IEEE Transactions on Power Systems · 2006 · 348 citations · 8 references
BusinessBusiness EconomicsEconomicsEnergy EconomicsForecastingWeather ForecastingElectricity MarketsRenewable EnergyRenewable Energy EconomicsShort-term ForecastingStochastic ProgrammingWind ForecastingImbalanceProduction ForecastsRenewable Energy ForecastingRenewable ForecastingShort-term Energy ForecastingWind Power ForecastingWind Power SystemsWind Speed Forecasting
Power markets are tailored to conventional generation, so wind power must rely on long forecasts, yet forecast errors inevitably generate imbalance costs for wind farm owners. This study proposes a new method to minimize imbalance costs for wind power in short‑term markets. Using stochastic programming, the authors model wind‑power forecast errors as a stochastic process to generate optimal production bids and compare the resulting imbalance costs to bids based solely on wind‑speed forecasts.
Present power markets are designed for trading conventional generation. For wind generation to participate in a short-term energy market, lengthy wind power production forecasts are required. Although wind speed forecasting techniques are constantly improving, wind speed forecasts are never perfect, and resulting wind power forecast errors imply imbalance costs for wind farm owners. In this paper, a new method for minimization of imbalance costs is developed. Stochastic programming is used to generate optimal wind power production bids for a short-term power market. A Wind power forecast error is represented as a stochastic process. The imbalance costs resulting from this strategy are then compared to the case when wind power production bids on a short-term power market are based directly on a wind speed forecast
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