A Hybrid Stochastic/Robust Planning Model for Integrated Energy System Considering Multiple Uncertainties

Complex energy flows exist in integrated energy systems (IESs), and the optimal assignment of different energy sources has become a primary challenge in the initial stage of its construction. In addition, multiple uncertainties, such as wind power fluctuations and multiple energy load fluctuations, are not well-respected in IES planning. In order to solve the above problems, the generation expansion planning model in IES is proposed. In the model the uncertainties of renewable energy resources (RESs) are described by scenarios, while the power price and fuel price are described by intervals, and a stochastic/robust (S/R) optimization is established to minimize the sum of annual investment cost, operation cost and reliability cost. Based on duality theory, the proposed multi-level S/R optimization model is transformed into a mixed integer linear programming (MILP) model, which can be solved using the CPLEX solver. The proposed model is tested on the IEEE 33-node distribution system with an 11-node cold-heat system. The validity and effectiveness of the proposed model are illustrated by case studies.