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Abstract Integrated energy systems (IES) face increasing challenges in achieving low-carbon and economically efficient operation because of the strong inter-temporal coupling of long-duration energy storage and the limited coordination of carbon and oxygen utilization. However, the coordinated integration of long-duration liquid CO₂ energy storage (LCES) scheduling with carbon–oxygen recycling has received limited attention.
Abstract To enhance the accuracy of day-ahead photovoltaic (PV) power forecasting and overcome the limited adaptability of individual models under varying weather conditions, this paper proposes a scenario-aware dynamic model selection method based on an actor-critic contextual policy learning approach.
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