Research On Unit Optimal Dispatch In Wind Farm

With the increase of wind power integration in power system, the stochastic variability of wind energy has been posing great challenges on traditional economic dispatch and operational security. Research on optimal dispatch in a wind farm with constrains on power system load and wind power forecasting could not only reduce operational redundancy and mechanical abrasion of wind turbines, avoid frequent on-off operation, but also decrease operation cost, improve power quality. All these above would eventually mitigate the negative impacts from wind power on power system, thus to maximize wind power penetration and economic benefits on the condition of secure system operation.Based on the wind power forecasting data, this dissertation mainly discussed the unit optimal dispatch algorithms aiming to reduce the wind turbine relative fatigue loading damage and the line loss of collection system in the wind farm. The main contribution of the dissertation can be summarized as follows:(1) A relative fatigue loss method for wind turbine key components in different operation conditions is established. Based on the wind resource data of a wind farm in Northern China, with the Rayleigh distribution of the wind speed cumulative distribution function, this dissertation simulated the fatigue load of1.5MW wind turbine using GH-Bladed and obtained the fatigue load spectrum of wind turbine components using the rain flow cycle count method. Then, according to the Miner laws and the simulation calculation, the relative fatigue damage of the wind turbine key components was calculated, providing evaluation criterion for unit optimal dispatch in the wind farm.(2) A wind power forecasting model is built based on neural network with the phase space reconstruction. Unit optimal dispatch in the wind farm is based on the wind turbine short-term and ultra short-term power forecasting. According to the principle of chaos-phase space reconstruction, it can be seen that the wind speed and the wind power time series data of wind turbines have the property of chaotic. On this basis, the phase space reconstruction was combined with neural network to establish chaos-BP and chaos-Elman, chaos-Volterra series wind power forecasting models. Tests showed that the forecasts of Elman model are more accurate than that of the others, improving accuracy and stability of the forecasting.(3) An unit optimal dispatch model is established minimizing the losses of collection system in a wind farm. With constraint conditions including wind farm output's satisfaction of grid dispatch, upper and lower limit of the wind generator active power output, upper and lower limit of the wind generator reactive power output power, upper and lower limit of the wind generator voltage and upper and lower limit of the transformer ratio, with the minimum collecting system network loss in the wind farm being the objective function, the mathematical model of optimal dispatch was established by using particle swarm optimization algorithm and genetic-particle swarm optimization algorithm respectively. The results showed that genetic-particle swarm optimization algorithm was better than particle swarm optimization algorithm in both optimization performance and computing efficiency.(4) A unit commitment model is established with an objective function of minimization of mechanical losses in a wind farm. Based on relative fatigue damage model built before, unit commitment model was established for the rational allocation of unit start-up in wind farm, which could obtain the minimum mechanical damage in the scheduling period, prolong the operation efficiency and life. And then the improved binary particle swarm optimization algorithm, genetic optimization algorithm, genetic-particle swarm optimization algorithm were utilized to obtain an optimized solution. The results showed that BPSO-GA improved the optimal performance better than GA and BPSO, and minimized the amount of fatigue damage in runtime. The calculation time of the BPSO-GA algorithm which introduced the particle swarm optimization parameters was slightly longer than that of the BPSO algorithm, but shorter than that of the GA algorithm. The calculation time of three models in decreasing order is:GA, BPSO-GA, BPSO.(5) A model for optimally allocating wind turbine output is presented based on units classification. After the analysis of large amount of historical data of unit in the wind farm, the average and root mean square difference of each wind turbine power and the wind speed were extracted as the characteristic value to analyze the unit performance, the units were classified with the SOFM neural network algorithm and the fuzzy c-means clustering algorithm based on simulated annealing and genetic algorithm. The units with better electrical performance in the classification were given priority in the execution of the power generation plan. Considering the line loss of the power generation plan, the rest of the units in the wind farm were optimized in two layers, the outer layer being the unit commitment optimization with the target of minimum relative fatigue damage of wind turbine, the inner layer being the optimal power dispatch of the units satisfying the grid requirement. Finally the power allocation model with the output of a wind farm in compliance with the dispatch requirements of the grid was established. Both the two kinds of classification algorithm can get unit commitments meeting the requirements of power grid dispatch. After comparison, however, it is found that the fatigue damage value of fuzzy clustering algorithm based on genetic simulated annealing algorithm is smaller than that of the SOFM neural network algorithm. Optimal dispatch based on wind turbines classification could optimize units operation in wind farm and improve the power quality of wind farm.