Abstract
For the integration of distributed generations such as large-scale wind and photovoltaic power generation, the characteristics of the distribution network are fundamentally changed. The intermittence, variability, and uncertainty of wind and photovoltaic power generation make the adjustment of the network peak load and the smooth control of power become the key issues of the distribution network to accept various types of distributed power. This paper uses data-driven thinking to describe the uncertainty of scenery output, and introduces it into the power flow calculation of distribution network with multi-class DG, improving the processing ability of data, so as to better predict DG output. For the problem of network stability and operational control complexity caused by DG access, using KELM algorithm to simplify the complexity of the model and improve the speed and accuracy. By training and testing the KELM model, various DG configuration schemes that satisfy the minimum network loss and constraints are given, and the voltage stability evaluation index is introduced to evaluate the results. The general recommendation for DG configuration is obtained. That is, DG is more suitable for accessing the lower point of the network voltage or the end of the network. By configuring the appropriate capacity, it can reduce the network loss, improve the network voltage stability, and the quality of the power supply. Finally, the IEEE33&69-bus radial distribution system is used to simulate, and the results are compared with the existing particle swarm optimization (PSO), genetic algorithm (GA), and support vector machine (SVM). The feasibility and effectiveness of the proposed model and method are verified.
Highlights
The world’s energy sources are accelerating in their transition to diversification, cleanliness, and low carbonization
The IEEE33&69-bus radial distribution system is used as an example to simulate and compare the obtained distributed generation (DG) location and capacity selection results with particle swarm optimization (PSO) and genetic algorithm (GA) algorithms
For the 33-node system, when the DG was connected to one position, the DG access capacity was similar to the other methods, and the configuration active power loss given by kernel extreme learning machine (KELM) was lower
Summary
The world’s energy sources are accelerating in their transition to diversification, cleanliness, and low carbonization. A large proportion of renewable energy has been connected to the grid, but its power generation output is random, which means the analysis and control of power system has become more important This fundamentally changes the planning and operation characteristics of a distribution network. J. et al [12], with the goal of the highest economic return on investment cycle, a distributed power supply location and capacity optimization method considering network dynamic reconfiguration was proposed. It can be seen from the above references that the kinds of DG location and capacity selection models have been constructed, and the optimization algorithm such as particle swarm optimization (PSO) and genetic algorithm (GA) are applied to solve the model. The validity and feasibility of the proposed method were verified, and the computation speed was faster
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