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.

Integrated energy systems (IESs) have attracted increasing attention in recent years due to the high energy efficiency and low emission of carbon dioxide. To deal with the limitation of single IES, distributed energy networks consisting of multi-IESs are proposed to improve the complementarity of both the energy supply and the demand. This paper mainly focuses on the day-ahead energy management of the whole energy network for the economic operation of the system, following which, a cooperative game is formulated to determine the optimal strategy of each IES to minimize the coalition daily cost. Meanwhile, an allocation mechanism is designed from the perspective of probability to allocate coalition cost to each IES. According to the results of the numerical study, the proposed approach can improve the economic performance of both the energy network and the individual IES by interchanging electrical and thermal energies in the network.

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