Interval optimization based operational strategy of integrated energy system under renewable energy resources and loads uncertainties

Summary Uncertainties from renewable energy resources (RESs) and energy demands have brought enormous challenges to the optimal operation of integrated energy system (IES). An interval optimization based operational strategy for IES is proposed to overcome uncertainties. Firstly, embarking from a deterministic IES operation model, an interval method is presented to quantify the uncertainties instead of possibility distribution so as to better characterize the impact of RESs and loads on the operation of the IES. Secondly, the interval optimization model under multiple uncertainties is presented. In the proposed model, the total daily cost is optimized and system operation constraints are fully considered. Thirdly, the order interval relation and possibility degree are adopted to transform the interval model to deterministic model, which is solved by CPLEX optimizer. Finally, case studies considering influence of different uncertainty objects and uncertainty possibility degree levels are performed and analyzed extensively. The simulation results show that the optimized interval numbers will be increased gradually as uncertainty fluctuation degree increased from ±5% to ±25%. Comparing with automatic robust convex optimization method, the robust optimized values are in accordance with the upper values of optimized interval number optimization method, and the midpoints of interval results optimized by interval method are 4.1%, 8.7%, 11.7%, 16.5%, and 8.0% less than robust optimization results, respectively. An interval optimization operation model for integrated energy systems considering uncertainties arising from various energy demands and renewable energy resources is proposed. To solve the interval optimization model, the interval optimization model is transformed into deterministic model by the order interval relation and interval possibility degree. Case studies considering influence of different uncertainty objects and uncertainty possibility degree levels are performed and analyzed extensively.