Combined Economic Emission Dispatch Problem Research Articles

An improved golden jackal optimization algorithm for combined economic emission dispatch problems

In this research paper, a new improved golden jackal optimization (IGJO) algorithm is applied to address the combined economic emission dispatch (CEED) problem, along with various thermal generator constraints such as valve point loading (VPL) effect, generator limits (GL) in power system. The hunting behavior of the golden jackals is mimicked in the golden jackal optimization (GJO) algorithm. The main aim of the CEED problem is to find the best optimal generation scheduling while minimizing both fuel cost and emission besides meeting the different power system constraints. The original GJO algorithm faces challenges when dealing with high-dimensional optimization problems, as it tends to get trapped in local optima. To address this issue the opposition-based learning (OBL) method was adopted in this GJO algorithm to obtain the global optimal solution and ensure enhanced performance in finding the solution for the CEED problems. To assess the competitiveness of the IGJO algorithm, it is used for various CEED test problems available in the literature, and results are contrasted with other recent heuristic optimization algorithms. Simulation results show that the proposed IGJO performs more effectively than the other compared algorithms in terms of solution quality, and robustness.

Data-driven method of solving computationally expensive combined economic/emission dispatch problems in large-scale power systems: an improved kriging-assisted optimization approach

Combined economic/emission dispatch (CEED) is generally studied using analytical objective functions. However, for large-scale, high-dimension power systems, CEED problems are transformed into computationally expensive CEED (CECEED) problems, for which existing approaches are time-consuming and may not obtain satisfactory solutions. To overcome this problem, a novel data-driven surrogate-assisted method is introduced firstly. The fuel cost and emission objective functions are replaced by improved Kriging-based surrogate models. A new infilling sampling strategy for updating Kriging-based surrogate models online is proposed, which improves their fitting accuracy. Through this way, the evaluation time of the objective functions is significantly reduced. Secondly, the optimization of CECEED is executed by an improved non-dominated sorting genetic algorithm-II (NSGA-II). The above infilling sampling strategy is also used to reduce the number of evaluations for original mathematic fitness functions. To improve their local convergence ability and global search abilities, the individuals that exhibit excellent performance in a single objective are cloned and mutated. Finally, information about the Pareto front is used to guide individuals to search for better solutions. The effectiveness of this optimization method is demonstrated through simulations of IEEE 118-bus test system and IEEE 300-bus test system.

Combined Economic Emission Dispatch in Presence of Renewable Energy Resources Using CISSA in a Smart Grid Environment

The geographically spatial and controlled distribution of fossil fuel resources, catastrophic global warming, and depletion of fossil fuel resources have forced us to integrate zero- or low-emissions energy resources, such as wind and solar, in the generation mix. These renewable energy resources are unexhausted, available around the globe, and free of cost. The advancement in wind and solar technologies has caused an appreciable decrease in installed the and global levelized costs of electricity via these sources. Therefore, the penetration of renewable energy resources in the generation mix can provide a promising solution to the above-mentioned problems. The aim of simultaneously reducing fuel consumption in terms of “Fuel Cost” and “Emission” in thermal power plants is called a combined economic emission dispatch problem. It is a combinatorial and multi-objective optimization problem. The solution of this problem is to allocate the load demand and losses on the committed units in such way that the overall costs of the generation and emission of thermal units are reduced, while the legal bounds (constraints) are met. It is a highly non-linear and complex optimization problem. The valve-point loading effect makes this problem non-convex. The addition of renewable energy resources (RERs) adds more complexities to this problem because they are intermittent. In this work, chaotic salp swarm algorithms (CISSA) are used to solve the combined economic emission dispatch problem. Chaos is used as an alternative to randomization for the tuning of the control variable to improve the trait of obtaining global extrema. Different test cases having different combinations of thermal, solar, and wind units are solved using the proposed algorithm. The results show the superiority of this study in comparison to the existent research results in terms of the cost of generation and emissions.

