A modified chaotic differential evolution algorithm for short-term optimal hydrothermal scheduling. (February 2015)

• Record Type: Journal Article
• Authors: Zhang, Jingrui; Lin, Shuang; Qiu, Weixia
• Is Part Of: International journal of electrical power & energy systems. Volume 65(2015:Feb.)
• Page Start: 159
• Page End: 168
• Publication Date: 2015-02
• Subjects: Short-term hydrothermal scheduling -- Differential evolution -- Regeneration -- Chaotic local search; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2014.09.041
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: The paper solved the short-term optimal hydrothermal scheduling (STOHS) problems. Modified chaotic differential evolution (MCDE) is applied to solve STOHS problems. Repair, selection, regeneration and chaotic local search are integrated into MCDE. Performance of MCDE has been compared with different versions of DE, PSO, etc. The comparisons show MCDE performs effectively and yields competitive solutions. Abstract: The short-term optimal hydrothermal scheduling (STOHS) plays one of the most important roles in power systems operation. The STOHS problem involves the solution of difficult constrained optimization problems that require good computational techniques. This paper proposes a modified chaotic differential evolution (MCDE) approach for the solution of this difficult optimization problem. A repair strategy and a novel selection operation are simultaneously introduced into the MCDE approach for handling constraints of the problem. The repair strategy preserves the feasibility of solutions generated and avoids the use of penalty factors as much as possible. The introduced selection operation makes a not clearly distinction between feasible solutions and infeasible ones at early stage of the algorithm and makes a clearly distinction at the later stage. Additionally, an adaptive regeneration operation is proposed to enhance population diversity and to avoid local optimums. Moreover, a chaotic local search technique is introduced also to accelerate the searching process of the algorithm. The proposed MCDE approach is applied to three well-known hydrothermal test systems in order to verify its feasibility and efficiency. The obtained results are compared with those obtained by other population-based heuristic approaches reported in literature. It is observed from the comparisons that the proposed MCDE approach performs effectively and can yield competitive solutions.