Affiliations: [a] State Grid Hebei Electric Power Company Shijiazhuang Power Supply Company, Shijiazhuang, Hebei, China
| [b] Shanghai Investigation, Design & Research Institute Co., Ltd, Shanghai, China
Correspondence:
[*]
Corresponding author. Haotian Wu, Shanghai Investigation, Design & Research Institute Co., Ltd, Shanghai 200335, China. E-mail: [email protected].
Note: [#] Co-Corresponding Author: Jie Zhao, State Grid Hebei Electric Power Company Shijiazhuang Power Supply Company, Shijiazhuang 050000, China. E-mail: [email protected].
Abstract: To effectively enhance the safety, stability, and economic operation capability of DC microgrids, an optimized control strategy for DC microgrid hybrid energy storage system (HESS)(The abbreviation table is shown in Table 2) based on model predictive control theory is proposed. Based on the characteristics of supercapacitors and batteries, system safety requirements, and various constraints, a predictive model for a hybrid energy storage DC microgrid is established. By defining its optimization indicators, designing an energy optimization management strategy, and transforming it into a quadratic programming problem for solution, the reasonable scheduling of power in the DC microgrid has been achieved. In addition, a power control method was proposed for the system without constraints. The simulation experiment results show that at the initial sampling time, the system operates normally, and the MPC algorithm allocates two types of energy storage devices to discharge to meet the net load demand, without absorbing electricity from the external network. At the 30th sampling point, the net load increases, and the MPC controller obtains the optimal solution of the control problem based on the known net load prediction data at the previous sampling time. It outputs the operating reference values of each output unit at the next time. Starting from the 100th to 199th sampling points, SOCUC falls below the lower limit of the safety interval, and the system enters situation 4 mode. The external network output assists the battery in working. At the 131st sampling point, the net load decreases, the system enters Situation 3 mode, and the battery operates independently. Until the 179th point, SOCB was also below the lower limit of its safety interval, and the system entered situation 5 mode, completely maintaining system power balance by external network power. Starting from point 201, the net load becomes negative, and the system charges the HESS according to instructions and stops the external power grid energy transmission. Conclusion: The feasibility and effectiveness of the proposed optimization management strategy have been verified.
Keywords: DC microgrid, model predictive control, mixed energy storage, objective function, secondary planning
