Abstract In order to improve the integrated energy system's ability to absorb new energy and explore the relationship between different optimization objectives. In this paper, the scheduling problem of the integrated electric-heat system with virtual energy storage is decomposed into the standby formulation problem and the new energy consumption optimal operation problem considering the opportunity constraints, and a multi-objective optimization model of the integrated electric-heat system is constructed, which simultaneously takes into account the economy, low carbon and comprehensive energy efficiency improvement. A bilinear Benders distributed computing framework is proposed to improve the solving efficiency by introducing the approximate chance constraints of multiple scenarios and scenario indicator variables. The original problem is decomposed into the main problem and several sub-problems for iterative solving. The normal boundary crossing method is used to obtain Pareto frontier. Example analysis shows that compared with the centralized algorithm, the solution speed of the proposed algorithm is significantly improved. The model considering the constraints of new energy consumption opportunities can balance the economy and robustness of operation by adjusting the confidence degree during scheduling. Meanwhile, virtual energy storage can adjust the charging and discharging power of the heat supply network according to the output of the new energy department, further improving the absorbing capacity of new energy and reducing the operating cost. Finally, the influence of different optimization goals of economy, low carbon and energy efficiency is analyzed by solving the Pareto frontier. First, the integrated electric-heat system standby optimal scheduling model based on opportunity constraints is proposed. The model includes the start-stop and climb constraints of generator sets, CHP units and EB units, as well as the charge and discharge constraints of electric energy storage and capacity constraints. At the same time, the virtual energy storage characteristics of heat supply network and its mass flow regulation mode are considered, and new energy opportunity constraints are constructed to cope with system uncertainties, so as to obtain a day-ahead backup plan that can balance conservatism and economy. Secondly, the problem is divided into the standby formulation problem and the optimal operation problem, in which the chance constraint is transformed into bilinear form by scenarioization. In order to solve the problem that the charge and discharge constraints of electric energy storage cannot generate Benders cuts, the integer cycle comparison strategy in the bilinear Benders method is adopted to achieve the global optimal. At the same time, a parallel framework supporting distributed computing is proposed to improve the solving efficiency. Finally, from the perspective of the integrated electric-heat system optimal operation, this paper establishes a multi-objective optimization model that takes into account the economy, low carbon and comprehensive energy efficiency improvement of the integrated electric-heat system, and uses the normal boundary crossing method to solve it, and analyzes the restrictive relationship between the three objectives. Finally, a numerical example is given to verify the effectiveness of the proposed method.
Lin Yumian,Xiong Houbo,Zhang Xiaoyan等. Distributed Multi-Objective Optimal Scheduling of Integrated Electric-Heat System Considering Chance Constraint of New Energy and Virtual Storage[J]. Transactions of China Electrotechnical Society, 2024, 39(16): 5042-5059.
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2024, Vol. 39 
