Study On Electricity Retailers' Risk Identification And Control Optimization Model In Light Of Electric Market Reform Environment In China

In the new round of power market reform,the liberalization of the power-selling-side market has enabled power customers with options to gradually expand from large power users to all power users,and it also indicates that China's power market operation model is changing from wholesale competition model to retail competition model.The power industry competition risk is borne by the power generation side alone in the past,however now power generation side and sale side share the risk of volume and price in the electricity market.At the same time,with the encouragement of industry policies,the number of China's electricity retailers has soared.As of February 2017,China has established 6389 electricity retailers.These companies include power grid companies and distribution companies with long-term experience in purchasing and selling electricity,as well as power-generation sales companies with experience in bidding for electricity and direct purchases of electricity with large consumers,and a large number of independent sales companies that do not have experience in purchasing and selling electricity.With the gradual establishment of the monthly bidding market,the spot market and the auxiliary service market,China's electricity retail industry has shown a rapid development trend,but there are major differences in the power business model and risk factors of different types of electricity retailers.Therefore,under the background of electric power reform,the risk identification and control of different types of electricity retailers are currently important research topics with realistic significance.Based on the development status quo of China's electricity retailers,the paper analyzes the pricing tendency of electricity purchaser and seller in the monthly bidding transaction.According to the qualifications and main business characteristics,the electricity retailers are divided into distribution network type,power generation type,social type and intermediate agent type,and their business characteristics and roles in the electricity market are analyzed.On this basis,according to the classification of the power sales business,value-added services,and transmission and distribution business,the paper analyzes the different business models of the electricity retailers,and respectively summarizes the concepts and characteristics of the different business models.Then,combined with four operating modes of the electricity market,the paper analyzes the competitive relationship between the electricity retailers under different operating modes.In the risk identification of electricity retailers,starting with the risk identification dimension,the paper analyzes the risk of electricity retailers in the electricity market at different time spans.In the paper,the risk phase of the power sales company is divided into preparatory phase,planning and construction phase,and operation and maintenance phase.Based on the characteristics and problem analysis of power industry financing system,the risk analysis in planning and construction each link,and the value-driven tree analysis of EVA,the risk drivers in each stage are identified.On this basis,the paper simplifies the duplicated indicators in the identified risk factors.The ISM is used to explain the causal relationship between the indicators,and a risk factor development relationship chart is constructed.Then according to the causal relationship between the risk factors,the cause factors are used as the key risk drivers.Based on the risk analysis,the risk of the electricity retailers in the medium and long-term contract transactions is mainly price risk.The paper builds the electricity price risk control models under the "one-to-one" and "one-to-many" trading modes for electricity retailers respectively.In the "one-to-one" trading mode,the generation cost and the purchase cost in other power markets are used as the bottom line for electricity purchaser and seller,and a bilateral negotiation model based on Zeuthen strategy and Bayesian learning is constructed.In the "one-to-many" trading mode,firstly a power price strategy model for the power sales company as an electricity purchaser to independently purchase power for CFDs is constructed.And on this basis,considering the rising price brought about by the oligopolistic market in the region,the power price strategy model for the electricity retailers as a purchase alliance to purchase power for CFDs is constructed.In view of the power price strategy of the electricity retailers in the monthly bidding market,this paper starts with different market bidding settlement methods,and theoretically analyzes the pricing tendency of electricity retailers under the unified clearing price mechanism and the PAB price mechanism.Then on the basis of the unified market clearing price mechanism,the paper uses the smart agents and RE learning algorithm to simulate the pricing behavior of electricity purchaser and seller in the monthly auction transactions.For the risk of deviation assessment,this paper establishes a model for the load-bundling bidding for customers by electricity retailers.And considering the ability of interruptible load and price-type demand response to regulate the power positive and negative deviations,the paper builds an electricity purchase and sale strategy model under the deviation assessment mechanism considering demand-side responses.Then,respectively taking profit maximization and risk minimization as the optimization objectives,the risk control model is established according to the electricity sales strategy of generation sales companies in different power markets and the power purchase portfolio strategy of purchase sales companies.And an example is taken to analyze the changing trend of the combination strategies of the electricity retailers under different targets.Finally,in the view of the specific sales companies for virtual power plants with distributed renewable energy,on the basis of cost-benefit analysis,the paper establishes the scheduling optimization model considering the uncertainty of renewable energy output,and constructed the virtual power plant joint scheduling model and benefit allocation model based on Shapley value method.On this basis,because in the spot market bidding,the virtual power plant simultaneously faces power deviation brought by the output uncertainty and the market price uncertainty risk.Taking penalty cost minimization and blocking cost minimization as the optimization goal,the paper constructs a collaborative bidding model for the virtual power plant with wind turbines,photovoltaic power plants and energy storage systems.Finally,analyzed the development trend of the destributed photovoltaic energy according to the relative government subsidy policies.