Dynamic Economic Dispatch Incorporating Multiple Wind Farms Based On Chance Constrained Programming

With the increase of energy demand and the depletion of fossil fuels,wind power generation has attracted great attention all around the world as a renewable energy.However,large-scale wind power integration brings new challenges and demands to dynamic economic dispatch and safe operation of power systems due to its uncertainty and variability.Thus traditional scheduling strategy is no longer applicable to systems that integrated with large-scale wind farms.Novel decision scheduling strategies are urged to find.On the base of wind power and load probabilistic forecasting,a stochastic dynamic economic dispatch model is proposed based on chance constrained programming,ensuring the uncertainty caused by wind power and load can be offset by spinning reserves under given confidence interval.Ramp rate constraints,transmission constraint,power balance constraint and operating reserve constraint are all taken into consideration.Compared with the existing dynamic economic scheduling integrated with wind farms,the model and simplification methods proposed in this thesis can better deal with the situation of multi-wind farm integration.Risk of load shedding and wind spillage are reduced according to given probability by optimizing the amount of reserves.Two methods are used to solve chance constrained programming.When the variables of chance constrained programing are not independent of each other,it is difficult to convert the chance constraints into deterministic constraints.Monte Carlo method can be used to check the feasibility of constraints.An improved Particle Swarm Optimization method based on Monte Carlo is used to solve the dynamic economic dispatch incorporating wind power.The global searching ability of PSO is improved by adaptive acceleration factor,nonlinear inertia weight,feasible regulation scheme and mutation regulation scheme.If chance constraints are separate and simple,then chance constrained programming can be solved by converting them into corresponding deterministic constraints.The focus and difficulty lie in the fast computation of cumulative distribution function of the joint variable and its inverse function.FFT is used to convert chance constraints into deterministic constraints by computing the probability distribution of joint variables,then the final deterministic model is solved by CPLEX,an excellent business optimization solver.Replacing the convolution process by FFT transformation can accelerate the optimization duration.The simulation results on modified IEEE-39 system with multiple wind farms demonstrate the feasibility and effectiveness of the proposed method.In conclusion,chance constrained programming is used to deal with the uncertainties caused by wind power generation.The simplification and solution of the model can provide guidance to dispatchers.