Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function

• Published in: Energies (Basel) Vol. 17; no. 21; p. 5389
• Main Authors: Leonel, Laís Domingues, Balan, Mateus Henrique, Camargo, Luiz Armando Steinle, Ramos, Dorel Soares, Castro, Roberto, Clemente, Felipe Serachiani
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2024
• Subjects: Alternative energy sources; Aluminum industry; Consumers; Decision making; Economic aspects; Electricity; energy procurement; Energy transition; Flexibility; Integer programming; integrated stochastic optimization model; Investment analysis; load-supply flexibility; Market prices; Mathematical functions; Natural gas; Optimization; Power plants; Production processes; regret cost function; Brazil
• DOI: 10.3390/en17215389
• ISSN: 1996-1073

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is of fundamental importance. This paper presents a decision-making structure for large consumers with flexibility to manage electricity or natural gas consumption to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of USD 3.98 millions over a six-month analysis period.