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E. Abraham
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Dealing with uncertainty in predicted inflows presents a major challenge in optimal reservoir flood control. Scenario-based stochastic control approaches address this by generating multiple inflow time series from probabilistic models, each representing a possible future with associated likelihoods. However, using too many scenarios increases computational complexity, while too few may compromise representativeness. Although the two critical steps of scenario generation and reduction have been extensively explored in other fields, their application to reservoir inflow dynamics remains limited. This study develops and applies a probabilistic data-driven model, specifically, a Bayesian Neural Network (BNN), for scenario generation. While the model exhibits limitations in predicting peak inflows due to data scarcity, it effectively captures temporal dependencies in inflow time series and achieves high short-term accuracy, as measured by the Nash–Sutcliffe Efficiency Coefficient (NSE) and Root Mean Squared Error (RMSE), though performance declines over longer horizons. For scenario reduction, four distance measures widely used in other domains, i.e., the Manhattan, Euclidean, Wasserstein, and energy distances, are evaluated. Experimental results show that the energy distance best preserves the statistical properties of the full scenario set, followed by the Manhattan and Euclidean distances. However, in terms of retaining extreme inflow scenarios, which are critical for flood control, the Manhattan and Euclidean distances outperform others based on a custom index measuring the envelope size of the original scenario set using the l1-norm. In terms of computational efficiency of scenario reduction approaches, the energy distance is the most expensive (quadratic in m, the number of reduced scenarios), while the Wasserstein scales linearly. In the examples used, reduced sets are shown to adequately capture extremes when the number of scenarios m≥30. Considering the trade-off between preserving extremes and computational cost, the Manhattan and Euclidean distances with m=30 are recommended as a practical choice for reservoir inflow scenario reduction.
...
Dealing with uncertainty in predicted inflows presents a major challenge in optimal reservoir flood control. Scenario-based stochastic control approaches address this by generating multiple inflow time series from probabilistic models, each representing a possible future with associated likelihoods. However, using too many scenarios increases computational complexity, while too few may compromise representativeness. Although the two critical steps of scenario generation and reduction have been extensively explored in other fields, their application to reservoir inflow dynamics remains limited. This study develops and applies a probabilistic data-driven model, specifically, a Bayesian Neural Network (BNN), for scenario generation. While the model exhibits limitations in predicting peak inflows due to data scarcity, it effectively captures temporal dependencies in inflow time series and achieves high short-term accuracy, as measured by the Nash–Sutcliffe Efficiency Coefficient (NSE) and Root Mean Squared Error (RMSE), though performance declines over longer horizons. For scenario reduction, four distance measures widely used in other domains, i.e., the Manhattan, Euclidean, Wasserstein, and energy distances, are evaluated. Experimental results show that the energy distance best preserves the statistical properties of the full scenario set, followed by the Manhattan and Euclidean distances. However, in terms of retaining extreme inflow scenarios, which are critical for flood control, the Manhattan and Euclidean distances outperform others based on a custom index measuring the envelope size of the original scenario set using the l1-norm. In terms of computational efficiency of scenario reduction approaches, the energy distance is the most expensive (quadratic in m, the number of reduced scenarios), while the Wasserstein scales linearly. In the examples used, reduced sets are shown to adequately capture extremes when the number of scenarios m≥30. Considering the trade-off between preserving extremes and computational cost, the Manhattan and Euclidean distances with m=30 are recommended as a practical choice for reservoir inflow scenario reduction.
Abstract
(2026)
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Derya Sadak, Pierre Karamountzos, Patricia Mares-Nasarre, Nick van de Giesen, Edo Abraham
This study addresses the potential role of Pumped Hydro Energy Storage (PHES) within the Kenyan electricity system pathways by combining high-resolution geospatial analysis for the site suitability and spatially explicit energy system optimisation model (ESOM) to evaluate optimal power system expansions to 2050. The modelling framework further incorporates a Gaussian copula-based Bayesian network to represent uncertainties related to demand and market pricing for net-zero target scenarios.
The study brings together three elements of PHES technologies by quantifying site-specific capital costs based on topology, implementing and optimising their scale and spatial patterns in future power systems, and addressing known uncertainties. Initially, techno-economically viable PHES sites are explored in Kenya by applying geospatial operations and redeveloping existing water bodies. Considering the country’s distinctive geography, climate, land use, and water supply, the potential sites have been assessed within the nexus framework. The results indicate that Kenya offers considerable potential for PHES, with unit capital expenditures ranging from $750/kW to $6000/kW, with many options being comparable to the lower end of global cost ranges. This spatial heterogeneity of PHES potential motivates a spatially explicit dispatch and expansion analysis to identify which sites are cost-effective, where, and at what scale in future electrification pathways.
For this purpose, the study introduces a spatially explicit ESOM, termed PyPSA-KE, based on the open-source PyPSA-Earth framework. The model is calibrated using Kenya-specific data and applied to investigate optimal power system expansion pathways to 2050 under carbon tax-based net-zero scenarios. Closed-loop PHES sites identified in the Global Atlas of PHES (Stocks et al., 2021) are represented explicitly, with site-specific capital costs and grid-connection distances derived from local topography. Results indicate a substantial potential for PHES deployment across Kenya for both daily and multi-day storage, complemented by battery storage to ensure peak demand is met. The absolute amounts of storage required in 2050 are highly sensitive to uncertain exogenous socio-techno-economic factors, most notably future electricity demand, battery cost trajectories, and the stringency of carbon taxation. Although the ESOM is deterministic, explicitly accounting for such uncertainty is essential, in line with a growing shift towards global sensitivity analysis in the literature (Yue et al., 2018). To this end, the study proposes a Bayesian framework enabling probabilistic characterisation and rapid exploration of long-term scenarios. A Gaussian-copula-based Bayesian network is constructed using Monte Carlo samples of PyPSA-KE outputs, generated by imposing probability distributions on key uncertain inputs. Despite limitations associated with network structure and the use of bivariate Gaussian copulas, the approach demonstrates strong potential to extract robust insights and inform policy discussions on long-term power system planning under deep epistemic uncertainty. ...
The study brings together three elements of PHES technologies by quantifying site-specific capital costs based on topology, implementing and optimising their scale and spatial patterns in future power systems, and addressing known uncertainties. Initially, techno-economically viable PHES sites are explored in Kenya by applying geospatial operations and redeveloping existing water bodies. Considering the country’s distinctive geography, climate, land use, and water supply, the potential sites have been assessed within the nexus framework. The results indicate that Kenya offers considerable potential for PHES, with unit capital expenditures ranging from $750/kW to $6000/kW, with many options being comparable to the lower end of global cost ranges. This spatial heterogeneity of PHES potential motivates a spatially explicit dispatch and expansion analysis to identify which sites are cost-effective, where, and at what scale in future electrification pathways.
For this purpose, the study introduces a spatially explicit ESOM, termed PyPSA-KE, based on the open-source PyPSA-Earth framework. The model is calibrated using Kenya-specific data and applied to investigate optimal power system expansion pathways to 2050 under carbon tax-based net-zero scenarios. Closed-loop PHES sites identified in the Global Atlas of PHES (Stocks et al., 2021) are represented explicitly, with site-specific capital costs and grid-connection distances derived from local topography. Results indicate a substantial potential for PHES deployment across Kenya for both daily and multi-day storage, complemented by battery storage to ensure peak demand is met. The absolute amounts of storage required in 2050 are highly sensitive to uncertain exogenous socio-techno-economic factors, most notably future electricity demand, battery cost trajectories, and the stringency of carbon taxation. Although the ESOM is deterministic, explicitly accounting for such uncertainty is essential, in line with a growing shift towards global sensitivity analysis in the literature (Yue et al., 2018). To this end, the study proposes a Bayesian framework enabling probabilistic characterisation and rapid exploration of long-term scenarios. A Gaussian-copula-based Bayesian network is constructed using Monte Carlo samples of PyPSA-KE outputs, generated by imposing probability distributions on key uncertain inputs. Despite limitations associated with network structure and the use of bivariate Gaussian copulas, the approach demonstrates strong potential to extract robust insights and inform policy discussions on long-term power system planning under deep epistemic uncertainty. ...
This study addresses the potential role of Pumped Hydro Energy Storage (PHES) within the Kenyan electricity system pathways by combining high-resolution geospatial analysis for the site suitability and spatially explicit energy system optimisation model (ESOM) to evaluate optimal power system expansions to 2050. The modelling framework further incorporates a Gaussian copula-based Bayesian network to represent uncertainties related to demand and market pricing for net-zero target scenarios.
