Multi-timescale optimization scheduling of integrated energy systems oriented towards generalized energy storage services

Abstract

Abstract This paper addresses the limitations of existing research that focuses on single-sided resources and two-timescale optimization, overlooking the coordinated response of various energy storage resources across different timescales in comprehensive energy systems. To tackle these shortcomings, the study integrates flexible demand-side resources, such as electric vehicles (EVs), hydrogen storage, and air conditioning clusters, as generalized energy storage. It explores their impact on the operation cost of the comprehensive energy system across three stages: day-ahead, intraday, and real-time. The paper establishes an optimization scheduling model for mobile energy storage, hydrogen storage, and virtual energy storage of air conditioning clusters, considering the physical and temporal constraints of different storage devices, aiming to minimize the operational cost. The day-ahead stage employs C&CG to address the uncertainty of wind and photovoltaic power generations, while the intraday stage synergizes hydrogen storage, gas turbines, and demand-side substitutable and transferable loads to mitigate renewable energy fluctuations. The real-time stage leverages the virtual energy storage model of air conditioning clusters for rapid response to renewable energy deviations. Case studies validate the effectiveness of the model, demonstrating that multi-timescale optimization of generalized energy storage in comprehensive energy systems can significantly reduce operational costs and enhance system reliability.