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Solar photovoltaic (PV) power has become one of the major renewable energy sources in modern power systems. Hence, solar forecasting is indispensable for addressing the uncertainties of PV power outputs. Since PV power generation mainly depends on solar radiation conditions, satellite-derived methods have been proven effective in large-scale short-term solar forecasting. These methods utilize satellite optical images to track cloud motion and do not require supplemental ground cameras. However, cloud motion between two consecutive satellite images is highly non-stationary, as the motion includes both displacement and shape variation. Conventional motion estimation methods may miss the detailed cloud variation information, increasing the errors in solar forecasting. In this study, a multi-head cloud motion vector prediction method is proposed to address this challenge. The method models cloud variation as the probabilistic sum of pixels moving under multiple optical flow vectors. Time-varying solar intensity is also considered in the method. In order to estimate these optical flow vectors, a learning-based intelligent model with a self-attention structure is established. The proposed method of multi-step-ahead satellite-derived solar forecasting is superior to conventional methods, as the results of case studies indicate that the proposed method improves the precision of intra-hourly PV power prediction.

The rapid growth of large-scale photovoltaic (PV) power generation has received considerable attention, where a major challenge is that PV power ramp events can adversely affect the operations of integrated power systems. Therefore, effective forecast and dispatch methodologies are critical. However, it is relatively difficult to predict an accurate PV power ramp rate. Another issue is that, since forecast and dispatch are typically considered in research as two separate problems, forecast modeling does not involve the knowledge of dispatch. In this case, unbiased PV forecast is unpractical for PV ramp events, increasing their negative effects. Besides, such a unified optimization with both forecast and dispatch cannot be solved easily since the problem is strongly nonlinear. Motivated by these challenges, a reinforcement forecasting framework is proposed for intraday economic dispatch in this study. First, the PV power ramp rate forecasting is categorized into two tasks of multi-classification and class expectation prediction, where the class continuity is considered in order to improve the forecast distribution. Next, a reinforcement learning agent is established for connecting PV forecast and system dispatch, which adjusts PV power generation schedules based on real-time system feedbacks. Comparative studies are conducted on an intraday system dispatch environment with energy storage. The results of the studies indicate that the proposed framework can provide higher economic benefits than those under benchmark schemes.