The study brings together three elements of PHES technologies by quantifying site-specific capital costs based on topology, implementing and optimising their scale and spatial patterns in future power systems, and addressing known uncertainties. Initially, techno-economically viable PHES sites are explored in Kenya by applying geospatial operations and redeveloping existing water bodies. Considering the country’s distinctive geography, climate, land use, and water supply, the potential sites have been assessed within the nexus framework. The results indicate that Kenya offers considerable potential for PHES, with unit capital expenditures ranging from $750/kW to $6000/kW, with many options being comparable to the lower end of global cost ranges. This spatial heterogeneity of PHES potential motivates a spatially explicit dispatch and expansion analysis to identify which sites are cost-effective, where, and at what scale in future electrification pathways.
For this purpose, the study introduces a spatially explicit ESOM, termed PyPSA-KE, based on the open-source PyPSA-Earth framework. The model is calibrated using Kenya-specific data and applied to investigate optimal power system expansion pathways to 2050 under carbon tax-based net-zero scenarios. Closed-loop PHES sites identified in the Global Atlas of PHES (Stocks et al., 2021) are represented explicitly, with site-specific capital costs and grid-connection distances derived from local topography. Results indicate a substantial potential for PHES deployment across Kenya for both daily and multi-day storage, complemented by battery storage to ensure peak demand is met. The absolute amounts of storage required in 2050 are highly sensitive to uncertain exogenous socio-techno-economic factors, most notably future electricity demand, battery cost trajectories, and the stringency of carbon taxation. Although the ESOM is deterministic, explicitly accounting for such uncertainty is essential, in line with a growing shift towards global sensitivity analysis in the literature (Yue et al., 2018). To this end, the study proposes a Bayesian framework enabling probabilistic characterisation and rapid exploration of long-term scenarios. A Gaussian-copula-based Bayesian network is constructed using Monte Carlo samples of PyPSA-KE outputs, generated by imposing probability distributions on key uncertain inputs. Despite limitations associated with network structure and the use of bivariate Gaussian copulas, the approach demonstrates strong potential to extract robust insights and inform policy discussions on long-term power system planning under deep epistemic uncertainty.
The study brings together three elements of PHES technologies by quantifying site-specific capital costs based on topology, implementing and optimising their scale and spatial patterns in future power systems, and addressing known uncertainties. Initially, techno-economically viable PHES sites are explored in Kenya by applying geospatial operations and redeveloping existing water bodies. Considering the country’s distinctive geography, climate, land use, and water supply, the potential sites have been assessed within the nexus framework. The results indicate that Kenya offers considerable potential for PHES, with unit capital expenditures ranging from $750/kW to $6000/kW, with many options being comparable to the lower end of global cost ranges. This spatial heterogeneity of PHES potential motivates a spatially explicit dispatch and expansion analysis to identify which sites are cost-effective, where, and at what scale in future electrification pathways.
For this purpose, the study introduces a spatially explicit ESOM, termed PyPSA-KE, based on the open-source PyPSA-Earth framework. The model is calibrated using Kenya-specific data and applied to investigate optimal power system expansion pathways to 2050 under carbon tax-based net-zero scenarios. Closed-loop PHES sites identified in the Global Atlas of PHES (Stocks et al., 2021) are represented explicitly, with site-specific capital costs and grid-connection distances derived from local topography. Results indicate a substantial potential for PHES deployment across Kenya for both daily and multi-day storage, complemented by battery storage to ensure peak demand is met. The absolute amounts of storage required in 2050 are highly sensitive to uncertain exogenous socio-techno-economic factors, most notably future electricity demand, battery cost trajectories, and the stringency of carbon taxation. Although the ESOM is deterministic, explicitly accounting for such uncertainty is essential, in line with a growing shift towards global sensitivity analysis in the literature (Yue et al., 2018). To this end, the study proposes a Bayesian framework enabling probabilistic characterisation and rapid exploration of long-term scenarios. A Gaussian-copula-based Bayesian network is constructed using Monte Carlo samples of PyPSA-KE outputs, generated by imposing probability distributions on key uncertain inputs. Despite limitations associated with network structure and the use of bivariate Gaussian copulas, the approach demonstrates strong potential to extract robust insights and inform policy discussions on long-term power system planning under deep epistemic uncertainty.
A framework for designing a water quality monitoring plan for decentralized rainwater harvesting drinking water supply systems
A case study in the Netherlands
Rainwater harvesting (RWH) is increasingly adopted as a decentralized water source, yet there are no regulations or policies ensuring its safety for drinking purposes. This paper introduces a five-step iterative framework to build a water quality monitoring plan for such systems comprising system description, monitoring parameters, monitoring frequency and timing, monitoring techniques, and data storage and analysis. To illustrate its practical application, the framework is applied to Superlocal, a community scale rainwater harvesting project in Kerkrade, the Netherlands, where rainwater is collected and treated locally to supply drinking water. Applying this framework, the manuscript provides detailed implementation guidance. In the absence of specific RWH regulations, the study adapts existing Dutch drinking water regulations (DWB and DWR) and utilizes a historical rainwater quality database to inform parameter selection, thresholds, and monitoring frequencies. The analysis reveals that water production and monitoring costs for Superlocal total €4.60/m3, considerably higher than for centralized systems in the Netherlands. As decentralized RWH systems are increasingly integrated into climate adaptation strategies for urban water, this work highlights barriers and enablers for advancing safe and sustainable decentralized water management. The study recommends policy interventions and cooperative management models to enhance the economic viability and safety of these systems.
...
Rainwater harvesting (RWH) is increasingly adopted as a decentralized water source, yet there are no regulations or policies ensuring its safety for drinking purposes. This paper introduces a five-step iterative framework to build a water quality monitoring plan for such systems comprising system description, monitoring parameters, monitoring frequency and timing, monitoring techniques, and data storage and analysis. To illustrate its practical application, the framework is applied to Superlocal, a community scale rainwater harvesting project in Kerkrade, the Netherlands, where rainwater is collected and treated locally to supply drinking water. Applying this framework, the manuscript provides detailed implementation guidance. In the absence of specific RWH regulations, the study adapts existing Dutch drinking water regulations (DWB and DWR) and utilizes a historical rainwater quality database to inform parameter selection, thresholds, and monitoring frequencies. The analysis reveals that water production and monitoring costs for Superlocal total €4.60/m3, considerably higher than for centralized systems in the Netherlands. As decentralized RWH systems are increasingly integrated into climate adaptation strategies for urban water, this work highlights barriers and enablers for advancing safe and sustainable decentralized water management. The study recommends policy interventions and cooperative management models to enhance the economic viability and safety of these systems.
Governing the water-energy-food nexus
A multi-stage governance assessment approach embracing complexity and regional diversity
Integrated management of water, energy, and food (WEF) systems requires governance approaches that account for biophysical interdependencies while remaining grounded in the institutional arrangements through which decisions are made and implemented. Despite the widespread appeal of the WEF nexus, its operationalization remains limited, particularly in data and capacity-constrained contexts. This study presents a sequenced diagnostic framework to assess WEF nexus governance, explicitly linking biophysical interactions to institutional and policy processes. The approach begins with a basin-level analysis of subsystem interactions to characterize a clearly defined nexus challenge. It then situates this challenge within the broader governance landscape, before proceeding to detailed analyses of policy coherence and governance architecture, including proposal and approval procedures, formal implementation arrangements, and guiding principles. Applied to the Tana River Basin in Kenya, the framework identifies 50 explicit synergies and 7 trade-offs across national sectoral policies, alongside uneven coordination mechanisms between sectors. The findings show how land-use, water allocation, agricultural priorities, climate adaptation, and ecosystem protection are shaped by the interaction between biophysical constraints and sectoral mandates. By linking policy alignment with institutional design, the framework identifies actionable entry points for improving cross-sector coordination. Designed for both preliminary assessments and stakeholder-engaged processes, the approach provides a practical pathway for operationalizing WEF nexus governance in real-world settings.
...
Integrated management of water, energy, and food (WEF) systems requires governance approaches that account for biophysical interdependencies while remaining grounded in the institutional arrangements through which decisions are made and implemented. Despite the widespread appeal of the WEF nexus, its operationalization remains limited, particularly in data and capacity-constrained contexts. This study presents a sequenced diagnostic framework to assess WEF nexus governance, explicitly linking biophysical interactions to institutional and policy processes. The approach begins with a basin-level analysis of subsystem interactions to characterize a clearly defined nexus challenge. It then situates this challenge within the broader governance landscape, before proceeding to detailed analyses of policy coherence and governance architecture, including proposal and approval procedures, formal implementation arrangements, and guiding principles. Applied to the Tana River Basin in Kenya, the framework identifies 50 explicit synergies and 7 trade-offs across national sectoral policies, alongside uneven coordination mechanisms between sectors. The findings show how land-use, water allocation, agricultural priorities, climate adaptation, and ecosystem protection are shaped by the interaction between biophysical constraints and sectoral mandates. By linking policy alignment with institutional design, the framework identifies actionable entry points for improving cross-sector coordination. Designed for both preliminary assessments and stakeholder-engaged processes, the approach provides a practical pathway for operationalizing WEF nexus governance in real-world settings.
