Planning Microgrids in Industrial Parks Considering the Uncertainty of Carbon Price and Renewable Energy: A Stochastic Approach

The concerns about limiting carbon emissions and controlling global warming promote the decarbonization of the energy sector. The microgrid can integrate different energy sources and increase renewable penetration by balancing the local supplies and demands flexibly. The introduction of the carbon pricing mechanism further impels the energy transition from fossil-fuel generation to renewable generation. However, the uncertainty of renewable outputs and carbon pricing challenges microgrid planning. In this paper, we propose a stochastic optimization model for planning microgrids in industrial parks. It includes three parts: the retirement of existing fossil fuel generators, the deployment of renewable generators, and the installation of energy storage systems. Typical scenarios are generated and reduced to simulate the fluctuations of renewable outputs and carbon price. An adaptive genetic algorithm is adopted to solve the proposed planning model. The case study result verifies the effectiveness of the proposed model and indicates that the proposed model can provide reasonable microgrid planning strategies.