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Solution Strategies for Developing Large-Scale Energy Systems
Abstract
Due to globalization, liberalization of emerging markets, and population growth, the demands for raw materials and manufactured products are rapidly increasing. Consequently, supply chains are expanding with alacrity at the global scale and becoming more and more synergistic at the regional scale. At the same time, there has been a societal push towards carbon neutral energy systems that meet their energy requirements via power generated from solar arrays and wind turbines.
Regrettably, the dispatchability of these two types of energy generators are plagued with fluctuations and stochastic intermittencies. To combat these complications, various technologies for storing energy have been put forth, namely, lithium-ion batteries, pumped storage hydro-power, and hydrogen-based dense energy carriers; however, each of these energy storage technologies have their own strengths and weaknesses. Therefore, there is a need for quantitative frameworks to ensure that the energy systems within supply chains of the future are optimally developed and can operate over a wide range of conditions.
To this end, this work presents a generic multi-period integrated infrastructure planning and operational scheduling approach for developing energy systems. The corresponding mathematical programming formulation can either be posed as a mixed-integer linear program or a mixed-integer quadratic program. The methodology is applicable to energy systems with multiple resources, locations, processing pathways, and planning periods in which infrastructure decisions can be carried out. It can also be generalized to generic supply chain problems.
Unfortunately, the complexity of the aforementioned mathematical programming formulation can pose serious computational difficulties even for a “state-of-the-art” mixed-integer programming solver. Consequently, this work proposes a computationally distributable solution strategy, using Benders decomposition and a column generation procedure, to improve the tractability of the problem. It also presents a set of valid inequalities that can be applied to the Benders master problem, which have been found to significantly improve the convergence of the procedure, and a three-phase matheuristic that is able to generate integer feasible upper bounds. o Finally, this work culminates in a solution framework for developing supply chains comprised of transportable modular production units; whereby the modular production units can be relocated between production facilities to meet the spatial and temporal changes in the availabilities, demands, and prices of the underlying commodities. This work presents a “flow-based” and a “path-based” mixed-integer linear programming formulation to model the problem and a three-phase matheuristic that is able to generate integer feasible upper bounds.
Citation
Allen, Richard (2022). Solution Strategies for Developing Large-Scale Energy Systems. Doctoral dissertation, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /197859.