Real-Time Self-Dispatch of a Remote Wind-Storage Integrated Power Plant Without Predictions: Explicit Policy and Performance Guarantee

Abstract

This paper investigates real-time self-dispatch of a remote wind-storage integrated power plant connecting to the main grid via a transmission line with a limited capacity. Because prediction is a complicated task and inevitably incurs errors, it is a better choice to make real-time decisions based on the information observed in the current time slot without predictions on the uncertain electricity price and wind generation in the future. To this end, the operation problem is formulated under the Lyapunov optimization framework to maximize the long-term time-average revenue of the wind-storage plant. Inter-temporal storage dynamics are represented by a virtual queue which is mean rate stable. An online method for real-time dispatch is proposed based on Lyapunov drift algorithm via a drift-minus-revenue function. The upper bound of such a function, which does not depend on future uncertainty, is minimized in each time slot. Explicit dispatch policies are obtained through multi-parametric programming technique so that no optimization problem is solved online. It is proved that the online algorithm can maintain all the constraints across the entire horizon and the expected optimality gap compared to the deterministic offline optimum with perfect uncertainty information is inversely proportional to the weight coefficient in the drift-minus-revenue function. Numerical tests using real wind and electricity price data validate the effectiveness and performance of the proposed method.