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Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid

Xiaogang Guo, Zhejing Bao, Zhijie Li and Wenjun Yan
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Xiaogang Guo: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Zhejing Bao: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Zhijie Li: Shandong Longkou Electricity Supply Company, State Grid Corporation of China, Longkou 267000, China
Wenjun Yan: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2018, vol. 11, issue 1, 1-17

Abstract: In this paper, an adaptively constrained stochastic model predictive control (MPC) is proposed to achieve less-conservative coordination between energy storage units and uncertain renewable energy sources (RESs) in a microgrid (MG). Besides the economic objective of MG operation, the limits of state-of-charge (SOC) and discharging/charging power of the energy storage unit are formulated as chance constraints when accommodating uncertainties of RESs, considering mild violations of these constraints are allowed during long-term operation, and a closed-loop online update strategy is performed to adaptively tighten or relax constraints according to the actual deviation probability of violation level from the desired one as well as the current change rate of deviation probability. Numerical studies show that the proposed adaptively constrained stochastic MPC for MG optimal operation is much less conservative compared with the scenario optimization based robust MPC, and also presents a better convergence performance to the desired constraint violation level than other online update strategies.

Keywords: adaptive control; chance constraints; microgrid; stochastic model predictive control; dispatching (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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