Abstract：The technical reformation of self-provided combined heat and power (CHP) units by park enterprises can significantly improve their fast regulation capability. However, the dynamic process of the CHP unit is not finely modeled in the scheduling process, which makes it difficult for the scheduling scheme to match the operation state of CHP units. This paper proposed a combined electrical and heating system (CEHS) dynamic optimal scheduling method considering the state operation constraints of the park's self-provided CHP units. In the park-type CEHS, the enterprise's self-provided CHP units are subject to the unified dispatching of the power grid, which supplies the enterprise's own power and part of the power for production and living in the radiation area. The shortage of power supply is met by the grid dispatching wind power and conventional thermal power units. The heat source of CHP unit exchanges heat with the first heating station to supply the internal heat load of industrial enterprises. Firstly, considering the characteristics of electric energy, flow, pressure and other state variables under the rapid regulation of CHP unit, the dynamic constraints of the unit in the form of differential algebraic equations (DAEs) are established. Secondly, a CEHS dynamic optimization scheduling model considering the state operation constraints of the self-provided CHP units in the park is constructed. Aiming at the problem that CEHS dynamic optimization with DAEs constraints is difficult to solve, a dynamic adaptive particle swarm optimization (DAPSO)-radial basis quasi sequential bi-level optimization strategy is proposed. The outer layer optimizes the CEHS decision variables with the objective of minimizing the CEHS operation cost. The DAPSO algorithm is used to iteratively optimize the scheduling scheme to obtain the output of each unit. The electric and thermal output values of CHP units are taken as the setting values for dynamic optimization in the inner layer. The inner layer aims at minimizing the performance index of the control process of the CHP units. By guiding the output variables to approach the desired set value, the unit control process converges and the control variables are smoothed as much as possible. For the dynamic optimization problem of the optimal control of the inner unit, the radial basis function (RBF) format is used to discretize the variables, and the discretized nonlinear programming problem is solved by the quasi sequential method. Finally, the correctness and effectiveness of the proposed method were verified based on the improved IEEE30 node system. The simulation results show that the wind power consumption capacity of the system can be improved after fully considering the control characteristics of CHP units. The flexibility of CHP unit modification provides the possibility for enterprise self-provided power plants to realize auxiliary services to the grid. The data show that the bi-level solution strategy of outer DAPSO optimal scheduling + inner quasi sequential method (RBF discrete format) dynamic optimization is a good balance of computational efficiency and solution accuracy. The optimal scheduling of CHP unit dynamic modeling has obvious advantages in system economy and unit safety.
黄悦华, 陈庆, 张磊, 叶婧, 卢天林. 考虑园区自备热电联产机组运行约束的电热耦合系统动态优化调度[J]. 电工技术学报, 2023, 38(16): 4433-4447.
Huang Yuehua, Chen Qing, Zhang Lei, Ye Jing, Lu Tianlin. Dynamic Optimal Scheduling of Combined Electrical and Heat System Considering State Operation Constraints of CHP Units in the Park. Transactions of China Electrotechnical Society, 2023, 38(16): 4433-4447.
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2023, Vol. 38 
