Research On Power Grid Dispatching Method Considering Generation-load Interaction And Multi-energy Collaborative Optimation

At present,facing the global fossil energy shortage and serious greenhouse effect,countries actively seek new energy to replace fossil energy.In China,with the vigorous promotion of the strong smart grid and ubiquitous power Internet of things,a high proportion of renewable energy is connected to the power grid.And artificial intelligence,sensor,advanced communication technology can obtain a comprehensive data of new energy,energy storage and load.These datas can be applied to power system optimization scheduling.At the same time,the rapid development of comprehensive energy technology makes the urban power grid gradually turn to multiple energy supply patterns,including electricity,water,heat,gas and others.As the continuous increase of installed scale of renewable energy,it brings many problems to the operation and scheduling of power system.In order to deal with these problems,renewable energy consumption technology emerges at the right moment.And it is convenient to optimize the multi-energy power,users’ load and energy storage power station by ubiquitous Internet of things advanced technology.The existing optimization scheduling strategies research on power system with renewable energy does not think a lot of the source-charge interaction and ignores the deep peak shaving of units and the joint scheduling of user loads under the new situation.In order to make up for the deficiencies of existing studies,this paper analyzes the characteristics of the power generation side and the users’ side.And this paper focuses on the optimal scheduling of the power system with renewable energy.The main tasks completed are as follows:(1)The generation-load characteristics of power system with renewable energy are studied.On the generation side,the output characteristics of photovoltaic power station and wind turbine are analyzed,and the uncertainty model based on probability function is established.On the load side,the uncertainty of system load considering demand response,electricity pricing-based demand response(PDR)and incentive demand response(IDR)is analyzed.And the corresponding uncertainty model of system load,the day-ahead pricing-based demand response virtual unit and the intraday incentive demand response virtual unit are established.(2)For the two-phase collaborative optimization problem of power system with large-scale renewable energy,this paper proposes a day-ahead and intraday low-carbon scheduling model for power systems with wind power,photovoltaic station and storage.In this model,the uncertainty of wind power,photovoltaic,load and demand response units is considered in different time scales,and thermal power units may be work in depth peak-shaving condition.The objective function is the total of system operating costs and carbon emission costs,and the improved bat algorithm based on genetic algorithm and two-point estimation method are used to solve this problem.The feasibility and superiority of the model and its solution method are verified by numerical simulation.(3)For the optimal operation and scheduling problem of urban power grid with high permeability of renewable energy,this paper proposes an intelligent management method for multi-energy coordination of urban power grid.This method consideres the operation constraints of all equipments in the system and safe operation constraints of power network.And a day-ahead optimization scheduling model for the city level integrated energy system is established based on the output prediction of uncertain components such as load and renewable energy in the integrated energy system.Then this model is solved by alternating direction multiplier algorithm.Finally,the validity of the method is verified by a practical example.