Capacity Expansion Model for Multi-Temporal Energy Storage in Renewable Energy Base Considering Various Transmission Utilization Rates

To accelerate the energy transition, China is actively advancing the construction of large-scale renewable energy bases, typically adopting an integrated approach of wind power, photovoltaic (PV), thermal power, and energy storage to accomplish the targets of power transmission. However, the generation characteristics of renewable energy are distinctly seasonal, leading to mismatches with the load demand characteristics of the receiving end over long-term scales. Therefore, it is essential to consider diverse temporal energy storage in planning flexibility resources. This paper proposes a capacity expansion model for multi-temporal energy storage that considers both the output characteristics of renewable energy and the load demands of the receiving end. Incorporating time series decomposition with an adaptive clustering method, typical scenarios are extracted to solve the capacity expansion model, effectively balancing computational efficiency and solution accuracy. Through a case study in Northwest China, we demonstrate that with the escalation of transmission utilization hours, the demand for longterm energy storage capacity escalates proportionately. This correlation becomes strikingly evident when the transmission utilization hour reaches 6000 h. Moreover, coordinated planning for multi-temporal energy storage can lead to a reduction in comprehensive generation costs by 1 %-18 %.