Multi-Timescale Optimal Dispatching Strategy for Coordinated Source-Grid-Load-Storage Interaction in Active Distribution Networks Based on Second-Order Cone Planning

Yang Mi, Yuyang Chen, Minghan Yuan, Zichen Li, Biao Tao and Yunhao Han ()
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Yang Mi: School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Yuyang Chen: School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Minghan Yuan: State Grid Shanghai Municipal Electric Power Company, Pudong District, Shanghai 200122, China; ymh127ymh@aliyun.com
Zichen Li: School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Biao Tao: School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Yunhao Han: School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Energies, 2023, vol. 16, issue 3, 1-21

Abstract: In order to cope with the efficient consumption and flexible regulation of resource scarcity due to grid integration of renewable energy sources, a scheduling strategy that takes into account the coordinated interaction of source, grid, load, and storage is proposed. In order to improve the accuracy of the dispatch, a BP neural network approach modified by a genetic algorithm is used to predict renewable energy sources and loads. The non-convex, non-linear optimal dispatch model of the distribution grid is transformed into a mixed integer programming model with optimal tides based on the second-order cone relaxation, variable substitution, and segmental linearization of the Big M method. In addition, the uncertainty of distributed renewable energy output and the flexibility of load demand re-response limit optimal dispatch on a single time scale, so the frequency of renewable energy and load forecasting is increased, and an optimal dispatch model with complementary time scales is developed. Finally, the IEEE 33-node distribution system was tested to verify the effectiveness of the proposed optimal dispatching strategy. The simulation results show an 18.28% improvement in the economy of the system and a 24.39% increase in the capacity to consume renewable energy.

Keywords: distribution network; renewable energy consumption; source-grid-load-storage; second-order cone planning; optimal scheduling; BP neural network (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: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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