Optimal Operation of Integrated Water-Power Systems Under Contingencies

With the sharply growing complexity and rapid deployment of smart technologies in our modern society, risk-aware management and coordination in day-to-day operation of the interlinked critical infrastructures is urgently needed. In particular, the interconnected water and power systems (WaPS) are in need of joint and cooperative operation to maximize the economic benefits during normal operating conditions and resilience services during emergencies. While contingency analysis is used to assist the system operators in gaining knowledge of the system's static security, such understanding is more challenging to achieve in the case of integrated WaPS. This article proposes a novel optimization model for under-emergency operation of the integrated WaPS, considering contingencies in both networks. In order to ensure the delivery of water demand, the proposed formulation considers the hydraulic constraints of the water networks, which is naturally a nonlinear model. The proposed nonlinear model is approximated using a piece-wise linearization approach to convert the optimization model into a mixed-integer linear programming formulation. The proposed analytics are applied to a modified IEEE 24-bus reliability test system that is jointly operated with two and three commercial-scale water networks. The proposed model is evaluated using various disaster severity levels (i.e., N - k contingency scenarios) and verify the promising performance of the proposed integrated WaPS model when facing failures and threatening high impact low probability emergencies.

Automated summary

This article proposes a novel optimization model for under-emergency operation of the integrated water and power systems, considering contingencies in both networks. The nonlinear model is approximated using a piece-wise linearization approach to convert the optimization model into a mixed-integer linear programming formulation, to address failures and threatening emergencies effectively.