Research On Optimal Dispatch Of Distributed Virtual Power Plant Aggregated By Residential Loads Based On Refined Modeling

The rapid development of smart grid technology has promoted the process of load intellectualization and bidirectional interaction between power grid and users.The research on demand response of user-side has become an important topic.Compared with the traditional network-load relationship,the intelligent grid-load interaction puts forward higher requirements on the accuracy and reliability of control and scheduling strategies of demand side.Resident load,as an important component of load,contains a variety of flexible load resources with high response potential.However,it is difficult to manage and regulate it due to its electricity characteristics.Therefore,this paper takes residential loads as the main research object.The refined modeling,potential assessment and optimal dispatch of flexible loads on residential side are studied.Firstly,according to the personalization and comfort requirements of household users,three typical household high-power loads were refined and studied.Based on the load physical models and the refined control models which reflect users’ subjective willingness,a precise modeling method for three typical high power controllable loads is proposed on residential side including heating,ventilating and air conditioning(HVAC)systems,electric water heaters and electric vehicles.The influences of the refined definition of various relevant technical parameters and environmental parameters,user’s personalized electricity consumption habits and the atypical power consumption process on electricity consumption of different loads are analyzed in details.Secondly,the optimal scheduling strategies of energy management for individual households are studied.The power consumption characteristics of various loads are analyzed.Constrained by the comfort requirements and benign use standard of loads,an optimal scheduling model for home energy management aiming at optimizing the electricity cost is established.The adaptive discrete particle swarm optimization algorithm is used to solve the optimal scheduling problem under different types of loads and different household scenarios respectively.The comparison of household electricity cost and load demand curves before and after optimization show the rationality and validity of the proposed model.Thirdly,the flexible loads of various residents in the residential area are aggregated.At the level of distributed virtual power plant,the quantitative evaluation index system of scheduling potential is constructed and the calculation method of response potential is put forward.Based on fuzzy C-means clustering method,HVAC load clustering analysis is carried out and different types of households are distinguished.Based on the refined models of various loads and the electricity consumption characteristics of different types of households,characteristic indicators are selected to construct index system for quantifying the scheduling potential.Through duty cycle analysis of HVAC load curve,fitting analysis of water temperature curve of electric water heater load and description of charging and discharging feasible region of electric vehicle load,the response capacity is solved.Thus,the response potential analysis model could be established.The analysis results of response potential of aggregated loads in residential example demonstrate that the proposed potential quantification method is feasible and practical.Finally,the research on the optimal scheduling strategy and grid-load interaction capability of distributed virtual power plants is carried out.At the level of distributed virtual power plant,the electricity tariff optimization model with the goal of maximizing its profit is established based on user stickiness theory.At the electricity market level,the grid side,distributed virtual power plant and residential user side are regarded as the tripartite dispatching participants.A two-layer optimal dispatching framework is constructed and the optimal dispatching models of upper and lower layers are established respectively.The upper layer is a non-cooperative game model for dispatching capacity with the goal of maximizing the profit of each distributed virtual power plant.The lower layer allocates the result of the upper bidding game,and it is a cooperative game model for dispatching capacity allocation with the goal of maximizing the revenue of each user load group.Based on the idea of optimal dispatching,the aggregated load control strategies of distributed virtual power plants participating in peak shaving are discussed.The results of hierarchical optimal dispatching game and the comparison of load curves before and after peak shaving illustrate that the proposed hierarchical optimal dispatching model and load control strategies for grid-load interaction are reasonable and effective.