Load Forecasting With Both Accuracy And Benefit Optimization For Two-stage Electricity Market

With the rapid development of global economy,the consumption of non-renewable energy has intensified.Electricity has played an important role in the future energy market as a flexible secondary energy source.Maintaining the stable and efficient operation of the power system is conducive to the effective and rational allocation of resources.Electricity market plays a vital role in assisting the planning and operation of the power system.China has gradually opend up the electricity market during the period of global power market deregulation.However,Chinese power market needs to learn more from other successful power markets in the world at the begining stage,and combine those experiences and its basic national conditions to open up a suitable development for China.Among the successful electricity markets in the world,the day ahead market is a venue where the Load Serving Entities(LSEs)submit and purchase their expected loads in the future,which can be derived from load forecasting model.Nowadays,most of load forecasting model only focus on predict accuracy,however,the accuracy of load forecasting may not be completely synchronized with economy because of price fluctuation.That means a more accurate schedule in day ahead market will likely reduce LSEs’ benefits.To face this issue,this paper adopts a scheme that combines efficiency on the basis of ensuring accuracy,and proposes a load forecasting model with Both Accuracy and Benefit Optimization(BABO)for two-stage electricity market.This model is based on the traditional load forecasting technique,the term cost is introduced into the objective function,the improved backpropagation and modified L-M algorithm is used the neural network for training.Case studies used load data in PJM and New York area,the verification results show that BABO has obvious effects on quantifying the efficiency of the LSEs and promoting the comprehensive improvement of its economy and accuracy.