Optimal planning of slow-ramping power production in energy systems with renewables forecasts and limited storage

Abstract

Abstract-We address the cost-efficient operation of an energy production system under renewables uncertainty. We develop an MDP model for an idealized system with the following features: (1) perfectly predictable power demand, (2) a renewable power source subject to uncertain forecast, (3) limited energy storage, (4) an unlimited fast-ramping power source, and (5) a slow-ramping power source which requires (optimal) planning. A finite-horizon stochastic optimization problem is introduced to minimize the overall cost of operating the system, and then solved numerically using standard approaches (based on backward induction) and available data. In contrast with the unit commitment problem which is traditionally optimized for a single planning frame, we show in simple scenarios that it may be beneficial to optimize over a few planning frames, and that there is no benefit to considering longer (e.g., infinite) horizons. We discretize the state space in an attempt to mitigate the curse of dimensionality usually associated with numerically solving MDPs. We note that few discretization states already yield a significant decrease in the total cost.

The Optimal Control of Storage for Arbitrage and Buffering, with Energy Applications

“…There have been numerous studies into the use of storage for buffering against both the increased variability and the increased uncertainty in electrical power systems, due to higher penetration of renewable penetration-the former due to the natural variability of such resources as wind power, and the latter due to the inherent uncertainty of forecasting. These studies have considered many different more detailed objectives; these range from the sizing and control of storage facilities co-located with the renewable generation so as to provide a smoother supply and so offset the need for network reinforcement [13,14,15], to studies on storage embedded within transmission networks so as to increase wind power utilisation and so reduce overall generation costs [16,17,18]. In addition there have been a number of studies into the more general use of storage for buffering, for example, so as to provide fast frequency response to power networks [19,20,12], or to provide quality of service as part of a microgrid [21,22].…”