A survey of water utilities’ digital transformation: drivers, impacts, and enabling technologies

npj Clean Water, Год журнала: 2023, Номер 6(1)

Опубликована: Июль 7, 2023

Abstract Climate change and urbanization challenge utilities’ pursuit of water security worldwide. While utilities are directly impacted by climate change, their operations also contribute to greenhouse gas emissions. Digital technologies have proven effective in improving operations, leading a more sustainable urban cycle. However, the global progress digital transformation remains largely understudied. Here, we present results an online survey involving 64 from 28 countries investigating impacts on utility sector, its drivers, key-enabling technologies. We found that distribution system is entry point further adoption whole Furthermore, technology driven primarily economic benefits, followed government regulation hydroclimatic factors. Starting results, out avenues for research targeting better understanding influence regulation, corporate mindset, consumer involvement successful transformation.

Язык: Английский

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Real-time techno-economical operation of preserving microgrids via optimal NLMPC considering uncertainties

Engineering Science and Technology an International Journal, Год журнала: 2024, Номер 57, С. 101823 - 101823

Опубликована: Сен. 1, 2024

In the modern era, managing optimal real-time control of microgrids during operation phase has been a significant challenge, requiring careful consideration both technical and economic factors. This paper introduces framework for islanded using preserving network. structure incorporates various distributed generation sources, including rotating non-rotating resources, along with energy storage systems. The optimization function within model predictive (MPC) manages essential network parameters, such as frequency voltage, while addressing objectives. To enhance precision account uncertainties in consumption integration continuous power flow is employed. approach aims to create that closely mirrors real-world conditions, ensuring more accurate representation microgrid dynamics. proposed demonstrates improvements performance compared Standard MPC Adaptive MPC, highlighting its potential efficient management. achieves notable reductions total voltage deviation 85.87% 87.62% respectively. Additionally, it delivers impressive enhancements 99.46% 96.62% Economically, significantly outperforms both, reducing costs by 39.29% 28.12% MPC.

Язык: Английский

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Dimensions of Superiority: How Deep Reinforcement Learning Excels in Urban Drainage System Real-time Control

Water Research X, Год журнала: 2025, Номер 28, С. 100313 - 100313

Опубликована: Фев. 5, 2025

Reducing combined sewer overflows and flooding is crucial for the efficient operation of urban drainage systems. Traditional real-time control (RTC) methods often fall short in efficiency performance, which prompts exploration innovative approaches. Deep reinforcement learning (DRL) has recently emerged as a promising technique to enhance RTC performance. This study evaluates effectiveness using multi-agent-based DRL approach. We developed comprehensive evaluation framework incorporating multiple quantitative indicators, including objectives, decision time, robustness, adaptability. To validate our framework, we conducted case on an system Suzhou, China, analyzing 31 historical rainfall events. Our findings reveal that can reduce overflow risks by 15.1 % 43.5 average compared conventional methods. Additionally, demonstrates superior efficiency, not only highlights potential management but also provides insights into its broader application enhancing resilience infrastructure

Язык: Английский

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Beyond engineering: A review of reservoir management through the lens of wickedness, competing objectives and uncertainty

Environmental Modelling & Software, Год журнала: 2023, Номер 167, С. 105777 - 105777

Опубликована: Июль 5, 2023

Traditionally, reservoir management has been synonymous with the operation of engineering infrastructure systems, majority literature on topic focusing strategies that optimize their and control. This is despite fact reservoirs have major impacts society environment, mechanics how to best manage a are often overshadowed by both environmental changes higher-order questions associated societal values, risk appetite politics, which highly uncertain there no "correct" answers. As result, attracted more controversy than any other type water infrastructure. In this paper, we address these often-ignored issues providing review through lens wickedness, competing objectives uncertainty. We highlight challenges identify research efforts required ensure systems serve environment into future.

