As a high proportion of clean energy is connected to the power grid, the occupancy rate of the system synchronous machine decreases, the inertia constant of the system decreases, and the difficulty of frequency adjustment continues to increase. The imbalance of frequency is mainly caused by the imbalance of active power, so the problem of frequency can be transformed into the problem of active power balance. According to the droop control principle, the concept of the equivalent unit regulating power coefficient is proposed, and the equivalent unit regulating power is determined by determining the system parameters and frequency offset. In order to reduce the frequency regulation cost in the integrated energy system, a feasible method considering the frequency regulation cost is the proposed variable droop control active power economic optimization method. First, the integrated energy system in this study consists of carbon capture power plants, a wind turbine generator system (WTGS), a photovoltaic power generation system, and energy storage batteries. All four types of power supply leave spare capacity to participate in frequency regulation through droop control. Second, the concept of the equivalent unit regulating power coefficient (equivalent coefficient) and the mathematical model of the equivalent unit regulating power coefficient of the integrated energy system are put forward. Then, within the allowable range of frequency fluctuations, considering carbon trading and ancillary service markets and aiming at the lowest frequency regulation cost, an economical optimal distribution method is established for active power in an integrated energy system including carbon capture power plants, wind power, photovoltaic, and energy storage. Taking a city in the north as an example, the improved moth flame algorithm is used to solve the problem. The simulation results show that the proposed model can improve the frequency regulation characteristics and reduce the frequency regulation cost.
The analysis of load characteristics is the basis and premise of load actively participating in power grid regulation. This paper proposes a multi-factor load classification method considering the load of clean energy power generation, the rapidity of load classification, and various subjective and objective factors that may affect the behavior of load consumption. First, it describes the characteristic index of load consumption behavior and analyzes the subjective and objective factors that affect the power grid consumption behavior. The effect of clean energy generation on load side is considered. Based on the load characteristics, the K-means algorithm is used for main clustering. Then, the confidence level of the uncertainty of the actual load adjustable capacity is analyzed by quantifying the load adjustable potential index and the fuzzy C-means clustering method was used for secondary clustering of the adjustable capacity. Finally, DBI and SC indexes are used to evaluate the clustering results, standard values of evaluation indexes are set, and unqualified clustering results are recalculated and corrected. 31 industrial users in a province are selected as research objects, and the load data of the past 365 days are collected to verify the effectiveness and practicability of the proposed method. The classification results show that the classification accuracy is still good when the noise is 30%, and the maximum deviation between the clustering results and the actual load regulation potential is 12%. It can meet the actual engineering error standard.
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