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.
With the increasing demand of energy and the growing intensity of energy crisis, various smart energy systems are developed on the distribution level, such as integrated energy system (IES). In this article, a trading mechanism among various energy retailers and consumers is designed under the open market environment. The trading problem is formulated as a multi‐leader and multi‐follower Stackelberg game, which is built with the strategy set of energy bidding price and purchasing pattern. In particular, consumer's demand is described with the price elasticity property of demand, and non‐cooperative competition behavior among retailers is analyzed mathematically. To implement the proposed game approach, a distributed algorithm is presented by combining particle swarm optimization (PSO) and interior point method (IPM). Finally, simulation results show that the proposed trading mechanism will contribute to the development of heterogeneous energy market and the overall profit of retailers in the traditional trading mechanism is 37.8% lower than the profit in the proposed mechanism.
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