Toward Value-oriented Renewable Energy Forecasting: An Iterative Learning Approach

TL;DR

This paper introduces a value-oriented renewable energy forecasting method that aligns forecast models with operational costs, improving dispatch efficiency and reducing costs through an iterative bilevel optimization approach.

Contribution

It develops a novel bilevel programming framework with an iterative solution strategy that directly incorporates operational costs into the forecasting process.

Findings

Forecasts reduce operational costs compared to traditional methods.

The approach is more computationally efficient than stochastic programming.

Forecasts are aligned with operational value, not just statistical accuracy.

Abstract

Energy forecasting is an essential task in power system operations. Operators usually issue forecasts and leverage them to schedule energy dispatch ahead of time. However, forecast models are typically developed in a way that overlooks the operational value of the forecasts. To bridge the gap, we design a value-oriented point forecasting approach for sequential energy dispatch problems with renewable energy sources. At the training phase, we align the loss function with the overall operation cost function, thereby achieving reduced operation costs. The forecast model parameter estimation is formulated as a bilevel program. Under mild assumptions, we convert the upper-level objective into an equivalent form using the dual solutions obtained from the lower-level operation problems. Additionally, a novel iterative solution strategy is proposed for the newly formulated bilevel program. Under such an iterative scheme, we show that the upper-level objective is locally linear regarding the forecast model output, and can act as the loss function. Numerical experiments demonstrate that, compared to commonly used statistical quality-oriented point forecasting methods, forecasts obtained by the proposed approach result in lower operation costs. Meanwhile, the proposed approach is more computationally efficient than traditional two-stage stochastic programs.