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The design of strategic investments in water, energy and food (WEF) infrastructures is challenging because the size, location, technology mix and pace of development is made uncertain by multiple factors. For example, the return on investment, which comes long after building a hydropower dam, is made uncertain by local, regional and global climate and socio-economic factors. This is exacerbated by the challenges associated with the impacts of climate change, especially in sub-Saharan Africa (SSA) where it is difficult to model these impacts, hence leading to high levels of uncertainty in future scenarios (2050 and beyond).Long-term investment planning and system operations for energy, depend on and compete with other sectors for, the availability of water (for hydropower and cooling thermal plants) and land resources (e.g. for biofuel production and arability). The efficient exploitation of land, energy and water resources and their synergised use for economic development therefore require an multidimensional integrated optimisation approach co-created with stakeholders in dialogue. This starts with planning followed by prioritised investments based on local, national and regional needs in the energy, agricultural and water sectors. This is mostly lacking in SSA at the moment. We gathered a selected group of experts in Accra, Ghana in November 2022 with a broad mix of experiences and expertise in the energy, water and agricultural sectors, who shared deeper insights and values of the need for integrated WEF planning to begin tackling challenges and opportunities identified in the Volta Basin in West Africa (starting with Ghana) and the Tana basin in Kenya. The main challenge identified was the disjointed planning of WEF infrastructures due to different financing mechanisms and siloed sectoral thinking; and participants raised emerging opportunities for planning infrastructure through transnational and regional cooperation as well as the need to build on existing and new initiatives devoid of entrenched political goals.In this contribution, we will present some of the main findings from the meeting in Accra and share knowledge on how transparent WEF modelling can be contextualised for local operational relevance, and through co-creation, how interactive engagement tools can be used for planning, policy- and decision-making.Keywords: WEF modelling, sub-Saharan Africa, WEF Infrastructures, Investment Planning, OptimisationThe work leading to these results has received funding from the European Horizon Europe Programme (2021-2027) under grant agreement No.101083763 (EPIC Africa). The opinions expressed in the document are of the authors only and in no way reflect the European Commission’s opinions. The European Union is not liable for any use that may be made of the information.

The Netherlands is a low-lying country situated in the Rhine-Meuse delta. A significant portion of the Netherlands is located below sea level, making the proper management of local and national waterways essential. Polders are used to manage groundwater levels, drain excess rainwater, and store water during times of drought. These polders often have pumping stations that pump water into drainage canals, like the Noordzeekanaal-Amsterdam-Rijnkanaal (NZK-ARK), which receives water from the Rhine river and four local water authorities and connects to the North Sea at IJmuiden through a pumping station and a series of undershot gates.The operators of the NZK-ARK utilize Model Predictive Control (MPC) to schedule the discharge of water through the gates and pumps. The combination of the pump and gate discharge allows the NZK-ARK to discharge excess water to the North Sea when the sea water level is both higher and lower than the water level in the canal. However, traditional MPC can lead to suboptimal schedules when uncertainty is introduced, resulting from, for example, incoming discharge, fluctuating electricity prices, and the availability of renewable energy. Stochastic MPC allows for the consideration of uncertainty in decision-making, optimizing control actions based on a range of potential scenarios. In the future, the objectives for the control system of the gates and pumps may become more complex and may need to take into account factors like renewable energy availability and electricity prices. Ensuring the effective and efficient management of water in the Netherlands is critical, and the use of polders for water storage and control of groundwater tables, and techniques like MPC and stochastic MPC play important roles in achieving this goal.In this study, we present a framework that combines probabilistic forecasting, scenario generation and reduction, and stochastic MPC to minimize energy costs associated with pumping at the NZK-ARK. This framework is based on probabilistic forecasts of electricity prices and incoming discharge and is specifically designed for use at the NZK-ARK. By considering the uncertainty present in electricity prices and incoming discharge, our framework allows for the optimization of control actions through the use of stochastic MPC. The ultimate goal of this approach is to reduce energy costs at the NZK-ARK by effectively managing the discharge of water through the pumps and gates while complying with local constraints.

Ethiopia’s energy demand is expected to increase sevenfold in the coming 30 years, resulting in increased variable renewable electricity (VRE) production by solar PV and wind. Energy storage acts as a buffer that mitigates the effects of over- or under-capacity in production by VRE. With 97% of global bulk energy storage, pumped hydro storage is the most widely used and mature energy storage technology. With its long operational life, high round-trip efficiency (80%) and stable cost trajectory, it is a competitive option for many VRE-rich (future) energy systems. However, barriers to pumped storage include heavy technical, site-specific restrictions, long construction times and high initial capital investment requirements.This study investigates if Ethiopia’s energy pathways benefit from adding pumped hydro storage, suitable regions for PHS, and to what extent storage would increase system resilience. The long-term energy planning tool OSeMOSYS is used, which allows for detailed investigation into system dynamics whilst parallelly minimising costs. OSeMOSYS enabled the investigation into Ethiopia by looking at an extensive host of techno-economic specifications and supply and demand dynamics from the electrification of transport and integration of variable renewables to residential cooking demands.This research studies thirteen scenarios which are separated into three main categories: Base Case (3), Emission Penalty (EMI) (6) and Varying Wind Capacity and Seasonality (WND) (6). The base case introduces pumped storage to the energy pathways, and the EMI scenario characterises three pathways for carbon pricing. In the WND scenario, wind power’s capacity factor and seasonality are altered to investigate the potential effects of using more accurate local data or prioritising some supply zones on the energy system configuration. Additionally, the most favourable locations for solar PV and wind are combined with potential PHS locations to find optimal sites for storage construction.The results of the research show that pumped hydro storage is adopted into the energy system in all scenarios, following both a diurnal and seasonal (dis)charge pattern. Variable renewable integration increases by an average of 10% from the addition of storage (78 GWh). The emission penalty increases the electrification of residential cooking demand and boosts VRE penetration but does not integrate storage integration further than the base case due to reaching the upper limit of the storage capacity set in the planning experiments.Pumped hydro storage was found to increase the resilience of the modelled energy systems to climate-driven seasonal uncertainties and prices due to fossil fuel and carbon price uncertainties by making them less dependent on fossil fuels, decreasing vulnerability for potential emission penalties, and seasonal capacity fluctuations. The introduction of PHS was also found not to increase overall system costs, making it, combined with the stable levelised cost of storage and high maturity, a prime candidate for large-scale energy storage in Ethiopia.

