Abstract
With the continuous development of society and under the background of sustainable development and resource conservation, the proportion of renewable energy in the global energy structure is increasing. At the same time, wind power has been widely used in many regions of the world because wind power technology is more advanced and mature than other renewable energy sources. In addition, with a large number of wind turbines connected to the grid, it not only helps automatic generation control (AGC) of power systems but also brings new challenges and difficulties. In this study, a multi-source cooperative control model of wind power participating in AGC frequency regulation is established to solve the dynamic problem of power distribution from real-time total power command to different AGC units. This study presents an optimal AGC-coordinated control method based on the multi-objective mayfly optimization (MMO) algorithm, which makes the fitting degree of power command output and actual output curve high and the adjustment mileage payment minimum, so as to achieve the best AGC performance. Finally, the simulation results show that this method can effectively decrease the total power deviation and adjustment mileage payment in the multi-source-coordinated control of AGC.
Highlights
Nowadays, renewable energy such as wind power, solar energy, and tidal energy, are developing rapidly, under the background of pursuing energy conservation, emission reduction, and sustainable development (Zhang et al, 2015; Yang et al, 2020a; Yang et al, 2020b; Xiong et al, 2020; Zhang et al, 2021a; Shetty and Priyam, 2021)
When wind power is highly involved in automatic generation control (AGC) frequency regulations, this study considers achieving the coordinated control between wind turbines and traditional water/thermal power units by reasonably distributing power output commands
In order to verify the effectiveness of mayfly optimization (MMO), the extended two-area load frequency control (LFC) model is tested in this study, and the multi-objective immune algorithm with non-dominated neighbor-based selection is introduced (NNIA) (Gong et al, 2014) along with the nondominated sorting genetic algorithm II (NSGA-II) (Deb et al, 2002) and the improved strength Pareto evolutionary algorithm (SPEA2) (Corne et al, 2001)
Summary
Renewable energy such as wind power, solar energy, and tidal energy, are developing rapidly, under the background of pursuing energy conservation, emission reduction, and sustainable development (Zhang et al, 2015; Yang et al, 2020a; Yang et al, 2020b; Xiong et al, 2020; Zhang et al, 2021a; Shetty and Priyam, 2021). When wind power participates in frequency regulation, it does not make full use of the advantages of high response speeds and climbing speeds of wind power and consider the characteristics of large fluctuations of wind turbine’s output power, so it is impossible to achieve the optimal control of AGC systems. This study presents a multi-objective mayfly optimization (MMO) algorithm This algorithm is used to optimize the power command distribution link in the working process of AGC, make full use of the advantages of high response speeds of upper wind turbines, and weaken the disadvantages of large fluctuations of wind turbine’s output, so as to achieve the coordinated control problem between wind turbines and traditional water/thermal power units (Bhattacharyya et al, 2020; Zervoudakis and Tsafarakis, 2020). The fifth section summarizes the work results of this study
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