Two-stage hybrid day-ahead solar forecasting

Abstract

Abstract-Power supply from renewable resources is on a global rise where it is forecasted that renewable generation will surpass other types of generation in a foreseeable future. Increased generation from renewable resources, mainly solar and wind, exposes the power grid to more vulnerabilities, conceivably due to their variable generation, thus highlighting the importance of accurate forecasting methods. This paper proposes a two-stage day-ahead solar forecasting method that breaks down the forecasting into linear and nonlinear parts, determines subsequent forecasts, and accordingly, improves accuracy of the obtained results. To further reduce the error resulted from nonstationarity of the historical solar radiation data, a data processing approach, including pre-process and post-process levels, is integrated with the proposed method. Numerical simulations on three test days with different weather conditions exhibit the effectiveness of the proposed two-stage model.

Microgrid Value of Ramping

“…Ramp up and ramp down constraints of dispatchable DG units are defined by (4)- (7), where (4),(6) and (5), (7) belong to intraday and inter-day intervals, respectively. Constraints (8)- (11) represent the minimum up and down time limits of dispatchable DG units, where (8) and (10) belong to intra-day intervals, while (9) and (11) belong to inter-day intervals. The limitation of energy storage power depends on the charging and discharging minimum and maximum limits, based on its mode (12)- (13).…”

Day-Ahead Solar Forecasting Based on Multi-Level Solar Measurements

“…Hybrid models are considered highly effective for solar forecasting in a way that they reinforce capabilities of each individual method. Hybrid models reap the benefits of two or more forecasting methods with the objective of achieving a better forecast result [18]- [21]. In [22], authors present a hybrid model consisting of various forecasting methods for a 48-hour-ahead solar forecasting in North Portugal.…”

Battery Swapping Station as an Energy Storage for Capturing Distribution-Integrated Solar Variability

“…The aggregated prosumers net loads variability between two successive time periods (i.e., Δt) is formulated in (12). Nevertheless, leveraging (11) and (12), the aggregated prosumers net load variability is entirely captured by the BSS through exchanged power with the utility grid.…”