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<p>This paper proposes a novel load-shedding model to optimize power flow and address voltage stability issues in electric power systems. Voltage stability is assessed using the voltage stability margin index, and when instability is detected, the model determines a new equilibrium point that minimizes the cost of load shedding while meeting operational and stability constraints. A key focus of this study is effectively capturing the uncertainties of generation and loads. Information-Gap Decision Theory (IGDT) is applied to model these uncertainties, while renewable generation scenarios are produced via the Monte Carlo method and reduced using a proposed scenario reduction approach. In the presence of outages, the Arithmetic Optimization Algorithm (AOA) is employed to determine optimal load shedding, prioritizing consumer importance rather than uniform disconnection. The proposed method is tested on the IEEE 14-bus and 118-bus systems. Results show that AOA converges faster than alternative methods, with standard deviation values at least 0.0049 and 0.0065 lower, respectively. Comparing load shedding with and without voltage control, reductions of 33.4, 54.6, and 38.5&#xa0;MW are achieved for the IEEE 14-bus system, and 23.0, 7.3, and 47.0&#xa0;MW for the IEEE 118-bus system, with a sustainability margin of 2. Considering uncertainty in generation increases the required shedding by 23% in the IEEE 14-bus and over 200% in the IEEE 118-bus compared to deterministic cases. Moreover, increasing uncertainty from 5% to 15% raises load shedding by 14.0% and 49.46%, respectively. This comprehensive framework demonstrates improved efficiency, faster convergence, and more resilient operation, offering a practical tool for operators to manage load shedding under uncertainty while preserving voltage stability.</p>

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Optimizing Power Flow and Voltage Stability: A Novel Load-Shedding Model Incorporating Uncertainty and Cost Considerations

  • 期刊论文

  • 发表日期：

    2026年3月12日

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  • Yuan Zhang

摘要

This paper proposes a novel load-shedding model to optimize power flow and address voltage stability issues in electric power systems. Voltage stability is assessed using the voltage stability margin index, and when instability is detected, the model determines a new equilibrium point that minimizes the cost of load shedding while meeting operational and stability constraints. A key focus of this study is effectively capturing the uncertainties of generation and loads. Information-Gap Decision Theory (IGDT) is applied to model these uncertainties, while renewable generation scenarios are produced via the Monte Carlo method and reduced using a proposed scenario reduction approach. In the presence of outages, the Arithmetic Optimization Algorithm (AOA) is employed to determine optimal load shedding, prioritizing consumer importance rather than uniform disconnection. The proposed method is tested on the IEEE 14-bus and 118-bus systems. Results show that AOA converges faster than alternative methods, with standard deviation values at least 0.0049 and 0.0065 lower, respectively. Comparing load shedding with and without voltage control, reductions of 33.4, 54.6, and 38.5 MW are achieved for the IEEE 14-bus system, and 23.0, 7.3, and 47.0 MW for the IEEE 118-bus system, with a sustainability margin of 2. Considering uncertainty in generation increases the required shedding by 23% in the IEEE 14-bus and over 200% in the IEEE 118-bus compared to deterministic cases. Moreover, increasing uncertainty from 5% to 15% raises load shedding by 14.0% and 49.46%, respectively. This comprehensive framework demonstrates improved efficiency, faster convergence, and more resilient operation, offering a practical tool for operators to manage load shedding under uncertainty while preserving voltage stability.