Optimal Control of Hybrid Energy Storage System of New Energy Power Generation System Based on Improved Particle Swarm Algorithm

Abstract

In order to ensure the stability and reliability of power supply and realize day and night power generation, wind and solar complementary power generation systems are built in areas with abundant solar and wind energy resources. However, the system investment cost is too high. Because of this, there are wind, light intermittent, and non-intermittent power generation systems. For issues such as stability, an energy storage system needs to be configured to stabilize power fluctuations. This paper aims to study the optimization control of hybrid energy storage system of new energy power generation system based on improved particle swarm algorithm. In this paper, the application of particle swarm algorithm to power system reactive power optimization has been researched in two aspects. Through optimization methods, reasonable adjustment of control variables, full use of equipment resources of the power grid, to improve voltage quality and reduce system operation network to ensure the stability of the voltage system. In addition, this paper selects the IEEE30 node test system and simulation data analysis, takes the hybrid energy storage system as the optimization object, and optimizes the reactive power of the newly improved particle swarm algorithm. The experiments in this paper show that the improved algorithm has a good effect in reactive power optimization, increasing the performance of the hybrid energy storage system by 27.02%. MPSO algorithm is also better than basic PSO algorithm. It can be seen from the figure that in the PSO algorithm, the algorithm basically tends to be stable after more than 40 iterations, and finally the algorithm converges to 0.089.

Application Analysis of MMC-HVDC AC Tie Line Transmission in New Energy Power Generation

“…Moreover, at present, there is no accurate method to calculate these parameters well, especially in power electronic systems, some commonly used tuning empirical formulas, such as the Fibonacci sequence method, and so on often cannot get a good control effect, therefore, in the design of the traditional nonlinear ADRC controller, a large number of parameters are usually obtained only by trial and error, which will lead to a lot of time and energy being wasted in the parameter adjustment process, and the debugging efficiency is extremely low. It is this significant defect of the traditional nonlinear ADRC that makes it not put into large-scale industrial applications [24]. Aiming at the shortcomings of the above traditional ADRC, the parameters in the ADRC are given physical meaning.…”

Real-time monitoring method for field operation of new energy power generation production based on mobile Internet

“…Under the condition of determining different risk levels, three monitoring methods, namely, the reference [5] method, the reference [6] method and the method in this paper, monitor the dynamic changes of the fitting value of the results in each monitoring point, and compare and analyze the results, and the results are shown in Figure 3. The method in this paper Reference [5] method Reference [6] method Figure 3 Comparison results of fitting degree of three monitoring methods According to the comparison results in Figure 3, among the three monitoring methods of power generation production site operation, the method in this paper shows better performance than the other two literature methods in the fitting value of each monitoring point. Specifically, the fitting values of this method are lower, all below 0.2%, which means that the monitoring results are more consistent with the actual situation and the error is smaller.…”

“…Reference [5] puts forward the optimal control of hybrid energy storage system of new energy generation system based on improved particle swarm optimization. This paper studies the application of particle swarm optimization in reactive power optimization of power system from two aspects.…”

Particle Swarm Optimization Algorithm With Adaptive Two-Population Strategy

“…In [52], the fuzzy expected value model problem is solved by combining stochastic PSO with a back propagation neural network. In [53], PSO is used to reduce the operating cost of a power system. In [54], the opposition-based PSO algorithm is proposed by considering the learning strategy of the opposition to PSO, which is used to solve the distribution and dispatching problem of active distribution networks.…”