| In order to reduce the cost of household electricity,promote friendly interaction between users and power grid to optimize user?side resource allocation and achieve low-carbon and high-efficiency power consumption.To a certain extent it will promote the development of energy conservation and emission reduction and demand response.This dissertation studies the smart home energy optimization management strategy participating in demand response.The research contents are as follows:(1)The classifications and implementation modes of demand response are summarized.On this basis,the real-time electricity price type and emergency demand response event type demand response model suitable for individual residential users are established from the perspective of residential users and demand response issuers.The results of examples reflect to some extent the possibility of implementing the above demand response projects on the residential side in the future.(2)The models of the typical electrical supply and use equipment are established.Based on the response mode of residential users,the typical household electrical supply and use equipments are classified.The physical characteristics and operational characteristics of each type of equipment are combined to establish a shiftable load model,a flexible load model,an energy storage equipment model,and an electric vehicle model.The flexible load model considers the energy storage characteristics of the building based on the first-order ETP model,the strong coupling between the indoor temperature and the air-conditioner state at each period-is decoupled by introducing the temperature characteristic quantity ?Teql,thereby simplifying the air-conditioning model.Case studies of the air conditioning control effect in different seasonal typical days of a certain area verify the validity and practicability of the air conditioning model and show the controllability and dispatching potential of each equipment.(3)A bilevel home energy optimization management strategy based on price-based demand response is proposed.Using the idea of layered optimization,the idea of optimizing home energy management from two aspects of power use side management and power supply side management is proposed.Firstly,the multi-variable mixed integer nonlinear optimization scheduling model is established with considering the user's comfort and the lowest electricity cost for the load side management model.The working state of each household electrical equipment is optimized every 15 minutes in one day and modify the scheduling model by adding integer constraints and the interior algorith1 is used to solve this model.The power supply side management is divided into three scenarios base on the different states of the electric vehicle to reasonably arrange the power supply sources of the users in each period.Finally,the examples of household energy optimal management for residential users under four application scenes on typical summer days are taken to verify the effectiveness of the strategy and show its role in guiding users to rationally arrange power consumption time and optimize resource allocation.(4)A bilevel home energy optimization management strategy based on incentive-based demand response is proposed.Firstly,the strategy considers the economics and users' comfort to design the judgment process of whether to participate in DR events.Then,considering the response potential of the user's electrical equipment,the shiftable load and the air conditioner are selected as the main body to complete the load reduction task,and three load redistribution rules are established for the shiftable load.A multivariable integer linear optimization model is established for the air conditioning load redistribution problem aimed at the lowest cost of air conditioning electricity.Finally the example of the user facing the same DR event on the summer/winter typical days verifie that the algorithm has the function of guiding the user to respond to the DR event,and demonstrates the effectiveness of it. |
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