Optimal adaptive decentralized under-frequency load shedding for islanded smart distribution network considering wind power uncertainty

Zhiqiang Luo, Hui Liu, Ni Wang, Teyang Zhao and Jiarui Tian

Applied Energy, 2024, vol. 365, issue C, No S0306261924005452

Abstract: The increasing share of renewable distributed generations has raised a challenge about the frequency stability. Under-frequency load shedding (UFLS) can deal with this challenge, but it usually considers a prorated load-shedding strategy and neglects the impact of uncertainty from renewable generation. This may result in unnecessary additional economic losses from the system blackout. In this paper, a risk-decision-based optimal adaptive decentralized UFLS method is proposed to coordinate the distribution of shedding load in the smart distribution network to avoid unnecessary additional economic losses, while considering the wind power uncertainty. Firstly, the uncertainty from wind power generation is modeled by confidence intervals and evaluated by the calculation of the conditional value at risk (CVaR). Then, to economically distribute the power deficit among load agents, a decentralized consensus on the incremental cost among decentralized agents is reached, where the interruption cost of the local agent varies with its load as a nonlinear relationship. Finally, a two-layer adaptive risk-decision decentralized UFLS control framework is proposed to enable the frequency stability and economic operation of the smart distribution network. With the proposed strategy, local information sharing and important global information discovery are achieved in a distributed way. Simulations on an improved IEEE 33-bus system show that the proposed strategy can effectively avoid rigid shedding and implement the optimal allocation of the system power deficit.

Keywords: Multiagent system (MAS); Wind power uncertainty; Conditional value at risk; Optimal consensus; Risk-decision; Under-frequency load shedding (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123162

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