Multi-target Low-carbon Power System Scheduling With Wind Farm

Energy generation scheduling must be under the premise of the protection of reliableelectricity supply, in accordance with the principles of energy efficiency, economy, priorityscheduling of renewable generation resources. Wind power as a clean energy, promote thedevelopment of low-carbon electricity.the study of the dynamic economic dispatchincluding wind farm power problems is of great significance under the economicrequirements of the electricity markets.Wind power brings new problems to dynamic economic dispatch. The randomnessand volatility of wind power not only increases the difficulty of dispatching, but also putsforward higher requirements to the ramping rates of conventional units and the spinningreserve. Dynamic economic dispatch including wind farm power systems is a highdimension, nonlinear, multi-constrained mixed integer optimization problem.This paper first introduces the basic concepts and optimization algorithm foreconomic dispatch including wind farms. Including the selection of the establishment ofthe objective function and constraint conditions, Mathematical model of power systemdynamic economic dispatch, Evolutionary algorithms and multi-objective optimization.Second, consider the cost of risk in Wind Power System Dynamic economic dispatchmodel. Minimum system cost of power generation as the objective function, consideringthe power balance, operating constraints, Generator Ramp Rate Constraints, spinningreserve capacity constraints, the establishment of the single-objective economic dispatchmodel.Then, taking into account the carbon emissions of conventional units, taking intoaccount both economic and environmental aspects, Minimum system cost of powergeneration and pollution gas emissions minimum, Consider the power balance, operatingconstraints, Generator Ramp Rate Constraints, spinning reserve capacity constraints, withwind farm target low-carbon power system scheduling model.Lastly, respectively, using improved particle swarm optimization, and multi-objectiveparticle swarm optimization algorithm, the above example, the simulation study, get goodresults, verify the validity of the model.