Journal
IEEE SYSTEMS JOURNAL
Volume 12, Issue 3, Pages 2589-2600Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2016.2614723
Keywords
Energy storage; microgrid; model predictive control; renewable energy; stochastic programming
Categories
Funding
- National Natural Science Foundation of China [61403303]
- China Post-Doctoral Science Foundation [2014M560776]
- Natural Science Basis Research Plan in Shaanxi Province of China [2016JQ5080]
- China Postdoctoral Science Foundation [2016T90918]
- State Key Laboratory of Electrical Insulation and Power Equipment [EIPE15306]
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Microgrids have emerged as a promising paradigm to integrate the renewable generation units, energy storage systems, and dispersed loads. However, the inherent fluctuation of renewable generations adds significant uncertainty to the supply side of microgrid. On the other hand, due to the small scale of the microgrid, the load demand in microgrid also has higher uncertainty than that observed in utility grids. Furthermore, the supply uncertainty and demand uncertainty may have different distribution characteristics, e.g., Gaussian and non-Gaussian uncertainties may exist simultaneously. These uncertainties pose significant challenges to the microgrid energy management. To address these challenges, this paper establishes a new stochastic energy scheduling scheme for microgrids. In this scheme, the energy scheduling is formulated as a stochastic model predictive control problem, which incorporates the uncertainties in both sides of supply and demand. Using machine-learning techniques, the corresponding stochastic optimization problem is converted to a standard convex quadratic programming, and thus can be solved efficiently. A key feature of this scheme is that it handles the coexistence of Gaussian and non-Gaussian uncertainties. Simulation results validate the effectiveness of the proposed scheme.
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