Research On Electricity Customer’s Credit Rating And Antistealing Power Considering Distributed Generation

In recent years, with the advancement of the electricity market reforms, the liberalization of distributed generation(DG), the promotion of large consumers direct purchase of electricity, the implementation of energy-saving policies, the traditional passive electricity buyers gradually transformed into active participants in power market activities. The influence of electricity customer’s behavior on electric power company’s operating risk is not only confined to electricity arrears and stealing problems, but also related to dispatching plan, equipment maintenance cycle, calls of auxiliary services about peak regulation and reserve supply, energy-saving quotas, which are relevant to electric power company’s operating costs and income. It is urgent for electric power company to develop a set of power customer’s credit evaluation system under new circumstances, which can evaluate the impact of customer’s credit on electric power company’s management risk scientifically and accurately. Therefore, in order to control power grid marketing risk, the paper proposes a new customer credit rating evaluation model with new indexes such as the electric quantity and the quality standard rate of gridconnected DG, the percentage of breach electricity quantity of the directpurchasing contract, the completion rate of carbon reduction quota. By using the optimum combination weighting evaluation method based on grey incidence degree, this model overcome the shortcoming of one-sidedness of traditional single method.On the other hand, anti-stealing problem is still severe in China. Antistealing work will face new challenges brought by DG, which will increased electric power company’s pressure and inspection cost. Considering the impact of DG on distribution network flow, this paper presents an adjusted plan for traditional substation area line loss calculation method, which provides a theoretical basis for judging the severity of stealing in each substation area. Moreover, this paper offers an appropriate solution to the problem of the hidden customer’s electricity consumption due to partial self-consumption generated by DG. By using the severity of stealing of each substation area narrow down anti-stealing inspection scope as first step, this paper suggests to further lock down inspection customers by using abnormal electricity consumption as second screening criteria. Finally, identify electricity filcher by using data mining method. This electricity anti-stealing management approach can select out suspicious electricity filcher efficiently and accurately, which help electric power company save inspecting resources, reduce marketing costs and realize risk management.