Optimization of Integrated Energy System Considering Photovoltaic Uncertainty and Multi-Energy Network

The access of photovoltaics can reduce the carbon emissions of the integrated energy system and can also improve the economics of terminal energy supply, but the uncertainty of photovoltaic output also brings greater challenges to the optimal operation of the system. This paper focuses on coordinated optimization for the multiple energy systems in consideration of demand response. Latin hypercube sampling and the K-means algorithm are used to generate acceptable scenarios to deal with the photovoltaic uncertainty. Demand response based on Time-of-Use (TOU) electricity price is employed to realize the peak load shifting, and in consequence to improve the system operation. The optimization objective is to minimize the operational cost, subject to the constraints of electric grids, natural gas grids, and hot water pipeline grids. Due to the nonconvex constraints of these grids, the constraints are relaxed by means of the mixed integer linear programming approach, and the whole problem is established as a mixed integer linear programming model. Case studies show that demand response in each energy system and the coordinated optimization between the multiple energy systems can reduce the operational cost of the whole system. Even though the photovoltaic uncertainty results in a higher operational cost, the system has a more reliable operating point.