Scheduling Strategy Research Of Power System Considering Wind Integration And Demand Response

The double pressure of fossil energy shortage and the goals of energy saving and emission reduction makes it urgent to transform energy structure of our country.Wind power generation has become an important compoment of energy structure with the advantages of low cost,mature technology,large-scale development and utilization,and so on.With the large-scale integration of wind power,the uncertainty of wind brings great challenges to power system's control and operation.Demand response changes the traditional power consumption pattern from the load side,this provides a new idea to overcome the uncertainty of wind power.This paper studies the schedule optimization strategy of power system with wind power integtation and demand response,and realizes the interaction and synergistic efficiency between source and load.Firstly,the basic content of demand response is studied.This chapter analyzes the advantages of demand response,the necessity of implementing demand response,the favorable conditions for the development of demand response,and the feasibility of demand response to the consumption of wind power,and establishs the mathematical models for different kinds of demand response.Secondly,the short-term wind power scenario quality evaluation framework is established,in which uncertainty of short-term wind power is divided into randomness at each individual lead time and volatility between periods.Simulation results show that the scenarios considering wind power 's correlation can better depict randomness and volatility of wind power,thus providing more powerful technical support for operation and reliability evaluation.Thirdly,for the unit commitment problem considering wind power and demand response,a two-stage stochastic optimization model is proposed.At the source side,the uncertainty of the wind power is described in terms of scenarios considering the correlation between time points,at the load side,the "Time of Use Pricing" and "Interruptible Load" are the reprents of demand response,the model achieves the co-optimization of the energy market and the reserve market.The simulation results of IEEE39 system show that the"Time of Use Pricing" plays a significant role in the peak shaving and valley filling,"Interruptible Load" takes part in load regulating during the peak load period.The superposition of the two methods can smooth the load curve and reduce the peak load pressure,at the same time,they enhance the flexibility of the system,which can effectively deal with the uncertainty of wind power and reduce the discard of wind power.Finally,this paper presents a new improved teaching-learning-based optimization algorithm(ITLBO)to solve the dynamic economic emission dispatch(DEED)problem based on the characteristic of period coupling.The original teaching and learning algorithm is used as the basic framework,the opposition-based learning(OBL)strategy,the single interval teaching and learning process,the single interval greedy selection strategy is adopted to enhance the local searching ability and the global searching ability.The simulation results show that the proposed ITLBO algorithm can obtain smooth and uniformly distributed Pareto optimal frontier and has a faster convergence rate compared with the original TLBO and other optimization methods.