Aligning development and climate goals means Africa’s energy systems will be based on clean energy technologies in the long term, but pathways to get there are uncertain and variable across countries. Although current debates about natural gas and renewables in Africa are heated, they largely ignore the substantial context specificity of the starting points, development objectives and uncertainties of each African country’s energy system trajectory. Here we—an interdisciplinary and majority African group of authors—highlight that each country faces a distinct solution space and set of uncertainties for using renewables or fossil fuels to meet its development objectives. For example, Ethiopia is headed for an accelerated green-growth pathway, but Mozambique is at a crossroads of natural gas expansion with implicit large-scale technological, economic, financial and social risks and uncertainties. We provide geopolitical, policy, finance and research recommendations to create firm country-specific evidence to identify adequate energy system pathways for development and to enable their implementation.

International audience; Water utilities are challenged to reduce their water losses through detecting, localizing, and repairing leaks as quickly as possible in their aging distribution systems. In this work, we solve this challenging problem by detecting multiple leaks simultaneously in a water distribution network for the Battle of the Leak Detection and Isolation Methods. The performance of leak detection and localization depends on how well the system roughness and demand are calibrated. In addition, existing leaks affect the diagnosis performance unless they are identified and explicitly represented in the model. To circumvent this chicken-and-egg dilemma, we decompose the problem into multiple levels of decision-making (a hierarchical approach) where we iteratively improve the water distribution network model and so are able to solve the multileak diagnosis problem. First, a combination of time series and cluster analysis is used on smart meter data to build patterns for demand models. Second, point and interval estimates of pipe roughnesses are retrieved using least squares to calibrate the hydraulic model, utilizing the demand models from the first step. Finally, the calibrated primal model is transformed into a dual model that intrinsically combines sensor data and network hydraulics. This dual model automatically converts small pressure deviations caused by leaks into sharp and localized signals in the form of virtual leak flows. Analytical derivations of sensitivities with respect to these virtual leak flows are calculated and used to estimate the leakage impulse responses at candidate nodes. Subsequently, we use the dual network to (1)detect the start time of the leaks, and (2)compute the Pearson correlation of pressure residuals, which allows further localization of leaks. This novel dual modeling approach resulted in the highest true-positive rates for leak isolation among all participating teams in the competition.

The Netherlands is a low-lying country in the Rhine-Meuse delta. Because a large part of the Netherlands is situated below sea level, proper management of local and national waterways is a necessity. Polders are used to manage groundwater levels, drain excess rainwater and store water for droughts. Typically, pumping stations in local Dutch polders pump water up to a drainage canal (in Dutch: ‘boezem’).The Noordzeekanaal—Amsterdam-Rijnkanaal (NZK-ARK) is one such drainage canal, receiving discharge from the Rhine and four local water authorities. The canal connects with the North Sea in IJmuiden, through a pumping station and a set of undershot gates. The combination of pump and gate discharge allow the canal to discharge excess water to the North Sea when the sea water level is both higher and lower than the water level in the canal.Pump and gate discharge is scheduled through Model Predictive Control (MPC), where reliable forecasts are necessary to reliably schedule discharge. The objectives for the control system of the gates and pumps are likely to become more complex in the future. For example, the availability of renewable energy, or electricity prices are to be taken into account when scheduling pump discharge. Research has shown that regular MPC can lead to suboptimal schedules when uncertainty is introduced, for example leading to high energy costs. Stochastic MPC allows for the consideration of uncertainty in decision making, optimising control actions over a set of possible scenario’s.One way of generating these scenarios is by using a probabilistic forecasts. A Quantile Regression Deep Neural Network (QR-DNN) can be used to forecast quantiles of a forecast variable. When enough quantiles are considered, a Cumulative Distribution Function (CDF) can be constructed. A Bayesian Network (BN) is a graph-structured network that can estimate multi-dimensional Probability Density Functions by conditionalizing random variables according to a user defined structure and observed data. The BN can be applied to sample from the marginal CDF’s generated by the QR-DNN, while respecting autocorrelation or considering exogenous variables that are not yet considered by the QR-DNN.In this research, we apply probabilistic forecasting methods to generate pump discharge scenarios that can be used in a stochastic MPC for the NZK-ARK. We use actual data from the four local water authorities discharging into the NZK-ARK, and apply a QR-DNN to generate marginal CDF’s of the expected pump discharge into the NZK-ARK. A BN is then applied to generate scenarios by conditionalizing the marginal CDF’s and take multidimensional samples with autocorrelation.

This article proposes an active fault isolation method for application to water distribution networks (WDNs) to localize leaks. The method relies on the classification of observed outputs to a discrete set of hypothetical faults. Due to parametric uncertainties, the outputs are random vectors that follow unknown probability distribution functions (PDFs). The output PDFs corresponding to the considered faults are approximated using smooth kernel density estimation (SKDE). They are used to calculate the posterior probability of each hypothesis, given the observed outputs, by applying Bayes' rule. The difficulty to classify observed outputs to the right fault comes from the overlap between output PDFs. An active algorithm is introduced that proactively minimizes the joint overlap between the output PDFs by designing optimal control inputs. Due to physical limitations on control inputs and depending on the intensity of uncertainties, full separation, and hence fault isolation, cannot be guaranteed for a single observed output. Therefore, subsequent observations are used in an iterative framework, where the posterior probabilities of the previous time step serve as the prior probabilities for the next time step. The method is applied to locate leaks in a benchmark WDN for different levels of uncertainty in customer water demand and leakage magnitude. Improvements in the performance are observed in comparison to the best considered passive method from literature.

