Research On Regulation Capability Evaluation And Improvement For Distributed Electric Heating Load Group

Due to the rapid economic development and the acceleration of the modernization process,the world is facing increasingly severe environmental and climatic problems,which makes the decarbonization of electrical energy supply an inevitable trend of current development.However,in a high proportion of renewable energy grids,its "source-side" active regulation capability is gradually limited,which puts a huge demand on the "load-side" flexible and adjustable resources With the support of the national clean heating policy,the northern region of China has a large number of electric heating loads and is developing rapidly.As an important flexible time-shifting load,when it reaches a certain scale,it will become a very considerable demand response resource in the operation of the power grid.However,at present,various types of electric heating loads are only in a state of regulation to meet the heating demand.How to deeply explore its regulation potential and incorporate it into the dispatching operation of the power system will be an important research direction.This article first analyzes the working characteristics of a single electric heating load,proposes a quantitative index that characterizes the regulation capacity of a single electric heating load,and analyzes the regulating characteristics of the electric heating load based on this index.Secondly,a large number of measured operating data are collected through electric heating operation control experiments.Quantitative analysis of the effect of outdoor temperature on the operating characteristics of electric heating loads based on measured data;Then introduced the equivalent thermal parameter model of electric heating load,and briefly analyzed the limitation of this model to evaluate the regulation capacity of a single electric heating load.In order to improve the evaluation accuracy of the electric heating load regulation ability,a genetic algorithm(GA)was used to optimize the parameters of the extreme learning machine(ELM).Taking the current indoor temperature,the next indoor temperature,the short-term outdoor average temperature,the switching state,and the power of the electric heating load as model inputs and the electric heating response time as the model output,a single electric heating load adjustment capability evaluation model based on neural networks is established.Finally,the validity of the proposed method is verified by measured data.Based on the measured data,based on the realization of a single electric heating load evaluation model,the electric heating load group adjustment capacity is evaluated,and the distribution characteristics of the load group adjustment capacity are summarized and summarized.According to its characteristics,the "relay" control of the electric heating load in the building and the "round control" strategy of the electric heating load between the buildings are proposed.The effect of different control durations on the capacity of electric heating load is analyzed through specific examples.The indoor temperature change interval of the user during load control is described to ensure the user's temperature comfort.The feasibility and effectiveness of the control strategy are verified.In order to include electric heating load in power system dispatching,a hierarchical structure based on load aggregator is proposed by analyzing the characteristics of distributed residential electric heating load.Based on this,the wind power fluctuations are stabilized as a scenario for research and analysis.The load aggregator compensation cost is taken as the goal.Taking into account the constraints of the electric heating load capacity,an electric heating load participation model to stabilize the wind power fluctuations is constructed.The adjustment capability evaluated after the relay-type control is combined for optimization,so that the combined optimization results can meet the system scheduling instructions.The effectiveness and economics of the model are demonstrated through calculation examples,and at the same time,it proves that the control strategy proposed in this paper can achieve broader scheduling adjustment goals.