Hybrid Physics and Data-driven Contingency Filtering for Security Operation of Micro Energy-water Nexus

This paper investigates a novel engineering problem,i.e.,security-constrained multi-period operation of micro energywater nexuses.This problem is computationally challenging because of its high nonlinearity,nonconvexity,and large dimension.We propose a two-stage iterative algorithm employing a hybrid physics and data-driven contingency filtering(CF)method and convexification to solve it.The convexified master problem is solved in the first stage by considering the base case operation and binding contingencies set(BCS).The second stage updates BCS using physics-based data-driven methods,which include dynamic and filtered data sets.This method is faster than existing CF methods because it relies on offline optimization problems and contains a limited number of online optimization problems.We validate effectiveness of the proposed method using two different case studies:the IEEE 13-bus power system with the EPANET 8-node water system and the IEEE 33-bus power system with the Otsfeld 13-node water system.

Machine learning for energy-water nexus: challenges and opportunities

Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainabil-ity.To this end,recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors.We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability,transport,and use to energy generation,fuel supply,and customer demand,and in the interde-pendencies among these systems that can leave these systems vul-nerable to cascading impacts from single disruptions.In this paper,we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves.We then survey machine learning techniques that have found application to date in energy-water nexus problems.We con-clude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.

A survey of analytical methods for inclusion in a new energy-water nexus knowledge discovery framework

The energy-water nexus,or the dependence of energy on water and water on energy,continues to receive attention as impacts on both energy and water supply and demand from growing popula-tions and climate-related stresses are evaluated for future infra-structure planning.Changes in water and energy demand are related to changes in regional temperature,and precipitation extremes can affect water resources available for energy genera-tion for those regional populations.Additionally,the vulnerabilities to the energy and water nexus are beyond the physical infrastruc-tures themselves and extend into supporting and interdependent infrastructures.Evaluation of these vulnerabilities relies on the integration of the disparate and distributed data associated with each of the infrastructures,environments and populations served,and robust analytical methodologies of the data.A capability for the deployment of these methods on relevant data from multiple components on a single platform can provide actionable informa-tion for interested communities,not only for individual energy and water systems,but also for the system of systems that they com-prise.Here,we survey the highest priority data needs and analy-tical methods for inclusion on such a platform.

Water-Energy Prototype Model for the NEMS Modeling Platform: Thermoelectric Water Demand and Its Implications on Regional Electricity Market

A simplified energy-water prototype model has been developed at the National Energy Technology Laboratory (NETL) as a part of a larger effort to comprehensively model energy-water interactions. The NETL Water-Energy Model (NWEM) prototype passively couples a variety of data on water supply, water availability, and power plant water use with the National Energy Modeling System (NEMS) power generation forecasts. NWEM operates at a watershed level and its efficacy in resolving local water supply and water-use trade-offs was demonstrated using data from Sandia National Laboratory along with a water supply scenario projected by the World Resources Institute (WRI). The prototype model only passively utilized a forecast of power generation from an existing forecast;the model’s choices were limited to purchases or retrofitting to meet future water supply constraints. NETL is continuing to integrate the water sub-module into the NEMS framework, which will allow active interaction between the water market and power markets, extending the industry’s ability to re-dispatch its generating units with the price of water as one of the variable costs.

Model predictive control for water management and energy security in arid/semiarid regions

This paper aims to develop a realistic operational optimal management of a water supply system in an arid/semiarid region under climate change conditions.The developed model considers the dynamic variation of water demand,rainfall,weather,and seasonal change in electricity price.It is mathematically developed as a multi-constraint non-linear programming model based on model predictive control principles.The model optimises the quantities of water supplied by each source every month and improves the energy efficiency in a water supply system with multiple types of sources.The effectiveness of the developed MPC model is verified by applying it to a case study and comparing the results with those obtained with an open loop model.Results showed that using the MPC model leads to a 4.16%increase in the water supply cost compared to the open loop model.However,when considering uncertainties in predicting water demands,aquifer recharges,rainfall,and evaporation rate,the MPC model was better than the open loop model.Indeed,the MPC model could meet the water demand at any period due to its predictability of variations,which was not the case with the open loop model.Moreover,a sensitivity analysis is conducted to verify the capacity of the developed model to deal with some phenomena due to climatic changes,such as in rainfall.

Quantitative evaluation framework and application for regional water sustainability under local energy transition

Quantitatively analysing the impacts on regional water sustainability under the energy transition is vital to regional water management and specific technology selection,which is also relevant to dealing with climate change.This study proposed a new multi-indicator evaluation framework for regional water sustainability under local energy transition to quantitatively evaluate the water withdrawal and water environment during the energy transition from a lifecycle perspective.An integrated regional energy-water evaluation model was also developed based on the Low Emissions Analysis Platform with a combination of lifecycle assessment and water footprint analysis.Shaanxi province in China was then taken as a case study,and the impacts of its energy transition on regional water environmental sustainability,including quantity and quality,were investigated under five scenarios.Results showed that the large-scale application of carbon capture,utilisation and storage technology and bio-energy equipped with carbon capture and storage technology could have additional advantages regarding CO_(2) emissions.However,such technologies exhibit a minimal effect on improving water environmental quality and reducing water demand for the first time due to the leakage of absorbents,CO_(2) and other risky substances during capture,transportation and storage from a lifecycle perspective.This finding drives the innovation of related breakthrough technologies with the promotion of water and end-treatment technologies in the future.