Severity‐Based Reliability Assessment of Energy Communities

We develop a probabilistic, severity‐based reliability framework for assessing the mismatch between renewable generation and power demand in an energy community. Building on a semi‐Markov model of the wind‐speed residual, we replace the binary working/failure description of the mismatch by a multilevel severity (multistate) classification of the uncovered demand, which induces a time‐varying partition of the state space driven by the periodic demand and seasonal‐mean profiles. Within this setting, we recall the semi‐Markov reliability machinery (availability, reliability, first‐passage/mean time to an unsafe condition) in threshold form, and introduce a set of severity‐ and magnitude‐aware indicators that the binary description cannot provide: the probability of each severity level, an Expected Severity Index, the Expected Uncovered Demand by severity level, the expected occupation time in unsafe levels, and a reliability‐constrained community‐design formulation. For long horizons, we obtain a cyclo‐stationary severity law that summarizes the average daily safety profile. The framework is illustrated through a data‐informed synthetic energy‐community case study for Los Angeles County (California), evaluated under two wind‐siting scenarios—an on‐site turbine and a nearby ridge wind farm—with a farm‐aggregate power curve. An exploratory battery analysis based on a simple rule‐based dispatch strategy is also presented, illustrating how the framework can support risk‐informed operation, preliminary storage assessment and resilience analysis of energy communities.