Stochastic two-stage multi-objective unit commitment of distributed resource energy systems considering uncertainties and unit failures

Jingfan Liu and Shijie Zhang

Reliability Engineering and System Safety, 2025, vol. 253, issue C

Abstract: Compared to centralized generation technology, distributed energy resource systems are susceptible to energy risks caused by boundary uncertainties and unit failures. This study introduces a stochastic two-stage multi-objective optimization method to address reliability-based unit commitment issues. In the day-ahead stage, operational state and reserve capacity are determined to minimize prescheduled operation costs based on forecasted parameters. In the real-time stage, a decision-dependent stochastic reliability method is proposed to simulate outage scenarios. Reserve resources within available units are allocated to mitigate forecasting errors and unit failures. Additionally, the grid interaction ratio and penalty cost are added to restrict the depth and frequency access to the grid. Four comparative cases analyze the effects of the proposed methodology. This method innovatively achieves the simulation of stochastic multi-unit outages and delete faulty units in the operation scheme. The optimal results show that the risks of electricity and cooling supply are underestimated, while the risks of heating are overestimated, compared to N-1 reliability. Furthermore, Pareto analysis of the multi-objective problem enhances independent operational capacity through utilization of reserve resources. Grid dispatch pressure is reduced since purchased power can be used as day-ahead planning. Thus, the methodology achieves collaborative optimization of reliability with a reduction of operation costs, offering effective guidance for engineering applications.

Keywords: Distributed energy resource system; Uncertainties and outages; Stochastic two-stage UC; Energy and reserve scheduling; Multi-objective programming (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:253:y:2025:i:c:s0951832024005921

DOI: 10.1016/j.ress.2024.110520

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