Day-Ahead Optimal Scheduling of Integrated Energy System Based on Type-II Fuzzy Interval Chance-Constrained Programming

Renewable energy sources (RES) generation has huge environmental and social benefits, as a clean source with great potential. However, the difference in uncertainty characteristics of RES electric–thermal loads poses significant challenge to optimal schedule an integrated system (IES). Therefore, for different multiple uncertainties IES, this paper proposes type-II fuzzy interval chance-constrained programming (T2FICCP)-based optimization model solve above problem. In model, sets are used describe numbers load uncertainty, thus constructing T2FICCP-based IES day-ahead economic scheduling model. The was resolved hybrid algorithm based on linear T2FICCP. simulations conducted total 20 randomly selected days obtain advance operation plan each unit cost system. research results show that T2FICCP less dependence output power forecasting error, so can effectively improve economy while ensuring safe stable