Multi-objective station-network synergy planning for regional integrated energy system considering energy cascade utilization and uncertainty

Regional integrated energy system (RIES) is conductive to integrating distributed renewable energy and represent a crucial form for constructing low-carbon energy systems in future cities. Addressing key issues in RIES planning such as station-network synergy optimization, uncertainty optimization, and multi-objective optimization, this paper proposes a synergy planning framework for RIES considering energy cascading utilization and uncertainty. Firstly, a dual-layer synergy planning model is established. In the upper layer, it resolves the station-network layout optimization problem considering user distribution and load complementarity. An improved P-median model is introduced to optimize the site selection and network layout of energy stations. In the lower layer, a two-stage stochastic programming model is proposed to address the configuration optimization problem for energy stations considering uncertainty. To enhance the energy utilization efficiency of RIES, exergy efficiency is incorporated into the multi-objective optimization model. A combination of ε-constrant methods and the TOPSIS method is employed for multi-objective solving and decision-making. The proposed models and methods are validated through a case study. The analysis of seven comparative experiments indicates that there is an optimal number of energy stations for the overall economic efficiency in RIES station-network layout optimization. Compared to single-objective optimization, multi-objective optimization considering both economic and exergy efficiency has advantages in improving energy utilization efficiency and reducing carbon emissions. When the load complementarity between multiple regions is higher, the interconnection gains between energy stations are greater. The research framework proposed in this study can provide essential references for designers of urban RIES.