An efficient local multi‐energy systems planning method with long‐term storage

Abstract

Long‐term storage will play a crucial role in future local multi‐energy systems (MES) with high penetration renewable energy integration for demand balancing. Local MES planning with long‐term energy storage is essentially a very large‐scale program because numerous decision variables, including binary variables, should be used to model long‐term energy dependencies for accurate operational cost estimation. How to largely reduce decision variables as well as guarantee the planning model accuracy becomes one main concern. To this end, this paper proposes a novel efficient aggregation and modeling method for local MES planning. The aggregation method first decomposes input time series data (renewable energy output and energy demand) into hourly and daily components, based on which more accurate aggregation results with a few typical scenarios can be derived. By incorporating similar decomposition into the operation model of energy devices, the planning model can describe the long‐term energy cycle and the hourly operation characteristic at the same time and yield accurate optimization results with limited complexity. Experimental results show that the proposed method can considerably decrease the complexity of the problem while maintaining agreement with the results based on the optimization of the full‐time series.

Guest Editorial: Special issue on computational methods and artificial intelligence applications in low‐carbon energy systems

“…1) The paper entitled "An Efficient Local Multi-Energy Systems Planning Method with Long-term Storage" proposes a framework for optimal configuration planning of a multienergy system with long-term storage that incorporates time series seasonal-trend decomposition into time series aggregation [10].…”

“…On the multi‐energy system dispatch front: 1)The paper entitled “An Efficient Local Multi‐Energy Systems Planning Method with Long‐term Storage” proposes a framework for optimal configuration planning of a multi‐energy system with long‐term storage that incorporates time series seasonal‐trend decomposition into time series aggregation [10]. 2)The paper entitled “A Data‐Driven Scheduling Approach for Integrated Electricity‐Hydrogen System Based on Improved DDPG” proposes an improved deep reinforecement learning real‐time scheduling algorithm for an integrated hydropower‐photovoltaic‐hydrogen system to maximize system revenues from the cooperation of various natural resources [11].…”