Two-Layer Optimization Method for Multi-energy Storage Capacity Configuration of High-Rise Office Buildings

The energy storage system enables the translation of the building’s electrical load over time, providing the system with increased flexibility in controlling the flow of energy. This study presents a two-layer collaborative optimization approach for high-rise office buildings that do not employ renewable energy. It takes into account both the equipment configuration and the operating parameters of the energy storage system, aiming to reduce the similarity detection rate. Taking a high-rise office located in a region with hot summers and warm winters as an example, the upper layer employs a multi-objective particle swarm optimization algorithm (MOPSO) to determine the capacity configuration of the energy storage system. This algorithm takes into account the initial investment and operating costs throughout the system’s life cycle. The lower layer utilizes mixed integer linear programming (MILP) to establish the operation strategy corresponding to the obtained capacity, and then feeds back to the upper layer. Ultimately, the optimal capacity configuration solution for the energy storage system is achieved, combining the TOPSIS method to facilitate decision-making and reduce complexity.