A compact time horizon compression method for planning community integrated energy systems with long-term energy storage

Long-term energy storage (LTES), such as hydrogen storage, has attracted significant attention due to its outstanding performance in storing energy over extended durations and seasonal balancing of power generation and consumption. However, planning for LTES usually necessitates the comprehensive coverage of its whole operation cycle, spanning from days to months, making the issue complex and intractable. To simplify the planning of a community integrated energy system (CIES) with LTES, this study proposes a time horizon compression (THC) method and formulates a concise long-term planning model for CIES with compressed time horizons. Then, robust optimization method with a budget uncertainty set is employed to develop a robust THC model, aimed at addressing data uncertainties in CIES planning. The proposed robust THC model is implemented in the planning of a CIES with high penetration of renewable energy sources, with the objective of minimizing the total annual cost. The results demonstrate that the proposed model can efficiently solve the complex CIES planning problem, resulting in a 42.77% acceleration in optimization speed. Additionally, the diversity and differentiation in THC configurations is investigated to enhance the implementation of THC in long-term CIES planning. The effectiveness of solution robustness and the significant effects of LTES on CIES are analyzed and validated in the case study.