Язык: Английский

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A Mixed‐Integer Linear Programming Framework for Optimization of Water Network Operations Problems

Water Resources Research, Год журнала: 2024, Номер 60(2)

Опубликована: Фев. 1, 2024

Abstract Water distribution systems (WDSs) are critical infrastructure used to convey water from sources consumers. The mathematical framework governing the of flows and heads in extended period simulations WDSs lends itself application a wide range optimization problems. Applying classical mixed integer linear programming (MILP) approach model hydraulics within an can contribute higher solution accuracy with lower computational effort. However, adapting models conform MILP formulation has proven challenging because intrinsic non‐linearity system complexity associated modeling hydraulic devices that influence state WDS. This paper introduces MILPNet, adjustable for be build solve extensive array MILPNet includes constraints represent mass balance energy conservation equations, devices, control rules, status checks. To structure, employs piece‐wise approximation programming. was implemented tested using Gurobi Python API. Modeling shown comparable EPANET, public domain software modeling, sensitivity analyses were conducted examine impacts assumptions on performance MILPNet. Additionally, demonstrated pump scheduling examples single rolling horizon scenarios. Our results show facilitate construction problems applications operations.

Язык: Английский

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Synthetic Forecast Ensembles for Evaluating Forecast Informed Reservoir Operations

Water Resources Research, Год журнала: 2024, Номер 60(2)

Опубликована: Фев. 1, 2024

Abstract Forecast informed reservoir operations (FIRO) is an important advance in water management, but the design and testing of FIRO policies limited by relatively short (10–35 year) hydro‐meteorological hindcasts. We present a novel, multisite model for synthetic forecast ensembles to overcome this limitation. This utilizes parametric non‐parametric procedures capture complex errors maintain correlation between variables, lead times, locations, ensemble members. After being fit data from hindcast period, can generate any period with observations. demonstrate approach case study FIRO‐based Ensemble Operations (EFO) control policy Lake Mendocino—Russian River basin, which conditions release decisions on forecasts Hydrologic System (HEFS). explore two generation strategies: (a) simulation meteorology force HEFS; (b) HEFS streamflow directly. evaluate using verification techniques event‐based validation, finding good agreement actual forecasts. then EFO performance over (1985–2010) only pre‐hindcast (1948–1984). Results show that highlight failure modes under plausible ensembles, improvements are still needed fully behavior ensembles. Overall, methodology advances novel way test robustness, key building institutional support FIRO.

Язык: Английский

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Real-time regulation of detention ponds via feedback control: Balancing flood mitigation and water quality

Journal of Hydrology, Год журнала: 2024, Номер 643, С. 131866 - 131866

Опубликована: Авг. 23, 2024

Язык: Английский

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Designing integrated and resilient multi-energy systems via multi-objective optimization and scenario analysis

Applied Energy, Год журнала: 2025, Номер 382, С. 125281 - 125281

Опубликована: Янв. 13, 2025

Язык: Английский

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Stability-guaranteed data-driven nonlinear predictive control of water distribution systems

Control Engineering Practice, Год журнала: 2025, Номер 157, С. 106243 - 106243

Опубликована: Янв. 23, 2025

Язык: Английский

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Reinforcement Learning of Multi‐Timescale Forecast Information for Designing Operating Policies of Multi‐Purpose Reservoirs

Water Resources Research, Год журнала: 2025, Номер 61(2)

Опубликована: Фев. 1, 2025

Abstract Hydrological forecasts have significantly improved in skill over recent years, encouraging their systematic exploitation multipurpose reservoir operations to improve reliability and resilience extreme events. Despite the growing availability of multi‐timescale forecasts, there is still a lack transparent integrated methods for selecting most suitable forecast products, variables, lead times specific operational challenges. In this work, we propose holistic approach based on Reinforcement Learning (RL) design dam operating policies informed by available products. Our extends traditional Evolutionary Multi‐Objective Direct Policy Search method parametrizing both policy information extraction process. We compare our RL with state‐of‐the‐art two‐step procedure which selection processing are performed before optimization. demonstrate value operation Lake Como (Italy) considering from short seasonal manage flood‐ drought‐related objectives. identifies solutions achieving an 18% improvement hypervolume indicator compared not 6% those designed using reference methodology. These improvements accompanied increased flexibility trade‐off analysis directly extracting within multi‐objective This study demonstrates feasibility benefits integrating extraction, particularly when multiple available.

Язык: Английский

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