Sub-Saharan Africa, home to one of the fastest-growing populations in the world, is experiencing rapid urbanization and the emergence of a growing middle class. Despite these developments, current agricultural production falls short of meeting the projected food demand, hindered by lack of access to modern energy, among other factors. Improving the productivity of smallholder farmers and expanding local crop processing are vital strategies to meet this increasing demand for food and to enhance the lives and livelihoods of African farmers. Supplying electricity for irrigation and crop processing can stimulate entrepreneurship, foster local wealth creation, and support the achievement of several Sustainable Development Goals (SDGs), while improving the cost-effectiveness of clean energy plans in rural areas. In this article, we argue that Africa's energy planning should focus on accelerating economic growth by providing clean and modern energy solutions that extend beyond basic household services. This includes integrating energy needs for small-scale irrigation and crop processing into household energy planning. Through various example projects, we demonstrate that the benefits of productive energy uses extend beyond mechanizing agriculture and crop processing. These initiatives enhance local economic development by integrating electricity into household business operations, such as milling and agro-processing, which create jobs and diversify income. They also enhance communication and provide critical market information, empowering rural communities. By prioritizing productive energy uses, these projects increase electricity consumption, improving the financial sustainability of electrification efforts.
...
Sub-Saharan Africa, home to one of the fastest-growing populations in the world, is experiencing rapid urbanization and the emergence of a growing middle class. Despite these developments, current agricultural production falls short of meeting the projected food demand, hindered by lack of access to modern energy, among other factors. Improving the productivity of smallholder farmers and expanding local crop processing are vital strategies to meet this increasing demand for food and to enhance the lives and livelihoods of African farmers. Supplying electricity for irrigation and crop processing can stimulate entrepreneurship, foster local wealth creation, and support the achievement of several Sustainable Development Goals (SDGs), while improving the cost-effectiveness of clean energy plans in rural areas. In this article, we argue that Africa's energy planning should focus on accelerating economic growth by providing clean and modern energy solutions that extend beyond basic household services. This includes integrating energy needs for small-scale irrigation and crop processing into household energy planning. Through various example projects, we demonstrate that the benefits of productive energy uses extend beyond mechanizing agriculture and crop processing. These initiatives enhance local economic development by integrating electricity into household business operations, such as milling and agro-processing, which create jobs and diversify income. They also enhance communication and provide critical market information, empowering rural communities. By prioritizing productive energy uses, these projects increase electricity consumption, improving the financial sustainability of electrification efforts.
Coastal low-lying agricultural areas are threatened by groundwater salinisation due to saline groundwater upconing and seawater intrusion, which can be amplified by climate change-driven sea-level rise and drainage practices. In Dutch coastal polders, such as those on the island of Texel, rainfall is the only source of freshwater for agriculture, forming shallow rainwater lenses that support crop growth. Under conventional freely discharging drainage systems, rainfall-derived freshwater is rapidly removed, reducing freshwater retention in the shallow subsurface and enhancing saline upconing. As ditch water is often brackish and alternative freshwater sources are unavailable, thinning or loss of rainwater lenses poses a serious risk of root-zone salinisation and freshwater stress for crops.
Level-controlled drainage and subsurface irrigation are promising approaches to address these challenges. In this work, we use numerical modelling to evaluate how level-controlled drainage influences freshwater availability for crop growth in comparison to conventional drainage. Level-controlled drainage systems are designed to retain excess rainfall during autumn and winter by limiting outflow, thereby enhancing freshwater storage in the shallow subsurface, while still allowing controlled discharge of surplus water to drainage ditches. During spring and summer, the system can be actively managed to use for subsurface irrigation, providing supplemental water to crops using an external water supply.
The level-controlled drainage concept with subsurface irrigation is evaluated within the framework of the AGRICOAST project, which aims to enhance freshwater availability and promote efficient water use in saline-prone coastal regions. While previous numerical studies primarily focused on saturated flow conditions, this study advances current understanding by explicitly accounting for variably saturated, density-driven groundwater flow and solute transport processes relevant to root-zone conditions. We simulate a hypothetical representative case for the island of Texel, exploring system performance under a range of hydrogeological settings, climatic conditions, and drainage configurations. Crop growth parameters are incorporated to better represent seasonal water demands and root-zone dynamics. Through scenario analysis, we assess the impacts of weather variability and salinity dynamics on freshwater availability and root-zone salinity, and evaluate the effectiveness of level-controlled drainage in mitigating salinization risks. The results demonstrate the potential of level-controlled drainage as a sustainable water management strategy to support freshwater availability for coastal agriculture under changing environmental conditions. ...
Level-controlled drainage and subsurface irrigation are promising approaches to address these challenges. In this work, we use numerical modelling to evaluate how level-controlled drainage influences freshwater availability for crop growth in comparison to conventional drainage. Level-controlled drainage systems are designed to retain excess rainfall during autumn and winter by limiting outflow, thereby enhancing freshwater storage in the shallow subsurface, while still allowing controlled discharge of surplus water to drainage ditches. During spring and summer, the system can be actively managed to use for subsurface irrigation, providing supplemental water to crops using an external water supply.
The level-controlled drainage concept with subsurface irrigation is evaluated within the framework of the AGRICOAST project, which aims to enhance freshwater availability and promote efficient water use in saline-prone coastal regions. While previous numerical studies primarily focused on saturated flow conditions, this study advances current understanding by explicitly accounting for variably saturated, density-driven groundwater flow and solute transport processes relevant to root-zone conditions. We simulate a hypothetical representative case for the island of Texel, exploring system performance under a range of hydrogeological settings, climatic conditions, and drainage configurations. Crop growth parameters are incorporated to better represent seasonal water demands and root-zone dynamics. Through scenario analysis, we assess the impacts of weather variability and salinity dynamics on freshwater availability and root-zone salinity, and evaluate the effectiveness of level-controlled drainage in mitigating salinization risks. The results demonstrate the potential of level-controlled drainage as a sustainable water management strategy to support freshwater availability for coastal agriculture under changing environmental conditions. ...
Coastal low-lying agricultural areas are threatened by groundwater salinisation due to saline groundwater upconing and seawater intrusion, which can be amplified by climate change-driven sea-level rise and drainage practices. In Dutch coastal polders, such as those on the island of Texel, rainfall is the only source of freshwater for agriculture, forming shallow rainwater lenses that support crop growth. Under conventional freely discharging drainage systems, rainfall-derived freshwater is rapidly removed, reducing freshwater retention in the shallow subsurface and enhancing saline upconing. As ditch water is often brackish and alternative freshwater sources are unavailable, thinning or loss of rainwater lenses poses a serious risk of root-zone salinisation and freshwater stress for crops.
Level-controlled drainage and subsurface irrigation are promising approaches to address these challenges. In this work, we use numerical modelling to evaluate how level-controlled drainage influences freshwater availability for crop growth in comparison to conventional drainage. Level-controlled drainage systems are designed to retain excess rainfall during autumn and winter by limiting outflow, thereby enhancing freshwater storage in the shallow subsurface, while still allowing controlled discharge of surplus water to drainage ditches. During spring and summer, the system can be actively managed to use for subsurface irrigation, providing supplemental water to crops using an external water supply.
The level-controlled drainage concept with subsurface irrigation is evaluated within the framework of the AGRICOAST project, which aims to enhance freshwater availability and promote efficient water use in saline-prone coastal regions. While previous numerical studies primarily focused on saturated flow conditions, this study advances current understanding by explicitly accounting for variably saturated, density-driven groundwater flow and solute transport processes relevant to root-zone conditions. We simulate a hypothetical representative case for the island of Texel, exploring system performance under a range of hydrogeological settings, climatic conditions, and drainage configurations. Crop growth parameters are incorporated to better represent seasonal water demands and root-zone dynamics. Through scenario analysis, we assess the impacts of weather variability and salinity dynamics on freshwater availability and root-zone salinity, and evaluate the effectiveness of level-controlled drainage in mitigating salinization risks. The results demonstrate the potential of level-controlled drainage as a sustainable water management strategy to support freshwater availability for coastal agriculture under changing environmental conditions.
Level-controlled drainage and subsurface irrigation are promising approaches to address these challenges. In this work, we use numerical modelling to evaluate how level-controlled drainage influences freshwater availability for crop growth in comparison to conventional drainage. Level-controlled drainage systems are designed to retain excess rainfall during autumn and winter by limiting outflow, thereby enhancing freshwater storage in the shallow subsurface, while still allowing controlled discharge of surplus water to drainage ditches. During spring and summer, the system can be actively managed to use for subsurface irrigation, providing supplemental water to crops using an external water supply.