Transitioning towards renewable heating is important to minimise the use of fossil fuels and abate carbon emissions, because heating accounts for 50% of the final energy consumption and 40% of carbon dioxide emissions globally. In the city of Amsterdam, the Netherlands, the aim is to transition towards renewable heating by 2040 and achieve carbon-neutral heating by 2050 through a neighbourhood-based approach. Such an approach entails that per neighbourhood a renewable heat solution is chosen based on criteria such as carbon emissions, reliability, affordability and feasibility. The impacts of urban heating systems however goes beyond a neighbourhood, and take place on multiple spatial and temporal scales. In this presentation we discuss how a transition towards renewable heating systems can influence the water-energy-land nexus on multiple scales in three ways.First, heating systems use water locally, but also indirectly through the water footprint embedded in energy carriers. We therefore present an analysis of the direct and indirect water use of heating pathways towards 2050. Second, heating systems which currently have the lowest carbon emissions, may not be the heat option with the lowest carbon emissions in the future. Current decisions for heat options can therefore create non-optimal solutions for minimising carbon emissions in the future. An optimization model to find a mix of heating systems to reduce committed emissions on a neighbourhood scale within a given time period for different scenarios for the insulation of buildings and the decarbonisation of electricity generation is therefore presented. At last, new norms and forms of organising neighbourhood-based heating systems may emerge, potentially creating or exacerbating social inequalities within and beyond the spatial boundaries of a neighbourhood. We therefore present the preliminary results of an analysis on energy justice based on in-depth interviews with urban professionals, dwellers and decision makers in Amsterdam. By presenting these three studies we aim to address the challenge of multi-scale impacts of transitioning towards renewable urban energy systems and show how energy-water-land nexus research can contribute to decision making for urban infrastructures.

In this paper we propose a Quantile Regression Deep Neural Network capable of forecasting multiple quantiles in one model using a combined quantile loss function, and apply it to probabilistically forecast the prices of 8 European Day Ahead Markets. We show that the proposed loss function significantly reduces the quantile crossing problem to (near) 0% in all markets considered, while in some cases simultaneously increasing forecasting performance based on classical point forecast metrics applied to the expected value of the probabilistic forecast. The models are optimized using an automated approach with an elaborate feature- and hyperparameter search space, leading to good model performance in all considered markets.

Infrastructure for heat provision in the built environment needs to change remarkably to support lowering carbon emissions and achieving climate mitigation targets before 2050. We propose a computational approach for finding a mix of heat options per neighbourhood that minimises cumulative carbon emissions between 2030 and 2050, referred to as committed emissions, while at the same time adhering to technological constraints at both the household and neighbourhood scales. To establish this approach, we integrated bottom-up heat demand modelling at neighbourhood scale with a mixed-integer non-linear optimisation problem. Nine scenarios with different pathways for the insulation of buildings and the decarbonisation in electricity generation were considered and applied to three neighbourhoods in the city of Amsterdam, the Netherlands. The results show that (i) the committed emissions are ten times lower between 2030 and 2050 in scenarios in which ambitious measures are taken for the insulation of buildings and the decarbonisation in electricity generation, (ii) only in these ‘ambitious scenarios’ low temperature heat systems, such as heat pumps and low temperature heat networks, are optimal solutions for minimising committed emissions, (iii) if less ambitious insulation and decarbonisation measures are taken, high temperature heat options can be part of the heat mix with lowest committed emissions, and (iv) the minimum heat density for low temperature heat networks is not always achieved, creating risks for carbon lock-ins when applying these heat networks. Our results clearly indicate that pathways for the retrofitting of buildings and the decarbonisation in electricity generation need to be taken into account jointly when designing renewable and low-carbon heat systems to optimally reduce carbon emissions towards 2050 and reduce future carbon lock-ins.

Integrating the operational planning of river, land and power infrastructure could safeguard the water, energy and food security in regions where these resources are under pressure by increasing demands and decreasing availabilities and production potentials. Our work focuses on the benefits of integration and cooperation in the operational planning of these resources and infrastructures between riparian states in transboundary river basins. Therefore we introduce a regional hard-linked WEF-nexus model that explicitly represents resource connectivity networks, gridded agro-hydrological potentials and constraints, national socio-economic demands and non-linear operational processes to optimise reservoir operations, water allocations, cropping patterns, electricity mixes and trade quantities on a monthly time-step over multiple years in a receding horizon fashion. This iterative process facilitates the modelling of changes as feedback against exogenous disturbances and, through the exchange of information between countries, different levels of cooperation. We optimize the total economic returns of resource allocation for four different transboundary cooperation scenarios over an historic planning period in the Eastern Nile basin, for each country and regionally, for multiple foresight settings and policy objectives. Compared to the reference scenario of unilateral planning, our results indicate an increase in regional economic returns for scenarios in which flow information is shared between countries (+8%), flow and trade information is shared (+9%) and resources are coordinated regionally (+13%), without this being accompanied by a significant decline in returns for any country. These increased returns successively come from an increase in the effectiveness of agricultural water consumption, especially in Sudan, a change in trade patterns for agricultural products and a shift in cropping patterns. These findings illustrate the importance of adequate representations of spatial and temporal heterogeneity and resource connectivity, and the need for a more diverse set of collaboration scenarios to quantify the costs and benefits of specific interventions and policies to facilitate comprehensively planning in transboundary river systems.

