An improved gravitational search algorithm for solving short-term economic/environmental hydrothermal scheduling
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This paper proposes an improved gravitational search algorithm (IGSA) to find the optimum solution for short-term economic/environmental hydrothermal scheduling (SEEHTS), which considers minimizing fuel cost as well as minimizing pollutant emission. In order to improve the performance of GSA, this paper firstly uses particle memory character and population social information to update velocity. Secondly, a chaotic mutation operator is embedded into GSA and a selection-operator-based greedy rule is adopted to update population. When dealing with the constraints of the SEEHTS, a modification strategy by dividing the violation water volume into several parts and randomly selecting intervals to adjust the water discharge gradually is proposed to handle the water dynamic balance constraints. Meanwhile, a new symmetrical adjusting strategy is adopted to handle reservoir storage constraints. Furthermore, the priority index strategy based on thermal power output is applied to handle system load balance constraints. To test the performance of the proposed method, simulation results have been compared with those obtained by particle swarm optimization, evolutionary programming and differential evolution reported in literature. The results show that the proposed IGSA provides the optimum solution with less fuel cost and smaller emission. So it demonstrates that IGSA is effective for solving SEEHTS problem.
KeywordsEconomic/environmental scheduling Gravitational search algorithm Constraints handling Chaotic mutation Priority index
This work was supported by Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control, China Three Gorges University, National Natural Science Foundation of China (No. 51379080) and Fundamental Research Funds for the Central Universities (No. 2014TS152).
- Lu S, Sun C (2011) Quadratic approximation based differential evolution with valuable trade off approach for bi-objective short-term hydrothermal scheduling. Expert Syst Appl 38(11):13950–13960Google Scholar
- Nayak NC, Rajan CCA (2010) Hydro-thermal commitment scheduling by tabu search method with cooling-banking constraints. In: Swarm, evolutionary, and memetic computing. Lecture notes in computer science, vol 6466. Springer, Heidelberg, pp 739–752Google Scholar
- Senthil KV, Mohan MR (2011) A genetic algorithm solution to the optimal short-term hydrothermal scheduling. Electr Power Energy Syst 33(4):827–835Google Scholar
- Sun C, Lu S (2010) Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization. Expert Syst Appl 37(6):4232–4241 Google Scholar
- Sushil K, Naresh R (2007) Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem. Electr Power Energy Syst 29(10):738–747Google Scholar
- Yuan X, Zhang Y, Yuan Y (2008a) Improved self-adaptive chaotic genetic algorithm for hydro generation scheduling. J Water Resour Plan Manag ASCE 134(4):319–325Google Scholar
- Yuan X, Wang L, Yuan Y (2008b) Application of enhanced PSO approach to optimal scheduling of hydro system. Energy Convers Manag 49(11):2966–2972Google Scholar
- Yuan X, Cao B, Yang B et al (2008c) Hydrothermal scheduling using chaotic hybrid differential evolution. Energy Convers Manag 49(12):3627–3633Google Scholar