A combined SDDP/Benders decomposition approach with a risk-averse surface concept for reservoir operation in long term power generation planning

作     者：Diniz, Andre Luiz Maceira, Maria Elvira P. Vasconcellos, Cesar Luis V. Penna, Debora Dias J. 

作者机构：CEPEL Brazilian Elect Energy Res Ctr Rio De Janeiro Brazil Univ Estado Rio De Janeiro UERJ Rio De Janeiro Brazil 

出 版 物：《ANNALS OF OPERATIONS RESEARCH》 (运筹学纪事)

年 卷 期：2020年第292卷第2期

页      面：649-681页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：CCEE CPAMP?s Validation Task Force Chamber for Commercialization of Electrical Energy EPE Electrical Energy Research Center Energy Research Office MME Ministry of Mines and Energy Rochester Academy of Science, RAS Office of Energy Research and Development, OERD 

主　　题：OR in energy Power generation planning Stochastic dual dynamic programming Benders decomposition Large-scale linear programming Risk aversion 

摘      要：Power generation planning in hydrothermal systems is a complex optimization task, specially due to the high uncertainty in the inflows to hydro plants. Since it is impossible to traverse the huge scenario tree of the multistage problem, stochastic dual dynamic programming (SDDP) is the leading technique to solve it, originally from an expected-cost minimization perspective. However, there is a growing need to apply risk-averse/robust formulations to protect the system from critical hydrological scenarios. This is particularly important for predominantly hydro systems, because environmental issues prevent the construction of large reservoirs, thus reducing their water regulating capability. This paper proposes a two-level SDDP/Benders decomposition approach to include a new risk averse surface (RAS) concept for reservoir operation in power generation planning. The upper level problem is a SDDP solving strategy with expected-cost minimization criterion, where recourse functions for each time step are built through forward/backward passes. The second level consists in multi-period deterministic optimization subproblems for each node of the scenario tree, which are solved to ensure a desired level of protection from a set of given critical scenario several months ahead. An inner iterative procedure for each SDDP stage/scenario is applied, where feasibility cuts are included in the upper level subproblems to derive the RAS surface, which are multidimensional rule curves for reservoir operation. Such curves ensure that the policy provided by the SDDP algorithm yields storage levels in the reservoirs that are high enough to protect the system against such critical scenarios. A max-type time-linking penalization scheme for violation of RAS constraints is also proposed, which avoids the multiple application of the penalty value for the same violation in consecutive time steps, which may result in large marginal costs. Results are presented for the large-scale Brazilian sys