Optimal Unit Commitment Of Power Systems Containing Wind Energies

With the worldwide increase of energy demand and the environmental issuesbecoming increasingly prominent, as the most promising renewable clean energysources, wind power has been paid more attention, and the wind power capacityincreased year by year. However, different from conventional generators, the large scaleintegration of wind power will bring about a series of challenges and requirements tothe power system optimal Scheduling and operation due to its uncertainty and volatility.As a result, it has great significance for making reasonable scheduling plan of powersystem containing wind farms to improve the utilization ratio of wind power. Focusingon the optimal scheduling of power units with wind farms integrated in power system,research works in this paper are as follows:Considering the uncertainties of load and wind power, a load and wind powermulti-period scenario model is established with the scenario reduction algorithm. Forthe lack of a uniform probability distribution of wind power prediction error, this paperestablished the empirical distribution function of wind power prediction errors, built theanalytic expression of wind power prediction errors through spline interpolation method,and Latin Hypercube Sample technique was used to model the loads and wind powerprediction errors. This paper adopted scenario analysis to describe the uncertainties ofload and wind power, established the combined multi-period scene model for load andwind power. Example results showed the feasibility and effectiveness of this method.Considering the effects of load and wind power fluctuations between differentscenarios on the optimal unit commitment, with the weighted sum of mean and varianceof the generating costs under all scenarios as the objective function, the optimal unitcommitment model was established considering the impacts of uncertainty with the loadand wind power, and the improved particle swarm optimization algorithm wasintroduced to solve the problem. Based on the border scenarios of load and wind power,determined the up and down spinning reserve through the limit fluctuations betweendifferent scenarios. To improve the algorithms’ convergence performance, the powerunits’ output range was dynamically adjusted with the guide of global optimal solution.The example results of a typical ten test system showed the correctness andeffectiveness of the proposed model and algorithm.Considering the optimal unit commitment is closely related with the environmental cost, load prediction error and wind power penetration, to analysis the impacts on theoptimal unit commitment when these parameters change, this paper provided the unitcommitment’ analysis of factor influence. Example results showed that environmentalcosts have little influence on the generating costs, but have significant impacts on theenvironmental benefits; load prediction error and wind power penetration have greatinfluence on the unit commitment.