Abstract
The long-term hydropower generation scheduling (LHGS) is one of the most important problems which aim to obtain a suitable scheme to maximize electrical benefits. In this paper, a gradient based Harmony search algorithm (GHS) is employed on the optimization of the cascade hydropower stations in the Jinsha River. Firstly, the original Harmony search algorithm (HS) is firstly improved by a dynamically adjustment strategy and global pitch adjustment strategy. Then random gradient strategy is adopted to improve the search speed of HS. In case study, the proposed GHS is used to solve the LHGS problem in the Jinsha River. Results show that GHS is effectiveness when dealing with LHGS problems.
Highlights
Hydropower is an important renewable energy source and plays an important role in the power grid
It is of great importance to the long-term hydropower generation scheduling (LHGS) of the four hydropower stations in the Jinsha River
It performed based on musical performances and is an effective way to solve the LHGS problem
Summary
Hydropower is an important renewable energy source and plays an important role in the power grid. There have been many researches on the dispatching of cascade hydropower stations, and indicating that LHGS is a non-linear problem with complicate constraints [1] which make LHGS difficult to solve Classic methods such as dynamic programming (DP) [2] and progressive optimality algorithm (POA) [3] have played an important role in solving LHGS, but they perform not well when deal with cascade hydropower stations [4]. Harmony Search (HS) is a phenomenonmimicking meta-heuristic proposed by Zong Woo Geem in 2001 [6] It performed based on musical performances and is an effective way to solve the LHGS problem. In order to overcome the shortcomings, in this paper, a random gradient based Harmony search algorithm (GHS) is proposed to solve the LHGS problem. The results obtained by GHS are better than that obtained by CS, GSA and HS, indicating that GHS is effectiveness when dealing with LHGS problems
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