Combined Economic and Emission Power Dispatch Control Using Substantial Augmented Transformative Algorithm

The purpose of the Combined Economic Emission Dispatch (CEED) of electric power is to offer the most exceptional schedule for production units, which must run with both low fuel costs and emission levels concurrently, thereby meeting the lack of system equality and inequality constraints. Economic and emissions dispatching has become a primary and significant concern in power system networks. Consequences of using non-renewable fuels as input to exhaust power systems with toxic gas emissions and depleted resources for future generations. The optimal power allocation to generators serves as a solution to this problem. Emission dispatch reduces emissions while ignoring economic considerations. A collective strategy known as Combined Economic and Emission Dispatch is utilized to resolve the above-mentioned problems and investigate the trade-off relationship between fuel cost and emissions. Consequently, this work manages the Substantial Augmented Transformative Algorithm (SATA) to take care of the Combined Economic Emission Dispatch Problem (CEEDP) of warm units while fulfilling imperatives, for example, confines on generator limit, diminish the fuel cost, lessen the emission and decrease the force misfortune. SATA is a stochastic streamlining process that relies upon the development and knowledge of swarms. The goal is to minimize the total fuel cost of fossil-based thermal power generation units that generate and cause environmental pollution. The algorithm searches for solutions in the search space from the smallest to the largest in the case of forwarding search. The simulation of the proposed system is developed using MATLAB Simulink software. Simulation results show the effectiveness and practicability of this method in terms of economic and emission dispatching issues. The performance of the proposed system is compared with existing Artificial Bee Colony-Particle Swarm Optimization (ABC-PSO), Simulated Annealing (SA), and Differential Evolution (DE) methods. The fuel cost and gas emission of the proposed system are 128904 $/hr and 138094.4652$/hr.

SMO Algorithm to Unravel CEED Problem using Wind and Solar

This research proposes a more advanced way to address Combined Economic Emission Dispatch (CEED) concerns. Economic Load Dispatch (ELD) and Economic Emission Dispatch (EED) have been implemented to reduce generating unit fuel costs and emissions. When both economics and emission targets are taken into account, the dispatch of an aggregate cost-effective emission challenge emerges. This research affords a mathematical modeling-based analytical technique for solving economic, emission, and collaborative economic and emission dispatch problems with only one goal. This study takes into account both the fuel cost target and the environmental impact of emissions. This bi-intention CEED problem is converted to a solitary goal function using a price penalty factor technique. In this case, a metaheuristic and an environment-inspired, intelligent Spider Monkey Optimization technique (SMO) are used to address the CEED dilemma. By following the generator’s scheduling process, the SMO method is used to regulate the output from the power generation system in terms of pollution and fuel cost. The Fission-Fusion social (FFS) structure of spider monkeys promotes them to utilize a global optimization method known as SMO during foraging behaviour. The emphasis is mostly on lowering the cost of generation and pollution in order to improve the efficiency of the power system and handle dispatch problems with constraints. The economic dispatch has been remedied, and the improved result demonstrates that the system’s performance is stable and flexible in real time. Finally, the system’s output demonstrates that the system has improved in resolving CEED difficulties. When compared to earlier investigations, the proposed model’s findings have improved. As the generating units, wind and solar are used to explore the CEED crisis in the IEEE 30 bus system.

Chaos-enhanced multi-objective tunicate swarm algorithm for economic-emission load dispatch problem

Climate change and environmental protection have a significant impact on thermal plants. So, the main principles of combined economic-emission dispatch (CEED) problem are indeed to reduce greenhouse gas emissions and fuel costs. Many approaches have demonstrated their efficacy in addressing CEED problem. However, designing a robust algorithm capable of achieving the Pareto optimal solutions under its multimodality and non-convexity natures caused by valve ripple effects is a true challenge. In this paper, chaos-enhanced multi-objective tunicate swarm algorithm (CMOTSA) for CEED problem. To promote the exploration and exploitation abilities of the basic tunicate swarm algorithm (TSA), an exponential strategy based on chaotic logistic map (ESCL) is incorporated. Based on ESCL in CMOTSA, it can improve the possibility of diversification feature to search different areas within the solution space, and then, gradually with the progress of iterative process it converts to emphasize the intensification ability. The efficacy of CMOTSA is approved by applying it to some of multi-objective benchmarking functions which have different Pareto front characteristics including convex, discrete, and non-convex. The inverted generational distance (IGD) and generational distance (GD) are employed to assess the robustness and the good quality of CMOTSA against some successful algorithms. Additionally, the computational time is evaluated, the CMOTSA consumes less time for most functions. The CMOTSA is applied to one of the practical engineering problems such as combined economic and emission dispatch (CEED) with including the valve ripples. By using three different systems (IEEE 30-bus with 6 generators system, 10 units system and IEEE 118-bus with 14 generating units), the methodology validation is made. It can be stated for the large-scale case of 118-bus systems that the results of the CMOTSA are equal to 8741.3 $/h for the minimum cost and 2747.6 ton/h for the minimum emission which are very viable to others. It can be pointed out that the cropped results of the proposed CMOTSA based methodology as an efficient tool for CEED is proven.

