Research On Smart Residential Community Agent Pricing And Service Considering Differences In User Satisfaction

Demand response can help users save electricity costs and reduce the gap between the peak load and the valley load.The smart residential load can directly participate in demand response with a household energy management system,modern measurement and communication equipment as physical support.As an important bridge between the power grid and residential load communication,agents benefit from the opening of the retail electricity market and obtain the right to pricing,which can further tap the potential of users’ demand response and provide users with high-quality electricity services.Agents increase revenue mainly by selling electricity to users,while users reduce electricity costs by participating in demand response.The contradictory relationship between the two can be described as the agentuser Stackelberg game model.However,on the one hand,demand response will change the user’s original electricity consumption habits while reducing electricity costs,resulting in a decline in user satisfaction,and there are two kinds of satisfaction differences:different users’satisfaction needs and the same user’s satisfaction needs in different periods of time;on the other hand,agents allocate energy storage and photovoltaic to improve their revenue and regulation capacity,but the actual output fluctuation of photovoltaic will cause some loss of revenue.Therefore,the agent-user Stackelberg game model is improved from two angles.From the perspective of users,the influence of satisfaction differences on the Stackelberg game is considered.From the perspective of agents,the Stackelberg game scheme is adjusted to reduce the potential loss of income.Based on the review of the development process and research status of the smart power residential community,the Stackelberg game framework considering the differences in user satisfaction is proposed.The upper level is the leader,and the optimization goal is to maximize the revenue of the agent,formulate the user price and transmit it to the lower level.The lower level is the follower,taking the minimum user electricity cost as the goal,considering the user satisfaction differences constraints,the user’s electricity scheme is generated and fed back to the upper level,and the iterative method is used to solve the upper and lower models with mixed-integer structure.The results of the cases show that the agent pricing of the smart power residential community can adjust the flexibility of the electricity market.After considering the differences in user satisfaction,it can not only increase the agent income and reduce the user electricity cost but also reduce the peak-valley difference of the overall power load of the residential community.At the same time,it can provide users with more personalized intelligent electricity service.The above model obtains the game results based on the day-ahead photovoltaic prediction,without considering the real-time power imbalance caused by the fluctuation of photovoltaic output in the real-time stage.Therefore,the conditional value at risk is used to measure the risk of real-time regulation cost increase caused by photovoltaic output fluctuation,and a Stackelberg game model of agent pricing and user electricity consumption optimization is established.Using the KKT condition,the user level model is equivalently converted to upper constraint,and the bi-level model is simplified to single layer model.The single layer model is processed by strong duality theorem and large M method,and the single layer mixed-integer linear programming model is finally obtained.The results of the cases show that the agent can reduce the risk of high real-time regulation cost by adjusting the day-ahead electricity purchase and sale,the charging and discharging plan of energy storage and the fine-tuning pricing.With the increase of the agent’s risk aversion,the risk of real-time regulation is gradually reduced.Considering the differences of user satisfaction can improve the enthusiasm of users to participate in demand response.The agent can effectively measure the real-time regulation risk caused by the fluctuation of photovoltaic output by using conditional value at risk,and reduce the real-time regulation risk without significantly affecting the user’s economy.