Emission Dispatch Problem Research Articles

Combined heat and power economic emission dispatch using improved bare-bone multi-objective particle swarm optimization

An improved bare-bone multi-objective particle swarm optimization (IBBMOPSO) is proposed to solve the combined heat and power economic emission dispatch problems. To conquer the population diversity deficiency and premature convergence of bare-bone particle swarm optimization, IBBMOPSO integrates four improved strategies, that is, (i) a non-linear adaptive particle updating strategy is presented to automatically tune the weights of the personal best position (pbest) and the global best position (gbest), and to shrink the standard deviation for generating new particles; (ii) an improved strategy by comparing the sparsity of the pbest and the target particle instead of the domination is proposed to update the pbest; (iii) an improved strategy by selecting a random Pareto optimal solution from a newly filtered subset of the external archive is designed to determine the gbest for each target particle; and (iv) a modified strategy by combining the slope and the crowding distance is presented to determine the Pareto optimal frontier. IBBMOPSO is firstly validated by nine multi-objective benchmark test functions. Then, it is then applied to three test systems and the simulation results demonstrate that IBBMOPSO can achieve higher-quality dispatching schemes with lower generating fuel cost and less pollutant gas emission compared with other algorithms.

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.

Artificial Intelligence Based Demand Side Management Approach to Solve Non-Linear Dynamic Economic Emission Dispatch Problem

Artificial Intelligence Based Demand Side Management Approach to Solve Non-Linear Dynamic Economic Emission Dispatch Problem

Multiobjective Multi Verse Optimization Algorithm to Solve Dynamic Economic Emission Dispatch Problem with Transmission Loss Prediction by an Artificial Neural Network

Multiobjective Multi Verse Optimization Algorithm to Solve Dynamic Economic Emission Dispatch Problem with Transmission Loss Prediction by an Artificial Neural Network

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.

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.

An Efficient Chameleon Swarm Algorithm for Economic Load Dispatch Problem

Economic Load Dispatch (ELD) is a complicated and demanding problem for power engineers. ELD relates to the minimization of the economic cost of production, thereby allocating the produced power by each unit in the most possible economic manner. In recent years, emphasis has been laid on minimization of emissions, in addition to cost, resulting in the Combined Economic and Emission Dispatch (CEED) problem. The solutions of the ELD and CEED problems are mostly dominated by metaheuristics. The performance of the Chameleon Swarm Algorithm (CSA) for solving the ELD problem was tested in this work. CSA mimics the hunting and food searching mechanism of chameleons. This algorithm takes into account the dynamics of food hunting of the chameleon on trees, deserts, and near swamps. The performance of the aforementioned algorithm was compared with a number of advanced algorithms in solving the ELD and CEED problems, such as Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Earth Worm Algorithm (EWA). The simulated results established the efficacy of the proposed CSA algorithm. The power mismatch factor is the main item in ELD problems. The best value of this factor must tend to nearly zero. The CSA algorithm achieves the best power mismatch values of 3.16×10−13, 4.16×10−12 and 1.28×10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the ELD problem. The CSA algorithm achieves the best power mismatch values of 6.41×10−13 , 8.92×10−13 and 1.68×10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the CEED problem. Thus, the CSA algorithm was found to be superior to the algorithms compared in this work.

A novel oppositional learning-based chaotic sine cosine algorithm for the dynamic thermal–wind economic dispatch problem

Wind power generation is efficiently utilized with traditional thermal power generation in the dynamic economic emission dispatch (DEED) problem to achieve the demand power of scheduled periods with minimum generation cost and emission of pollutants. This study uses a novel oppositional learning-based chaotic sine cosine algorithm (SCA) (OL-CSCA) to solve a new model of wind energy-based DEED, called dynamic thermal–wind economic emission dispatch (DTWEED). The concepts of oppositional learning and chaos are introduced into the SCA to strengthen its optimization performance. The effectiveness of the proposed model is tested by solving four cases (generation cost, spinning reserve based generation cost, emission and combined cost–emission) using OL-CSCA. The novelty of DTWEED is shown by comparing the results of different cases with the traditional DEED model. Comparison of the results of OL-CSCA for different cases of DEED and DTWEED with the results of SCA and other algorithms demonstrated its robustness and proficiency.

A New Particle Swarm Optimization Based Strategy for the Economic Emission Dispatch Problem Including Wind Energy Sources

Power dispatch has become an important issue due to the high integration of Wind Power (WP) in power grids. Within this context, this paper presents a new Particle Swarm Optimization (PSO) based strategy for solving the stochastic Economic Emission Dispatch Problem (EEDP). This problem was solved considering several constraints such as power balance, generation limits, and Valve Point Loading Effects (VPLEs). The power balance constraint is described by a chance constraint to consider the impact of WP intermittency on the EEDP solution. In this study, the chance constraint represents the tolerance that the power balance constraint cannot meet. The suggested framework was successfully evaluated on a ten-unit system. The problem was solved for various threshold tolerances to study further the impact of WP penetration.

