Scheduling optimization and risk analysis for energy-intensive industries under uncertain electricity market to facilitate financial planning. (June 2023) - Gangwar, Sachin; Fernández, David; Pozo, Carlos; Folgado, Rubén; Jiménez, Laureano; Boer, Dieter
Highlights: A novel methodology for scheduling of energy intensive industries under the uncertainty of spot electricity market. Development of a scenario generator tool using ARIMA for electricity price forecasting. Integration of deterministic optimization with multi scenario approach. Risk metric analysis to facilitate decision making in choosing the best suited scenario. Implementation of the methodology in a real-life cryogenic air separation plant. Abstract: The planning of energy-intensive processes is intrinsically uncertain due to their dependence on the volatile energy market, with scheduling having a vast impact on the final production cost of these plants. Traditional stochastic methods are mathematically very complex, which translates into a significant computational effort that might prevent a timely response to varying electricity prices. To encounter this uncertainty, we develop a reliable hybrid simulation-optimization approach for optimizing the production plant scheduling, combining scenario analysis with risk analysis. The proposed methodology is demonstrated with real data from a cryogenic air separation plant in Tarragona (Spain). This approach also informs decision-makers about risk or expected shortfall associated with the implied scenario. The generic methodology used here can be easily adapted to schedule facilities in other energy-intensive sectors such as cement, metallurgy or pulp and paper. Graphical abstract: Image, graphical abstract