Research On Load Aggregation Considering User Response Willingness And Its Scheduling Strategy

With the introduction of the "carbon peaking and carbon neutrality " goal,the energy structure has changed,and the penetration rate of renewable energy has been increasing.The way to maintain the safe and stable operation of the power system faces severe challenges.The demand-side load resources participate in the optimal scheduling of the system through flexible aggregation,which can effectively suppress the problem of new energy fluctuations.However,the traditional load aggregation method does not fully consider the difference in the user’s response willingness,which will affect the user’s power consumption experience to a certain extent during the scheduling process.Meet the needs of refined management and control of the power grid.Massive flexible loads are taken as the research object of this thesis,and conducts research from three aspects: load classification,load hierarchical aggregation and potential analysis,and participation in new energy consumption.The main work is as follows:(1)Load classification based on fuzzy style K-plane clustering algorithmThe refined load aggregation is based on the accurate and effective classification of different loads.The existing research is mainly to classify the overall power consumption data of users.However,the current load tends to be diversified and different loads are due to internal reasons and user personal habits.,different load equipment presents different operating conditions.This thesis analyzes and processes the electricity consumption data accumulated during the operation of equipment with different loads by introducing the fuzzy style K-plane clustering algorithm,divides the load clusters with different electricity consumption habits,and analyzes the electricity consumption habits.Verification and analysis are carried out with the measured data of loads in a certain area,and compared with the existing clustering algorithms,the results show that the proposed clustering algorithm can mine the style characteristics of load electricity consumption habits and significantly improve the accuracy of classification.(2)Hierarchical Aggregation Strategy Considering Shiftable/Temperature-Controlled Loads and Its Scheduling Potential MiningThe traditional load aggregation scheme does not fully consider the difference of user response willingness,which affects the user experience in potential evaluation and subsequent control to a certain extent.This thesis proposes a hierarchical aggregation strategy that takes into account shiftable loads and temperature-controlled loads,and considers user response willingness in the process of hierarchical aggregation,and establishes an analysis model for aggregation scheduling potential that considers user response willingness.Through the analysis and processing of load characteristics,the equipment with similar characteristics is clustered and aggregated,and a user response willingness model considering user background information is established.Aggregate scheduling potential of temperature-controlled loads in temperature-controlled mode.Through numerical example simulation and analysis and verification,the strategy proposed in this thesis can improve the accuracy of load aggregation and fully tap the potential of participating in system optimization scheduling after load aggregation.(3)Temperature-controlled load aggregation control strategy for renewable energy consumptionTemperature-controlled load generally participates in system scheduling through direct control.In order to reduce the impact of direct load control on users,based on the analysis and mining of the potential of temperature-controlled load aggregation scheduling,this thesis first selects the aggregation suitable for participating in new energy consumption through the gravitational search algorithm.Then,the temperature control model of the temperature control load aggregation group based on the dendritic neural network is established,and the tracking and consumption task is completed by changing the temperature setting value.The simulation results show that the load aggregation group optimization strategy can effectively select the load and allocate the consumption tasks reasonably.The proposed aggregation control strategy can effectively complete the new energy consumption task and maximize the realization of "load follows the source".