{"id":15695,"date":"2026-06-01T14:59:47","date_gmt":"2026-06-01T14:59:47","guid":{"rendered":"https:\/\/www.proefschriftmaken.nl\/portfolio\/alessio-belmondo-bianchi-di-lavagna\/"},"modified":"2026-06-01T14:59:55","modified_gmt":"2026-06-01T14:59:55","slug":"alessio-belmondo-bianchi-di-lavagna","status":"publish","type":"us_portfolio","link":"https:\/\/www.proefschriftmaken.nl\/en\/portfolio\/alessio-belmondo-bianchi-di-lavagna\/","title":{"rendered":"Alessio Belmondo Bianchi di Lavagna"},"content":{"rendered":"","protected":true},"excerpt":{"rendered":"","protected":true},"author":7,"featured_media":15696,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"us_portfolio_category":[45],"class_list":["post-15695","us_portfolio","type-us_portfolio","status-publish","post-password-required","hentry","us_portfolio_category-new-template"],"acf":{"naam_van_het_proefschift":"Data-Driven Modeling and Optimization of Flexible and Efficient Power and Water Systems Management","samenvatting":"De versnellende transitie naar koolstofarme energiesystemen, gecombineerd met de toenemende druk op watervoorraden, transformeert de manier waarop samenlevingen kritieke infrastructuren plannen, exploiteren en co\u00f6rdineren. Elektriciteitsnetten worden geconfronteerd met groeiende uitdagingen door de toenemende vraag, aangedreven door de elektrificatie van meerdere sectoren. Tegelijkertijd stelt de toenemende onzekerheid door variabele opwekking van hernieuwbare energie nieuwe eisen aan flexibiliteit, betrouwbaarheid en marktontwerp. Watersystemen worden gelijktijdig geconfronteerd met een groeiende vraag, verminderde beschikbaarheid van zoet water en de noodzaak om energie-intensieve behandeling en distributie te beheren onder steeds volatielere energieprijzen. Deze trends maken de exploitatie van water nauwer gekoppeld aan de dynamiek van het energiesysteem.\n\nTegelijkertijd cre\u00ebert de diepe fysieke en economische onderlinge afhankelijkheid tussen energie en water nieuwe mogelijkheden voor flexibiliteit, effici\u00ebntie en veerkracht. Het realiseren van dit potentieel vereist besluitvormingskaders die de gekoppelde fysica, marktinteracties en belangrijkste onzekerheden op een ge\u00efntegreerde manier weergeven. De motivatie van dit proefschrift is om dergelijke ge\u00efntegreerde en onzekerheidsbewuste besluitvormingsinstrumenten te bieden voor gekoppelde water-energiesystemen. Dit stelt systeembeheerders en planners in staat om sectoroverschrijdende synergie\u00ebn te benutten en tegelijkertijd de betrouwbaarheid, betaalbaarheid en milieuprestaties te waarborgen.\n\nDit proefschrift richt zich op de ontwikkeling van marktgebaseerde, datagestuurde wiskundige optimalisatiekaders voor de planning en exploitatie van gekoppelde elektriciteits- en watersystemen onder onzekerheid. Het centrale doel is om een systeembenadering mogelijk te maken voor investerings- en operationele beslissingen. Deze beslissingen houden expliciet rekening met de fysica van elektriciteits- en waterinfrastructuren, hun onderlinge afhankelijkheid en de technisch-economische kenmerken van de onderliggende technologie\u00ebn. De kernbijdrage ligt in het gebruik van klassieke en nieuwe wiskundige technieken, waaronder herformuleringen, convexe relaxaties en op machine learning gebaseerde surrogaten. Deze technieken maken anders onhandelbare ge\u00efntegreerde water-energieproblemen computationeel haalbaar, terwijl de fysieke nauwkeurigheid en het marktrealisme behouden blijven.\n\nIn het bijzonder worden op maat gemaakte benaderingsmethoden ontwikkeld op basis van convexe relaxaties en input-convexe neurale netwerken om complexe niet-lineaire hydraulische relaties weer te geven. Deze benaderingen zijn ingebed in optimale stroomformuleringen voor elektriciteit en water. Daarnaast wordt onzekerheid gemodelleerd door middel van distributierobuuste kans-beperkte formuleringen op basis van datagestuurde momentinformatie. Deze aanpak ondersteunt beslissingen die haalbaar en economisch robuust blijven onder de slechtst denkbare voorspellingsfouten, zonder toevlucht te nemen tot computationeel veeleisende scenario-gebaseerde benaderingen.\n\nHet proefschrift bestaat uit zeven hoofdstukken. Hoofdstuk 1 introduceert het onderzoeksonderwerp en de maatschappelijke context, motivatie, onderzoeksdoelstellingen, onderzoeksvragen en methodologie. Het schetst de belangrijkste uitdagingen van de water-energienexus en bespreekt de huidige stand van zaken in de literatuur. Hoofdstuk 2 ontwikkelt een distributierobuust, marktgebaseerd kader voor het mobiliseren van op water gebaseerde flexibiliteit in transmissienetten. Hoofdstuk 3 is een directe uitbreiding hiervan naar waterdistributienetten op basis van een praktijkcasus. Hoofdstuk 4 behandelt waterstofproductie uit niet-drinkbare waterbronnen door een technisch-economisch investerings- en exploitatieplanningskader voor ge\u00efntegreerde water-stroom-waterstofsystemen te ontwikkelen. Hoofdstuk 5 onderzoekt onzekerheidsbewuste investeringsplanning voor energieopslag via arbitrage in dagvooraf- en real-time markten met behulp van innovatieve blokorders. Hoofdstuk 6 introduceert een neuraal netwerk-ge\u00efnformeerd kader voor de berekening van optimale waterstromen.