solution of minimizing both the fuel costs and pollutants emission. This paper performs all ELD, emission dispatch and CEED on an islanded and renewable-integrated microgrid separately using a recently developed novel Whale optimization Algorithm (WOA). Four various scenarios of load sharing among the DERs are studied. The results are then compared with other recently developed bio inspired algorithms to corroborate the effectiveness of the proposed technique. Further statistical analysis such as ANOVA test and Wilcoxon signed rank test are performed to prove the superiority of the proposed approach over the various other optimization techniques used.

Multi-objective economic emission dispatch using interior search algorithm

Multi-objective economic emission dispatch using interior search algorithm

Exchange market algorithm for multi-objective economic emission dispatch and reliability

This paper proposes exchange market algorithm (EMA) to solve economic emission dispatch and reliability (EEDR) problem in thermal power plants. The aim of EMA, inspired by the method of selling and purchasing of shares by elite stockholders, is to solve the optimization problems. The EMA is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. Existence of two seeking operators in EMA provides a high ability in exploiting global optimum point. In economic emission dispatch and reliability, it is tried to utilize generating units in a way that we have high reliability in supplying the system load demand as well as the minimum fuel costs. In this paper, the EMA is successfully implemented on two systems with 6 and 26 generating units considering economic and emission dispatch and system reliability. The obtained results are compared with other advanced techniques. The results well demonstrate the practical advantage of the exchange market algorithm over the other approaches.

Multi-objective Economic Emission Dispatch Solution Using Dance Bee Colony with Dynamic Step Size

Energy planning considering environment aspect is a vital research area for power system operation and control. This paper introduces an efficient variant namely dance bee colony with dynamic step size adjustment for solving the multi objective economic emission dispatch considering valve point effects. The particularity and robustness of the proposed algorithm is validated on two practical test systems IEEE 30-Bus and to 40 units considering valve point effect and power losses. Results compared to many recent competitive methods confirm the efficiency of the proposed method in term of solution quality and convergence characteristics.

Multi-objective economic emission dispatch considering combined heat and power by normal boundary intersection method

Pollutant gas emission concern is one of the most challenging issues in electric power systems putting excessive pressure on different participants of electricity markets. Integrating renewable energy sources and application of Combined Heat-and-Power to thermal power plants which are applied by system operators have many advantages, such as reducing green-house gas emission. This paper addresses a multi-objective electric model to integrate the generation of thermal units considering heat and power dispatch. The objective functions of the proposed multi-objective framework comprise simultaneous minimization of cost and thermal units’ emission as well as maximizing heat generation. Normal-Boundary Intersection method is implemented to solve the proposed model and TOPSIS decision making approach has been employed to find the optimal Pareto solution as the best tradeoff between cost, green-house gas emission and heat generation. The results obtained in this paper are compared with the ones derived from other techniques recently used and verify the effectiveness and efficiency of the proposed multi-objective method. Besides, it is found that the solutions obtained by incorporating the lexicographic optimization and Normal Boundary Intersection method are exceptional in the case of fuel cost, emission and execution time.

Multi-objective economic emission dispatch considering wind power using evolutionary algorithm based on decomposition

Due to the increasing deterioration of environmental problem, multi-objective Economic Emission Dispatch (EED) problem has become one of the active research areas in recent years. Meanwhile, the renewable energy such as wind energy is an important approach to reduce pollution emissions, as well as the dependence on fossil fuels. In this paper, a newly developed optimization technique, called Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), has been applied to optimize the cost and emission of wind–thermal power system. MOEA/D provides a simple but efficient framework which decomposes a Multi-objective Optimization Problem (MOP) into a number of scalar optimization subproblems and optimizes them simultaneously. The stochastic nature of wind power is modeled by Weibull probability distribution function and the uncertainty of wind power is considered as system constraints with stochastic variables. To validate the effectiveness of the MOEA/D method, it is first applied to solve the traditional EED problem of standard IEEE 30-bus 6-generator system as the benchmark. Then, the effect of wind power penetration on cost and emission is analyzed by MOEA/D in a 6-generator system and a 40-generator system with wind farms based on the proposed EED model. A comparative analysis with other similar optimization methods reveals that the MOEA/D method is able to generate better performance in terms of both solution quality and computational efficiency.

Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration

This paper presents a new and efficient method for solving EPD (economic power dispatch) problem. To solve this problem we have combined two meta-heuristic methods, the FFA and the mGA. The acceleration of the convergence speed, the improved solution quality and the balance between exploration and exploitation are achieved with FFA–mGA approach. The searching process starts with the FFA by initializing a group of random fireflies. After that, the search is pursued by the mGA. Then, the best results (better than FFA) found from mGA are still communicated to the FFA as an initial space search.The process is repeated than until the final solution is reached. The strength of the proposed approach is tested and validated on the standard IEEE 30-bus system by solving several cases as: the fuel cost minimization, Emission minimization, Emission and cost minimization simultaneously, fuel cost minimization with Sine Components, Piece-wise quadratic cost functions minimization and finally, the effect of the integration of wind energy in the system. The obtained results are compared with the classic FFA and the GA (genetic algorithm) and those in literature. The results show that the proposed approach provides accurate solutions for any type of the objective functions.

Scenario-Based Multi-Objective Optimal Dispatch Considering Forecast Uncertainties

Renewable energy recourses and Wind Power Generators are playing an ever-increasing role in power generation. In this paper, a two stage scenario-based approach considering load/wind forecast uncertainty is implemented for multi-objective economic emission dispatch problem which concludes minimize total fuel cost and emission simultaneously. Besides, an improved optimization algorithm of multi-objective based on Particle Swarm Optimization (MOIPSO) is implemented to extract the best solution for the stochastic problem. The proposed method is tested on a power system having 6-unit in order to measure its efficiency and feasibility.

Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem

This paper proposes an efficient optimization approach, namely quasi-oppositional teaching learning based optimization (QOTLBO) for solving non-linear multi-objective economic emission dispatch (EED) problem of electric power generation with valve point loading. In this article, a non-dominated sorting QOTLBO is employed to approximate the set of Pareto solution through the evolutionary optimization process. The proposed approach is carried out to obtain EED solution for 6-unit, 10-unit and 40-unit systems. For showing the superiority of this optimization technique, numerical results of the four test systems are compared with several other EED based recent optimization methods. The simulation results show that the proposed algorithm gives comparatively better operational fuel cost and emission in less computational time compared to other optimization techniques.