Research On Group-Controlling Strategy And Energy Optimization Method Of Photovoltaic Microgrid System

Photovoltaic microgrid as a supplementary form of large power grid has been gradually emerging, it has the characteristics of clean without pollution, no using restrictions, no regional restrictions, simple maintenance. In recent years of practical using, photovoltaic microgrid is moving at the direction of large capacity and distribution, which requires the photovoltaic array to group and be equipped with multiple inverters at the same time, distributed generation is formed in the adjacent area. For photovoltaic array group, inverters group and microgrids control management, a series of new problems are triggering at the face of the external conditions changing. For example:how to optimize in the photovoltaic array group, how to face the collaborative power of photovoltaic array and inverters, how to solve the problem of redundant switching among many inverters, how to optimize at the energy schedule of several microgrids in the adjacent area. These problems revolve around the various components of PV microgrid system, the research is carried on the group control strategy and energy optimization methods, the main contents are as follows:(1) Group tracking control strategy for a group of photovoltaic array. Due to the local shadow of PV array in the external environment often occurs, the shape of P-V curve will turn into multiple peaks by a singe peak and the conventional maximum power tracking algorithm can not be applied. A standard particle swarm optimation algorithm was studied in this paper and the constraints of convergence were deduced by using the functional analysis, the correctness was verified by Matlab simulation in combination of various parameters. Then under meeting the convergence condition, an improved particle swarm optimization algorithm was concluded by studying the influence on the optimal trajectory for random variables, and was applied to the group power optimization in the photovoltaic array. Finally, by the simulation and experiment, the results show that the improved particle swarm optimization algorithm can quickly and accurately carry out the maximum power tracking when PV array is in the condition of local shading.(2) Group power optimaization strategy for photovoltaic array and inverters. The low light irradiation such as the rainy day seriously affects the photovoltaic output power, so the output power of photovoltaic array group is often far less than the rated power of inverters group, which will cause a significant reduction in the efficiency of photoelectric conversion. Study on the group control method between the distributed photovoltaic array and inverters, the switch matrix is applied to form the flexible connection between them and a novel topology network structure of PV array and inverters. At the same time, the composition structure and working mechanism of the topology network are described. In order to cooperate with the topology network, a novel adaptive clustering algorithm is proposed to realize the power matching between the PV array group and inverters group. Finally, by simulation analysis and experimental verification, the results show that the group control strategy can be optimized power matching between the photovoltaic array and inverters in the condictions of low light irradiation, thus increasing the power efficiency of photovoltaic microgrid.(3) Redundant group control strategy for microgrid with multi-inverters. In microgrid system with multi-inverters, the conventional droop control exists the problem of poor output characteristics and the working mechanism of master-slave method depends entirely on the master module. These two methods currently used can not really realize the redundancy control of multiple inverters. In view of this situation, the research designed 3 layers of progressive control management. The first layer of control is to improve the traditional droop method, adding virtual impedance in the output inductor, will change the equivalent output impedance of the inverter. The value of output impedance is determined only by the filter inductance, and a double loop droop controller with high stability is designed. The second layer is grid synchronization control, in view of the positive feedback of the frequency and amplitude reference value, collecting the voltage amplitude and phase information at the two sides of public connection point, simultaneous feedback synchronization is adjusted in the process of droop control. The third layer control is compensation, combined with the realization principle of compensation transfer function and the structure of droop control, a compensation model for voltage frequency and amplitude is designed to compensate the steady error in the process of droop control. Finally, by the example simulation and experiment, the results show that the control strategy has good steady and dynamic performance in the face of the redundant switching of multiple inverters. The redundant group management is really realized for inverters group in microgrid system.(4) Energy optimization method for microgrids. Aim at the microgrids system with a loose structure and strong dynamic coupling, the paper presents an energy optimization method based on distributed predictive control to solve the energy optimization dispatch problem between the microgrids. Firstly, according to the energy flow characteristics of microgrids, the energy management model and predictive model of the microgrids system are established. Secondly, the distributed predictive control algorithm is introduced, which is based on the Nash optimization index to add the influence on the overall system, and the implementation of algorithm is described in detail. Finally, by the simulation of system which is formed three microgrids, it proves that the validity of this energy optimization method.