A Chaotic Krill Herd Optimizer for Efficient Combination of Renewable Energy Sources in Isolated Microgrid Mode

Microgrids consist of small energy resources such as the solar power, wind power, and many distributed generation units. This article focuses on the application of a chaotic krill herd (CKH) algorithm for solving economic dispatch (ED) and combined economic emission dispatch (CEED) problems in a microgrid. The ED problem finds the power scheduling of renewable energy resources (RESs) units to minimize the entire production costs of generation while all system constraints are preserved into their acceptable limitations. Meanwhile, achieving this value from the fuel cost and fossil fuel fired power plant emission should be as little as possible. The proposed CKH is a bio-inspired optimization algorithm that is built in the Lagrangian and evolutionary behavior of krill people in nature. The Chaotic guide is a variation in qualities combined with KH. A singer map is integrated with the KH algorithm and tested on different scenarios for improving its global research and enhancing its performance. The elitism scheme is also served to save the best krill during amid the procedure when updating the krill. The results clearly show the superiority of the CKH algorithm to search for the best function values when compared with the standard KH and other well-known meta-heuristic search algorithms.

Multi-objective membrane search algorithm: A new solution for economic emission dispatch

Many countries or regions are committed to reducing emissions in response to global climate issues. As an industry with a large proportion of emissions, power generation companies are facing increasing pressure to reduce emissions. Based on the Membrane Search Algorithm (MSA) designed by us, this paper proposes a multi-objective problem-solving algorithm, denoted as multi-objective MSA (MOMSA), in which the constrain-handling rules are designed to solve the Combined Heat and Power Economic Emission Dispatch (CHPEED) problem in a nonconvex and nonlinear space. The proposed method obtains the Pareto front of CHPEED 5-unit and 7-unit systems, and the recommended compromise solution has fewer emissions for the same fuel cost. In addition, the extremely challenging ultra large scale Combined Economic Emission Dispatch (CEED)problem is also studied, and the fuel cost and emissions of the compromise solution are more competitive. The research results show that MOMSA has excellent space exploration ability and can provide better emission reduction dispatching for CEED and CHPEED problems without complex parameter optimization.

An efficient bi-objective approach for dynamic economic emission dispatch of renewable-integrated microgrids

To overcome the challenges of conventional power systems, such as increasing power demand, requirements of stability and reliability, and increasing integration of renewable energy sources, the concept of microgrids was introduced and is currently one of the most important solutions for solving the mentioned problems. Generally, microgrids have two operating modes, namely grid-connected and islanded modes. Based on the literature and its unique characteristics, the islanded mode is more challenging than the other one. In this paper, a new self-adaptive comprehensive differential evolution (SACDE) algorithm is proposed for solving economic load dispatch (ELD) and combined economic emission dispatch (CEED) problems, achieving optimal power consumption in isolated microgrids. Initially, SACDE is employed for solving the ELD problem as a single-objective function, meaning that the operational cost is just considered as the objective function, and thereby, the resources are scheduled accordingly. Then, a multi-objective platform based on SACDE is also proposed to solve the CEED problem. It means two objective functions, including operational cost and emission, are simultaneously optimized. For evaluating the performance of the proposed method, three different scenarios under various cases are considered. According to the results, when SACDE is employed to solve the single objective function (cost minimization) problem, it has better performance than other methods. In terms of the bi-objective scheme (cost and emission minimization), SACDE is significantly superior to the price penalty factor technique which is frequently used in previous studies.

