Integrating wind power into power systems contributes to existing variability in system operations. Current methods to mitigate this variability and uncertainty focus on using conventional generator ramping capability. There is also the option of using wind power itself to mitigate the variability and uncertainty that it introduces into the system. This paper introduces the concept of a flexible dispatch margin as a means for wind to participate in mitigating net variability and net uncertainty. In providing a flexible dispatch margin, wind generators under-schedule in the hour-ahead energy market in order to have additional expected flexibility available for the real-time market. The implementation of the flexible dispatch margin is analyzed in a two-stage optimization model with recourse to the flexible dispatch margin, flexible demand and generator ramping. This modeling framework combines Monte Carlo simulations with AC OPF analysis, using the IEEE 39-bus test system. Results show that use of the flexible dispatch margin decreases the reliance on peaking generators to mitigate net variability and uncertainty, and also decreases the frequency of price spike events, particularly as wind penetration increases from 10% to 30%. The analysis emphasizes the importance of increasing flexible resource capability as power system variability and uncertainty increase.
Uncertainty and variability in the wind resource create obstacles for the participation of wind power in forward markets, such as regional day ahead electricity markets. Studies performed in various states have developed methods to improve wind forecasting and so reduce the inherent uncertainty in a day ahead schedule for wind power generation. This paper addresses the issue of the variability in wind power generation by estimating the next ten-minute production level for a hypothetical wind farm, and then dispatching additional dedicated resources, such as responsive load or a gas turbine, in order to reduce the net variability of the generation in the next tenminutes. Historical wind data from ISO-ne are used with an auto-regressive moving average model to develop the next ten-minute forecast. Preliminary results estimate the capacity required for the dedicated resources to maintain the wind output within a specified percentage of the submitted day ahead schedule.
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