How accurate is capacity configuration optimization of energy storage in microgrids?
Zeqing Zhang; Capacity configuration optimization of energy storage for microgrids considering source–load prediction uncertainty and demand response. 1 November 2023; 15 (6): 064102. The fluctuation of renewable energy resources and the uncertainty of demand-side loads affect the accuracy of the configuration of energy storage (ES) in microgrids.
What factors affect the configuration of energy storage in microgrids?
The fluctuation of renewable energy resources and the uncertainty of demand-side loads affect the accuracy of the configuration of energy storage (ES) in microgrids. High peak-to-valley differences on the load side also affect the stable operation of the microgrid.
Does es capacity and Dr reduce the cost of a microgrid?
The simulation results show that the optimal configuration of ES capacity and DR promotes renewable energy consumption and achieves peak shaving and valley filling, which reduces the total daily cost of the microgrid by 22%. Meanwhile, the DR model proposed in this paper has the best optimization results compared with a single type of the DR model.
What are the results of a microgrid configuration?
Capacity Configuration Results of Microgrid Equipment. First, Option C employs low-capacity PV, WT, electric storage, and hydrogen storage with high-capacity diesel to minimize the combined annual cost of the microgrid. Although the cost is reduced, the configuration yieldshigh carbon emissions and poor environmental friendliness.
What are isolated microgrids?
Isolated microgrids can be of any size depending on the power loads. In this sense, MGs are made up of an interconnected group of distributed energy resources (DER), including grouping battery energy storage systems (BESS) and loads.
Do peak-to-valley differences affect the stability of a microgrid?
High peak-to-valley differences on the load side also affect the stable operation of the microgrid. To improve the accuracy of capacity configuration of ES and the stability of microgrids, this study proposes a capacity configuration optimization model of ES for the microgrid, considering source–load prediction uncertainty and demand response (DR).
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