Abstract
The large-scale renewable energy power plants connected to a weak grid may cause bus voltage fluctuations in the renewable energy power plant and even power grid. Therefore, reactive power compensation is demanded to stabilize the bus voltage and reduce network loss. For this purpose, time-series characteristics of renewable energy power plants are firstly reflected using K-means++ clustering method. The time group behaviors of renewable energy power plants, spatial behaviors of renewable energy generation units, and a time-and-space grouping model of renewable energy power plants are thus established. Then, a mixed-integer optimization method for reactive power compensation in renewable energy power plants is developed based on the second-order cone programming (SOCP). Accordingly, power flow constraints can be simplified to achieve reactive power optimization more efficiently and quickly. Finally, the feasibility and economy for the proposed method are verified by actual renewable energy power plants.
Highlights
In the renewable energy power technology trend, the grid-connected renewable energy power plant is gradually built up from a low-voltage and small-scale level to a large-scale and high-voltage level
According to the literature reports [4,5,6,7,8], most renewable energy power plants in operation use a centralized reactive power configuration scheme based on standard technical requirements in the European Union and USA
To minimize the renewable energy power plants’ investment and operating cost, voltage stability, and the reactive power optimization configuration model in renewable energy generation clusters are solved based on the genetic algorithm
Summary
In the renewable energy power technology trend, the grid-connected renewable energy power plant is gradually built up from a low-voltage and small-scale level to a large-scale and high-voltage level. To minimize the renewable energy power plants’ investment and operating cost, voltage stability, and the reactive power optimization configuration model in renewable energy generation clusters are solved based on the genetic algorithm. Based on the K-means++ algorithm, the typical scenarios of renewable energy power plant output in one year are extracted, and the process is shown in Figure 1 and listed as follows. Step 2: Calculate Euclidean distance ( Dij ) between the sample ( X i ) and the central sample( X j ); Step 5: Based on the selected central samples, the time groups of renewable energy power plants day–output curves are calculated through the K-means clustering algorithm, including the center for each group, which is a typical scenario of renewable energy power plants. Cluster, we defined a node connected with the whole system
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