Applying different decomposition schemes using the progressive hedging algorithm to the operation planning problem of a hydrothermal system

• Published in: Electric power systems research Vol. 83; no. 1; pp. 19 - 27
• Main Authors: Gonçalves, Raphael E.C., Finardi, Erlon Cristian, Silva, Edson Luiz da
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2012; Elsevier
• Subjects: Applied sciences; Electric power plants; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Hydrothermal systems; Medium-term operation planning; Miscellaneous; Multi-stage linear stochastic optimization; Progressive hedging method; Multi-stage linear stochastic optimization; Hydrothermal systems; Progressive hedging method; Medium-term operation planning; Performance evaluation; Electric power plant; Multistage method; Output power; Stochastic method; Algorithm; Implementation; Stochastic programming; Case study; Computation time; Production cost; Economic aspect; Medium term; Planning; Large scale system
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2011.09.006

► We applied the Progressive Hedging algorithm to Operation Planning Problem. ► We propose an alternative decomposition scheme to get lower CPU time. ► We compared the CPU performance between two decomposition schemes. ► Besides, we also study PH features in order to improve the convergence process. ► We also implemented a parallel computational approach via PH. This paper addresses the solution of the Medium-Term Operation Planning (MTOP) problem. This operation scheduling problem aims to define the output of each power plant to minimize the expected production cost over a medium-term planning horizon. In hydrothermal systems, the MTOP is strongly influenced by the amount of water inflow to the reservoirs of hydroelectric plants, which is uncertain. Thus, the System Operator (SO) must consider these uncertainties in the problem resolution, which can be solved by means of Stochastic Programming (SP) techniques, such as the Progressive Hedging (PH) proposed in this paper. This paper presents suitable decomposition schemes to reduce the CPU time, such that it is possible to use a detailed model for the problem. Additionally, a parallel computational approach based on the PH algorithm is also implemented. To demonstrate the efficiency of the proposed schemes, a large hydrothermal system is investigated in the case studies.