Analysis Of Electricity Price Elasticity Coefficients In Retail Power Market

In order to determine the electricity price reasonably and to extend the powermarket, it is important to master the principle that the energy consumption quantityvaries with the price in power market (called Demand Response principle). However,the current retail power marker in our country has not opened and the option of themarket participants is limited. On the other hand, there are not enough technologiesand data needed by the customer's price sensitivity analysis (called Price Elasticity ofDemand) in traditional energy sell patterns. All these have complicated the electricityprice elasticity analysis. To solve this problem, this paper first bases on the fact that the performance ofTime of Use enables users to choose the price indirectly by choosing their using time.And it digs out the price elasticity by using the simple linear regression method,according to the industry consumptions in the current retail power market of provinceLiaoning. Because the energy consumption quantity is affected by many factors,multiple regression method has been used for analysis of the typical industries. Theresults have reflected the relationship between energy consumption quantity and pricetogether with other factors. In the aspect of the tendency of price influence on energyconsumption quantity, the same results have been got from above two models. Thishas proved the feasibility of the first model. However, deeper study has proved that the energy consumption quantity isaffected not only by current price, but also by adjacent prices. Therefore, this paperpresents the constructing process of price elasticity matrix of demand, and analysesthe difference of elasticity matrix structure based on different user classes and pricesorts. Then it presents a simplified method for the elasticity matrix, and shows thecalculating process in our current retail power market by an example. The result hasshown that the self-elasticity coefficients and the cross-elasticity coefficients arecompensative. This method can not only reflect the relation between energyconsumption quantity and price more reasonably, but also enlarge the examplenumber by presenting a new method for data proceeding to remove the effect of placedifference and time cycle. The actual applications have proved that the modelsprovided in this paper can reflect the Demand Response principle in current retailmarket, and own some practical effects. Considering that the multivariate data analysis with few observations is ofteninvolved in current retail power market, this paper has also analyzed the advantages ofpartial least squares regression in multivariate data analysis with few observations andits application areas in retail power market. And an example is given to show its usein cost analysis for distribution traders. The results have shown that the calculatedindexes of cost analysis are reasonable and can be used to estimate the distributiontraders. Above methods have been used as the most important arithmetic in pricesubsystem of power system analysis and decision-making system for LiaoningProvince Power Company.