Triple-layer joint optimization of capacity and operation for integrated energy systems by coordination on multiple timescales

The introduction of energy conversion and storage devices, such as power-to-gas (P2G) and seasonal energy storage, can realize the multi-timescale electricity, heat and natural gas complementarity for integrated energy systems (IESs), promoting renewable energy consumption. However, this brings a large number of decision variables, strong coupling, and nonlinearity, increasing the complexity and difficulty of the optimal design for IESs. Therefore, this study proposes a generic triple-layer optimal design framework for IESs via coordination on multiple timescales. The proposed framework decomposes the optimal design of IESs into long-, medium-, and short-timescale optimization layers that are tractable. The long-timescale optimization layer realizes the seasonal source–load matching by incorporating a refined electricity–gas complementarity model; medium-timescale optimization layer utilizes the inter-day energy transfer to deal with extreme loads caused by rare weather conditions; and short-timescale optimization layer aims to realize real-time energy balances and gain economic benefits by intraday peak shaving. Each layer not only optimizes related capacity and operation variables jointly for its own timescale but also exchanges coupling information with adjacent layers via designed interactive iteration mechanisms. The implementation of the proposed framework is further investigated in the design of an electricity–gas–heating–cooling coupled IES. Finally, a case study is performed to verify the feasibility and effectiveness of the proposed optimal design method. Simulation results show that the proposed method realizes source–load matching across multiple timescales, reducing the annualized total cost of the IES by 2.4 % and 3.3 % compared to the conventional single-timescale optimal design method under the grid connection and off-grid modes, respectively. The renewable energy consumption and device utilization rate of the optimized IES exhibit obvious improvements. In particular, the wind energy consumption of the IES increases by 15 times after introducing P2G and seasonal energy storage. Furthermore, sensitivity analyses indicate that the natural gas price considerably affects the optimization of the capacity configuration and economic performance of the IES, far exceeding that of the electricity price.