\n\nHet proefschrift sluit af met hoofdstuk 7, dat bevindingen uit alle voorgaande hoofdstukken integreert en in de bredere context van de water-energienexus en de energietransitie plaatst. De synthese benadrukt het belang van het combineren van hanteerbare optimalisatie, robuuste onzekerheidsmodellering en op machine learning gebaseerde surrogaten om marktcompatibele en fysiek onderbouwde besluitvormingsondersteuning voor gekoppelde infrastructuren mogelijk te maken. Het reflecteert op hoe watersystemen kunnen evolueren van passieve elektriciteitsconsumenten naar actieve aanbieders van flexibiliteit. Ten slotte schetst het hoofdstuk aanbevelingen voor beleidsmakers, exploitanten en onderzoekers, en identificeert het toekomstige onderzoeksrichtingen.","summary":"The accelerating transition towards low-carbon energy systems, combined with increasing pressure on water resources, is transforming how societies plan, operate, and coordinate critical infrastructures. Power systems face rising challenges due to growing demand driven by the electrification of multiple sectors. At the same time, increasing uncertainty from variable renewable energy generation places new requirements on flexibility, reliability, and market design. Water systems simultaneously confront growing demand, reduced freshwater availability, and the need to manage energy-intensive treatment and distribution under increasingly volatile energy prices. These trends make water operation more tightly coupled to energy system dynamics.\n\nAt the same time, the deep physical and economic interdependence between energy and water creates new opportunities for flexibility, efficiency, and resilience. Realizing this potential requires decision-making frameworks that jointly represent the coupled physics, market interactions, and key uncertainties in an integrated manner. The motivation of this thesis is to provide such integrated and uncertainty-aware decision-support tools for coupled water\u2013energy systems. This enables system operators and planners to exploit cross-sector synergies while safeguarding reliability, affordability, and environmental performance.\n\nThis thesis focuses on developing market-based, data-driven mathematical optimization frameworks for the planning and operation of coupled power and water systems under uncertainty. The central aim is to enable a system-level approach to investment and operational decisions. These decisions explicitly account for the physics of power and water infrastructures, their interdependence, and the techno-economic characteristics of the underlying technologies. The core contribution lies in employing classical and novel mathematical techniques, including reformulations, convex relaxations, and machine-learning-based surrogate models. These techniques make otherwise intractable integrated water\u2013energy problems computationally feasible while preserving physical accuracy and market realism.\n\nIn particular, tailored approximation methods based on convex relaxations and input convex neural networks are developed to represent complex nonlinear hydraulic relationships. These approximations are embedded within optimal power and water flow formulations. Additionally, uncertainty is modeled through distributionally robust chance-constrained formulations based on data-driven moment information. This approach supports decisions that remain feasible and economically robust under worst-case forecast errors without resorting to computationally demanding scenario-based approaches.\n\nThe thesis consists of seven chapters. Chapter 1 introduces the research topic and its societal context, motivation, research objectives, research questions, and methodology. It outlines the main challenges of the water\u2013energy nexus and reviews the state of the art in integrated power\u2013water modeling, market-based coordination, and uncertainty-aware optimization. The chapter also identifies key knowledge gaps related to uncertainty representation, computational tractability, and the role of emerging coupling technologies. Finally, it presents the overarching research questions and explains how subsequent chapters address them using a combination of mathematical optimization, data-driven modeling, and market-oriented analysis.\n\nChapter 2 develops a distributionally robust, market-based framework for mobilizing water-based flexibility in power transmission networks. The chapter formulates a distributionally robust optimization model that co-optimizes day-ahead energy schedules and real-time balancing. Flexible pumping loads in the water distribution system are treated as controllable resources that can respond to renewable generation deviations and price signals. Uncertainty in renewable generation is captured through moment-based ambiguity sets. Availability-cost-based incentives for flexible assets are introduced, enabling a systematic analysis of how different cost structures and uncertainty levels affect social welfare, generator revenues, and pump operating costs. Numerical case studies demonstrate that coordinated operation of power and water systems can enhance system flexibility and reduce net pumping costs, while preserving market compatibility and maintaining reliable water service.\n\nChapter 3 is a direct extension of this short-term market-based coordination framework developed in Chapter 2. It moves from the transmission-level, stylized setting to a distribution-level water network based on a real-world case study, and explicitly incorporates multiple sources of renewable generation uncertainty by representing aggregated solar and wind forecast errors within a joint ambiguity set. The chapter also introduces an improved convex relaxation of the hydraulic equations together with an iterative solution algorithm that increases the accuracy of the hydraulic approximation. These developments do not change the basic idea of water-based flexibility, but they do provide a more realistic assessment of how much flexibility a real distribution network can offer, and of its economic value when participating in electricity markets as an active provider of balancing services.