Prescribing net demand for two-stage electricity generation scheduling

TL;DR

This paper introduces a bilevel programming approach to prescribe net demand in two-stage electricity scheduling, accounting for cost asymmetries and demand regimes, resulting in significant cost savings over traditional methods.

Contribution

It develops a novel bilevel model that uses historical data and clustering to improve net demand prescriptions, optimizing for system cost rather than just imbalance minimization.

Findings

Significant cost reductions in case studies.

Improved demand forecasting tailored to operating regimes.

Enhanced scheduling efficiency compared to standard practices.

Abstract

We consider a two-stage generation scheduling problem comprising a forward dispatch and a real-time re-dispatch. The former must be conducted facing an uncertain net demand that includes non-dispatchable electricity consumption and renewable power generation. The latter copes with the plausible deviations with respect to the forward schedule by making use of balancing power during the actual operation of the system. Standard industry practice deals with the uncertain net demand in the forward stage by replacing it with a good estimate of its conditional expectation (usually referred to as a point forecast), so as to minimize the need for balancing power in real time. However, it is well known that the cost structure of a power system is highly asymmetric and dependent on its operating point, with the result that minimizing the amount of power imbalances is not necessarily aligned with minimizing operating costs. In this paper, we propose a bilevel program to construct, from the available historical data, a prescription of the net demand that does account for the power system's cost asymmetry. Furthermore, to accommodate the strong dependence of this cost on the power system's operating point, we use clustering to tailor the proposed prescription to the foreseen net-demand regime. By way of an illustrative example and a more realistic case study based on the European power system, we show that our approach leads to substantial cost savings compared to the customary way of doing.