The building energy system faces uncertainties from renewable energy power generation and energy demand, and the design using deterministic method will introduce the risk of suboptimal decisions. In this paper, a stochastic programming model is formulated for the building energy system planning problem under source and load uncertainties. Facing the computational burden caused by massive stochastic annual scenarios, a two-level scenario reduction method of typical annual scenario reduction and typical daily scenario reduction is proposed, which ensures the solvability of the stochastic programming model and takes into account the uncertainty of design boundary. To illustrate the model’s application, the design of an integrated energy system for an industrial park is investigated. The results show that the stochastic planning method can maximize the life-cycle economic benefit of the integrated energy system under uncertain design boundaries comparing with the deterministic planning method. In addition, the flexibility of the energy storage system can resist a certain degree of load forecasting deviation and improve energy supply reliability of the system.
Keywords integrated energy system, uncertainty, stochastic optimization, scenario reduction