Medium-short Term Genration Scheduling Models And Methods For Power Systems With Wind Power

Generation scheduling is vital to ensure the security operation of power system and to rearlize the high efficient allocation of generation resources. In recent years, in order to promote energy and environmental sustainability, wind power, which is a mature renewable generation resource, has been vigorously developed. The wind farm scale has been expanded and the grid-connected wind power capacity has been increased. However, due to the randomness, intermittence and unpredictability of wind power, a series of problems are brought to security operation and generation dispatching in the power system. In addition, with the improvement of systematic management level for generation scheduling, all of the active output, reserve allocating and maintenance scheduling should be considered in the optimal scheduling model to coordination generation resources and maximize the overall benefit. Based on such backgrounds, this dissertation focuses on the medium-short term generation scheduling for power system with wind power integration. The main research work involves:In aspect of active optimal scheduling with10-15min interval, to deal with the uncertainties of reserve determination and line power flow calculation brought by wind power, the coordinated active power and reserve dispatching model for wind power integrated power systems considering line security verification is proposed. Firstly, in the modeling process, combining the confidence level, the influence of load and wind power forecast errors on the reserve is analyzed, the relationship of reserve and wind output power is established, and the demand of reserve is accurately quantified. Secondly, considering the fluctuant features of line power flow arouse by wind power uncertainty, the multi-scenario model of line power constraints is introduced. However, the multi-scenario model takes a large amount of computation. To improve the efficiency, the checked scenario reduction method and checked lines reduction strategy are developed, which are based on linear programming theorem, and consider the output range of units in scheduling period. Finally, numerical examples show that the dispatch schemes given by the proposed model are provided to be more feasible and safety with the satisfaction of economy.On the basis of the above research, a multi-objective optimization scheduling model is formulated. In the proposed model, the environmental benefits and the operational risk caused by lacking reserve are taken to account, pollutant emissions and reserve risk indicator are introduced to the objective function, and the reserve risk indicators are developed. To solve this model, an improved differential evolution algorithm for multiobjective optimization (IDEMO) is proposed. In the IDEMO, the chaotic searching strategy is introduced to improve the availability rate of initial population, the adjustment strategies for control parameters are used to strengthen global optimal searching capability, and dynamic crowding distance is employed to keep the diverity of population. After that, the decision matrix is given by pareto solution set of the IDEMO, and the optimal solution set is odered by similarity to ideal solution (TOPSIS) based on the entropy, so the optimal solution can be decided. Numerical examples show that the proposed algorithm is able to provide excellent candidate plans for multi-objective optimization scheduling problem.In aspect of day-ahead unit commitment, an interval optimization combined with point estimation method (IO-PEM) is proposed to solve stochastic security constrained unit commitment (SCUC) problem. Firstly, accordance with the confidence interval of the wind power, the fluctuation of wind power is described by the most compact constraints set and the corresponding extreme scenarios. And the stochastic security constrained unit commitment is solved by interval optimization method. Such method can accelerate the solution speed in the premise to ensure that the scheduling results meet security constraints. Secondly, in order to evaluate the adjusting energy consumption with the consideration of wind power output fluctuation, point estimation method is introduced to improve the interval optimization approach. As a result, the optimal scheduling plan can be more economical. Finally, the model is optimized by mix-integer linear programming. And simulation results show that, the proposed method only need to caculate a few scenarios for keeping security and economy of the dispatching scheme, so the proposed method can effectively solve the stochastic SCUC problem.In aspect of monthly unit commitment, the joint optimization model of monthly unit commitment and maintenance scheduling for wind power integrated power system is proposed to handle the randomness of wind power and optimize maintenance scheduling. Firstly, based on historical data, the stochastic behavior of wind power is represented by weibull distribution, and the impact of wind power uncertainty on constraints is coped with chance-constrained programming theory. The relating probability model is formulated, and the methods of probability model transferring to determined model with wind farms is proposed. Secondly, maintenance expenses and the constraints of maintenance are introduced. Since unit commitment and maintenance scheduling have different scheduling intervals, the index variables of them are setted respectively, incidence matrix is created, generation and maintenance scheduling are coordinative optimized as well. Finally, the proposed model is solved by mixed integer linear programming. Simulation results show that proposed model can effectively balance the relationship between the costs and risks, the results of unit commiment and maintenance scheduling are more reasonable, and the optimal scheduling can provide an effective mean of mid term generation scheduling for power systems with wind power.As previously mentioned, in this dissertation the influence of wind power randomness on generation scheduling under different time scales is studied, the relevant approaches are furnished, and this work can provide a theoretical basis and method support for medium-short term economic and security operation in the environment of large-scale wind power integration.