The level-controlled drainage concept with subsurface irrigation is evaluated within the framework of the AGRICOAST project, which aims to enhance freshwater availability and promote efficient water use in saline-prone coastal regions. While previous numerical studies primarily focused on saturated flow conditions, this study advances current understanding by explicitly accounting for variably saturated, density-driven groundwater flow and solute transport processes relevant to root-zone conditions. We simulate a hypothetical representative case for the island of Texel, exploring system performance under a range of hydrogeological settings, climatic conditions, and drainage configurations. Crop growth parameters are incorporated to better represent seasonal water demands and root-zone dynamics. Through scenario analysis, we assess the impacts of weather variability and salinity dynamics on freshwater availability and root-zone salinity, and evaluate the effectiveness of level-controlled drainage in mitigating salinization risks. The results demonstrate the potential of level-controlled drainage as a sustainable water management strategy to support freshwater availability for coastal agriculture under changing environmental conditions.
Kenya has one of the fastest electrification rates in Sub-Saharan Africa. Despite the increase in electrification rates, rural and underserved regions remain a critical challenge requiring a cost-effective strategy that maximises the use of stand-alone and off-grid solutions. This paper uses the Open-Source Spatial Electrification Tool coupled with a binomial logistic regression model of urbanisation to explore least-cost electrification scenarios for universal access in Kenya. The premise is that as more areas are electrified and the population increases, more regions will likely become urban, leading to changes in their electricity demand. The regression model reveals at least four regions where new urban settlements will likely be concentrated: central Kenya, the coastline, and the border regions to the west and north of Kenya. Electrification scenarios prioritising off-grid ($5.2 billion) and stand-alone solutions ($1.8 billion) significantly reduce the required investment compared to scenarios prioritising grid extension ($8.1 billion). Given the crucial role of stand-alone solutions in minimising costs associated with electricity access, this paper suggests a shift in policy to promote the uptake of stand-alone systems over the previous focus on grid extension and large-scale projects that have dominated Kenya's energy policy landscape.
...
Kenya has one of the fastest electrification rates in Sub-Saharan Africa. Despite the increase in electrification rates, rural and underserved regions remain a critical challenge requiring a cost-effective strategy that maximises the use of stand-alone and off-grid solutions. This paper uses the Open-Source Spatial Electrification Tool coupled with a binomial logistic regression model of urbanisation to explore least-cost electrification scenarios for universal access in Kenya. The premise is that as more areas are electrified and the population increases, more regions will likely become urban, leading to changes in their electricity demand. The regression model reveals at least four regions where new urban settlements will likely be concentrated: central Kenya, the coastline, and the border regions to the west and north of Kenya. Electrification scenarios prioritising off-grid ($5.2 billion) and stand-alone solutions ($1.8 billion) significantly reduce the required investment compared to scenarios prioritising grid extension ($8.1 billion). Given the crucial role of stand-alone solutions in minimising costs associated with electricity access, this paper suggests a shift in policy to promote the uptake of stand-alone systems over the previous focus on grid extension and large-scale projects that have dominated Kenya's energy policy landscape.
Modeling changes in nutrient retention ecosystem service using the InVEST-NDR model
A case study in the Gumara River of Lake Tana Basin, Ethiopia
Journal article
(2025)
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Wubneh B. Abebe, Minychl G. Dersseh, Joanna R. Blaszczak, Goraw Goshu, Wuletawu Abera, Edo Abraham, Muluneh A. Mekonnen, Nicola Fohrer, Seifu A. Tilahun, Michael E. McClain, William A. Payne
Aquatic ecosystems provide valuable ecosystem services (e.g., habitat for fisheries) to surrounding communities but environmental degradation can diminish the quality of these ecosystem services. The Lake Tana basin, including the Gumara River and its associated wetlands in Ethiopia, has experienced rapid environmental change in the last several decades. Changes in the export of nutrients from the uplands might contribute to the rapid degradation of aquatic ecosystem services due to the expansion of water hyacinths and declines in fish biodiversity and yields. We estimate how human modification and climate change have impacted watershed nutrient retention from 1986 to 2020. Here we (1) examine trends in surface water chemistry, watershed land use/land cover change, and flow alterations; (2) estimate the watershed nutrient delivery ratio (NDR), a metric of watershed nutrient retention, through time; and (3) examine how fishery yields and water hyacinth infestation in Lake Tana at the outlet of the Gumara River change during a period of rapid increase in nutrient export from the Gumara River. Estimates of the surface load and export of both phosphorus (P) and nitrogen (N) from the Gumara River watershed were approximately stable between 1986 and 2009, but from 2014 to 2020 exports increased by 69 % for P and 80 % for N. Potential factors driving this rapid increase include an expansion in irrigation for agriculture, land conversion to eucalyptus plantations, decreases in dry season flow, and an increase in mean annual precipitation since 2009. In addition, the increase in nutrient export from the Gumara River watershed coincides with a near extirpation of fish in the Gumara River and a ten-fold expansion of water hyacinth downstream in Lake Tana. Human activity and hydrological alteration in the Gumara River watershed have resulted in water quality changes, declines in fish populations, and the expansion of invasive species. Long-term monitoring and watershed modeling can help inform the management of regionally important aquatic ecosystems such as the Gumara River and Lake Tana.
...
Aquatic ecosystems provide valuable ecosystem services (e.g., habitat for fisheries) to surrounding communities but environmental degradation can diminish the quality of these ecosystem services. The Lake Tana basin, including the Gumara River and its associated wetlands in Ethiopia, has experienced rapid environmental change in the last several decades. Changes in the export of nutrients from the uplands might contribute to the rapid degradation of aquatic ecosystem services due to the expansion of water hyacinths and declines in fish biodiversity and yields. We estimate how human modification and climate change have impacted watershed nutrient retention from 1986 to 2020. Here we (1) examine trends in surface water chemistry, watershed land use/land cover change, and flow alterations; (2) estimate the watershed nutrient delivery ratio (NDR), a metric of watershed nutrient retention, through time; and (3) examine how fishery yields and water hyacinth infestation in Lake Tana at the outlet of the Gumara River change during a period of rapid increase in nutrient export from the Gumara River. Estimates of the surface load and export of both phosphorus (P) and nitrogen (N) from the Gumara River watershed were approximately stable between 1986 and 2009, but from 2014 to 2020 exports increased by 69 % for P and 80 % for N. Potential factors driving this rapid increase include an expansion in irrigation for agriculture, land conversion to eucalyptus plantations, decreases in dry season flow, and an increase in mean annual precipitation since 2009. In addition, the increase in nutrient export from the Gumara River watershed coincides with a near extirpation of fish in the Gumara River and a ten-fold expansion of water hyacinth downstream in Lake Tana. Human activity and hydrological alteration in the Gumara River watershed have resulted in water quality changes, declines in fish populations, and the expansion of invasive species. Long-term monitoring and watershed modeling can help inform the management of regionally important aquatic ecosystems such as the Gumara River and Lake Tana.
Flood control of reservoir systems
Learning-based explicit and switched model predictive control approaches
Journal article
(2025)
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Ja-Ho Koo, Ali Moradvandi, Edo Abraham, Andreja Jonoski, Dmitri P. Solomatine
Effective reservoir flood control demands real-time decision-making that balances multiple objectives. However, traditional optimization approaches are often too computationally intensive and become intractable when considering dynamically changing preferences of operators, modelled as weights of different objectives. This study aims to develop tractable real-time flood control strategies that maintain performance while reducing computational complexity. We propose two data-driven approaches based on Model Predictive Control (MPC): (1) an explicit MPC using deep neural networks to directly determine optimal outflow schedules, and (2) a switched MPC that produces optimal weights of objectives based on hydrological conditions. Both methods leverage offline learning from an online Parameterized Dynamic MPC framework incorporating state-dependent weights. We tested these approaches on South Korea’s Daecheong multipurpose reservoir using historical flood events with various patterns. The explicit MPC demonstrated reliable performance under conditions similar to its training data. However, it showed frequent changes in outflow schedules and constraint violations for scenarios outside training data. In contrast, the switched MPC maintained robustness across all test scenarios due to a linear optimization process in a receding horizon manner, though with slightly reduced performance compared to the explicit MPC under scenarios inside the range of training data. Most significantly, both approaches reduced computation time from approximately 10 minutes to less than one second, making real-time implementation feasible. This dramatic improvement enables prompt decision-making during rapidly evolving flood events while maintaining near-optimal control performance.
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Effective reservoir flood control demands real-time decision-making that balances multiple objectives. However, traditional optimization approaches are often too computationally intensive and become intractable when considering dynamically changing preferences of operators, modelled as weights of different objectives. This study aims to develop tractable real-time flood control strategies that maintain performance while reducing computational complexity. We propose two data-driven approaches based on Model Predictive Control (MPC): (1) an explicit MPC using deep neural networks to directly determine optimal outflow schedules, and (2) a switched MPC that produces optimal weights of objectives based on hydrological conditions. Both methods leverage offline learning from an online Parameterized Dynamic MPC framework incorporating state-dependent weights. We tested these approaches on South Korea’s Daecheong multipurpose reservoir using historical flood events with various patterns. The explicit MPC demonstrated reliable performance under conditions similar to its training data. However, it showed frequent changes in outflow schedules and constraint violations for scenarios outside training data. In contrast, the switched MPC maintained robustness across all test scenarios due to a linear optimization process in a receding horizon manner, though with slightly reduced performance compared to the explicit MPC under scenarios inside the range of training data. Most significantly, both approaches reduced computation time from approximately 10 minutes to less than one second, making real-time implementation feasible. This dramatic improvement enables prompt decision-making during rapidly evolving flood events while maintaining near-optimal control performance.
