In electrical power systems, the impact of interruptions due to failures can be reduced through expansion planning studies. While high investments result in very expensive and more reliable decisions, reduced investments can lead to unreliable systems. Therefore, it is evident that economic and reliability constraints are conflicting, which makes decision-making difficult in planning and operation stage. The reliability theory, based on probabilities and stochastic processes, allows modeling the random behavior of equipment to estimate performance indices such as Loss of Load Cost. However, parameters as equipment failure rate and repair time are subject to random variations due to limited or nonexistent operating histories, aging and statistical errors. This paper proposes a technique for considering uncertainties on stochastic equipment data in power systems expansion planning. Based on the Monte Carlo Simulation, the proposed technique uses Interval Arithmetic as a method for calculating uncertainty through the theory of imprecise probabilities (P-Box). The application in a test system and a real transmission system allows observing the behavior of the reliability cost as well as the final cost of alternatives for expansion of these systems with the consideration of uncertainties along the expansion horizon.

中文翻译：

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考虑随机参数不确定性的复合电力系统可靠性评估


在电力系统中，可以通过扩展规划研究来减少因故障造成的中断的影响。虽然高投资会导致非常昂贵且更可靠的决策，但投资减少可能会导致系统不可靠。因此，经济性和可靠性约束相互矛盾，导致规划和运营阶段的决策困难。基于概率和随机过程的可靠性理论允许对设备的随机行为进行建模，以估计负载成本损失等性能指标。然而，由于有限或不存在的运行历史、老化和统计误差，设备故障率和维修时间等参数可能会发生随机变化。本文提出了一种在电力系统扩展规划中考虑随机设备数据不确定性的技术。基于蒙特卡罗模拟，所提出的技术使用区间算术作为通过不精确概率（P-Box）理论计算不确定性的方法。测试系统和实际传输系统中的应用允许观察可靠性成本的行为以及扩展这些系统的替代方案的最终成本，同时考虑扩展范围内的不确定性。