Multi-Stage Stochastic Programming to Joint Economic Dispatch for Energy and Reserve With Uncertain Renewable Energy

• Published in: IEEE transactions on sustainable energy Vol. 11; no. 3; pp. 1140 - 1151
• Main Authors: Lu, Runzhao, Ding, Tao, Qin, Boyu, Ma, Jin, Fang, Xin, Dong, Zhaoyang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Convergence; Criteria; Decision trees; Dynamic programming; Economic dispatch; Electric power grids; Energy; Indexes; Iterative methods; Load modeling; Optimization; Power dispatch; Programming; Renewable energy; Renewable energy sources; Renewable resources; stochastic dual dynamic programming (SDDP); Stochastic processes; Stochastic programming; Uncertainty
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2019.2918269

To address the uncertain renewable energy in the day-ahead optimal dispatch of energy and reserve, a multi-stage stochastic programming model is established in this paper to minimize the expected total costs. The uncertainties over the multiple stages are characterized by a scenario tree and the optimal dispatch scheme is cast as a decision tree which guarantees the flexibility to decide the reasonable outputs of generation and the adequate reserves accounting for different realizations of renewable energy. Most importantly, to deal with the "Curse of Dimensionality" of stochastic programming, stochastic dual dynamic programming (SDDP) is employed, which decomposes the original problem into several sub-problems according to the stages. Specifically, the SDDP algorithm performs forward pass and backward pass repeatedly until the convergence criterion is satisfied. At each iteration, the original problem is approximated by creating a linear piecewise function. Besides, an improved convergence criterion is adopted to narrow the optimization gaps. The results on the IEEE 118-bus system and real-life provincial power grid show the effectiveness of the proposed model and method.