Rolling Optimization Method of Reserve Capacity Considering Wind Power Frequency Control

doi:10.12204/j.issn.1000-7229.2022.02.014

Abstract

Abstract:

It is necessary for wind power to have reserve capacity and participate in system frequency regulation to build a new generation of power system with renewable energy as the main body. However, the existing reserve-capacity allocation methods rarely consider the active participation of wind power in frequency regulation, and most methods cannot optimize the economy and stability at the same time. A rolling optimization method of reserve capacity considering wind power frequency control is proposed, which is suitable for wind farms to participate in system frequency regulation by using hierarchical centralized frequency-control method. In this method, economy and frequency stability are taken into account by multi-objective chance-constrained programming. The parameters of wind power and load prediction error are corrected in rolling calculation. The optimal allocation schemes of reserve capacity of wind farm, thermal power plants and hydropower plants with different confidence are obtained by using hybrid intelligent algorithm. The simulation results of IEEE 39-node test system show that the method solves the limitation of traditional reserve-capacity allocation method, improves the frequency stability and operation economy of the system.

Key words: reserve capacity, chance constraint, multi-objective optimization, wind farm frequency control, hybrid intelligent algorithm

CLC Number:

TM762.1

XING Chao, XI Xinze, HE Tingyi, LI Shengnan, LIU Mingqun. Rolling Optimization Method of Reserve Capacity Considering Wind Power Frequency Control[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(2): 117-125.

Figures/Tables 12

Fig.1 Frequency-control method of the system with wind power

Fig.2 Control system of wind Farm

Fig.3 Flow chart of rolling optimization method for system reserve capacity

Fig.4 Diagram of IEEE 39-node test system with wind power

Table 1 Parameters of IEEE 39-node system

Table 2 Anticipated accident set

Fig.5 Simulation result of Scenario I

Fig.6 Simulation result of Scenario II

Fig.7 Simulation result of Scenario III

Fig.8 Simulation result of Scenario IV

Table A1 Parameter values of the synchronous generators

Table A2 Table A2 Parameter values of genetic algorithm

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