Solution of combined economic and emission dispatch problems of power systems without penalty

ABSTRACT In the present paper, the Optimization Without Penalty-based Optimization by Morphological Filter algorithm (OWP-based OMF) has been proposed to find the optimal solution for the Combined Economic and Emission Dispatch (CEED) problem. The objective function of the CEED problem is to minimize fuel cost and emission simultaneously while satisfying the load demand, equality and inequality constraints. The evaluation of OWP-based OMF performances is carried out on three test systems with 6, 10 and 40 generating units, with different constraints and various costs. The present paper shows that OWP-based OMF gives an accurate and effective solution of the CEED problem and outperforms the other tested algorithms.

Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem

In recent years, a variety of meta-heuristic nature-inspired algorithms have been proposed to solve complex optimization problems. However, these algorithms suffer from the shortcoming that multiple hyperparameters need to be set carefully. Therefore, to solve the problem, the kernel search optimization (KSO) algorithm inspired by the kernel method has been proposed. KSO can simplify the optimization process by transforming the optimization process of nonlinear function into the linear optimization process. Despite its advantage, the original KSO requires a large amount of computation, and has no powerful exploitation search, resulting in its inability to obtain more accurate results. In the present study, a local search of the hill-climbing algorithm is adopted, and the calculation of the kernel parameter is simplified to improve the original KSO. In an experiment using 50 benchmark functions, the new algorithm outperformed KSO and some well-known algorithms in accuracy and running time. Moreover, when applied in the real-world economic emission dispatch problem, the improved algorithm achieved a better performance than other algorithms compared. An online repository will support this research at https://aliasgharheidari.com.

A hybridized approach for design and optimization of combined economic emission dispatch

ABSTRACT This paper proposes a new algorithm, known as Refraction based Whale Optimization Algorithm (RWOA), for providing optimal solutions to the Combined Economic Emission Dispatch problem based on the production of an optimum solar energy system. It validates the robustness of the proposed algorithm in Hybrid Renewable Energy Systems (HRES). To control the emission and economic cost, it is necessary to connect the wind turbine with the thermal power plant. Finally, the performance of the proposed approach is compared with the other conventional approaches concerning the economic cost, emission cost transmission loss. Accordingly, the results, shows that the proposed RWOA method is 92.81%, 99.2%, 92.8%, 80%, 37.8%, 47.34%, and 45.83% superior to GA, GWO, WOA, DA, GWSO, LGSO and WWO at 40th iteration.

A New Chaotic Artificial Bee Colony for the Risk-Constrained Economic Emission Dispatch Problem Incorporating Wind Power

Due to the rapid increase in the consumption of electrical energy and the instability of fossil fuel prices, renewable energy, such as wind power (WP), has become increasingly economically competitive compared to other conventional energy production methods. However, the intermittent nature of wind energy creates certain challenges to the power network operation. The combined economic environmental dispatch (CEED) including WP is one of the most fundamental challenges in power system operation. Within this context, this paper presents a new attempt to solve the probabilistic CEED problem with WP penetration. The optimal WP to be incorporated in the grid is determined in such a way that the system security is within acceptable limits. The system security is described by various fuzzy membership functions in terms of the probability that power balance cannot be met. These membership functions are formulated based on the dispatcher’s attitude. This probabilistic and non-convex CEED problem is solved using a new technique combining chaos theory and artificial bee colony (ABC) technique. In this improved version of ABC (IABC), chaotic maps are used to generate initial solutions, and the random numbers involved in the standard ABC are substituted by chaotic sequences. The effectiveness of IABC is tested on two groups of benchmark functions and practical cases. The impacts of dispatcher’s attitude and risk level are investigated in the simulation section.

Pareto-based Multi-objective Firefly Algorithm with Hierarchical Clustering for Environmental-Economic Power Dispatch Problem

This paper proposes a multi-objective firefly algorithm (MOFFA) to solve the environmental-economic power dispatch (EEPD) problem. Also, it presents a modified firefly algorithm (FFA) to solve the economic power dispatch (EPD) and the emission dispatch (ED) problems as single goals. The modifications proposed on the traditional FFA aim to improve its exploration and ensure the feasibility of the obtained solutions. The proposed MOFFA uses an external Pareto set to keep the non-dominated solutions, where a hierarchical clustering algorithm is used to get a representative and controlled set of Pareto-optimal solutions. The best compromise solution is also extracted from the Pareto set using an approach based on the fuzzy set theory. The constraints of EEPD, EPD, and ED problems are power balance constraint, generation limits constraint, and transmission power losses. To verify the effectiveness of the proposed algorithms, two methodologies are adopted and tested on the IEEE 30-bus test and the 10-unit test system with valve-point loading. In methodology 1, the EPD problem and the ED problem are solved separately using the modified FFA. In methodology 2, the EEPD is solved as a true multi-objective optimization problem using the proposed MOFFA. The simulation results and the statistical analysis ensure the high-quality solutions of the proposed algorithms and prove the ability of the MOFFA to produce well-distributed Pareto-optimal solutions.