Sustainable energy systems can only be achieved when reducing both carbon emissions and water use for energy generation. Water-energy nexus studies are therefore crucial for supporting environmental policy oriented towards the mobilisation of resources in an optimally integrated way. Decarbonizing heating infrastructures is an important part of achieving low-carbon energy systems because they globally account for 50% of the final energy consumption and 40% of the carbon dioxide (CO2) emissions. In our study, we quantitatively assess the changing water usage of the energy sector due to the integration of low carbon heating infrastructures. Multiple future energy mix scenarios were assessed by building a multi-scale energy and water use model that quantifies the direct and virtual water footprint of space heating and hot water use in households, services and industry. In this presentation we show an analysis on the water use of heating pathways towards the year 2050 for the Netherlands and its capital, the city of Amsterdam. Additionally, we present preliminary results from our research about the trade-offs between carbon emission reductions, insulation measures and energy reliability in neighbourhoods in Amsterdam.

Integrating the planning of a multi-reservoir system in nexus with agricultural and electricity infrastructure could alleviate security concerns for these resources in regions where demand is growing while water and land scarcity are exacerbated by climate change and anthropogenic pressures. This study focuses on the benefits of resource integration and cooperation in the Eastern Nile basin. To overcome common limitations of equilibrium and soft-linked partial equilibrium models (e.g. high levels of spatial aggregation, non-insightful cooperation scenarios and a lack of heterogeneity), we propose a regional hard-linked WEF-nexus model that explicitly represents resource connectivity networks for water and electricity, and describes heterogeneity in resource availability, production potentials and physical constraints. Using a non-linear operational process, we optimise reservoir operations, water allocations, cropping patterns, electricity mixes and trade quantities on a monthly time-step over multiple years in a receding horizon fashion to maximize economic benefits for each country and regionally. This iterative implementation allows the modelling of operational changes as feedback against exogenous climate disturbances and enables information exchange between upstream-downstream countries. Thus, we describe four different levels of transboundary cooperation with their corresponding constraints and policy objectives. Compared to the reference scenario of unilateral planning, our results indicate an increase in regional economic returns for scenarios in which river flow information is shared between countries (+9%), river flow and trade information are shared (+10%) and WEF resources are coordinated regionally (+15%). These increased returns successively come from an increase in the effectiveness of agricultural water consumption, especially in Sudan, a change in trade patterns for agricultural products and a shift in cropping patterns. These findings underscore the importance of adequate representations of spatial and temporal heterogeneity of resources and their connectivity, as well as the need for a more diverse set of collaboration scenarios to facilitate planning in transboundary river systems.

Sustainable energy systems can only be achieved when reducing both carbon emissions and water use for energy generation. Although the water use for electricity generation has been well studied, integrated assessments of the water use by low-carbon heat systems are lacking. In this paper we present an analysis of the water use of scenarios for heat and electricity production for the year 2050 for the Netherlands and its capital, Amsterdam. The analysis shows that (i) the water withdrawal for heating can increase up to the same order of magnitude as the current water withdrawal of thermoelectric plants due to the use of aquifer thermal energy storage, (ii) the virtual water use for heating can become higher than the operational water consumption for heating, and (iii) the water use for electricity production becomes a relevant indicator for the virtual water use for heat generation because of the increase of power-to-heat applications.

In this manuscript we explore European feature importance in Day Ahead Market (DAM) price forecasting models, and show that model performance can deteriorate when too many features are included due to over-fitting. We propose a greedy algorithm to search over candidate countries for European features to be used in a DAM price forecasting model, that can be applied to several regression and machine learning modelling methodologies. We apply the algorithm to build price forecasting models for the Dutch market, using candidate countries selected through an integrated analysis based on open-source European electricity market data. Feature importance is visualised using an Auto Regressive and Random Forest model. We explain these results using cross-border flow and DAM price data. Two types of models (LEAR and the Deep Neural Network) are considered for the DAM price forecasting with and without European features. We show that in the Dutch case, taking European market integration into account improves the Mean Absolute Error (MAE) of the best performing DAM price forecasting model by 3.1%, the relative MAE (rMAE) by 3.85%, and the Symmetrical Mean Absolute Percentage Error (sMAPE) by 0.31 p.p., compared to the best forecasting model without European features. Through statistical testing we show that European features improve the accuracy of the forecasts with statistical significance.

International audience; The Food-Energy-Water (FEW) nexus for urban sustainability needs to be analyzed via an integrative rather than a sectoral or silo approach, reflecting the ongoing transition from separate infrastructure systems to an integrated social-ecological-infrastructure system. As technology hubs can provide food, energy, water resources via decentralized and/or centralized facilities, there is an acute need to optimize FEW infrastructures by considering cost-benefit-risk tradeoffs with respect to multiple sustainability indicators. This paper identifies, categorizes, and analyzes global trends with respect to contemporary FEW technology metrics that highlights the possible optimal integration of a broad spectrum of technology hubs for possible cost-benefit-risk tradeoffs. The challenges related to multiscale and multiagent modeling processes for the simulation of urban FEW systems were discussed with respect to the aspects of scaling-up, optimization process, and risk assessment. Our review reveals that this field is growing at a rapid pace and the previous selection of analytical methodologies, nexus criteria, and sustainability indicators largely depended on individual FEW nexus conditions disparately, and full-scale cost-benefit-risk tradeoffs were very rare. Therefore, the potential full-scale technology integration in three ongoing cases of urban FEW systems in Miami (the United States), Marseille (France), and Amsterdam (the Netherlands) were demonstrated in due purpose finally.