Floating photovoltaics in the long-term energy planning of Easter Nile Basin countries
Synergising water conservation, land use, and emissions
This manuscript presents a method for integrating floating photovoltaics (FPVs) into long-term energy planning, addressing rising electricity demands amidst water stress, land competition, and climate vulnerabilities. This integrated framework is applied to four Eastern Nile Basin countries, where renewable technologies are projected to dominate the power mix. Here, FPVs are evaluated for cost-effectiveness, water savings, and land efficiency. The study advances the OSeMOSYS energy planning framework by spatially explicitly modelling water savings and land values for various energy technologies, incorporating CO2 emissions and land use costs in the optimisation. To this end, new methodologies for land-use accounting and FPV potential for reducing evaporation in hydropower reservoirs were developed. We then evaluate FPV potential across a network of hydropower plants, incorporating electricity trade links between basin countries and simulating under different CMIP six climate change scenarios and tax scenarios. Across all scenarios, results indicate that FPVs can cost-effectively provide up to 3% of the region’s electricity generation by 2065, saving up to 376 million cubic meters of water annually. Scenarios introducing carbon and land-use taxes increase FPV’s share in the power generation mix to 4.5% and enable earlier FPV deployment. While climate impacts minimally affect FPV’s role, the technology slightly reduces CO2 emissions (0.4%) and land use (0.8%) in the baseline scenario without taxes. Compared to baseline scenario, a carbon tax alone reduces emissions by 11%–23% but raises land use by up to 8% due to increased renewable technologiesdeployment. On the other hand, land tax alone would reduce land use by 5%–8% with minimal impact on emissions. However, combining land and carbon taxes affects emissions (cuts up to 22%) and land use (a decrease of 1.6% or an increase of 1.2%). The study concludes that FPVs offer a promising solution for cost-effective and sustainable power expansion in the Eastern Nile Basin.
...
This manuscript presents a method for integrating floating photovoltaics (FPVs) into long-term energy planning, addressing rising electricity demands amidst water stress, land competition, and climate vulnerabilities. This integrated framework is applied to four Eastern Nile Basin countries, where renewable technologies are projected to dominate the power mix. Here, FPVs are evaluated for cost-effectiveness, water savings, and land efficiency. The study advances the OSeMOSYS energy planning framework by spatially explicitly modelling water savings and land values for various energy technologies, incorporating CO2 emissions and land use costs in the optimisation. To this end, new methodologies for land-use accounting and FPV potential for reducing evaporation in hydropower reservoirs were developed. We then evaluate FPV potential across a network of hydropower plants, incorporating electricity trade links between basin countries and simulating under different CMIP six climate change scenarios and tax scenarios. Across all scenarios, results indicate that FPVs can cost-effectively provide up to 3% of the region’s electricity generation by 2065, saving up to 376 million cubic meters of water annually. Scenarios introducing carbon and land-use taxes increase FPV’s share in the power generation mix to 4.5% and enable earlier FPV deployment. While climate impacts minimally affect FPV’s role, the technology slightly reduces CO2 emissions (0.4%) and land use (0.8%) in the baseline scenario without taxes. Compared to baseline scenario, a carbon tax alone reduces emissions by 11%–23% but raises land use by up to 8% due to increased renewable technologiesdeployment. On the other hand, land tax alone would reduce land use by 5%–8% with minimal impact on emissions. However, combining land and carbon taxes affects emissions (cuts up to 22%) and land use (a decrease of 1.6% or an increase of 1.2%). The study concludes that FPVs offer a promising solution for cost-effective and sustainable power expansion in the Eastern Nile Basin.
Incorporating Risk in Operational Water Resources Management
Probabilistic Forecasting, Scenario Generation, and Optimal Control
Journal article
(2025)
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Ties van der Heijden, Miguel Angel Mendoza-Lugo, Peter Palensky, Nick van de Giesen, Edo Abraham
This study presents an innovative approach to risk-aware decision-making in water resource management. We focus on a case study in the Netherlands, where risk awareness is key to water system design and policy-making. Recognizing the limitations of deterministic methods in the face of weather, energy system, and market uncertainties, we propose a scalable stochastic Model Predictive Control (MPC) framework that integrates probabilistic forecasting, scenario generation, and stochastic optimal control. We utilize Combined Quantile Regression Deep Neural Networks and Non-parametric Bayesian Networks to generate probabilistic scenarios that capture realistic temporal dependencies. The energy distance metric is applied to optimize scenario selection and generate scenario trees, ensuring computational feasibility without compromising decision quality. A key feature of our approach is the introduction of Exceedance Risk (ER) constraints, inspired by Conditional-Value-at-Risk (CVaR), to enable more nuanced and risk-aware decision-making while maintaining computational efficiency. In this work, we enable the Noordzeekanaal–Amsterdam-Rijnkanaal (NZK-ARK) system to participate in Demand Response (DR) services by dynamically scheduling pumps to align with low hourly electricity prices on the Day Ahead and Intraday markets. Through historical simulations using real water system and electricity price data, we demonstrate that incorporating uncertainty can significantly reduce operational costs—by up to 44 percentage points compared to a deterministic approach—while maintaining safe water levels. The modular nature of the framework also makes it adaptable to a wide range of applications, including hydropower and battery storage systems.
...
This study presents an innovative approach to risk-aware decision-making in water resource management. We focus on a case study in the Netherlands, where risk awareness is key to water system design and policy-making. Recognizing the limitations of deterministic methods in the face of weather, energy system, and market uncertainties, we propose a scalable stochastic Model Predictive Control (MPC) framework that integrates probabilistic forecasting, scenario generation, and stochastic optimal control. We utilize Combined Quantile Regression Deep Neural Networks and Non-parametric Bayesian Networks to generate probabilistic scenarios that capture realistic temporal dependencies. The energy distance metric is applied to optimize scenario selection and generate scenario trees, ensuring computational feasibility without compromising decision quality. A key feature of our approach is the introduction of Exceedance Risk (ER) constraints, inspired by Conditional-Value-at-Risk (CVaR), to enable more nuanced and risk-aware decision-making while maintaining computational efficiency. In this work, we enable the Noordzeekanaal–Amsterdam-Rijnkanaal (NZK-ARK) system to participate in Demand Response (DR) services by dynamically scheduling pumps to align with low hourly electricity prices on the Day Ahead and Intraday markets. Through historical simulations using real water system and electricity price data, we demonstrate that incorporating uncertainty can significantly reduce operational costs—by up to 44 percentage points compared to a deterministic approach—while maintaining safe water levels. The modular nature of the framework also makes it adaptable to a wide range of applications, including hydropower and battery storage systems.
Optimising rainwater harvesting systems under uncertainty
A multi-objective stochastic approach with risk considerations
Journal article
(2025)
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Alireza Shefaei, Arash Maleki, Jan Peter van der Hoek, Nick van de Giesen, Edo Abraham
Optimising rainwater harvesting (RWH) systems’ design involves sizing the storage and catchment areas to enhance cost-effectiveness, self-sufficiency, and water quality indicators. This paper considers the design of RWH systems under long-term uncertainty in precipitation and demands. In this work, we formulate and solve a multi-objective stochastic optimisation problem that allows explicit trade-offs under uncertainty, maximising system efficiency and minimising deployment cost. We use the yield after spillage (YAS) approach to incorporate the physical and operational constraints and the big-M method to reformulate the nonlinear min\max rules of this approach as a mixed-integer linear programming (MILP) problem. By posing a risk averseness measure on efficiency as a conditional value at risk (CVaR) formulation, we guarantee the designer against the highest demand and driest weather conditions. We then exploit the lexicographic method to effectively solve the multi-objective stochastic problem as a sequence of equivalent single-objective problems. A detailed case study of a botanical garden in Amsterdam demonstrates the framework's practical application; we show significant improvements in system efficiency of up to 15.5% and 28.9% in the driest scenarios under risk-neutral and risk-averse conditions, respectively, compared to deterministic approaches. The findings highlight the importance of taking into account multiple objectives and uncertainties when designing RWH systems, allowing designers to optimise efficiency and costs based on their specific requirements without extensive parameterisation.
...
