(717h) Optimal Design of Power-to-Fuels Supply Networks for Grid-Scale Energy Storage
In this work, we address the design of a supply network of power-to-fuels plants to meet given base load electricity demand across a set of geographical locations. Here, we only allow the use of solar PV panels and wind turbines as energy collection units. The synthesis of the following four chemicals is considered: methane, methanol, dimethyl ether (DME), and ammonia, which exhibit different characteristics in terms of energy density and feasible transportation modes. The first three chemicals are produced from hydrogen and carbon dioxide. Ammonia is a carbon-free energy carrier and can be produced using hydrogen and nitrogen. The main technical challenge in this supply chain design problem is the intermittency in the renewable energy availability, which demands that detailed production scheduling is also considered in the design of the production plants (Zhang et al., 2019). To this end, we have developed a mixed-integer linear programming (MILP) model that integrates design and operational decisions at the supply chain and site levels. We further propose a two-phase heuristic solution algorithm for solving large instances of the MILP.
The proposed framework has been applied to a specific region in Spain where the decarbonization of the power grid has a special incidence (around 1800 MW of coal-based power generation is expected to be decommissioned in the next few years). Using real-world data, the near-optimal locations of the production plants and energy storage capacities have been determined, along with the production and distribution plans for a full year. The results demonstrate the benefits of using chemicals as energy storage at the grid level.
Gür, T.M., 2018. Review of electrical energy storage technologies, materials and systems: challenges and prospects for large-scale grid storage. Energy & Environmental Science, 11, 2696.
Zhang, Q., Martín, M., Grossmann, I.E. (2019). Integrated design and operation of renewables-based fuels and power production networks. Computers & Chemical Engineering, 122, 80-92.