With population growth and a rising demand for meat based diets and energy, the global water demand will grow significantly over the next two decades. Agriculture is the largest global consumer of the available water resources, responsible for almost 70% of annual water withdrawals. Therefore, a pivotal step in addressing the alarming water-scarcity problem is improving water use efficiency in agriculture. This is a complex problem that many farmers face yearly: how to distribute available water optimally to maximize seasonal yield, while considering the uncertainty in future water resources (eg. seasonal rainfall).In our work, we consider the general problem of optimal soil moisture regulation of multiple fields (e.g., a plantation) for a full growth season, where allocating water optimally over the growth season is considered together with daily irrigation scheduling for multiple fields. This adds complexity to the control problem, as operational constraints need to be included (such as a limited number of fields that can be irrigated in a day) and trade-offs need to be made between irrigation and potential yield of the different fields. Furthermore, the growth stages of the fields can be different, as often not all fields can be planted and harvested at the same time.We propose a methodology to reduce this complex problem into two separate optimisation problems, which are solved using a two-level structure consisting of a scheduler for seasonal allocation and a model predictive controller for daily irrigation. In this approach, the scheduler determines the optimal allocation of water over the fields for the entire growth season to maximize the summation of each field’s crop yield, by considering a linear approximation of the multiplicative crop productivity function. In addition, the model predictive controller minimizes the daily water stress by regulating the soil moisture of the fields within a water-stress-free zone. This requires a model of the interaction between the soil, the atmosphere, and the crop. A simple water balance model is created for which the saturation dynamics are modeled explicitly using conditionally switched depletion dynamics to improve model quality. To further improve the controller's performance, we create an evapotranspiration model by considering the expected development of the crop over the season using remote-sensing-based measurements of the canopy cover. The presented methodology can handle resource and hydraulic infrastructure constraints. Therefore, our approach is generic as it is not restricted to a specific irrigation method, crop, soil type, or local environment. The performance of the two-level approach is evaluated through a closed-loop simulation in AquaCrop-OS of a real sugarcane plantation in Mozambique. Our optimal control approach boosts water productivity by up to 30% compared to local heuristics and can respect water use constraints that arise in times of drought.

The influence of stochastic water demand on model-based leak localizationGlobally, water demand is rising and resources are diminishing. In the context of climate change and a growing world population, a further increase in water scarcity seems inevitable. Aiming towards a sustainable future, water should be used as efficient as possible by minimizing water losses, which can be higher than 50% in some drinking water networks.3 To minimize losses it is crucial to detect, localize and repair leaks as soon as possible.Leaks cause changes in flow and pressure. By monitoring the network with pressure and flow sensors and coupling these measurements with hydraulic computer models, leaks can be detected and located. The success of this so-called model-based leak localization depends heavily on our knowledge of water demand, since every water consumption affects the pressure and flow in the network as well. Nowadays, demand is modelled based on water billing information and the network’s inflow. This study proposes a new strategy by modelling stochastic demands. Realistic residential demands are generated in high spatial and temporal resolution based on Dutch water use statistics with SIMDEUM4. Subsequently, the stochastic demands are used within hydraulic simulations. The influence of demand fluctuations on pressure in the system is analyzed using Monte-Carlo sampling and the corresponding effects on model-based leak detection and localization are investigated.The proposed method is applied on a real Dutch water distribution network, containing inflow and six pressure measurements. Statistical information like the number of residents, households and annual billing information in the area is known. The corresponding hydraulic model is calibrated on pipe roughness by minimizing the mean squared error of the modelled and measured pressure at the sensor locations. Pressure driven simulations are performed and the resulting pressure changes at the sensors are simulated. Through the stochastic simulations in combination with Monte-Carlo sampling, confidence intervals for pressure changes at the sensor locations are determined and compared with the real measurements. The performance of leak detectability and localization is subsequently examined. This study shows that stochastic water demand simulations provide a better understanding on the reliability of model-based leak localization. By using these simulations, confidence intervals of demand related pressure changes at the sensor locations can be determined which affect the performance of leak detectability and localization under the variability of water demand. A better grip on the reliability of leak localization yields in a more efficient quest for leaks. 3EurEau 2017, Europe’s water in figures, an overview of European drinking water and waste water sectors, The European Federation of National Associations of Water Services4Blokker, E. J. M. (2010), Stochastic water demand modelling for a better understanding of hydraulics in water distribution networks, PhD thesis, Delft University of Technology

International audience; The paper presents the results of the Battle of Postdisaster Response and Restoration (BPDRR) presented in a special session at the first International water distribution systems analysis & computing and control in the water industry (WDSA/CCWI) Joint Conference, held in Kingston, Ontario, Canada, in July 2018. The BPDRR problem focused on how to respond and restore water service after the occurrence of five earthquake scenarios that cause structural damage in a water distribution system. Participants were required to propose a prioritization schedule to fix the damages of each scenario while following restrictions on visibility/nonvisibility of damages. Each team/approach was evaluated against six performance criteria: (1)time without supply for hospital/firefighting, (2)rapidity of recovery, (3)resilience loss, (4)average time of no user service, (5)number of users without service for eight consecutive hours, and (6)water loss. Three main types of approaches were identified from the submissions: (1)general-purpose metaheuristic algorithms, (2)greedy algorithms, and (3)ranking-based prioritizations. All three approaches showed potential to solve the challenge efficiently. The results of the participants showed that for this network, the impact of a large-diameter pipe failure on the network is more significant than several smaller pipes failures. The location of isolation valves and the size of hydraulic segments influenced the resilience of the system during emergencies. On average, the interruptions to water supply (hospitals and firefighting) varied considerably among solutions and emergency scenarios, highlighting the importance of private water storage for emergencies. The effects of damages and repair work were more noticeable during the peak demand periods (morning and noontime) than during the low-flow periods; and tank storage helped to preserve functionality of the network in the first few hours after a simulated event.

