Multi-Cycle Unit Commitment Coping With High Wind Power Uncertainty

With the growing concern about energy and the environment, wind power as the most visible renewable energy generation, has been vigorously developed across the world. However, the growth of integrated wind power also brings many dispatching and control challenges, due to the nature of wind energy and the characteristics of wind plants. High wind power uncertainty due to unsatisfactory forecast accuracy usually generates additional integration costs, which influence the power system scheduling and dispatching, both in the power systems with developed electricity market and in the power systems of China. Unit commitment (UC) has always been regarded as one of the most crucial processes in system scheduling and the crucial basis for dispatching. How to address the wind power uncertainty in UC has attracted much more concern in recent decades.Based on mathematical optimization theories and the idea of multi-period and multi-time scale coordination optimizing, this thesis performs meticulous and thorough work on the research of monthly and short term UC, and the coordination optimizing of them, to cope with the high wind power uncertainty.Firstly, considering the long start-up time and high start-up costs of the coal units in China, and the mid-long term electricity trading, a monthly and short term UC coordinatation strategy is presented. A corresponding two-stage monthly UC model which considering short term scheduling is developed also. A dynamic clustering idea is presented which clusters conventional coal units into two optimizing groups, which are monthly start-up optimizing group and short term start-up optimizing group. It is based on the advantages of the two scheduling periods that monthly scheduling is more conducive to optimize resource distribution and to reduce the total start up costs, whereas short term scheduling contributes to reduce the load rate and reserve requirement due to the significant reduction in the wind power uncertainty. Montly wind speed statistical results are used to generate wind power scenarios, which are integrated in the second-stage model. Simulation results show that the average load rate of thermal units could be improved and further economic benefits could be obtained by coordination optimization of monthly and short-term UC through thermal unit clustering.Secondly, a novel multi-level coordination strategy for short term UC is proposed. It is based on the small probability characteristics of the extra reserve demanded to cater for wind uncertainty and the characteristics of wind power forecast error decreasing gradually with the shorter time scales. In the multi-level coordination strategy, the traditional UC strategy is expanded to three coordinated UC levels, which are primary UC (PUC), secondary UC with the time scale of a few hours (Usually4-8h) in advance, and tertiary UC (TUC) that adjusts the up/down states of fast units. The spinning reserve in PUC is reduced by relaxing system reliability level, so that the operating costs can be reduced. To guarantee the system reliability, SUC and TUC are carried out when the spinning reserve capacity cannot meet the actual demand in intraday operation. To obtain the most economic UC scheme, the optimal spinning reserve capacity in PUC should be determined, which is quantified with a simulation analysis method in this paper. Simulation results show that the spinning reserve capacity could be reduced by coordination of UC with different time scales, therefore the total operation costs could be reducedBased on the idea of multi-level unit commitment coordinated optimizing, and considering the economic advantage of scenario-based stochastic unit commitment model, a novel three-stage UC decision framework is presented. An additional middle stage is added into traditional two stage UC decision framework, to modify UC schedule of sub-fast start units several hours earlier when the reliability requirement cannot be satisfied, estimated using the updated wind realization and the hours-ahead wind power forecasting. It is analized that the intraday hours-ahead forecasted wind power and intraday UC adjustment event are both with twofold randomness when being considered in day-ahead scheduling. As a result, the birandom programming is applied to the proposed three-stage UC modeling with wind power uncertainties. To formulate the reliability constraints, equilibrium chance measures are used in programming formulation. A birandom simulation based hybrid GA is selected to solve the proposed three-stage UC model. Simulation results show that more accuate wind power information could be used when applying the proposed birandom model, therefore more economic UC decisions could be obtained.Based on the proposed monthly-short term unit commitment coordination strategy, and the unit commitment models, two multi-cycle and multi-time scale unit commitment coordination strategies are designed to cope with the high wind power uncertainty.