Solution to economic emission load dispatch by simulated annealing: case study

Júnior, Jorge de Almeida Brito; Nunes, Marcus Vinícius Alves; Nascimento, Manoel Henrique Reis; Rodríguez, Jorge Laureano Moya; Leite, Jandecy Cabral

doi:10.1007/s00202-017-0544-0

Cited by 18 publications

(25 citation statements)

References 21 publications

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“…Recently, several evolutionary programs are investigated for solving ELD problem. The literature documents the most relevant . The evolutionary algorithms in general require limited knowledge of the problem.…”

Section: Introductionmentioning

confidence: 99%

“…The most promising meta‐heuristic approaches for ELD are fast converging evolutionary programming, ant colony, population variant differential evolution, genetic algorithm, Tabu search, differential harmony, bacterial foraging, artificial bee colony, firefly algorithm, simulated annealing, semi‐definite programming, gravitational search algorithm, and hybrid univariate marginal distribution algorithm . It could be concluded based on literature that some evolutionary approaches suffer from serious drawbacks as premature convergence, time consuming, and initial solution sensitivity. Moreover, they differ widely according to the implementation complexity, the computation speed, and the storage requirements …”

Section: Introductionmentioning

confidence: 99%

“…It could be concluded based on literature that some evolutionary approaches suffer from serious drawbacks as premature convergence, time consuming, and initial solution sensitivity. Moreover, they differ widely according to the implementation complexity, the computation speed, and the storage requirements …”

Section: Introductionmentioning

confidence: 99%

“…To remedy the deficiency of a single evolutionary technique and enhance the performance, the merging between two meta‐heuristic methods is advised. The synergy improves the search capabilities and overcomes the limitations of each method.…”

Section: Introductionmentioning

confidence: 99%

“…The approach of combining two evolutionary techniques provides better solution for ELD than that of single optimization method. However, the hybrid scenario is more complicated and requires more computation requirements than the elementary method …”

Section: Introductionmentioning

confidence: 99%

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Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

Hatata

Hafez

2018

Int Trans Electr Energ Syst

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Summary Economic load dispatch (ELD) became recently a mandatory tool for reliable and economic power system operation. ELD improves the security of the power supply while minimizing the generation costs. Daily load curve imposes conflicting requirements on ELD scheme, such as fulfilling demand during peak periods while limiting the cost. Demand response (DR) is an intelligent approach that ensures load demand fulfillment without introducing additional costs. DR allows shifting the electricity consumption to the non‐peak periods. Therefore, DR could be considered as a virtual power plant. ELD is usually solved as nonlinear constraint optimization problem. Three simple, fertile, and robust meta‐heuristic optimizations are competing in this article for solving ELD incorporating DR as virtual power plant. These are ant lion optimizer (ALO), particle swarm optimization (PSO), and artificial immune system (AIS). The objectives are minimizing the overall cost, system losses, and emission levels. Variety of distinctive study systems are considered to test the functionality and feasibility of the proposed algorithms; they are 6‐bus, 30‐bus, and 118‐bus IEEE systems. MATLAB environment is used for coding ALO, PSO, AIS, and the systems under concern. The comprehensive results show that incorporating DR in ELD reduces the costs/losses/emissions without affecting the power system security and reliability. The comparative results verify the robustness and reliability of ALO in minimizing the overall operating cost and emission levels of the power systems under concern while confining with the constraints. Moreover, they show that ALO has the highest accuracy and convergence efficiency while enjoying the reduced computation storage and execution time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

Hatata

Hafez

2018

Int Trans Electr Energ Syst

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A new approach to economic-emission load dispatch using NSGA II. Case study

Moraes

Bezerra

Rodríguez³

et al. 2018

Int Trans Electr Energ Syst

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Summary Economic‐emission load dispatch optimization problem is an essential task in power plants with internal combustion engines. In power plants, in addition to electricity, a lot of air pollution by exhaust gases is generated. There are many international standards that establish the permissible limits of different substances but still have not developed an expression to evaluate the environmental impact caused by all components of the exhaust gases as a whole. A new method to evaluate this impact is developed in this paper. The developed mathematical expression was called “emission index.” To get a better idea of the environmental impact of each type of engine, the “specific emission index”, which is the emission index divided by the power delivered by the engine. This paper also presents a mathematical model for a multiobjective optimization of economic‐emission load dispatch using nondominated sorting genetic algorithm II.

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Multiobjective dynamic economic emission dispatch using evolutionary algorithm based on decomposition

Zhu

Qiao

Dong

et al. 2019

IEEJ Transactions Elec Engng

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Due to the pressures of energy and environment problems, the dynamic economic emission dispatch (DEED), which is more in line with actual dispatching requirements, has become an important research issue in recent years. In this paper, the highly dimensional, strongly coupled, nonlinear and nonconvex multiobjective DEED model is established considering both the fuel costs and pollution emissions objectives. Furthermore, an improved multiobjective evolutionary algorithm based on decomposition with constraints handling (IMOEA/D‐CH) is proposed to obtain the optimal dispatching schemes. The IMOEA/D‐CH method first decomposes the DEED problem into a number of scalar optimization subproblems and then evolves them simultaneously using the neighborhood information. The real‐time heuristic constraints adjustment and modification methods and the adaptive threshold punishment mechanism are adopted in allusion to the complex constraints of the dispatching model. An evolutionary control strategy is also utilized to avoid excessive evolution of the algorithm toward a certain objective. In addition, the fuzzy decision‐making strategy is used to provide the decision maker with the optimal compromise solutions. To validate the effectiveness of the IMOEA/D‐CH method, the IEEE 30‐bus 6‐generator system and 10‐generator system and 118‐bus 14‐generator system are studied as test cases. The simulation results indicate the validity and superiority of the proposed method compared with the other reported methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Solution to economic emission load dispatch by simulated annealing: case study

Cited by 18 publications

References 21 publications

Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

A new approach to economic-emission load dispatch using NSGA II. Case study

Multiobjective dynamic economic emission dispatch using evolutionary algorithm based on decomposition

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