Proactive security-constrained unit commitment (SCUC) is one of the effective resilience-oriented responses to keep the secure operation of the power system during natural disasters. The approaches proposed in the previous research works cannot guarantee high-quality response timely to various emergencies during typhoon disasters. This article proposes a proactive SCUC model solved by an approximate dynamic programming (ADP) algorithm to improve system resilience during typhoon disasters, while meeting the requirements of the computational time. Both the uncertainty of the typhoon motion path and the wind speed-dependent transmission line failure probability are considered in the scenario generation. The optimization process is formulated as a Markov decision process, and a piecewise linear function (PLF)-based ADP is employed for solving the model. With the exogenous information extracted from generated scenarios and recorded in the slopes of PLFs, the ADP algorithm can result in a near-optimal solution within a short computational time. Simulations are carried out using the modified IEEE RTS-79 system. The significant computational time saving and high-quality solution demonstrate the effectiveness of the proposed approach.

Proactive Security-Constrained Unit Commitment Against Typhoon Disasters: An Approximate Dynamic Programming Approach

Siano P.;
2023-01-01

Abstract

Proactive security-constrained unit commitment (SCUC) is one of the effective resilience-oriented responses to keep the secure operation of the power system during natural disasters. The approaches proposed in the previous research works cannot guarantee high-quality response timely to various emergencies during typhoon disasters. This article proposes a proactive SCUC model solved by an approximate dynamic programming (ADP) algorithm to improve system resilience during typhoon disasters, while meeting the requirements of the computational time. Both the uncertainty of the typhoon motion path and the wind speed-dependent transmission line failure probability are considered in the scenario generation. The optimization process is formulated as a Markov decision process, and a piecewise linear function (PLF)-based ADP is employed for solving the model. With the exogenous information extracted from generated scenarios and recorded in the slopes of PLFs, the ADP algorithm can result in a near-optimal solution within a short computational time. Simulations are carried out using the modified IEEE RTS-79 system. The significant computational time saving and high-quality solution demonstrate the effectiveness of the proposed approach.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4853067
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