A Developed Approach to Solve Economic and Emission Dispatch Problems Based on Moth-Flame Algorithm

The solution of Combined Economic and Emission dispatch (CEED) problems aims to minimize the operating fuel cost and emission levels simultaneously, while satisfying load demand and operational constraints. The CEED problem can be formulated as a nonlinear optimization problem with equality and inequality operating constraints. Moth-flame optimization algorithm (MFO) is one of the newest bio inspired optimization techniques. However, this paper proposes a modified moth-flame optimization algorithm (MMFO) to find the optimal solution of CEED problems. MMFO is mainly based on the concept of MFO with modification the path of moths in new spirals around the flame. The developed MMFO algorithm can be used to improve the convergence characteristics of CEED optimal solutions compared to other optimization algorithms. The performance of MMFO is compared with different optimization techniques. Valve point effect, environmental emissions, and transmission line losses are included in the CEED analysis to provide more practical results. MMFO is validated using different standard test systems. The simulated results show the effectiveness and the superiority of the developed MMFO against other well-known optimization techniques.

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

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

Improved tunicate swarm algorithm: Solving the dynamic economic emission dispatch problems

This study proposes improved tunicate swarm algorithm (ITSA) for solving and optimizing the dynamic economic emission dispatch (DEED) problem. The DEED optimization target is to reduce the fuel cost and pollutant emission of the power system. In addition, DEED is a complex optimization problem and contains multiple optimization goals. To strengthen the ability of the ITSA algorithm for solving DEED, the tent mapping is employed to generate initial population for improving the directionality in the optimization process. Meanwhile, the gray wolf optimizer is used to generate the global search vector for improving global exploration ability, and the Levy flight is introduced to expand the search range. Three test systems containing 5, 10 and 15 generator units are employed to verify the solving performance of ITSA. The test results show that the ITSA algorithm can provide a competitive scheduling plan for test systems containing different units. ITSA proposed algorithm gives the optimal economic and environmental dynamic dispatch scheme for achieving more precise dispatch strategy.

Development and application of slime mould algorithm for optimal economic emission dispatch

In this paper, an Improved version of the Slime Mould Algorithm (ISMA) is proposed and applied to efficiently solve the single-and bi-objective Economic and Emission Dispatch (EED) problems considering valve point effect. ISMA is developed to improve the performance of the conventional Slime Mould Algorithm (SMA). In ISMA, the solution positions are updated depending on two equations borrowed from the sine–cosine algorithm (SCA) to obtain the best solution. Multi-objective SMA (MOSMA) and Multi-objective ISMA (MOISMA) are developed based on the Pareto dominance concept and fuzzy decision-making. In the multi-objective EED problem, MOSMA and MOISMA are applied to minimize the total fuel costs and total emission with the valve point effect simultaneously. The proposed single-and bi-objective economic emission dispatch algorithms are validated using five test systems, 6-units, 10-units, 11-units, 40-units, and 110-units. The performance of the proposed algorithm is compared with Harris Hawk Optimizer (HHO), JellyfishSearch optimizer (JS), Tunicate Swarm Algorithm (TSA), Particle swarm optimization (PSO), and SMA algorithms. The results show that the proposed algorithms are more robust than other well-known algorithms. Feasible solutions using the proposed algorithms are also achieved, which adjust the schedule of generation without violation of the operating generation limits.

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.

A multi-objective differential evolution algorithm and a constraint handling mechanism based on variables proportion for dynamic economic emission dispatch problems

Dynamic economic emission dispatch (DEED) problems have become an important research issue in power system operations. It is a multi-objective optimization problem (MOP) with high dimensional, nonlinear, nonsmooth, and nonconvex, considering the power balance constraints, valve point effects, prohibited operating zones, and ramp rate limits. Therefore, an efficient multi-objective optimization technology is needed to solve conflicting objectives in the DEED problems. Besides, an efficient constraint handling mechanism is the key step in solving real-world problems. In this paper, a proportional dynamic adjustment decision (PDAD) variables method is proposed to deal with the constraints in the DEED problems by considering the difference in the power generation range of the unit. While, the constraint handling mechanism of the slack variable method is improved, and a dynamic slack variable (DSL) method is proposed. Besides, a non-dominant sorting differential evolution algorithm with a self-adaptive parameter operator and a local search operator (NSDESa_LS) is developed to solve the DEED problems. Finally, the performance of the proposed method is compared with state-of-the-art methods on 5-, 10-, and 40-unit systems. The results show that the proposed NSDESa_LS-PDAD method has a superior performance.