Efficient planning and optimization of integrated energy system considering double uncertainty of source and load

The design process of integrated energy system will face the uncertainty of renewable energy generation and energy demand, and the risk of sub-optimal decision will be introduced when the deterministic method is used for design. In this paper, an efficient planning and optimization model for integrated energy systems considering dual source-load uncertainty was proposed. A large set of primitive stochastic scenarios was first generated using Monte Carlo simulation, and the primitive stochastic yearly scenarios were doubly reduced to typical years and typical days in order to ensure that the stochastic planning model was solvable, taking into account the uncertainty of the boundary conditions. Secondly, to confirm the validity of the scenarios obtained based on the reduced scenarios, this paper tests the validity of the stochastic planning model by substituting the stochastic planning scenarios into the original set of stochastic scenarios for production simulation using an integrated energy system in an industrial park as a case study. The results show that the typical day boundary fully considers the uncertainty of the original design boundary, and can ensure the solution of the stochastic programming model. Compared with the deterministic optimization model, the stochastic optimization model produces a more conservative scheme with less allocation of renewable energy and more dependence on conventional energy and power grid. At the same time, the results of production simulation show that the stochastic programming method has more advantages in economy, and the equal annual cost of the planning scheme is 2.02% lower than that of the deterministic planning scheme.