Research On Optimization Allocation Of Distributed Generation

With the development of the global economy and the growth of the worldpopulation, traditional energy has been gradually exhausted and the environmenthas been polluted. The traditional ways of electricity generation which consumelarge amounts of energy have become a huge burden to the environment. In order toalleviate the situation and achieve the sustainable development, people put forwarddistributed generation.The distributed generation is a kind of small generator which is set at customerside in power system. It is not only capable of supplying power to users directly,but also capable of merging into power grid and supplying power with the publicpower grid together. The distributed generation has influence on the power flow,power loss, node voltage, reliability of the network and so on. The distributedgeneration also brings about much more uncertainty to the grid at the same time.Therefore, how to optimize the allocation while considering the characteristics ofdistributed generation becomes an important issue.The paper managed to figure out the type and position of the distributiongenerator based on the power flow calculation of network. Firstly, with naturalconditions considered, it created the model of objective functions which arededicated to minimizing the cost of generator and power loss of network andimproving the voltage level. This paper used the Genetic Algorithm as theoptimization algorithm while using three phase back-forward sweep method tocalculate the power flow. At last, a case of IEEE33node system was built to verifythe validity of the models and algorithms.Secondly, taking the output uncertainty of renewable energy distributedgeneration into account, this paper realized the optimized allocation of distributedgeneration capacity with objective functions of capacity credit and total cost. Windturbine and diesel were chosen as representatives of distributed generation andconventional generation respectively. The capacity credit was calculated throughMonte Carlo simulation on the premise of setting up random production model ofdistributed generation. Then the optimization of the genetic algorithm was implemented based on the objectives of maximum capacity credibility andminimum cost. The results indicated that the wind turbine possesses some capacitycredibility which should be considered comprehensively in the programming.