\n\nChapter 4 addresses hydrogen production from non-potable water resources by developing a techno-economic investment and operation planning framework for integrated water\u2013power\u2013hydrogen systems. A mixed-integer optimization model is formulated that jointly selects electrolysis technologies, water treatment configurations, and operational strategies, while considering participation in multiple electricity markets. The framework combines water quality constraints, treatment process options, and electrolysis performance characteristics to evaluate trade-offs between capital and operating costs, emissions, and flexibility value. Representative case studies show how non-potable water sources and carefully designed technology portfolios can support cost-effective hydrogen production from non-potable water. They also demonstrate how hydrogen plants can act as flexible loads that interact optimally with power system price dynamics.\n\nChapter 5 explores uncertainty-aware energy storage investment planning through arbitrage in day-ahead and real-time electricity markets using novel block orders. A distributionally robust optimization framework is developed to capture uncertain prices originating from imperfect renewable generation forecasts using data-driven ambiguity sets. It also represents energy storage investment and operation over multiple time scales. The model examines how cross-temporal energy and flexibility arbitrage opportunities, market product design, and uncertainty influence optimal storage sizing, siting, and dispatch in systems with high shares of variable renewable energy generation. Numerical results illustrate that robust formulations can mitigate revenue risk, improve the economic viability of storage, and enhance power system reliability. This provides guidelines for both investors and market designers.\n\nChapter 6 introduces a neural-network-informed optimal water flow framework. Machine learning-based surrogate models are used to represent complex nonlinear hydraulic relationships in water distribution networks. Input convex neural networks are developed and trained as convex surrogates for pump and pipe hydraulics. These surrogates are embedded within optimal water flow formulations to obtain convex or mixed-integer convex problems. The approach preserves key physical properties while substantially improving computational tractability compared to traditional nonlinear models. Benchmarking against classical hydraulic solvers and optimization formulations shows that the proposed neural-network-informed models achieve high accuracy and significant computational gains. This enables large-scale, potentially uncertainty-aware planning and operation of coupled power\u2013water systems.\n\nThe thesis concludes with Chapter 7 that integrates findings from all previous chapters and places them in the broader context of the water\u2013energy nexus and the energy transition. The synthesis emphasizes the importance of combining tractable optimization, robust uncertainty modeling, and machine-learning-based surrogates to enable market-compatible and physically grounded decision support for coupled infrastructures. It reflects on how water systems can evolve from passive electricity consumers into active flexibility providers. Emerging technologies such as hydrogen production and energy storage can be systematically co-optimized within integrated frameworks. Finally, the chapter outlines recommendations for policymakers, operators, and researchers, and identifies future research directions. These include extending the developed methods to larger systems, additional sectors, richer sources of uncertainty, and more complex market designs.","auteur":"Alessio Belmondo Bianchi di Lavagna","auteur_slug":"alessio-belmondo-bianchi-di-lavagna","publicatiedatum":"9 juli 2026","taal":"EN","url_flipbook":"https:\/\/ebook.proefschriftmaken.nl\/ebook\/alessiobelmondobianchidilavagna?iframe=true","url_download_pdf":"https:\/\/ebook.proefschriftmaken.nl\/download\/23bef609-86c6-4e02-9273-e0009e3b4c6e\/optimized","url_epub":"","ordernummer":"18955","isbn":"978-94-6534-419-5","doi_nummer":"","naam_universiteit":"Wageningen University","afbeeldingen":15697,"naam_student:":"","binnenwerk":"","universiteit":"Wageningen University","cover":"","afwerking":"","cover_afwerking":"","design":""},"_links":{"self":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio\/15695","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio"}],"about":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/types\/us_portfolio"}],"author":[{"embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/comments?post=15695"}],"version-history":[{"count":1,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio\/15695\/revisions"}],"predecessor-version":[{"id":15698,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio\/15695\/revisions\/15698"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/media\/15696"}],"wp:attachment":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/media?parent=15695"}],"wp:term":[{"taxonomy":"us_portfolio_category","embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio_category?post=15695"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}