The Food-Energy-Water (FEW) nexus for urban sustainability needs to be analyzed via an integrative rather than a sectoral or silo approach, reflecting the ongoing transition from separate infrastructure systems to an integrated social-ecological-infrastructure system. As technology hubs can provide food, energy, water resources via decentralized and/or centralized facilities, there is an acute need to optimize FEW infrastructures by considering cost-benefit-risk tradeoffs with respect to multiple sustainability indicators. This paper identifies, categorizes, and analyzes global trends with respect to contemporary FEW technology metrics that highlights the possible optimal integration of a broad spectrum of technology hubs for possible cost-benefit-risk tradeoffs. The challenges related to multiscale and multiagent modeling processes for the simulation of urban FEW systems were discussed with respect to the aspects of scaling-up, optimization process, and risk assessment. Our review reveals that this field is growing at a rapid pace and the previous selection of analytical methodologies, nexus criteria, and sustainability indicators largely depended on individual FEW nexus conditions disparately, and full-scale cost-benefit-risk tradeoffs were very rare. Therefore, the potential full-scale technology integration in three ongoing cases of urban FEW systems in Miami (the United States), Marseille (France), and Amsterdam (the Netherlands) were demonstrated in due purpose finally.

Distributing water optimally is a complex problem that many farmers face yearly, especially in times of drought. In this work, we propose optimization‐based feedback control to improve crop yield and water productivity in agriculture irrigation for a plantation consisting of multiple fields. The interaction between soil, water, crop (sugarcane in this work), and the atmosphere is characterized by an agro‐hydrological model using the crop water productivity modeling software AquaCrop‐OS. To optimally distribute water over the fields, we propose a two‐level optimal control approach. In this approach, the seasonal irrigation planner determines the optimal allocation of water over the fields for the entire growth season to maximize the crop yield, by considering an approximation of the crop productivity function. In addition, the model predictive controller takes care of the daily regulation of the soil moisture, respecting the water distribution decided on by the seasonal planner. To reduce the computational complexity of the daily controller, a mixed‐logic dynamical model is identified based on the AquaCrop‐OS model. This dynamical model incorporates saturation dynamics explicitly to improve model quality. To further improve performance, we create an evapotranspiration model by considering the expected development of the crop over the season using remote‐sensing‐based measurements of the canopy cover. The performance of the two‐level approach is evaluated through a closed‐loop simulation in AquaCrop‐OS of a real sugarcane plantation in Mozambique. Our optimal control approach boosts water productivity by up to 30% compared to local heuristics and can respect water use constraints that arise in times of drought.

This paper presents an extended Model Predictive Control scheme called Multi-objective Model Predictive Control (MOMPC) for real-time operation of a multi-reservoir system. The MOMPC approach incorporates the non-dominated sorting genetic algorithm II (NSGA-II), multi-criteria decision making (MCDM) and the receding horizon principle to solve a multi-objective reservoir operation problem in real time. In this study, a water system is simulated using the De Saint Venant equations and the structure flow equations. For solving multi-objective optimization, NSGA-II is used to find the Pareto-optimal solutions for the conflicting objectives and a control decision is made based on multiple criteria. Application is made to an existing reservoir system in the Sittaung river basin in Myanmar, where the optimal operation is required to compromise the three operational objectives. The control objectives are to minimize the storage deviations in the reservoirs, to minimize flood risks at a downstream vulnerable place and to maximize hydropower generation. After finding a set of candidate solutions, a couple of decision rules are used to access the overall performance of the system. In addition, the effect of the different decision-making methods is discussed. The results show that the MOMPC approach is applicable to support the decision-makers in real-time operation of a multi-reservoir system.

Severe water scarcity in recent years has magnified the economic, social, and environmental significance of water stress globally, making optimal planning in water resources necessary for sustainable socio-economic development. One of the regions that is most affected by this is the Sistan region and its Hamoun wetland, located in south-east Iran. Water policies are essential to sustain current basin ecosystem services, maintaining a balance between conflicting demands from agriculture and the protection of wetland ecosystems. In the present study, a multi-objective optimization model is linked with the Water Evaluation and Planning (WEAP) software to optimize water allocation decisions over multiple years. We formulate and parameterize a multi-objective optimization problem where the net economic benefit from agriculture and the supply of environmental requirements were maximized, to analyze the trade-off between different stakeholders. This problem is modeled and solved for the study area with detailed agricultural, socio-economic, and environmental data for 30 years and quantification of ecosystem services. By plotting Pareto sets, we investigate the trade-offs between the two conflicting objectives and evaluate a possible compromise. The results are analyzed by comparing purely economic versus multi-objective scenarios on the Pareto front. Finally, the disadvantages and advantages of these scenarios are also qualitatively described to help the decision process for water resources managers.

Many sequential mathematical optimization methods and simulation-based heuristics for optimal control and design of water distribution networks rely on a large number of hydraulic simulations. In this paper, we propose an efficient inexact subspace Newton method for hydraulic analysis of water distribution networks. By using sparse and well-conditioned fundamental null space bases, we solve the nonlinear system of hydraulic equations in a lower-dimensional kernel space of the network incidence matrix. In the inexact framework, the Newton steps are determined by solving the Newton equations only approximately using an iterative linear solver. Since large water network models are inherently badly scaled, a Jacobian regularization is employed to improve the condition number of these linear systems and guarantee positive definiteness. After presenting a convergence analysis of the regularised inexact Newton method, we use the conjugate gradient (CG) method to solve the sparse reduced Newton linear systems. Since CG is not effective without good preconditioners, we propose tailored constraint preconditioners that are computationally cheap because they are based only on invariant properties of the null space linear systems and do not change with flows and pressures. The preconditioners are shown to improve the distribution of eigenvalues of the linear systems and so enable a more efficient use of the CG solver. Since contiguous Newton iterates can have similar solutions, each CG call is warm-started with the solution for a previous Newton iterate to accelerate its convergence rate. Operational network models are used to show the efficacy of the proposed preconditioners and the warm-starting strategy in reducing computational effort.