An innovative hybrid algorithm for solving combined economic and emission dispatch problems

As environmental concerns have grown, the combined economic emission dispatch (CEED) problem has gotten a lot of attention. Both the cost of fuel and the emission pollution caused by it must be kept to a minimum. As a result, this paper presents an innovative hybrid approach (ihPSODE) for solving CEED problems. This hybrid technique incorporated novel differential evolution (nDE) and particle swarm optimization (nPSO). Where nDE introduces an improved mutation and crossover approach (to prevent untimely convergence) as well as nPSO introduces a new acceleration coefficient, inertia weight and position improve equation (to alleviate the stagnation). So as to balance among local and global search ability, after ihPSODE population evaluation, the best half individuals are determined and the rest individuals are discarded. Then, nPSO is used in the current population (to sustain exploration and exploitation) and nDE is employed in the nPSO generated population (to improve convergence accuracy). The competence of the proposed algorithms (ihPSODE, nPSO and nDE) is inspected on 23 unconstrained benchmark function and then solved 3 test system (3-, 6- and 40-unit) of economic load dispatch (ELD) and 3 test system (3-, 10- and 40-unit) of CEED problem. The experiments have denoted that the proposed algorithms show competitive results and significant performances.

Combined Economic Emission Dispatch with and without Consideration of PV and Wind Energy by Using Various Optimization Techniques: A Review

Combined economic emission dispatch (CEED) problems are among the most crucial problems in electrical power systems. The purpose of the CEED is to plan the outputs of all production units available in the electrical power system in such a way that the cost of fuel and polluted emissions are minimized while respecting the equality and inequality constraints of the system and efficiently responding to the power load required. The rapid depletion of these sources causes limitation and increases the price of fuel. It is therefore very important that scientific research in the last few decades has been oriented toward the integration of renewable energy systems (RES) such as wind and PV as an alternative source. Furthermore, the CEED problem including RES is the most important problem with regard to electrical power field optimization. In this study, a classification of optimization techniques that are widely used, such as traditional methods, non-conventional methods, and hybrid methods, is summarized. Many optimization methods have been presented and each of them has its own advantages and disadvantages for solving this complex CEED problem, including renewable energy. A review of different optimization techniques for solving this CEED problem is explored in this present paper. This review will encourage researchers in the future to gain knowledge of the best approaches applicable to solve CEED problems for practical electrical systems.

Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm

Electricity can be provided to small-scale communities like commercial areas and villages through microgrid, one of the small-scale, advanced, and independent electricity systems out of the grid. Microgrid is an appropriate choice for specific purposes reducing emission and generation cost and increasing efficiency, reliability, and the utilization of renewable energy sources. The main objective of this paper is to elucidate the combined economic emission dispatch CEED problem in the microgrid to attain optimal generation cost. A combined cost optimization approach is examined to minimize operational cost and emission levels while satisfying the load demand of the microgrid. With this background, the authors proposed a novel improved mayfly algorithm incorporating Levy flight to resolve the combined economic emission dispatch problem encountered in microgrids. The islanded mode microgrid test system considered in this study comprises thermal power, solar-powered, and wind power generating units. The simulation results were considered for 24 hours with varying power demands. The minimization of total cost and emission is attained for four different scenarios. Optimization results obtained for all scenarios using IMA give a comparatively better reduction in system cost than MA and other optimization algorithms considered revealing the efficacy of IMA taken for comparison with the same data. The proposed IMA algorithm can solve the CEED problem in a grid-connected microgrid.

Probability distribution arithmetic optimization algorithm based on variable order penalty functions to solve combined economic emission dispatch problem

