Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.

Optimal Day-ahead Dynamic Pricing of Grid-connected Residential Renewable Energy Resources Under Different Metering Mechanisms

Nowadays,grid-connected renewable energy resources have widespread applications in the electricity market.However,providing household consumers with photovoltaic(PV)systems requires bilateral interfaces to exchange energy and data.In addition,residential consumers’contribution requires guaranteed privacy and secured data exchange.Dayahead dynamic pricing is one of the incentive-based demand response methods that has substantial effects on the integration of renewable energy resources with smart grids and social welfare.Different metering mechanisms of renewable energy resources such as feed-in tariffs,net metering,and net purchase and sale are important issues in power grid operation planning.In this paper,optimal condition decomposition method is used for dayahead dynamic pricing of grid-connected residential renewable energy resources under different metering mechanisms:feed-intariffs,net metering,and net purchase and sale in conjunction with carbon emission taxes.According to the stochastic nature of consumers’load and PV system products,uncertainties are considered in a two-stage decision-making process.The results demonstrate that the net metering with the satisfaction average of 68%for consumers and 32%for the investigated electric company leads to 28%total load reduction.For the case of net purchase and sale mechanism,a satisfaction average of 15%for consumers and 85%for the electric company results in 11%total load reduction.In feed-in-tariff mechanism,in spite of increased social welfare,load reduction does not take place.