(529e) An Efficient Solution Strategy for Solving Enterprise-Wide Multi-Period Planning and Scheduling Problems: A Case Study on a Hydrogen-Based Economy

Energy is vital for the general welfare of mankind; yet its traditional cultivation from hydrocarbons can have harmful effects on the environment. To address these complications, there has been a societal push towards renewable energy generation. However, such a transition has intrinsic challenges and complications including but not limited to spatial, temporal, and stochastic elements. To tackle some of these obstacles, researchers in academia and practitioners in industry have proposed the transition into a hydrogen-based economy. This will require an exponential increase in hydrogen’s deployment from its current negligible share [1,2,3]. Moreover, a successful transition demands substantial investments in infrastructure over the next few decades to make the venture commercially feasible [4]. This in turn, requires a quantitative decision-making framework to construct optimal infrastructure planning decisions across multiple locations and multiple time periods.

In this work, we expand upon our previously developed optimization framework for the simultaneous design and operation of hydrogen-based energy systems. In our hitherto framework, we introduced a bespoke matheuristic that reduced the required computational time to solve the optimization problem by approximately two orders of magnitude on large-scale instances [5]. In continuation, we present a two-stage matheuristic for tackling the multi-period and multi-location infrastructure planning problem. The meta-heuristic embedded in the matheuristic, decouples the planning and scheduling periods to compute on the fly upper bounds to the optimization problem in the branch-and-bound tree via its linear programming relaxation. This new solution strategy allows us to optimally solve enterprise-wide multi-period planning and scheduling problems in a fraction of the time that it takes a state-of-the-art commercial solver.

References:

[1] Moreno-Benito, M., Agnolucci, P., & Papageorgiou, L. G. (2017). Towards a sustainable hydrogen economy: Optimisation-based framework for hydrogen infrastructure development. Computers & Chemical Engineering, 102, 110-127.

[2] Palys, M. J., & Daoutidis, P. (2020). Using hydrogen and ammonia for renewable energy storage: A geographically comprehensive techno-economic study. Computers & Chemical Engineering, 136, 106785.

[3] Demirhan, C. D., Tso, W. W., Powell, J. B., & Pistikopoulos, E. N. (2021). A multi-scale energy systems engineering approach towards integrated multi-product network optimization. Applied Energy, 281, 116020.

[4] Shell scenarios sketch: A US net-zero CO2 energy system by 2050. (2020). Shell Oil Company.

[5] Allen, R. C., Baratsas, S.G., Kakodkar, R., Avraamidou, S., Powell, J. B., Heuberger, C.F., Demirhan, C. D. & Pistikopoulos, E.N. (2021). An optimization framework for solving integrated planning and scheduling problems for dense energy carriers. 11th IFAC International Symposium on Advanced Control of Chemical Processes (ADCHEM). In press.