Mitigating the Impact of Photovoltaic Power Ramps on Intraday Economic Dispatch Using Reinforcement Forecasting

Abstract

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.