An integrated state-estimation framework for interdependent water and energy systems

Faegheh Moazeni, Javad Khazaei, Prasenjit Mitra

Research output: Contribution to journalArticlepeer-review

Abstract

Recent studies have shown indisputable evidence indicating water and energy networks are two interdependent cyber-physical networks and should be operated cooperatively to maximize their efficiency and resiliency. However, current state-estimation models of water and energy infrastructures are implemented independently in their respective control centers. Such independent state-estimation in energy or water network might not be able to detect false data injections in the other network. In this paper, an integrated state-estimation framework is proposed to process the consistency of the data and estimate the parameters of a combined water and energy network. Compared to standalone state-estimation, an integrated framework proposed in this work makes it more difficult for the attackers to launch stealthy false data injections, as more parameters are involved in the state-estimation that need to be modified for a successful attack. The findings of this research confirm that by accounting for interlinks between hydrological and energy systems, the security of both systems will be improved. The interconnection between the state variables and measurements of the water-energy network is reflected in the energy consuming components of the water system, and is achieved through the measurements of pump's active power consumption in the proposed integrated estimation framework. The framework includes Newton's iterative method for an alternating current (AC) state-estimation procedure for the energy network. The state-estimation of the energy network includes the estimation of voltages using measured parameters of the energy network such as active/reactive power flow/injection or voltages. Moreover, a three-tier state-estimation process based on Newton's iterative method for the water network is contemplated to attain nodal heads, nodal demands, and the flow rate of the pipes without pumps. An integrated bad data detection algorithm is also supplemented to the proposed framework to detect potential bad data in water distribution system or energy network and enhance the resilience of the integrated water energy systems.

Original languageEnglish (US)
Article number125393
JournalJournal of Hydrology
Volume590
DOIs
StatePublished - Nov 2020

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

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