A hierarchical demand assessment methodology of peaking resources in multi-areas interconnected systems with a high percentage of renewables

L.I. Junhui, Yahui Pan, Gang Mu, Guohang Chen, Xingxu Zhu, Ganggui Yan, Cuiping Li and Chen Jia

Applied Energy, 2024, vol. 367, issue C, No S0306261924007542

Abstract: The uncertainty of wind power poses a serious challenge in peak regulation for multi-area interconnected power system, and this paper proposes a method for quantitatively assessing the peaking resource demand of a multi-area interconnected power system, which takes into account the complementary characteristics of inter-area regulating resources and the wind power forecast error. First, wind power historical data are analysed with a data-driven model-based wind power forecast error, to reduce the impact of renewable energy uncertainty on power system peaking demand. Subsequently, a multi-area peaking demand analysis method considering power flow constraints, which is proposed for the effective utilization of peaking resources in areas with complementary characteristics. The method can adequately quantify the area power margin, adjustable power, and area grid peaking demand. On this basis, a two-layer assessment model of multi-area peaking resource demand is constructed, the areas are divided into different categories of peaking aggregation based on the area peaking characteristics. The upper layer takes the area operating cost as the optimisation objective, to derive the optimal output of each area in the system, and the lower layer ensures the optimal matching of power among clusters, which is solved by using a Nash equilibrium. Finally, the effectiveness and superiority of the method is verified by case analysis, and the numerical results show that compared with the traditional global optimal method, the cost of operation and transmission has been decreased by 21.5% and the security of power flows has been significantly enhanced.

Keywords: Peak shaving; Grid constraints; Interconnected system; Provide and demand balance; Demand quantification (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.apenergy.2024.123371

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