Research On Trading Strategies Considering Demand Response Under Incomplete Information Scenario

At present,the penetration rate of new energy in the microgrid continues to increase.The output of new energy is characterized by randomness and volatility.This increased the fluctuation in power output at the power supply side and increased risks to the safe operation of the power grid.The energy is exchanged more frequently between microgrids in the microgrid cluster,the opacity of information exchanged between microgrids means that the microgrid can not know about the demand of other microgrid when reporting energy demand to the energy operator,and which create the incomplete information scenario for electricity trading.The existence of false demand information reporting and uncertainty of new energy output can lead to the deviation in the quantity of electricity demanded and used,and cause fluctuations in the energy consumption of the microgrid cluster and economic losses.Therefore,it is necessary to study the energy trading strategies of microgrid cluster under incomplete information scenario.In this paper,the characteristics of different kinds of loads in the microgrid are analyzed in the architecture of the microgrid cluster which contains wind,photovoltaic,storage and loads,and a microgrid power trading strategies considering demand response under incomplete information scenario is studied.First,the interactive mechanism used in energy operator to microgrids,microgrid to microgrid is analyzed,the operation models of wind,photovoltaic and energy storage device are built,and the loads are classified according to their characteristics.In view of the problems of false electricity demand information reported and uncertainty of new energy output,this paper proposed a method to suppress false information report based on electricity price reward and punishment mechanism using dynamic integrity factor and a method based on quantifying economic risk using conditional value-at-risk theory to optimize the micro-grid electricity trading strategies respectively,and studied the method’s impact on demand response and economy.To suppress the behavior of false electricity demand information report in incomplete information scenario,this paper proposed a price reward and punishment mechanism based on dynamic integrity factor,and established a bi-layer electricity trading strategies for the microgrid cluster.Firstly,based on the transaction volume reported on the microgrid,a reward and punishment model for electricity prices based on dynamic integrity factors is established.Secondly,transaction models for energy supply and demand are constructed at the upper level and lower level.Finally,the numerical analysis shows that the trading mechanism can be used to reduce the power fluctuations and the possibility of reporting false information in the microgrid cluster,and improve the economy and security of all participants.In order to reduce the potential economic risk caused by the uncertainty of new energy output,this paper adopted the conditional value-at-risk theory(CVa R)to quantify the economic risk and proposed a day ahead trading strategies considering electricity tariff and CVa R for microgrid cluster.First,a new model considering uncertainty is built based on the deviation of new energy output prediction and its probability distribution to achieve the prediction of new energy output and scenario building,and Latin hypercube sampling method is used to generate multiple sets of scenarios.The backward scenario reduction technique based on the Kantorovich distance is used to reduce the quantity of scenarios to obtain typical output scenarios.Second,a quantitative model of economic risk due to uncertainty in new energy generation is proposed by using CVa R.A multi-objective optimization model considering the economics,comfortable and potential risks of microgrid operation is built.Fuzzy theory and the binary comparison weighting method are used to process the multi-objectives.Finally,an arithmetic analysis is carried out by combining different simulation scenarios.The result shows that the trading strategies can be used to balance the micro-grid economy,comfortable and potential risk,and reduce the peak-to-valley difference of the power traded in the microgrid cluster.