Abstract
The integration of large-scale wind power adds a significant uncertainty to power system planning and operating. The wind forecast error is decreased with the forecast horizon, particularly when it is from one day to several hours ahead. Integrating intraday unit commitment (UC) adjustment process based on updated ultra-short term wind forecast information is one way to improve the dispatching results. A novel three-stage UC decision method, in which the day-ahead UC decisions are determined in the first stage, the intraday UC adjustment decisions of subfast start units are determined in the second stage, and the UC decisions of fast-start units and dispatching decisions are determined in the third stage is presented. Accordingly, a three-stage birandom UC model is presented, in which the intraday hours-ahead forecasted wind power is formulated as a birandom variable, and the intraday UC adjustment event is formulated as a birandom event. The equilibrium chance constraint is employed to ensure the reliability requirement. A birandom simulation based hybrid genetic algorithm is designed to solve the proposed model. Some computational results indicate that the proposed model provides UC decisions with lower expected total costs.
Highlights
With growing concern about energy and the environment, wind power as the most visible renewable energy generation has been vigorously developed across the world
We develop an equilibrium chance-constrained three-stage birandom unit commitment formulation, which contains an additional middle stage to modify Unit commitment (UC) schedule of subfast units several hours earlier
The intraday UC adjustment stage is assumed to be carried out 4 hours earlier before the real time
Summary
With growing concern about energy and the environment, wind power as the most visible renewable energy generation has been vigorously developed across the world. High wind power uncertainties due to unsatisfactory forecast accuracy usually generate additional integration costs, which may be reduced by employing adaptive scheduling policies and developing more appropriate models. How to address the wind power uncertainty in unit commitment has attracted much more concern in recent decades. Increasing the system reserve level explicitly had been presented when wind uncertainty addressing problem appeared. Ortega-Vazquez and Kirschen proposed a programming method to determine optimal SR requirement which minimizes the expected cost including interruptions and the operating costs [3], in which an uncommon conclusion was drawn that an increased wind power penetration did not necessarily require larger amounts of SR; the system reliability level might be compromised correspondingly
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