Optimising rainwater harvesting (RWH) systems’ design involves sizing the storage and catchment areas to enhance cost-effectiveness, self-sufficiency, and water quality indicators. This paper considers the design of RWH systems under long-term uncertainty in precipitation and demands. In this work, we formulate and solve a multi-objective stochastic optimisation problem that allows explicit trade-offs under uncertainty, maximising system efficiency and minimising deployment cost. We use the yield after spillage (YAS) approach to incorporate the physical and operational constraints and the big-M method to reformulate the nonlinear min\max rules of this approach as a mixed-integer linear programming (MILP) problem. By posing a risk averseness measure on efficiency as a conditional value at risk (CVaR) formulation, we guarantee the designer against the highest demand and driest weather conditions. We then exploit the lexicographic method to effectively solve the multi-objective stochastic problem as a sequence of equivalent single-objective problems. A detailed case study of a botanical garden in Amsterdam demonstrates the framework's practical application; we show significant improvements in system efficiency of up to 15.5% and 28.9% in the driest scenarios under risk-neutral and risk-averse conditions, respectively, compared to deterministic approaches. The findings highlight the importance of taking into account multiple objectives and uncertainties when designing RWH systems, allowing designers to optimise efficiency and costs based on their specific requirements without extensive parameterisation.
Journal article
(2025)
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Anteneh G. Dagnachew, Seleshi G. Yalew, Meron Tesfamichael, Chukwumerije Okereke, Edo Abraham
Africa's abundant renewable energy resources and vast land areas present an unprecedented opportunity for the development of a green hydrogen economy. Several countries in Africa have already initiated ambitious projects to establish green hydrogen economies, mainly targeting export markets in Europe. This perspective article explores the potential of green hydrogen in Africa and examines the challenges and risks associated with green hydrogen development on the continent. We believe that the role of hydrogen in the transformation of Africa should go beyond energy export and aim to harness its competitive advantage for various industries that require low-cost green hydrogen. We argue that prevailing extractive and geopolitical realities pose a major barrier in Africa’s path towards harnessing the full potential of green hydrogen for its development. To break this cycle, Africa must demand equitable partnerships, invest in technology transfer, and foster collaboration to drive a transformative green hydrogen revolution that is aligned with Agenda 2063s vision for a self-empowered and sustainable Africa.
Key policy insights:
- Green-hydrogen has the potential to provide affordable energy to transport and industry, accelerating sustainable development in Africa.
- The current resource development practice in Africa will constrain the continent’s ability to chart an independent course towards a sustainable green hydrogen economy.
- There is a need for clear regulation, incentives, and financial support for the adoption of green hydrogen technology.
- It is important that national governments create local demand which can stimulate new and sustainable jobs, and bilateral and regional collaborations to build and operate hydrogen infrastructure.
- Despite the high technical potential, Africa’s role in the global hydrogen market is hindered by limitation in access to finance, technology, infrastructure, and policy stability.
- Africa must assert itself on the global stage, demanding equitable partnerships and investments that foster technological transfer and knowledge sharing that benefit wider socio-economic development. ...
Key policy insights:
- Green-hydrogen has the potential to provide affordable energy to transport and industry, accelerating sustainable development in Africa.
- The current resource development practice in Africa will constrain the continent’s ability to chart an independent course towards a sustainable green hydrogen economy.
- There is a need for clear regulation, incentives, and financial support for the adoption of green hydrogen technology.
- It is important that national governments create local demand which can stimulate new and sustainable jobs, and bilateral and regional collaborations to build and operate hydrogen infrastructure.
- Despite the high technical potential, Africa’s role in the global hydrogen market is hindered by limitation in access to finance, technology, infrastructure, and policy stability.
- Africa must assert itself on the global stage, demanding equitable partnerships and investments that foster technological transfer and knowledge sharing that benefit wider socio-economic development. ...
Africa's abundant renewable energy resources and vast land areas present an unprecedented opportunity for the development of a green hydrogen economy. Several countries in Africa have already initiated ambitious projects to establish green hydrogen economies, mainly targeting export markets in Europe. This perspective article explores the potential of green hydrogen in Africa and examines the challenges and risks associated with green hydrogen development on the continent. We believe that the role of hydrogen in the transformation of Africa should go beyond energy export and aim to harness its competitive advantage for various industries that require low-cost green hydrogen. We argue that prevailing extractive and geopolitical realities pose a major barrier in Africa’s path towards harnessing the full potential of green hydrogen for its development. To break this cycle, Africa must demand equitable partnerships, invest in technology transfer, and foster collaboration to drive a transformative green hydrogen revolution that is aligned with Agenda 2063s vision for a self-empowered and sustainable Africa.
Key policy insights:
- Green-hydrogen has the potential to provide affordable energy to transport and industry, accelerating sustainable development in Africa.
- The current resource development practice in Africa will constrain the continent’s ability to chart an independent course towards a sustainable green hydrogen economy.
- There is a need for clear regulation, incentives, and financial support for the adoption of green hydrogen technology.
- It is important that national governments create local demand which can stimulate new and sustainable jobs, and bilateral and regional collaborations to build and operate hydrogen infrastructure.
- Despite the high technical potential, Africa’s role in the global hydrogen market is hindered by limitation in access to finance, technology, infrastructure, and policy stability.
- Africa must assert itself on the global stage, demanding equitable partnerships and investments that foster technological transfer and knowledge sharing that benefit wider socio-economic development.
Key policy insights:
- Green-hydrogen has the potential to provide affordable energy to transport and industry, accelerating sustainable development in Africa.
- The current resource development practice in Africa will constrain the continent’s ability to chart an independent course towards a sustainable green hydrogen economy.
- There is a need for clear regulation, incentives, and financial support for the adoption of green hydrogen technology.
- It is important that national governments create local demand which can stimulate new and sustainable jobs, and bilateral and regional collaborations to build and operate hydrogen infrastructure.
- Despite the high technical potential, Africa’s role in the global hydrogen market is hindered by limitation in access to finance, technology, infrastructure, and policy stability.
- Africa must assert itself on the global stage, demanding equitable partnerships and investments that foster technological transfer and knowledge sharing that benefit wider socio-economic development.
Journal article
(2025)
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Ali Moradvandi, Sjoerd Heegstra, Pamela Ceron-Chafla, Bart De Schutter, Edo Abraham, Ralph E.F. Lindeboom
Temperature plays a critical role in performance and stability of anaerobic digestion processes, subject to frequent meteorological fluctuations. However, state-of-the-art modeling and process control approaches for anaerobic digestion often do not consider the temporal dynamics of the temperature, which can influence microbial communities, kinetics, and chemical equilibrium, and consequently, biogas production efficiency. Therefore, to account for anaerobic digesters operating under fluctuating meteorological conditions, the Anaerobic Digestion Model no. 1 (ADM1) is mechanistically extended in this paper to incorporate temporal changes into temperature-dependent parameters by defining inhibition functions for microbial activities using the cardinal temperature model, and accounting for the lag in microbial adaptation to temperature fluctuations using a time-lag adaptation function. Thereafter, given that temperature fluctuations are a significant disturbance, a control framework based on Model Predictive Control (MPC) is developed to regulate the feeding flow rate and to ensure stable production rates despite temperature disturbances without relying on direct temperature control. An adaptive MPC approach is formulated based on a linear input–output model, where the parameters of the linear model are updated online to capture the nonlinear dynamics of the process and frequent changes in the dynamics accurately. In addition, a fuzzy logic system is employed to assign a reference trajectory for the production rate based on the temperature and its rate of change. Integrating this fuzzy logic system with the MPC controller enhances the production rate on warm days and avoids the operational failure in production on cold days. Additionally, to enhance biogas production rates, the feasibility of utilizing a portion of the produced biogas for external heating purposes is also investigated. It is demonstrated that by utilizing the proposed MPC approach, the additional amount of feed for the digester to produce methane required for a self-consumption biogas-fueled heating system can be calculated according to the meteorological variations. This enhances the process performance and stability. Finally, a thermally optimized dome digester semi-buried in the ground, operating under climate conditions of The Netherlands is considered as a case study to validate the extended model in agreement with biological and physicochemical behaviors of real-world applications, and to demonstrate the effectiveness of the proposed control system in handling temperature changes and enhancing performance.
...
