Metaheuristic optimization algorithms comparison adopted for the profit maximization of electricity market participants

The electricity market faces numerous challenges due to the growing demand for energy, increasing penetration of renewable energy sources, and the need for grid reliability and efficiency. To address these challenges, optimization algorithms have emerged as essential tools for optimizing various asp...

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Published in:Journal of Electrical Systems Vol. 20; no. 6s; pp. 1032 - 1042
Main Authors: Banker, Sumit, Chakravorty, Jaydeep, Bariya, Chetan, Chaudhari, Tejal, Brahmbhatt, Bhavik, Priyadarshi, Mitesh
Format: Journal Article
Language:English
Published: Paris Engineering and Scientific Research Groups 29.04.2024
Subjects:
Dynamic programming
Electric power demand
Energy management
Genetic algorithms
Heuristic methods
Integer programming
Linear programming
Machine learning
Maximization
Mixed integer
Nonlinear programming
Optimization algorithms
Optimization techniques
Particle swarm optimization
Power dispatch
Profit maximization
Renewable energy sources
Simulated annealing
Unit commitment
ISSN:1112-5209
Online Access:Get full text
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Abstract The electricity market faces numerous challenges due to the growing demand for energy, increasing penetration of renewable energy sources, and the need for grid reliability and efficiency. To address these challenges, optimization algorithms have emerged as essential tools for optimizing various aspects of the electricity market, including generation, transmission, distribution, and demand-side management. The review can be done by providing an overview of the key components and challenges of the electricity market, including generation dispatch, unit commitment, economic dispatch, transmission network optimization, and demand response management. It then systematically examines a wide range of optimization techniques employed in addressing these challenges, including linear programming, mixed-integer linear programming, nonlinear programming, dynamic programming, genetic algorithms, particle swarm optimization, simulated annealing, and machine learning-based approaches. This paper presents a comparison of optimization algorithms, RCEDUMDA (Ring-Cellular Encode-Decode Univariate Marginal Distribution Algorithm) and CLHC2RCEDUMDA (Hill Climbing to Ring Cellular Encode-Decode Univariate Marginal Distribution Algorithm) for the profit maximization of Electricity Market consumers & prosumers.
AbstractList The electricity market faces numerous challenges due to the growing demand for energy, increasing penetration of renewable energy sources, and the need for grid reliability and efficiency. To address these challenges, optimization algorithms have emerged as essential tools for optimizing various aspects of the electricity market, including generation, transmission, distribution, and demand-side management. The review can be done by providing an overview of the key components and challenges of the electricity market, including generation dispatch, unit commitment, economic dispatch, transmission network optimization, and demand response management. It then systematically examines a wide range of optimization techniques employed in addressing these challenges, including linear programming, mixed-integer linear programming, nonlinear programming, dynamic programming, genetic algorithms, particle swarm optimization, simulated annealing, and machine learning-based approaches. This paper presents a comparison of optimization algorithms, RCEDUMDA (Ring-Cellular Encode-Decode Univariate Marginal Distribution Algorithm) and CLHC2RCEDUMDA (Hill Climbing to Ring Cellular Encode-Decode Univariate Marginal Distribution Algorithm) for the profit maximization of Electricity Market consumers & prosumers.
Author Banker, Sumit
Priyadarshi, Mitesh
Chakravorty, Jaydeep
Brahmbhatt, Bhavik
Bariya, Chetan
Chaudhari, Tejal
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Copyright 2024. This work is published under https://creativecommons.org/licenses/by/4.0/legalcode (the“License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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SubjectTerms Dynamic programming
Electric power demand
Energy management
Genetic algorithms
Heuristic methods
Integer programming
Linear programming
Machine learning
Maximization
Mixed integer
Nonlinear programming
Optimization algorithms
Optimization techniques
Particle swarm optimization
Power dispatch
Profit maximization
Renewable energy sources
Simulated annealing
Unit commitment
Title Metaheuristic optimization algorithms comparison adopted for the profit maximization of electricity market participants
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