Prescribing net demand for two-stage electricity generation scheduling
Year: 2023
Type: article
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.
Source: Operations Research Perspectives
Authors: J.M. Morales, M.A. Muñoz, S. Pineda
Institution: Universidad de Málaga
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,800
Funders: Universidad de Málaga, Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía, Federación Española de Enfermedades Raras, Ministerio de Ciencia e Innovación, Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España, Junta de Andalucía, Smithsonian Conservation Biology Institute, European Research Council, European Regional Development Fund, Agencia Estatal de Investigación, European Social Fund, H2020 European Research Council
Awards: G1175115578, G1855990336, G2229418981, G2262748287, G2309174360, G2660406951, G2829722718, G300979063, G3480869486, Advanced Analytics to Empower the Small Flexible Consumers of Electricity, G410571670, G425668148, G4297911362, G430777504, G451917667, G4896368323, G5080475149, G5248924513, G5421974903, G5539308778, G5744120364, G5805502524, G5967599077, G6230056669, G7084143925, G8570089637, G8704500437, G99296377
OpenAlex ID: W4318015197