Temperature plays a critical role in performance and stability of anaerobic digestion processes, subject to frequent meteorological fluctuations. However, state-of-the-art modeling and process control approaches for anaerobic digestion often do not consider the temporal dynamics of the temperature, which can influence microbial communities, kinetics, and chemical equilibrium, and consequently, biogas production efficiency. Therefore, to account for anaerobic digesters operating under fluctuating meteorological conditions, the Anaerobic Digestion Model no. 1 (ADM1) is mechanistically extended in this paper to incorporate temporal changes into temperature-dependent parameters by defining inhibition functions for microbial activities using the cardinal temperature model, and accounting for the lag in microbial adaptation to temperature fluctuations using a time-lag adaptation function. Thereafter, given that temperature fluctuations are a significant disturbance, a control framework based on Model Predictive Control (MPC) is developed to regulate the feeding flow rate and to ensure stable production rates despite temperature disturbances without relying on direct temperature control. An adaptive MPC approach is formulated based on a linear input–output model, where the parameters of the linear model are updated online to capture the nonlinear dynamics of the process and frequent changes in the dynamics accurately. In addition, a fuzzy logic system is employed to assign a reference trajectory for the production rate based on the temperature and its rate of change. Integrating this fuzzy logic system with the MPC controller enhances the production rate on warm days and avoids the operational failure in production on cold days. Additionally, to enhance biogas production rates, the feasibility of utilizing a portion of the produced biogas for external heating purposes is also investigated. It is demonstrated that by utilizing the proposed MPC approach, the additional amount of feed for the digester to produce methane required for a self-consumption biogas-fueled heating system can be calculated according to the meteorological variations. This enhances the process performance and stability. Finally, a thermally optimized dome digester semi-buried in the ground, operating under climate conditions of The Netherlands is considered as a case study to validate the extended model in agreement with biological and physicochemical behaviors of real-world applications, and to demonstrate the effectiveness of the proposed control system in handling temperature changes and enhancing performance.
Model predictive control (MPC) is an optimal control strategy suited for flood control of water resources infrastructure. Despite many studies on reservoir flood control and their theoretical contribution, optimisation methodologies have not been widely applied in real-time operation due to disparities between research assumptions and practical requirements. To address this gap, we include practical objectives, such as minimising the magnitude and frequency of changes in the existing outflow schedule. Incorporating these objectives transforms the problem into a multi-objective nonlinear optimisation problem that is difficult to solve in real time. Additionally, it is reasonable to assume that the weights and some parameters, considered the operators’ preferences, vary depending on the system state. To overcome these limitations, we propose a framework that converts the original intractable problem into parameterised linear MPC problems with dynamic optimisation of weights and parameters. This is done by introducing a model-based learning concept. We refer to this framework as Parameterised Dynamic MPC (PD-MPC). The effectiveness of this framework is demonstrated through a numerical experiment for the Daecheong multipurpose reservoir in South Korea. We find that PD-MPC outperforms standard MPC-based designs without a dynamic optimisation process for the objective weights and model parameter. Moreover, we demonstrate that the weights and parameters vary with changing hydrological conditions.
...
Model predictive control (MPC) is an optimal control strategy suited for flood control of water resources infrastructure. Despite many studies on reservoir flood control and their theoretical contribution, optimisation methodologies have not been widely applied in real-time operation due to disparities between research assumptions and practical requirements. To address this gap, we include practical objectives, such as minimising the magnitude and frequency of changes in the existing outflow schedule. Incorporating these objectives transforms the problem into a multi-objective nonlinear optimisation problem that is difficult to solve in real time. Additionally, it is reasonable to assume that the weights and some parameters, considered the operators’ preferences, vary depending on the system state. To overcome these limitations, we propose a framework that converts the original intractable problem into parameterised linear MPC problems with dynamic optimisation of weights and parameters. This is done by introducing a model-based learning concept. We refer to this framework as Parameterised Dynamic MPC (PD-MPC). The effectiveness of this framework is demonstrated through a numerical experiment for the Daecheong multipurpose reservoir in South Korea. We find that PD-MPC outperforms standard MPC-based designs without a dynamic optimisation process for the objective weights and model parameter. Moreover, we demonstrate that the weights and parameters vary with changing hydrological conditions.
Integrating Floating Photovoltaics in Long-term Energy Planning of Eastern Nile Basin Countries
Synergies Between Water Conservation, Land Use, and Emissions Reduction
This study addresses the pressing need for sustainable energy infrastructure in the Eastern Nile Basin region, focusing on the integration of Floating Solar Photovoltaics (FPVs) in long-term energy planning. FPVs offer advantages over land-based photovoltaics, such as reducing capital costs by utilizing existing infrastructure at hydropower dams and reducing evaporation. Given the region's growing population and high competition for water, our research introduces a novel framework that explores the dual benefits of water conservation and reduced land use, alongside policy targets for lowering carbon emissions through increased integration of renewables in the power mix.
The study advances existing models by incorporating FPV technology into the OSeMOSYS tool, an open-source model for optimizing national energy generation mixes. Our research presents a spatially explicit framework for long-term energy system planning that integrates land use and water conservation metrics at reservoirs within the energy planning process. The role of FPVs in the region’s energy pathways is evaluated by endogenizing the costs of CO2 emissions and land use, while considering water savings. Our analysis develops and implements a new methodology for land-use accounting and pricing, and assesses the potential of FPVs to reduce evaporation across a network of hydropower reservoirs. This expanded modeling framework is then utilized to analyze various scenarios, including different hydrological regimes under CMIP climate change projections and policy measures such as the introduction of taxes on carbon emissions and land-use, and regional electricity trade links.
Results indicate that FPVs can cost-effectively provide up to 3% of the region's electricity generation by 2065, saving up to 376 million cubic meters of water annually. Scenarios introducing carbon and land-use taxes increase FPV's share in the power generation mix to 4.5% and enable earlier FPV deployment. While climate impacts minimally affect FPV's role, the technology slightly reduces CO2 emissions (0.4%) and land use (1.6%) in the baseline scenario without taxes. A carbon tax alone reduces emissions by 11-23% but raises land use by up to 8% due to increased wind, hydro, and solar deployment. Land tax alone reduces land use by 5-8% with minimal impact on emissions. However, combining land and carbon taxes reduces emissions (by 12% to 22%) and land use (a decrease of 1.6% or an increase of 1.2%). The optimal locations for FPV deployment are identified as Lake Nasser (2.1 GW), Renaissance Dam (6.4 GW), and Merowe Dam (1.2 GW), leveraging existing hydropower infrastructure. These findings demonstrate that FPVs represent a promising adaptation strategy for energy planning offering multiple co-benefits including reduced water evaporation, efficient land use, and emissions mitigation, particularly when supported by appropriate environmental pricing policies. ...
The study advances existing models by incorporating FPV technology into the OSeMOSYS tool, an open-source model for optimizing national energy generation mixes. Our research presents a spatially explicit framework for long-term energy system planning that integrates land use and water conservation metrics at reservoirs within the energy planning process. The role of FPVs in the region’s energy pathways is evaluated by endogenizing the costs of CO2 emissions and land use, while considering water savings. Our analysis develops and implements a new methodology for land-use accounting and pricing, and assesses the potential of FPVs to reduce evaporation across a network of hydropower reservoirs. This expanded modeling framework is then utilized to analyze various scenarios, including different hydrological regimes under CMIP climate change projections and policy measures such as the introduction of taxes on carbon emissions and land-use, and regional electricity trade links.
Results indicate that FPVs can cost-effectively provide up to 3% of the region's electricity generation by 2065, saving up to 376 million cubic meters of water annually. Scenarios introducing carbon and land-use taxes increase FPV's share in the power generation mix to 4.5% and enable earlier FPV deployment. While climate impacts minimally affect FPV's role, the technology slightly reduces CO2 emissions (0.4%) and land use (1.6%) in the baseline scenario without taxes. A carbon tax alone reduces emissions by 11-23% but raises land use by up to 8% due to increased wind, hydro, and solar deployment. Land tax alone reduces land use by 5-8% with minimal impact on emissions. However, combining land and carbon taxes reduces emissions (by 12% to 22%) and land use (a decrease of 1.6% or an increase of 1.2%). The optimal locations for FPV deployment are identified as Lake Nasser (2.1 GW), Renaissance Dam (6.4 GW), and Merowe Dam (1.2 GW), leveraging existing hydropower infrastructure. These findings demonstrate that FPVs represent a promising adaptation strategy for energy planning offering multiple co-benefits including reduced water evaporation, efficient land use, and emissions mitigation, particularly when supported by appropriate environmental pricing policies. ...
This study addresses the pressing need for sustainable energy infrastructure in the Eastern Nile Basin region, focusing on the integration of Floating Solar Photovoltaics (FPVs) in long-term energy planning. FPVs offer advantages over land-based photovoltaics, such as reducing capital costs by utilizing existing infrastructure at hydropower dams and reducing evaporation. Given the region's growing population and high competition for water, our research introduces a novel framework that explores the dual benefits of water conservation and reduced land use, alongside policy targets for lowering carbon emissions through increased integration of renewables in the power mix.
The study advances existing models by incorporating FPV technology into the OSeMOSYS tool, an open-source model for optimizing national energy generation mixes. Our research presents a spatially explicit framework for long-term energy system planning that integrates land use and water conservation metrics at reservoirs within the energy planning process. The role of FPVs in the region’s energy pathways is evaluated by endogenizing the costs of CO2 emissions and land use, while considering water savings. Our analysis develops and implements a new methodology for land-use accounting and pricing, and assesses the potential of FPVs to reduce evaporation across a network of hydropower reservoirs. This expanded modeling framework is then utilized to analyze various scenarios, including different hydrological regimes under CMIP climate change projections and policy measures such as the introduction of taxes on carbon emissions and land-use, and regional electricity trade links.
