A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power

• Published in: Energy (Oxford) Vol. 36; no. 2; pp. 1018 - 1029
• Main Author: Liao, Gwo-Ching
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.02.2011; Elsevier
• Subjects: Algorithms; Applied sciences; Chaos theory; Chaotic quantum genetic algorithm; Dynamic economic dispatch; Dynamical systems; Dynamics; econometric models; Economic data; Economics; Electric energy; Emission reduction; Energy; energy conservation; Energy economics; Energy saving; Exact sciences and technology; General, economic and professional studies; Genetic algorithms; Methodology. Modelling; Natural energy; Optimization; power generation; Power system integrated wind power; wind power; Wind power generation; Emission reduction; Chaotic quantum genetic algorithm; Power system integrated wind power; Energy saving; Dynamic economic dispatch; Cost minimization; Optimal strategy; Energy cost; Wind energy; Genetic algorithm; Evolutionary algorithm; Economic optimization; Electric power production; Constrained optimization
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2010.12.006

An optimization algorithm is proposed in this paper to solve the economic dispatch problem that includes wind farm using the Chaotic Quantum Genetic Algorithm (CQGA). In addition to the detailed models of economic dispatch introduction and their associated constraints, the wind power effect is also included in this paper. The chaotic quantum genetic algorithm used to solve the economic dispatch process and discussed with real scenarios used for the simulation tests. After comparing the proposed algorithm with several other algorithms commonly used to solve optimization problems, the results show that the proposed algorithm is able to find the optimal solution quickly and accurately (i.e. to obtain the minimum cost for power generation in the shortest time). At the end, the impact to the total cost savings for power generation after adding (or not adding) wind power generation is also discussed. The actual implementation results prove that the proposed algorithm is economical, fast and practical. They are quite valuable for further research. ► Quantum Genetic Algorithm can effectively improve the global search ability. ► It can achieve the real objective of the global optimal solutions. ► The CPU computation time is less than that other algorithms adopted in this paper.