In the present manuscript, we investigate and demonstrate the use of outer approximation methods for simultaneously optimising the placement and operation of control valves in water distribution networks. The problem definition results in a mixed-integer nonlinear program with nonconvex constraints. We simplify the formulation, compared to previous literature, in order to reduce the degree of nonlinearity in the constraints and decrease the total problem size. We then formulate the application of outer approximation based methods for the generation of good quality local optimal solutions for the considered co-design problem. Finally, we present the results of applying the developed techniques to two case studies, and also comparing the performances of the outer approximation approaches with those of other local mixed integer nonlinear programming solution methods.

For electricity grids with an increasing share of intermittent renewables, the power generation mix can have significant daily variations. This leads to time-dependent emission intensities and volatile electricity prices in the day-ahead and spot market tariffs that can be better utilised by energy intensive industries such as water supply utilities. A multi-objective optimisation method for scheduling the operation of pumps is investigated in this paper for the reduction of both electricity costs and greenhouse gas emissions for a benchmark water distribution system. A set of energy supply scenarios has been formulated based on future projections from National Grid plc (UK) in order to investigate the range of cost savings and emission reductions that could be possibly achieved. Pump scheduling options with fixed time-of-use and day ahead market tariffs are analysed in order to compare potential reduction tradeoffs for both electricity costs and greenhouse gas emissions using Pareto optimality. The presented analysis concludes that the explicit inclusion of greenhouse gas emission reductions in optimising the scheduling of pumps operation in water distribution systems could provide considerable benefits; however, more compelling fiscal and regulatory incentives are needed.

La resiliencia hidráulica puede ser formulada como una medida de la capacidad de la red de distribución de agua para mantener un nivel de servicio mínimo bajo condiciones de fallo. Este artículo explora un modelo híbrido que une medidas de resiliencia hidráulica y de teoría de grafos. La propuesta es extender el concepto de distancia geodésica en las tuberías, teniendo en cuenta pérdidas de energía asociadas con el caudal. Nuevos algoritmos basados en caminatas aleatorias evalúan rutas hidráulicamente factibles e identifican diferentes niveles de resiliencia en nodos. Aquellos de menor valor en dicha evaluación son analizados en una segunda fase, considerando la disponibilidad y capacidad de sus rutas de abastecimiento. La criticidad de una tubería, medida a través de su impacto en la interrupción del suministro a los nodos de la red, también se analiza dentro de este novedoso marco con resultados fiables y computacionalmente eficientes.

In this paper, we consider the proactive control of flow velocities to maximise the self-cleaning capacity of the drinking water distribution systems under normal operations both through a change of the network topology and through an optimal control of pressure reducing valve (PRV) settings. Inspired by line outage flow distribution in electrical networks, we show how a fast network graph analysis of link closures can be used to estimate the potential changes in flow velocities, which are then used to determine the most favourable pipes for closure. Where closing of pipes cannot be used because of other conflicting objectives, we consider the optimal control of PRVs to maximise self-cleaning at peak demand periods. We formulate a novel optimisation problem to maximise the network operations for increased self-cleaning capacity, while satisfying hydraulic and regulatory pressure constraints at demand nodes. A new smooth objective function approximation for cleaning capacity of the network is proposed along with a scalable sequential convex programming method to solve the resulting valve optimization problems. We use a published benchmark network as a case study to show the efficacy of these new approaches.

The application of cationic polymers to enhance membrane fluxes in anaerobic membrane bioreactors has been proposed by several authors. However, literature shows contradictory results on the influence of those chemicals on the biological activity. In this research, we studied the effect of a cationic polymer on the production of methane from acetate by acetoclastic methanogens. We assessed the effect of polymer concentration on the accumulated methane production (AMP) and the specific methanogenic activity (SMA) in batch tests. Batch tests results showed lower SMA values at higher concentrations of polymer and no effect on the final AMP. Different inhibition models were calibrated and compared to find the best fit and to hypothesize the prevailing inhibition mechanisms. The assessed inhibition models were: competitive (M1a), non-competitive (M2a), un-competitive (M3a), biocide-linear (M4a), and biocide-exponential (M5a). The parameters in the model related to the polymer characteristics were adjusted to fit the experimental data. M2a and M3a were the only models that fitted both experimental SMA and AMP. Although M1a and M4a adequately fitted the experimental SMA, M1a simulations slightly deviated from the experimental AMP, and M4a considerably underpredicted the AMP at concentrations of polymer above 0.23 gCOD L-1. M5a did not adequately fit either experimental SMA and AMP results. We compared models a (M1a to M5a), which consider the inhibition by the concentration of polymer in the bulk liquid, with models b (M1b to M5b) considering the inhibition being caused by the total concentration of polymer in the reactor. Results showed that the difference between a and b models' simulations were negligible for all kinetic models considered (M1, M2, M3, M4, and M5). Therefore, the models that better predicted the experimental data were the non-competitive (M2a and M2b) and un-competitive (M3a and M3b) inhibition models, which are biostatic inhibition models. Consequently, the decreased methanogenic activity caused by polymer additions is presumably a reversible process.

