(200a) Incorporation of Network Topology for Resilient Design and Optimal Operation in the Framework of Water-Energy Nexus

Authors: 
Tsolas, S. D., Texas A&M University
Karim, M. N., Texas A&M University
Hasan, M. M. F., Artie McFerrin Department of Chemical Engineering, Texas A&M University
The modern world is supported by complex infrastructures with intricate interdependencies, such as power distribution grid and water distribution networks. Poor maintenance or catastrophic events on some connections can propagate and affect the whole network’s performance, leading to power outages and water shortages. In addition, these networks are highly interdependent, constituting what is known as the Water-Energy Nexus (WEN) [1,2]. Energy needs water to be generated, and water needs energy to be treated and distributed. This poses additional challenges as a power outage will significantly hamper the water distribution capacity of a geographical region. Similarly, power plants utilizing water cooling, might need to reduce their generating capacity to operate safely during a water shortage, or shale activity might face increased pricing and even declined water rights.

It is crucial to design resilient regional water-energy networks, which can withstand disruptions in their connectivity or in their facilities’ capacities and still manage to satisfy the end-use power and water demands. Plenty of works have discussed the effect of network topology in the performance of ideal or real networks under disruptive cascading failures [3,4]. However, accounting for network resilience in the design phase is still an unresolved question in the literature. To this end, we utilize our proposed WEN superstructure to represent a regional water-energy system [5]. The superstructure is comprised of energy and water resources, energy and water consumers, and intermediate potential energy and water sources to be allocated. The intermediate sources are characterized by a conversion factor (eg. power per fuel, water output to water input), an intensity factor (water needed for power, power required for water treatment), a capacity factor and a generating design capacity. The objective of the MINLP formulation is to minimize the total network cost (resource supply, generation, transportation), satisfy consumer demands, and obtain optimal selection of generating technologies, capacities and connectivity.

To account for the resulting nexus’ resilience, we incorporate network topology-imposing constraints in the design phase. Specifically, we utilize the binary variables of selection of facilities and connecting streams, to dictate, (i) node centrality, (ii) the degree of how much centralized/distributed is the nexus (network density), and (iii) the number of alternate paths for a product to reach from a resource to consumer (equivalent to the betweenness centrality). In the expense of network cost, the resulting network becomes more resilient by imposing larger node degrees, more distributed generation, and more alternative paths for power and water to reach the consumers. Resilience is then quantified by testing the resulting nexus for feasibility (satisfaction of demands) after removing connections and restricting the capacity or shutting down sources. Then, the optimal pareto curves are obtained for network cost versus resilience. Finally, the combined effect of nexus design attributes, such as minimum allowed flow and capacity factor, and network properties, such as average degree and network density, is investigated and a framework to obtain resilient networks for grass-root designs and retrofitting existing infrastructures is developed.

References:

[1] Garcia, D. J., & You, F. (2016). The water-energy-food nexus and process systems engineering: a new focus. Computers & Chemical Engineering, 91, 49-67.

[2] Tsolas, S. D., Karim, M. N., & Hasan, M. F. (2018). Optimization of water-energy nexus: A network representation-based graphical approach. Applied energy, 224, 230-250.

[3] Albert, R., Albert, I., & Nakarado, G. L. (2004). Structural vulnerability of the North American power grid. Physical review E, 69(2), 025103.

[4] Yazdani, A., & Jeffrey, P. (2011). Complex network analysis of water distribution systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 21(1), 016111.

[5] Tsolas, S. D., Karim, M. N., & Hasan, M. F. (2018). Systematic Design, Analysis and Optimization of Water-Energy Nexus. Foundations of Computer-Aided Process Design 2019, Paper ID 101