The optimization objective of combined economic emission dispatch (CEED) problem is to minimize the total cost of power generation and the emission of harmful gases under the premise of satisfying the load demand and operation constraints. In today's world, energy and environmental problems are becoming more and more serious. Solving CEED problems can reduce costs, save energy, and reduce environmental pollution. By adopting the maximum/maximum price penalty factor, the multi-objective CEED optimization problem is transformed into a single objective optimization problem, and a probability distribution arithmetic optimization algorithm (AOA) based on variable order penalty function was proposed to solve the CEED problem. The energy attenuation strategy was proposed to replace the variation trend of Math optimizer accelerated (MOA) in the original AOA, which better balances the exploration and exploitation of AOA. Five probability distribution functions, including exponential distribution, uniform distribution, normal distribution, beta distribution and gamma distribution, are used to replace a control parameterμ, so as to enhance its searching ability, improve the convergence speed, and enhance the ability to jump out of the local optimal. Based on the CEC-BC-2017 benchmark functions, the original AOA is compared with the five improved AOA to select the best improved strategy, and the effectiveness of the improved strategy is verified by simulation comparison with other intelligent optimization algorithms. In dealing with the constraint of CEED problem, five kinds of penalty functions with varying order were proposed, which are sine function, hyperbolic tangent function, arctangent function, V-type function and hyperbolic secant function, respectively, to replace the traditional fixed penalty function. The CEED problem with 6 units are selected and solved under the total demand of 150 MW, 175 MW, 200 MW and 225 MW respectively. The experimental results show that the AOA based on probability distribution is better than other optimization algorithms in solving CEED problems, and the variable order penalty strategy improves the solution quality and convergence speed compared with the fixed penalty strategy.

Engaging undergraduate students to analyze the results of published research articles

Engineering institutions offer technical education by designing curriculum based on expected graduate attributes. The ability of undergraduate students (UG) to analyze complex engineering problems is one of the attributes and it is addressed by enabling students to work on certain projects or problems. The research experience during undergraduate studies can enhance the overall educational experience of students. But implementing research activities for undergraduate students can be challenging. To engage UG students, researchers have tried different strategies to enhance the research experience of students. This paper presents a case study in which, third-year students of Electrical Engineering were engaged to analyze the results of the combined economic emission dispatch problem (CEED) reported in past literature. As a part of in-semesterexamination (ISE) for the course Power System Operation and Control, the students were given different research papers published in reputed international journals and associated with the CEED problem. The students were asked to identify the main equations, collect supporting data from literature and develop a computer program in MATLAB to analyze results in the research article under study. Each student presented findings on the assigned paper, the computer program developed, with explanations and conclusions. The research findings of all students were appropriately consolidated to prepare the manuscript as a review article for submission to a suitable international journal. The proposed methodology can be easily implemented in other courses by properly defining thescope of research work expected from students. The research experience of students (experimental group) was measured by conducting a test and the results were compared with other students (control group). The results show that students under experiment performed better than other students. Keywords— Analysis; CEED; in-semester-examination; MATLAB; undergraduate research.

A modified Marine predators algorithm for solving single- and multi-objective combined economic emission dispatch problems

Decreasing the emission of greenhouse gases and the fuel cost are the main principles of the combined economic emission dispatch (CEED) optimization problem. In this work, a modified version of the Marine Predators Algorithm (MMPA), is proposed and applied for solving single- and bi-objective CEED problems. MMPA is suggested to improve the performance of conventional MPA. It includes a comprehensive learning approach to share the best experiences among all the individuals to avoid premature convergence. The MMPA increases the populations' effectiveness to reach the optimum fitness solution and this is helpful in cases of single- and bi-objective functions. The proposed MMPA has been mathematically assessed with twenty-eight 50-dimensional benchmarks of CEC2017. Also, it is successfully tested on four electrical power generation systems (each of them with 3, 5, 6, and 26 generating units respectively). The results achieved by MMPA are compared with those obtained by the original MPA and other recent optimization methods. Concerning the bi-objective problem, the Pareto approach is combined with the proposed MMPA and conventional MPA techniques to reach a group of non-dominated (ND) solutions, and then the fuzzy method is utilized to choose the best compromise solution. The reached results display that the proposed MMPA technique gives better performance than the conventional MPA and the others.

A hybrid optimization-based approach to solve environment constrained economic dispatch problem on microgrid system

