Search

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. • A compact time horizon compression (THC) method is proposed for CIES planning. • Long-term energy storage is planned and investigated for seasonal energy balance. • Robust optimization is used for robust CIES designs with a budget uncertainty set. • The results indicate that THC reduces 42.77% solving time while keeping accuracy. • Applied conditions are analyzed and proposed for appropriate THC implementation. [ABSTRACT FROM AUTHOR]

Operational flexibility is to depict the ability of a power system in guaranteeing a secure and high-performance system operation, which is essential for distribution systems (DSs) with high renewable energy generation (REG) penetration. However, due to the dispersed integration of energy storage systems and uncertain REGs, the available regulation capability exhibits spatio-temporal coupling. It poses a challenge to quantify flexibility considering multiple locations and time periods. This paper proposes a novel analytical region-based method to characterize the flexibility considering spatio-temporal coupling, where the total dispatch budget and carbon emission constraints are considered to guarantee economic and environmental benefits. Leveraging the basic feasible solution theory, we derive an analytical formulation of the flexibility region, expressed as a set of linear inequalities. Note that no inner or outer approximation techniques are employed in the deduction procedure. The effectiveness and scalability of the proposed method are validated on test systems. Case studies demonstrate that the proposed method enables precise quantification and visualization of operational flexibility for DSs. The derived flexibility region is a closed polyhedron in a high-dimensional space, signifying the maximum ranges of uncertainties that can be accommodated. • A region-based method is developed for quantification and visualization of operational flexibility in distribution systems. • An analytical expression of the flexibility region under spatio-temporal coupling is derived from the dispatch model. • Total dispatch budget and carbon emission constraints are considered in flexibility quantification. [ABSTRACT FROM AUTHOR]