Abstract As power systems transition towards decarbonised energy mixes with increasing wind penetration, prolonged low‐wind‐power (LWP) events have emerged as a critical source of adequacy risk. However, a comprehensive analysis of LWP events remains constrained by the scarcity of long‐term historical wind‐power generation records in regions with high renewable penetration. To address this limitation, this study proposes a TabNet‐based wind power modelling framework for 90 wind farms in Guangxi, China, leveraging historical meteorological records and measured operational data. ERA5 reanalysis data are used to reconstruct hourly wind‐power generation over a 43‐year period (1980–2022). An adaptive mean‐below‐threshold method featuring a tolerance mechanism is proposed to identify LWP events over the 43‐year period. Based on this, the intra‐annual and inter‐annual characteristics of LWP events are analysed, and their relationship with the El Niño–Southern Oscillation and the Eastern Pacific (EP) index is investigated. The results indicate that both the annual duration and frequency of LWP events exhibit upward trends over the study period, with the upward trends becoming more pronounced after 2000. Furthermore, the EP index demonstrates a lagged association with LWP duration and frequency in Guangxi, with decaying La Niña years exhibiting the clearest positive anomalies in LWP duration and frequency among the investigated ENSO categories. These findings offer climate‐informed insights for wind power adequacy assessment and renewable integration planning.
Abstract Photovoltaic‐storage‐charging microgrids (PSCMs) are being increasingly deployed in extreme environments, including desert, polar, coastal and high‐altitude regions, to provide a reliable and sustainable power supply. However, these harsh environments significantly accelerate the degradation of photovoltaic arrays, battery storage systems and electric vehicle charging infrastructures. Effectively managing PSCMs under these conditions requires energy management systems with control strategies that explicitly account for environment‐dependent degradation dynamics. This paper provides a comprehensive review of degradation‐aware energy management for PSCMs operating in extreme environments. First, extreme operating environments are classified using quantitative thresholds, and their specific degradation effects on PSCM components are systematically analysed. Next, degradation modelling approaches comprising analytical models, data‐driven approximations and physics‐informed enhancements are reviewed and compared. Current energy management strategies, ranging from model‐based methods to data‐driven reinforcement learning and hybrid architectures, are then examined. Finally, future research directions are outlined, focusing on generalizable multi‐stress degradation models, principled degradation‐aware control frameworks and comprehensive datasets and digital twin testbeds.
Abstract Weather‐dependent renewable energy makes modern power systems more sensitive to extreme weather. Therefore, reliable identification of power system‐oriented extreme weather scenarios is important for reliability assessment, resilience analysis, and risk‐oriented operation. However, these scenarios are heterogeneous and suffer from severe class imbalance, which makes reproducible labelling and robust model training difficult. This paper proposes a hybrid classical‐quantum learning framework for power system‐oriented extreme weather scenario identification. First, a structured definition and labelling framework is developed to extract extreme weather scenarios from large‐scale weather‐power datasets by jointly considering meteorological abnormality, power system response, and external event evidence. Second, a quantum generative module is introduced as a training‐only augmentation tool to synthesize minority‐class samples, while validation and testing remain strictly based on real observations. Third, a hybrid classical‐quantum classifier is constructed by embedding variational quantum circuits as trainable feature‐mapping modules within a classical learning pipeline, aiming to examine their feasibility under small‐sample and imbalanced‐data conditions. In addition to conventional generation and classification metrics, risk‐oriented indicators are used to evaluate whether the identified scenarios are meaningful for power system operation. The proposed framework is validated using real‐world weather‐power data from China. The results show that the quantum generative adversarial network preserves minority‐class risk characteristics, while convolutional neural network‐quantum recurrent neural network (CNN‐QRNN) attains the numerically highest minority macro‐F1 of 0.2998 among the compared models, with a macro‐F1 of 0.3972.
Abstract Modern power systems require increasingly flexible and responsive power conversion, driving the need for power converters with enhanced controllability and adaptability, where artificial intelligence (AI) plays a key role in enabling intelligent decision making, adaptive control and system level optimization. However, current AI applications in power systems remain largely restricted to cloud‐based post processing or device‐level demonstrations, lacking end‐to‐end hardware platforms that support the full pipeline from programming to deployment within a closed loop formed by physical converter hardware and AI modules. To address this gap, this paper proposes an edge AI‐powered power converter (EAI‐PC) hardware platform that utilizes locally deployed edge intelligence to establish bidirectional data exchange between converter low‐level control and EAI upper‐layer command dispatch. The platform features an end‐to‐end AI‐to‐converter closed‐loop deployment framework, enabling AI‐generated commands to be directly executed on physical converter hardware. By interconnecting converters (AC/DC and DC/DC) with an EAI module (using NVIDIA Jetson Orin Nano) via an industrial CAN protocol, this platform supports hardware‐level algorithm verification in the power system domain. The proposed EAI‐PC platform is experimentally evaluated across system configurations from single to multiple interconnected converters, assessing converter performance, command delivery and three AI deployment cases (two offline and one online): a multilayer perceptron (MLP) for power‐sharing prediction, a spatio‐temporal graph neural network (STGNN) for photovoltaic (PV) and load forecasting, and a bidirectional long short‐term memory (Bi‐LSTM) for online battery dispatch in load management and peak shaving.
Abstract Renewable power purchase agreements (PPAs) expose generators and offtakers to capture‐price risk because realised revenue depends on prices during renewable production hours. Downside protection written on the production‐weighted settlement price is economically relevant, but difficult to mark and hedge because the index is non‐tradable and depends jointly on power prices and renewable output. This article develops a futures‐anchored statistical mark‐to‐model and hedging framework for long‐dated Asian floors embedded in renewable PPAs when renewable generation remains stochastic. The framework is not interpreted as a unique arbitrage‐free price in a complete market. Instead, it isolates the hedgeable component through liquid German baseload futures and updates the remaining price‐generation exposure using deterministic seasonal profiles and a vector autoregression for deseasonalised German day‐ahead price and wind‐generation residuals. Moment matching and a delta‐method approximation yield a tractable Bachelier‐style floor mark and operational futures hedge ratios. In pre‐delivery backtests for the 2024 and 2025 delivery years, the stochastic hedge reduces daily residual variance by 94.7% and 89.0%, respectively compared with 69.1% and 47.9% for a deterministic‐generation hedge. The results show that stochastic renewable production is a first‐order input for practical risk management of renewable PPA optionality.