Results indicate that FPVs can cost-effectively provide up to 3% of the region's electricity generation by 2065, saving up to 376 million cubic meters of water annually. Scenarios introducing carbon and land-use taxes increase FPV's share in the power generation mix to 4.5% and enable earlier FPV deployment. While climate impacts minimally affect FPV's role, the technology slightly reduces CO2 emissions (0.4%) and land use (1.6%) in the baseline scenario without taxes. A carbon tax alone reduces emissions by 11-23% but raises land use by up to 8% due to increased wind, hydro, and solar deployment. Land tax alone reduces land use by 5-8% with minimal impact on emissions. However, combining land and carbon taxes reduces emissions (by 12% to 22%) and land use (a decrease of 1.6% or an increase of 1.2%). The optimal locations for FPV deployment are identified as Lake Nasser (2.1 GW), Renaissance Dam (6.4 GW), and Merowe Dam (1.2 GW), leveraging existing hydropower infrastructure. These findings demonstrate that FPVs represent a promising adaptation strategy for energy planning offering multiple co-benefits including reduced water evaporation, efficient land use, and emissions mitigation, particularly when supported by appropriate environmental pricing policies.
The study advances existing models by incorporating FPV technology into the OSeMOSYS tool, an open-source model for optimizing national energy generation mixes. Our research presents a spatially explicit framework for long-term energy system planning that integrates land use and water conservation metrics at reservoirs within the energy planning process. The role of FPVs in the region’s energy pathways is evaluated by endogenizing the costs of CO2 emissions and land use, while considering water savings. Our analysis develops and implements a new methodology for land-use accounting and pricing, and assesses the potential of FPVs to reduce evaporation across a network of hydropower reservoirs. This expanded modeling framework is then utilized to analyze various scenarios, including different hydrological regimes under CMIP climate change projections and policy measures such as the introduction of taxes on carbon emissions and land-use, and regional electricity trade links.
Results indicate that FPVs can cost-effectively provide up to 3% of the region's electricity generation by 2065, saving up to 376 million cubic meters of water annually. Scenarios introducing carbon and land-use taxes increase FPV's share in the power generation mix to 4.5% and enable earlier FPV deployment. While climate impacts minimally affect FPV's role, the technology slightly reduces CO2 emissions (0.4%) and land use (1.6%) in the baseline scenario without taxes. A carbon tax alone reduces emissions by 11-23% but raises land use by up to 8% due to increased wind, hydro, and solar deployment. Land tax alone reduces land use by 5-8% with minimal impact on emissions. However, combining land and carbon taxes reduces emissions (by 12% to 22%) and land use (a decrease of 1.6% or an increase of 1.2%). The optimal locations for FPV deployment are identified as Lake Nasser (2.1 GW), Renaissance Dam (6.4 GW), and Merowe Dam (1.2 GW), leveraging existing hydropower infrastructure. These findings demonstrate that FPVs represent a promising adaptation strategy for energy planning offering multiple co-benefits including reduced water evaporation, efficient land use, and emissions mitigation, particularly when supported by appropriate environmental pricing policies.
An identification algorithm of switched Box-Jenkins systems in the presence of bounded disturbances
An approach for approximating complex biological wastewater treatment models
Journal article
(2024)
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Ali Moradvandi, Edo Abraham, Abdelhak Goudjil, Bart De Schutter, Ralph E.F. Lindeboom
This paper focuses on the development of linear Switched Box–Jenkins (SBJ) models for approximating complex dynamical models of biological wastewater treatment processes. We discuss the adaptation of these processes to the SBJ framework, showing the model's generality and flexibility as a class of switched systems that can offer accurate predictions for complex and nonlinear dynamics. This approach of modeling enables real-time data reconciliation from experiments and allows the design of model-based control strategies. Through the extension of the Outer Bounding Ellipsoids (OBEs) algorithm, the paper introduces an online two-stage parameter identification algorithm that effectively handles bounded disturbances for SBJ models. Using the OBE method relaxes the stochastic assumptions on disturbances, which may not be satisfied in practice, particularly for biological and environmental fluctuations. The proposed decomposed OBE algorithm separately identifies the switching patterns and parameters of linear submodels, conducting parameter identification in two distinct phases for input/output and disturbance/output submodels. The efficacy of this approach is shown via simulation results validated against both ADM1 and PBM models, demonstrating the proposed algorithm's capability to accurately predict outputs from different biological wastewater treatment models.
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This paper focuses on the development of linear Switched Box–Jenkins (SBJ) models for approximating complex dynamical models of biological wastewater treatment processes. We discuss the adaptation of these processes to the SBJ framework, showing the model's generality and flexibility as a class of switched systems that can offer accurate predictions for complex and nonlinear dynamics. This approach of modeling enables real-time data reconciliation from experiments and allows the design of model-based control strategies. Through the extension of the Outer Bounding Ellipsoids (OBEs) algorithm, the paper introduces an online two-stage parameter identification algorithm that effectively handles bounded disturbances for SBJ models. Using the OBE method relaxes the stochastic assumptions on disturbances, which may not be satisfied in practice, particularly for biological and environmental fluctuations. The proposed decomposed OBE algorithm separately identifies the switching patterns and parameters of linear submodels, conducting parameter identification in two distinct phases for input/output and disturbance/output submodels. The efficacy of this approach is shown via simulation results validated against both ADM1 and PBM models, demonstrating the proposed algorithm's capability to accurately predict outputs from different biological wastewater treatment models.
The Dual Model under Pressure
How Robust Is Leak Detection under Uncertainties and Model Mismatches?
Journal article
(2024)
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Enrique Campbell, Edo Abraham, Johannes Koslowski, Olivier Piller, David B. Steffelbauer
This paper investigates the robustness of one innovative model-based method for leak detection, namely the Dual Model. We evaluate the algorithm’s performance under various leakage scenarios in the L-Town network, despite uncertainties and model mismatches in (i) base demand, (ii) pipe roughness, (iii) the number of sensors, and (iv) network topology. Our investigation results indicate that the Dual Model is highly sensitive to discrepancies in the first three parameters. However, the impact can be mitigated through sensor-specific calibration, such as adjusting sensor elevations. Moreover, the Dual Model has demonstrated robustness to minor topology mismatches, like those introduced by closed valves.
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This paper investigates the robustness of one innovative model-based method for leak detection, namely the Dual Model. We evaluate the algorithm’s performance under various leakage scenarios in the L-Town network, despite uncertainties and model mismatches in (i) base demand, (ii) pipe roughness, (iii) the number of sensors, and (iv) network topology. Our investigation results indicate that the Dual Model is highly sensitive to discrepancies in the first three parameters. However, the impact can be mitigated through sensor-specific calibration, such as adjusting sensor elevations. Moreover, the Dual Model has demonstrated robustness to minor topology mismatches, like those introduced by closed valves.
Author Correction
Africa needs context-relevant evidence to shape its clean energy future
Journal article
(2024)
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Yacob Mulugetta, Youba Sokona, Philipp A. Trotter, Samuel Fankhauser, Jessica Omukuti, Lucas Somavilla Croxatto, Bjarne Steffen, Meron Tesfamichael, Edo Abraham, More authors...
Correction to: Nature Energyhttps://doi.org/10.1038/s41560-022-01152-0, published online 24 October 2022 In the version of the article initially published, Gebrekidan Gebresilassie Eshetu’s name appeared incorrectly as Eshetu Gebrekidan Gebresilassie and has now been corrected in the HTML and PDF versions of the article.
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Correction to: Nature Energyhttps://doi.org/10.1038/s41560-022-01152-0, published online 24 October 2022 In the version of the article initially published, Gebrekidan Gebresilassie Eshetu’s name appeared incorrectly as Eshetu Gebrekidan Gebresilassie and has now been corrected in the HTML and PDF versions of the article.
This white paper aims to provide an introduction to the topic of Design for Justice for a wide audience. It demonstrates ongoing research on this topic by the TU Delft community and contributes to the exchange of relevant knowledge and expertise, as one of the outcomes of the activities organised for the Delft Design for Values Institute’s annual theme ‘Design for Justice’. This document includes recommendations on how to foster Design for Justice, which are not just relevant for designers, engineers, and academic researchers, but also for educators and policy makers.
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This white paper aims to provide an introduction to the topic of Design for Justice for a wide audience. It demonstrates ongoing research on this topic by the TU Delft community and contributes to the exchange of relevant knowledge and expertise, as one of the outcomes of the activities organised for the Delft Design for Values Institute’s annual theme ‘Design for Justice’. This document includes recommendations on how to foster Design for Justice, which are not just relevant for designers, engineers, and academic researchers, but also for educators and policy makers.