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Power system unreliability tracing model allocates the system's reliability index to individual components, identifying potential weaknesses. This study expands its scope by considering the impact of storage resources. Unreliable factors leading to load shedding are categorized into two groups: objective factors inherent to the component and insufficient storage resources. The latter requires a retrospective analysis of other components that caused unreliability previously. When allocating responsibility for load shedding at a certain time, it begins by allocating it among components based on differences between fixed expected output and actual supply. Expected output insufficiency is considered as the unreliable factor. This insufficiency due to insufficient storage resources is then decomposed into segments, each caused by excessive output in earlier instances of the same component. The expected output excess is attributed to the expected output insufficiency of other components in previous times, for which responsibility has been allocated to each component. Consequently, the expected output insufficiency at a particular time can be traced back based on a temporal recursive model, with the load shedding further allocated to components before that time. Case studies based on several systems demonstrate that the proposed model's allocation results are reasonable and more accurate than the traditional model. [ABSTRACT FROM AUTHOR]

The increase in wind power penetration leads to the risk of energy supply interruption in integrated electricity‐heat energy systems (IHES). This paper presents an analytical‐based approach for the efficient operational reliability evaluation of the IHES. The wind power distribution characteristic model considering correlation is constructed based on Hidden Markov Model (HMM). To reduce computational complexity, a critical system state identification (CSSI) method is proposed that characterises both random device failures and wind power uncertainty. Under each critical system state, the analytical function relationship between the reliability index and the uncertainty factors is established based on the virtual stochastic response surface (VSRS) method. The analytical function can directly calculate the reliability index when the wind power output varies, avoiding a large number of repeated calculations of the optimal load‐shedding model of the system and reducing the time required for reliability assessment. This allows the operator to evaluate the operational reliability of the integrated energy system in real‐time. The numerical simulation of the test system combining the IEEE 33‐node distribution system and the existing 28‐node heating system proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Power outages in urban area carry heavy social and economic costs. Although social cost, especially public sentiment, is concerned by engineers and managers, it has been only qualitatively investigated without a rigorous model in the state‐of‐the‐art research and practice of service restoration (SR) for a long time. To fill this gap, this paper investigates a hybrid model which takes public sentiment into consideration by quantifying public sentiment triggered by power outage. Furthermore, conventional SR method focused on the optimization model with ideal conditions, which leaves a large room for improvement in complex environment. To improve the robustness of the model, the authors propose a reinforcement learning framework to analyze emergency management process without prior rules. At each time step, the optimal decision can be made automatically by a learned model. The numerical simulations with modified IEEE 33‐bus and IEEE 123‐bus systems demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Contingency screening is vital for maintaining the transmission system reliability. Bilevel programming is a powerful tool for the accurate identification of critical events. To accelerate the contingency screening with bilevel programming, this study proposes a root-event-based dynamic space pruning algorithm. A root event is an event in which all the failed components have contributions to the event impact. Considering the fact that numerous N − k events describe the same critical scenario, the proposed space pruning principles partition the N − k event space into the subspace of root events and the complement subspace. To accelerate the screening of subspace of root events, bilevel programming is constructed to identify new critical events having high risk and causing more severe load shedding than the root event. Besides, relaxed bilevel programming is proposed to accelerate the screening of the complement subspace. The relaxed scheme replaces the constraints of power flow by constraints of transmission capacity, and enables the identification of most of the critical events in the complement subspace with a low computational cost. Case studies on the IEEE RTS and a provincial grid in China indicate that the proposed algorithm significantly accelerates the screening process without missing critical events. [ABSTRACT FROM AUTHOR]

Reliability evaluation of bulk power systems (BPSs) has inherent computational complexity due to the numerous system states and the time‐consuming system state analysis, including power flow calculation, load curtailment, recognition of split power systems and network reconfiguration. In this study, a novel uniform‐design based method is proposed to improve the computational efficiency of power system reliability evaluation. The main idea is that the uniform‐design technique is used to generate the system states in reliability evaluation, which makes the sampled states more uniform and representative in the overall state space compared to the enumerated system states in an analytical method or the random‐generated system states in Monte Carlo simulation. As a result, the sample size and the computational time can be significantly reduced. In addition, the confidence intervals of the reliability indices, such as LOLP, FLOL and EENS, are given. And the estimation errors of reliability indices are discussed. The proposed method is tested on several BPSs, including the IEEE‐RTS79, IEEE‐RTS96 and a real BPS in China. All the case studies indicate that the proposed technique can significantly improve the computational efficiency of BPS reliability evaluation. [ABSTRACT FROM AUTHOR]