On the Added Value of State-of-the-Art Probabilistic Forecasting Methods Applied to the Optimal Scheduling of a PV Power Plant with Batteries

Abstract

Efforts have been made to develop methods to quantify the uncertainty related to the power production of renewable energy power plants, allowing producers to ensure more reliable engagements related to their future power delivery. Even though diverse probabilistic approaches have been proposed in the literature, giving promising results, the added value of adopting such methods is still unclear. This paper comprehensively assesses the profits obtained when probabilistic forecasts generated with state-of-the-art methods are fed into a stochastic programming decision-making model to optimally schedule an existing PV power plant operating in highly unstable weather. Different representative probabilistic forecasting methods are assessed and compared against deterministic forecasts submitted to varying levels of uncertainty, used to schedule the power plant in standalone operation and hybrid operation with batteries. The main findings reveal that although probabilistic forecasts offer potential benefits in handling uncertainty and utilizing battery assets to mitigate forecast errors, deterministic forecasts consistently yield higher profits than probabilistic forecasts. It is shown that this disparity is primarily attributed to the scenario diversity present in probabilistic forecasts, which leads to over-conservative decisions and the loss of temporal correlation with PV power production variations, resulting in increased imbalances and penalties.

Keywords

• renewable energy
• photovoltaic power plant
• uncertainty quantification
• scenario generation
• optimal scheduling
• stochastic programming