Decision making under the uncertainty of electricity market for scheduling and planning

Identifier:  TDX:4078

Handle: https://hdl.handle.net/20.500.11797/TDX4078

Authors:  Gangwar, Sachin

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. To develop this approach, we have used a MILP formulation, time series forecasting with ARIMA, and Monte-Carlo scenario generator. The MILP effectively copes with changing electricity prices while maximizing the plant performance. To this end, decisions on flow rates, starts-up and shutdowns of unit operations, and purchase orders are optimized according to the market needs of the existing industrial facility (i.e., demand of liquid N2, O2 and Ar and gaseous N2 and O2).