Uncertainty-Informed Operation Coordination in a Water-Energy Nexus

Journal Article · 01 August 2022 · IEEE Transactions on Industrial Informatics

DOI: https://doi.org/10.1109/tii.2022.3195695 · OSTI ID:1995236

Alhazmi, Mohannad  ^[1]; Dehghanian, Payman  ^[2]; Nazemi, Mostafa  ^[3]; Oikonomou, Konstantinos ^[4]

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1. King Saud University, Riyadh (Saudi Arabia)
2. George Washington University, Washington, DC (United States)
3. MPR Associates, Inc., Alexandria, VA (United States)
4. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

The widespread deployment of smart heterogeneous technologies and the growing complexity in our modern society calls for effective coordination of the interdependent lifeline networks. In particular, operation coordination of electric power and water infrastructures is urgently needed as the water system is one of the most energy-intensive networks, an interruption in which may quickly evolve into a dramatic societal concern. This paper develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interconnected power and water systems (PaWS). Joint probabilistic constraint (JPC) programming is employed to capture the uncertainties in wind resources and water demand forecasts. The proposed integrated stochastic model is presented as a non-linear non-convex optimization problem, where the non-linear hydraulic constraints in the water network are linearized using piece-wise linearization technique, and the non-convexity is efficiently tackled with a solution methodology to convert the proposed model with JPCs to a tractable mixed-integer linear programming (MILP) formulation that can be quickly solved to optimality. Here, the suggested framework is applied to a 15-node commercial-scale water network jointly operated with a power transmission system using a modified IEEE 57-bus test system. The numerical results demonstrate the of the proposed stochastic framework, resulting in cost reduction (13% on average when compared to the traditional setting) and energy saving of the integrated model under different realizations of uncertain renewable energy sources (RESs) and water demand scenarios. Additionally, the scalability of the proposed model is tested on a modified IEEE 118-bus test system connected to five water networks.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: National Science Foundation; USDOE
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 1995236
• Report Number(s): PNNL-SA-157416
• Journal Information: IEEE Transactions on Industrial Informatics, Journal Name: IEEE Transactions on Industrial Informatics Journal Issue: 5 Vol. 19; ISSN 1551-3203
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English