Genetic Algorithm-based Cost Optimization Model for Power Economic Dispatch Problem

Oluwadare, Samuel Adebayo; Babatunde, Iwasokun Gabriel; Ogunmakinde, Olabode Emmanuel; Olusi, O.; Akinwonmi, Akintoba Emmanuel

doi:10.9734/bjast/2016/24347

Cited by 5 publications

(2 citation statements)

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“…The binary genetic algorithm is one of the heuristic search technique based on the evolutionary ideas of natural selection and genetics [43]. Different from the evolutionary programming, the genetic algorithm is mainly based on crossover operator in finding the optimal solution.…”

Section: Implementation Of Binary Genetic Algorithm (Bga)mentioning

confidence: 99%

Application of Meta-Heuristic Techniques for Optimal Load Shedding in Islanded Distribution Network with High Penetration of Solar PV Generation

2017

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Abstract:Recently, several environmental problems are beginning to affect all aspects of life. For this reason, many governments and international agencies have expressed great interest in using more renewable energy sources (RESs). However, integrating more RESs with distribution networks resulted in several critical problems vis-à-vis the frequency stability, which might lead to a complete blackout if not properly treated. Therefore, this paper proposed a new Under Frequency Load Shedding (UFLS) scheme for islanding distribution network. This scheme uses three meta-heuristics techniques, binary evolutionary programming (BEP), Binary genetic algorithm (BGA), and Binary particle swarm optimization (BPSO), to determine the optimal combination of loads that needs to be shed from the islanded distribution network. Compared with existing UFLS schemes using fixed priority loads, the proposed scheme has the ability to restore the network frequency without any overshooting. Furthermore, in terms of execution time, the simulation results show that the BEP technique is fast enough to shed the optimal combination of loads compared with BGA and BPSO techniques.

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Section: Implementation Of Binary Genetic Algorithm (Bga)mentioning

confidence: 99%

Application of Meta-Heuristic Techniques for Optimal Load Shedding in Islanded Distribution Network with High Penetration of Solar PV Generation

2017

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“…However, numerical methods can result in problems with complicated and large power systems where problems of dimensionality or local optimality might exist. Previously, meta-heuristics methods have been used to solve the economic dispatch and power electrical system problem, including the genetic algorithm (GA) [7][8][9], particle swarm optimization (PSO) [10][11][12], ant colony optimization (ACO) [13,14], bee colony optimization (BCO) [15][16][17], the cuckoo search algorithm (CSA) [18], the shuffled frog leaping algorithm (SFLA) [19], the firefly algorithm (FA) [20] and simulated annealing (SA) [21,22]. These methods often provide fast and reasonable solutions, including global optimization with short time searching.…”

Section: Introductionmentioning

confidence: 99%

Multiple of Hybrid Lambda Iteration and Simulated Annealing Algorithm to Solve Economic Dispatch Problem with Ramp Rate Limit and Prohibited Operating Zones

Takeang

Aurasopon

2019

J. Electr. Eng. Technol.

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Aims This paper presents a multiple hybrid methods combining the lambda iteration and simulated annealing methods (MHLSA) to solve an economic dispatch (ED) problem with smooth cost function characteristics. The constraints of the economic dispatch were load demand and transmission loss. Methods The proposed MHLSA algorithm is a hybrid of the lambda iteration and simulated annealing methods and increases efficiency by adding multiple search mechanisms. It is a sequential execution of an individual hybrid algorithm of the lambda iteration and simulated annealing algorithm (HLSA) using only one personal microcomputer. The MHLSA algorithm introduces additional techniques for improvement of the search processes, such as the hybrid method, scope for multiple adaptive searches, and multiple searches. Results To show its effectiveness, the MHLSA is applied to two systems consisting of three and six power generating units. The optimized results from MHLSA are compared with those from HLSA and from conventional approaches, such as bee colony optimization (BCO), hybrid particle swarm optimization (HPSO) and simulated annealing (SA). Results confirm that the proposed MHLSA approach is capable of obtaining the greater speed of convergence and a higher quality solution efficiently. Conclusions Thus, it has great potential to be implemented in different types of power system optimization problem.

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Energy Management of Microgrids Using Load Shifting and Multi-agent System

Abdelsalam

Zedan

ElDesouky

2020

J Control Autom Electr Syst

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Genetic Algorithm-based Cost Optimization Model for Power Economic Dispatch Problem

Cited by 5 publications

References 0 publications

Application of Meta-Heuristic Techniques for Optimal Load Shedding in Islanded Distribution Network with High Penetration of Solar PV Generation

Application of Meta-Heuristic Techniques for Optimal Load Shedding in Islanded Distribution Network with High Penetration of Solar PV Generation

Multiple of Hybrid Lambda Iteration and Simulated Annealing Algorithm to Solve Economic Dispatch Problem with Ramp Rate Limit and Prohibited Operating Zones

Energy Management of Microgrids Using Load Shifting and Multi-agent System

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