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

Runzhao Lu, Tao Ding, Boyu Qin, Jin Ma, Xin Fang, Zhaoyang Dong

Research output: Contribution to journalArticlepeer-review

100 Scopus Citations

Abstract

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.
Original languageAmerican English
Pages (from-to)1140-1151
Number of pages12
JournalIEEE Transactions on Sustainable Energy
Volume11
Issue number3
DOIs
StatePublished - 2020

NREL Publication Number

  • NREL/JA-5D00-77551

Keywords

  • economic dispatch
  • renewable energy
  • stochastic dual dynamic programming (SDDP)
  • stochastic programming

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