A stochastic MPC based approach to integrated energy management in microgrids. (August 2018)

• Record Type: Journal Article
• Authors: Zhang, Yan; Meng, Fanlin; Wang, Rui; Zhu, Wanlu; Zeng, Xiao-Jun
• Is Part Of: Sustainable cities and society. Volume 41(2018)
• Page Start: 349
• Page End: 362
• Publication Date: 2018-08
• Subjects: Microgrid -- Model predictive control -- Stochastic programming -- Energy storage -- Distributed generators; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2018.05.044
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: A stochastic model predictive control (SMPC) approach to integrated microgrid energy management is developed. Forecasting uncertainties are represented by typical scenarios obtained through a two-stage scenario reduction technique. An equivalent deterministic mixed integer quadratic programming is formulated based on the selected typical scenarios. Comparative simulation results demonstrate that our proposed SMPC method outperforms other state-of-the-art approaches. Abstract: In this paper, a stochastic model predictive control (SMPC) approach to integrated energy (load and generation) management is proposed for a microgrid with the penetration of renewable energy sources (RES). The considered microgrid consists of RES, controllable generators (CGs), energy storages and various loads (e.g., curtailable loads, shiftable loads). Firstly, the forecasting uncertainties of load demand, wind and photovoltaic generation in the microgrid as well as the electricity prices are represented by typical scenarios reduced from a large number of primary scenarios via a two-stage scenario reduction technique. Secondly, a finite horizon stochastic mixed integer quadratic programming model is developed to minimize the microgrid operation cost and to reduce the spinning reserve based on the selected typical scenarios. Finally, A SMPC based control framework is proposed to take into account newly updated information to reduce the negative impacts introduced by forecast uncertainties. Through a comprehensive comparison study, simulation results show that our proposed SMPC method outperforms other state of the art approaches that it could achieve the lowest operation cost.