Study On Optimization Of Operating Reserve In Wind Power Integrated Power System Based On Multiple Timescale

The wind power is curtailed at load valley,and the lack of system peak regulation capacity became one of the main reasons for restricting the wind acceptance level.The main factors influencing the system peaking regulation are the capacity insufficiency of peaking regulation units on the power supply side,and that peak-valley load difference expands unceasingly on the load side.With the deepening of China's electricity market reform,it is more likely to use market-oriented means to stimulate the enthusiasm of the unit for promoting the acceptance of wind power than using the planned means.The peak regulation right transaction encourages thermal units to realize the potential for peak regulation and provides accommodation for wind power.However,the randomness and fluctuation of wind power will cause huge trading risks in the normal transaction,so a new transaction type should be considered to meet the demands of the market with the participation of wind power.At the same time,the price signals can guide the use of electricity and modify the load curve.It is significant for reducing peak regulation pressure of power supply side to study how to guide load response to promote wind accommodation.Therefore,taking consideration of system peak regulation,this paper undertake research on promoting wind power accommodation with the utilization of peak regulation transaction and load response.The main research contents include the following aspects:(1)Putting forward the normal pattern of peak regulation right transaction with the consideration of randomness of the wind farm.Through analyzing the wind farms' utility and thermal units' costs on both sides of peak regulation right transaction,a multi-objective model of peak regulation right transaction with the goal of achieving maximum social benefits and second wind power acceptance capacity is established.The model is solved by using the multi-objective particle swarm optimization(MOPSO)algorithm.In addition,the affect of the change in transaction capacity of wind farm peak regulation right on the second acceptance of wind power and social benefits are analyzed.Some influences of wind power prediction error,spinning reserve price,wind power capacity planned to be accepted,wind power and thermal power benchmark price on peak regulation right transaction and second wind power accommodation are discussed.All these provide the reference for peak regulation right transaction to promote wind accommodation between wind farms and thermal units.(2)Analysing the impact of wind power randomness on peak regulation right trades,the paper,combined with the avoiding risk characteristic of the power option,promotes a peak regulation right form that considers option theory,and put forward the concept of peak regulation right price and peak regulation capacity price.The multi-objective model is established,which achieves the maximum in social benefits,wind farm benefits and thermal units' benefits.Taking the case compares the influence of peak regulation transaction type of this paper promote and normal type,and different clearing types and peak regulation right price on second wind power acceptance capacity and benefits of wind farms and thermal units.The result verifies that the peak regulation transaction considering option theory can gain more benefits for wind farms and thermal units than normal type transaction when the capacities of second wind power acceptance are the same,and can reduce the capacity of extra spinning reserve units in the electricity system.(3)The paper conducts research about different types of users participate in the different action of promoting wind accommodation from the users' perspective of view and establishes the model with various types of users.This paper puts forward day-ahead price optimization and wind power system bi-level scheduling programming model with incentive-based demand response,which considers the uncertainty of wind power.Latin hypercube sampling is used to generate a large number of wind power scenarios,and simultaneous backwards reduction is used to reduce scenarios.The model is solved by using the quantum particle swarm optimization(QPSO)algorithm.The case verifies that the model put forward in this paper can peak load shifting and increase the wind accommodation,and reduce the thermal power generation cost and user electricity purchasing cost.In addition,some influences of different scheduling mode,time of use and day-ahead price,wind power prediction error,Price-based users' cost and Incentive-based users' quote price on scheduling results are discussed.