Study on coupling optimization model of node enterprises for energy storage-involved photovoltaic value chain in China

In recent years, with continuous focus on clean energy and environmental protection, the scale of photovoltaic generation industry in China has been gradually expanded, making great achievements. However, it also faces huge challenges and problems such as fierce market competition, serious photovoltaic curtailment, high cost, etc. In order to promote the sustainable development of photovoltaic industry, this paper constructs an energy storage-involved photovoltaic value chain (ES-PVC) consisting of three nodes for upstream, midstream and downstream, in which photovoltaic power suppliers, battery energy storage business and electric vehicle manufacturers locate respectively. The coupling problem for node enterprises in the value chain is studied by multi-objective optimization and G1 method, which is measured eventually via a single value transformed by ideal point method. Considering the particularity of issue, the paper proposes an improved genetic algorithm (IGA) to fit and solve the model. A best value chain is acquired via proposed approach whose feasibility and applicability are verified through comparative analysis with analytic hierarchy process (AHP) and linear assignment method (LAM) and sensitivity analysis. The implications and limitations are presented in the conclusion, aiming to provide theoretical reference for value management of photovoltaic enterprise and to get further research. •Construction of energy storage-involved photovoltaic value chain (ES-PVC).•Research on the coupling relationship between node in value chain.•Genetic algorithm was improved to adapt proposed model.•The proposed approach is robust and reliability compared with the classical methods such as AHP and LAM.