The optimal placement and operation of pressure control valves in water distribution networks is a challenging engineering problem. When formulated in a mathematical optimisation frame work, this problem results in a nonconvex mixed integer nonlinear program (MINLP), which has combinatorial computational complexity. As a result, the considered MINLP becomes particularly difficult to solve for large-scale looped operational networks. We extend and combine network model reduction techniques with the proposed optimisation framework in order to lower the computational burden and enable the optimal placement and operation of control valves in these complex water distribution networks. An outer approximation algorithm is used to solve the considered MINLPs on reduced hydraulic models. We demonstrate that the restriction of the considered optimisation problem on a reduced hydraulic model is not equivalent to solving the original larger MINLP, and its solution is therefore sub-optimal. Consequently, we investigate the trade-off between reducing computational complexity and the potential sub-optimality of the solutions that can be controlled with a parameter of the model reduction routine. The efficacy of the proposed method is evaluated using two large scale water distribution network models.

In this paper, we study the multiobjective co-design problem of optimal valve placement and operation in water distribution networks, addressing the minimization of average pressure and pressure variability indices. The presented formulation considers nodal pressures, pipe flows and valve locations as decision variables, where binary variables are used to model the placement of control valves. The resulting optimization problem is a multiobjective mixed integer nonlinear optimization problem. As conflicting objectives, average zone pressure and pressure variability can not be simultaneously optimized. Therefore, we present the concept of Pareto optima sets to investigate the trade-offs between the two conflicting objectives and evaluate the best compromise. We focus on the approximation of the Pareto front, the image of the Pareto optima set through the objective functions, using the weighted sum, normal boundary intersection and normalized normal constraint scalarization techniques. Each of the three methods relies on the solution of a series of single-objective optimization problems, which are mixed integer nonlinear programs (MINLPs) in our case. For the solution of each single-objective optimization problem, we implement a relaxation method that solves a sequence of nonlinear programs (NLPs) whose stationary points converge to a stationary point of the original MINLP. The relaxed NLPs have a sparse structure that come from the sparse water network graph constraints. In solving the large number of relaxed NLPs, sparsity is exploited by tailored techniques to improve the performance of the algorithms further and render the approaches scalable for large scale networks. The features of the proposed scalarization approaches are evaluated using a published benchmarking network model.

The operation of water distribution networks (WDN) with a dynamic topology is a recently pioneered approach for the advanced management of District Metered Areas (DMAs) that integrates novel developments in hydraulic modeling, monitoring, optimization, and control. A common practice for leakage management is the sectorization of WDNs into small zones, called DMAs, by permanently closing isolation valves. This facilitates water companies to identify bursts and estimate leakage levels by measuring the inlet flow for each DMA. However, by permanently closing valves, a number of problems have been created including reduced resilience to failure and suboptimal pressure management. By introducing a dynamic topology to these zones, these disadvantages can be eliminated while still retaining the DMA structure for leakage monitoring. In this paper, a novel optimization method based on sequential convex programming (SCP) is outlined for the control of a dynamic topology with the objective of reducing average zone pressure (AZP). A key attribute for control optimization is reliable convergence. To achieve this, the SCP method we propose guarantees that each optimization step is strictly feasible, resulting in improved convergence properties. By using a null space algorithm for hydraulic analyses, the computations required are also significantly reduced. The optimized control is actuated on a real WDN operated with a dynamic topology. This unique experimental program incorporates a number of technologies set up with the objective of investigating pioneering developments in WDN management. Preliminary results indicate AZP reductions for a dynamic topology of up to 6.5% over optimally controlled fixed topology DMAs.

This manuscript investigates the problem of optimal placement of control valves in water supply networks, where the objective is to minimize average zone pressure (AZP). The problem formulation results in a nonconvex mixed integer nonlinear program (MINLP). Due to its complex mathematical structure, previous literature has solved this nonconvex MINLP using heuristics or local optimization methods, which do not provide guarantees on the global optimality of the computed valve configurations. In our approach, we implement a branch and bound method to obtain certified bounds on the optimality gap of the solutions. The algorithm relies on the solution of mixed integer linear programs, whose formulations include linear relaxations of the nonconvex hydraulic constraints. We investigate the implementation and performance of different linear relaxation schemes. In addition, a tailored domain reduction procedure is implemented to tighten the relaxations. The developed methods are evaluated using two benchmark water supply networks and an operational water supply network from the UK. The proposed approaches are shown to outperform state-of-the-art global optimization solvers for the considered benchmark water supply networks. The branch and bound algorithm converges to good quality feasible solutions in most instances, with bounds on the optimality gap that are comparable to the level of parameter uncertainty usually experienced in water supply network models.

We propose an optimization-based method for designing a lower order Luenberger-type state estimator, while providing L 2 -gain guarantees on the error dynamics when the estimator is used with the higher order system. Suitable filter parameters can be computed by modelling the bilinear system as a linear differential inclusion and solving a set of bilinear matrix inequality constraints. Since these constraints are nonconvex, in general, we also show that one can solve a suitably defined semi-definite program to compute a bound on the level of suboptimality. The design method also allows one to explicitly take account of linear parameter uncertainties in order to provide a priori robustness guarantees. The H-infinity estimator not only has lower real-time computational requirements compared with a Kalman filter, but also does not require knowledge of the noise spectrum. For a numerical example, we consider the estimation of the radiation force for a wave energy converter, where a low-order model is used to approximate the radiation dynamics.

A dynamic topology aggregates zones in water distribution networks (WDNs) for improved pressure management and resilience to failure. Based on a sequential convex programming (SCP) approach, we propose an optimization method for the control of pressure reducing valves (PRV) in WDNs with dynamic topology. By restricting the SCP iterations to the feasible search space, we show that reliable convergence of the method is achieved. Using an experimental study in a large operational network, the optimization of PRV settings with a dynamic topology is shown to result in pressure reductions of 3.7% compared to optimized PRVs in a closed DMA structure.