Generation of electricity comes with the emission of toxic gases into the atmosphere by the fossil fueled generators. Along with the promotion in the utilization of renewable energy sources (RES), it is also the duty of the power engineers to arrive at a compromised solution such that less emission of toxic gases occurs with economic generation of electricity. This paper proposes a balanced trade-off method for solving environment constrained economic dispatch (ECED) problems. A novel comparative analysis is performed among proposed ECED method with existing price-penalty-factor (PPF) and fractional programming (FP) methods for solving combined economic emission dispatch (CEED) problems on a 3-unit dynamic test system to sort out the method, which yields a better trade off solution between generation cost and pollutants emitted. An algorithm, following the hunting strategy of wolves, is improvised by incorporating strategies from population-based sine-cosine algorithm along with position updating methods of crows to form a robust hybrid algorithm, which was used as the optimization tool for the study. Involvement of RES diminished the generation cost to 5.5% for both economic dispatch and PPF based CEED, and 6.5% decrease in emission of pollutants was observed due to the same. The generation cost and amount of emitted pollutants, evaluated using proposed ECED approach, were much closer to the economic dispatch and emission dispatch values respectively compared to PPF based and FP based CEED solution. Furthermore, statistical analysis endorses the superiority of the proposed hybrid optimizer over other algorithms presented in the state-of-art literature.

Dynamic Combined Economic Emission Dispatch integrating Plug-in Electric Vehicles and Renewable Energy Sources

The increasing rate of Green House Gases (GHGs) emission is expected to reduce by the integration of Plug-in Electric Vehicles (PEVs) and Renewable Energy Sources (RESs). A ten-unit thermal system with PEVs and RESs (i.e. wind and solar power) is rigorously analysed for dynamic Combined Economic Emission Dispatch (CEED) and the efficacy of the integrated system is investigated. Findings with smart planning and control of PEVs as loads and/or energy sources for developing sustainable and continuous power supply are discussed in this paper. The multi-objective CEED problem is solved by a fuzzy decision-making (FDM) approach using the Constriction Factor-Based Particle Swarm Optimisation (CFBPSO) algorithm in ten-unit thermal generators with the integration of PEVs and RESs. In both single- and multi-objective problems, the outcomes of the optimal dynamic CEED problem are further improved by the incorporation of PEVs, solar and wind power in the proposed model. With the FDM approach, the optimum CEED solution is achieved.

A comparative analysis between price‐penalty factor method and fractional programming method for combined economic emission dispatch problem using novel hybrid CSA‐JAYA algorithm

A comparative analysis between price‐penalty factor method and fractional programming method for combined economic emission dispatch problem using novel hybrid CSA‐JAYA algorithm

Solving combined economic emission dispatch problem in wind integrated power systems

<p>A meta-heuristic based optimization method for solving combined economic emission dispatch (CEED) problem for the power system with thermal and wind energy generating units is proposed in this paper. Wind energy is environmentally friendly and abundantly available, but the intermittency and variability of wind power affects the system operation. Therefore, the system operator (SO) must aware of wind forecast uncertainty and dispatch the wind power accordingly. Here, the CEED problem is solved by including the nonlinear characteristics of thermal generators, and the stochastic behavior of wind generators. The stochastic nature of wind generators is handled by using probability distribution analysis. The purpose of this CEED problem is to optimize fuel cost and emission levels simultaneously. The proposed problem is changed into a single objective optimization problem by using weighted sum approach. The proposed problem is solved by using particle swarm optimization (PSO) algorithm. The feasibility of proposed methodology is demonstrated on six generator power system, and the obtained results using the PSO approach are compared with results obtained from genetic algorithm (GA) and enhanced genetic algorithms (EGA).</p>

Constrained Multiobjective Equilibrium Optimizer Algorithm for Solving Combined Economic Emission Dispatch Problem

This research implements a recent evolutionary-based algorithm of equilibrium optimizer to resolve the constrained combined economic emission dispatch problem. This problem has two objective functions that represent the minimizing of generation costs and minimizing the emission of environmental pollution caused by generators. The proposed algorithm integrates the dominant criteria for multiobjective functions that allow the decision-maker to detect all the Pareto boundaries of constrained combined economic emission dispatch problem. In order to save the effort for the decision-maker to select the best compromise alternative, a cluster study was carried out to minimize the size of the Pareto boundary to an acceptable size, representing all the characteristics of the main Pareto frontier. On the other hand, in order to deal with the infringement of constraints, a repair algorithm was used to preserve the viability of the particles. The proposed algorithm is applied to solve the standard 30-bus IEEE system with 6 generators to validate its robustness and efficiency to produce a well-distributed Pareto frontier for constrained combined economic emission dispatch problem. Compared with other studies, good results in solving constrained combined economic emission dispatch problem are obtained and a reasonable reduced Pareto set is found.