Research On Distributionally Robust Optimization Of Renewable Energy Power Accommodation

Climate change is a major global challenge faced by all mankind.As the largest energy consumer and carbon emitter in the world,China has pledged to peak carbon dioxide emissions before 2030 and achieve carbon neutrality before 2060.It is important for the goal to develop renewable energy power represented by wind and photovoltaic power,and build a new power system with new energy as the main body.Wind and solar are uncontrollable resources.which are greatly affected by the environment and weather.Their power is random and fluctuating.During power system dispatch,it may be necessary to curtail wind and photovoltaic power for the safe and economic operation.That will cause a waste of renewable energy.The grid-connected renewable energy power generation puts forward higher requirements on the dispatching ability of power system.Therefore,it is significant for power system with renewable energy to build a more accurate economic dispatch model.It can maintain the safe and stable operation of power system,promote the accommodation of renewable energy power and improve the economics of power system operation.For this reason.this thesis carries out research on the dispatch model of power system with renewable energy.The main work of this thesis is as follows:Firstly,this thesis briefly introduces the theory of distributionally robust optimization,including its characteristics and applicable conditions.This thesis also classifies distributionally robust optimization method according to the different construction method of ambiguity set,and analyzes the advantages and disadvantages of each construction method of ambiguity set.This part lays a theoretical foundation for the subsequent construction of the distributionally robust optimization scheduling model.Secondly,this thesis constructs a distributionally robust optimization scheduling model considering the penalty of wind and photovoltaic power curtailment for the power system with renewable energy power based on the theory of distributionally robust optimization.The objective function not only includes the generation cost,start-up cost and stop cost of thermal units,but also introduces the penalty cost of wind and photovoltaic power curtailment,which can further promote renewable energy power accommodation.The constraints include power balance between generation supply and demand,the maximum and minimum capacity power,the ramp-up and ramp-down limits of units.The ambiguity set of uncertain parameters is built based on the first-order deviation moment function which describes the uncertainty of renewable energy power.It can cover more probability information of uncertain parameters,make the ambiguity set more complete,and provide strong decision support for decision makers.Because the model is a nonlinear optimization model,it is difficult to solve directly.In this thesis,linear decision rules and Lagrange duality theory are used to transform the original model into an equivalent mixed integer linear programming model.Finally,this thesis carried out case study,A modeling toolbox XProg 1.0 of the Matlab R2015b is used for programming.Compared with robust optimization model,distributionally robust optimization model is more economical.Taking the first-order deviation moment function into this model can reduce the conservatism of decision-making and further improve its quality.Flexible transformation for units can reduce the cost of power system to a certain extent,and also make more space for accommodation of renewable energy power.Based on the optimization results under different wind and photovoltaic power curtailment,the cost of power system increases as wind and photovoltaic power curtailment decreases.In order to maintain security and stability of power system,decision makers should set reasonable targets.