As the proportion of renewable energy in power system continues to increase, that power system will face the risk of a multi-time-scale supply and demand imbalance. The rational planning of energy storage facilities can achieve a dynamic time–delay balance between power system supply and demand. Based on this, and in order to realize the location and capacity optimization determination of multiple types of energy storage in power system, this paper proposes a collaborative optimization planning framework for multiple types of energy storage. The proposed planning framework is modelled as a two-stage MILP model based on scenarios via the stochastic optimization method. In the first stage, investment decisions are made for two types of energy storage: battery energy storage (short term) and hydrogen energy storage (long term). In the second stage, power system operation simulation is conducted based on typical scenarios. Finally, the progressive hedging (PH) algorithm is applied to realize the efficient solving of the proposed model. A modified IEEE 39-bus test system is used to verify the validity of the proposed multiple types of energy storage collaborative optimization planning model and PH algorithm.

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the “dual carbon” objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in southern China over recent years.

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the "dual carbon" objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in China over recent years.