Unit commitment and operating reserves with probabilistic wind power forecasts
Audun Botterud, Zhi Zhou, Jun Wang, Jorge F. Valenzuela, Jean Sumaili, Ricardo Jorge Bessa, Hrvoje Keko, Vladimiro Miranda
Abstract
Audun Botterud, Zhi Zhou, Jun Wang, Jorge F. Valenzuela, Jean Sumaili, Ricardo Jorge Bessa, Hrvoje Keko, Vladimiro Miranda
Abstract
In this paper we discuss how probabilistic wind power forecasts can serve as an important tool to efficiently address wind power uncertainty in power system operations. We compare different probabilistic forecasting and scenario reduction methods, and test the resulting forecasts on a stochastic unit commitment model. The results are compared to deterministic unit commitment, where dynamic operating reserve requirements can also be derived from the probabilistic forecasts. In both cases, the use of probabilistic forecasts contributes to improve the system performance in terms of cost and reliability.
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In this paper we discuss how probabilistic wind power forecasts can serve as an important tool to efficiently address wind power uncertainty in power system operations. We compare different probabilistic forecasting and scenario reduction methods, and test the resulting forecasts on a stochastic unit commitment model. The results are compared to deterministic unit commitment, where dynamic operating reserve requirements can also be derived from the probabilistic forecasts. In both cases, the use of probabilistic forecasts contributes to improve the system performance in terms of cost and reliability.
Key concepts: Probabilistic logic, Power system simulation, Wind power, Reliability (semiconductor), Probabilistic forecasting, Wind power forecasting, Electric power system, Computer science