TimeGAN-Based Data-Driven Scheduling for Shared Energy Storage Systems in Smart Buildings

Shared energy storage can effectively address the issues of low utilization and high costs caused by individual energy storage configurations by regulating resources across multiple regions. To further exploit the potential of shared energy storage in demand-side resources, this paper introduces electric vehicles and ice storage air conditioning, both with flexible energy storage characteristics, to construct a generalized shared energy storage model for the coordinated optimization of energy usage in smart building clusters. In response to the uncertainty of photovoltaic (PV) output on the energy input side, a time generative adversarial networks (TimeGAN) is employed to simulate a large number of PV output scenarios. By combining daily irradiance data, the static and dynamic features of these scenarios are mined, and typical scenarios are identified using K-medoids clustering. Additionally, a tiered carbon trading mechanism is introduced to limit the carbon emissions of the energy system. An optimization scheduling model for smart buildings is established, considering operational costs, carbon emissions, and user comfort, and is solved using CPLEX. Case studies demonstrate that the proposed method can generate high-quality PV output scenarios, improve regional PV consumption rates, and effectively balance user comfort and costs.