Pub Date : 2026-02-01DOI: 10.1016/j.segy.2026.100229
Amela Ajanovic, Reinhard Haas
The transition to a smart energy system necessitates the defossilisation of energy use while maintaining security and efficiency. Green hydrogen has been positioned as an energy carrier with potential to contribute to this transition. This study examines the economic viability of hydrogen use with special focus on the transport sector, analyzing cost trends in electrolysis, fuel cell vehicle deployment, and market dynamics. Despite its potential, hydrogen adoption faces significant barriers, including high production costs, infrastructure limitations, and inefficiencies in conversion processes. The study highlights the challenges of integrating hydrogen into energy systems, explores the competitiveness of hydrogen-powered vehicles across different transport modes, and assesses its role in a market-driven energy landscape. While hydrogen may play a role in hard-to-electrify sectors, its widespread adoption remains uncertain. Hydrogen in transport is likely to remain a niche solution, used only where other low-carbon alternatives are not feasible. Whether it can truly become a cornerstone of Europe's sustainable energy future will depend not just on technological progress but on carefully designed policies, realistic economic planning, and transparent long-term strategies.
{"title":"Economic perspectives of hydrogen and fuel cell vehicles in the transition to smart energy systems","authors":"Amela Ajanovic, Reinhard Haas","doi":"10.1016/j.segy.2026.100229","DOIUrl":"10.1016/j.segy.2026.100229","url":null,"abstract":"<div><div>The transition to a smart energy system necessitates the defossilisation of energy use while maintaining security and efficiency. Green hydrogen has been positioned as an energy carrier with potential to contribute to this transition. This study examines the economic viability of hydrogen use with special focus on the transport sector, analyzing cost trends in electrolysis, fuel cell vehicle deployment, and market dynamics. Despite its potential, hydrogen adoption faces significant barriers, including high production costs, infrastructure limitations, and inefficiencies in conversion processes. The study highlights the challenges of integrating hydrogen into energy systems, explores the competitiveness of hydrogen-powered vehicles across different transport modes, and assesses its role in a market-driven energy landscape. While hydrogen may play a role in hard-to-electrify sectors, its widespread adoption remains uncertain. Hydrogen in transport is likely to remain a niche solution, used only where other low-carbon alternatives are not feasible. Whether it can truly become a cornerstone of Europe's sustainable energy future will depend not just on technological progress but on carefully designed policies, realistic economic planning, and transparent long-term strategies.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"21 ","pages":"Article 100229"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Industrial electrification is increasing to reduce fossil fuel dependence, alongside a growing share of volatile renewables. A secure and reliable energy supply is crucial for industry, leading to a shift from centralised to decentralised grid structures. DC microgrids becoming increasingly popular in industry, since they enable energy recuperation from braking, reduce components and cables, and integrate storage and local generation to manage supply interruptions or peak loads. EVs add further synergies by serving as mobile storage units, helping to store and redistribute locally generated renewable energy. This paper analyses how EV integration in droop-controlled DC grids can contribute to a more stable, low-emission and peak-reduced load profile to the supply grid through load shifting and bridge interruptions. A droop-controlled DC grid model has been developed, incorporating an EV charging park based on probability functions. Scalable scenarios allow for diverse condition analysis using an energy management system that utilises fuzzy logic and sequential MILP optimisation. It has been shown that a 7% improvement of coefficient represented grid-serving behaviour is possible by load shifting. It has also been demonstrated that an optimised EMS can reduce the demand-based CO2 emissions by 41 kg for a representative day compared to a fuzzy logic EMS. At the same time peak load is decreased yielding a more constant residual load. These results highlight the potential of a controlled bidirectional charging infrastructure in DC grids and underscore the need to explicitly consider charging processes to ensure a residual load as constant as possible.
{"title":"Analysis of bidirectional EV charging infrastructures within industrial DC grids","authors":"Henning Rahlf , Lukas Knorr , Simon Althoff , Henning Meschede","doi":"10.1016/j.segy.2026.100227","DOIUrl":"10.1016/j.segy.2026.100227","url":null,"abstract":"<div><div>Industrial electrification is increasing to reduce fossil fuel dependence, alongside a growing share of volatile renewables. A secure and reliable energy supply is crucial for industry, leading to a shift from centralised to decentralised grid structures. DC microgrids becoming increasingly popular in industry, since they enable energy recuperation from braking, reduce components and cables, and integrate storage and local generation to manage supply interruptions or peak loads. EVs add further synergies by serving as mobile storage units, helping to store and redistribute locally generated renewable energy. This paper analyses how EV integration in droop-controlled DC grids can contribute to a more stable, low-emission and peak-reduced load profile to the supply grid through load shifting and bridge interruptions. A droop-controlled DC grid model has been developed, incorporating an EV charging park based on probability functions. Scalable scenarios allow for diverse condition analysis using an energy management system that utilises fuzzy logic and sequential MILP optimisation. It has been shown that a 7% improvement of coefficient represented grid-serving behaviour is possible by load shifting. It has also been demonstrated that an optimised EMS can reduce the demand-based CO<sub>2</sub> emissions by 41 kg for a representative day compared to a fuzzy logic EMS. At the same time peak load is decreased yielding a more constant residual load. These results highlight the potential of a controlled bidirectional charging infrastructure in DC grids and underscore the need to explicitly consider charging processes to ensure a residual load as constant as possible.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"21 ","pages":"Article 100227"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1016/j.segy.2025.100224
Hamza Abid , Brian Vad Mathiesen , Iva Ridjan Skov , Poul Alberg Østergaard
Denmark's abundant wind resources and expertise in offshore wind position it to support Europe's decarbonisation and energy security goals, especially as Europe seeks to diversify energy sources and invest in hydrogen and hydrogen-derived fuels. To achieve these objectives, Denmark and other Northern European countries aim to establish large-scale offshore energy hubs. This study examines two key aspects: (1) optimal infrastructure for connecting energy hubs to the mainland, and (2) the most advantageous export strategy, focusing on hydrogen or hydrogen-derived Sustainable Aviation Fuel (e-SAF) for the German market. The techno-economic analysis evaluates High Voltage Direct Current (HVDC) cables and hydrogen pipelines, assessing the Levelized Cost of Hydrogen (LCOH) for offshore and onshore electrolysis. Sensitivity analyses explore the economic benefits of utilizing excess heat in district heating. Using EnergyPLAN, a system-level assessment compares a decarbonized Danish energy system in 2045 with scenarios involving hydrogen and e-SAF exports. Results show that onshore electrolysis with HVDC is preferable for distances under 350 km, leveraging excess heat utilization for district heating and lower infrastructure costs. Hydrogen pipelines are advantageous only for greater distances. Utilizing excess heat from onshore electrolysis can reduce the LCOH by 10–30 %, depending on the selling price and utilization of heat. Direct hydrogen exports become profitable when hydrogen prices exceed 2.7 €/kg (80 €/MWh), while e-SAF export is favourable above 170 €/MWh (1.65 €/litre), aligning with fossil jet fuel competitiveness under carbon taxation. These findings underscore Denmark's potential to lead in renewable energy exports while bolstering European energy security and decarbonisation.
{"title":"The future of Danish offshore wind: Integration and export strategies for energy hubs in the North Sea","authors":"Hamza Abid , Brian Vad Mathiesen , Iva Ridjan Skov , Poul Alberg Østergaard","doi":"10.1016/j.segy.2025.100224","DOIUrl":"10.1016/j.segy.2025.100224","url":null,"abstract":"<div><div>Denmark's abundant wind resources and expertise in offshore wind position it to support Europe's decarbonisation and energy security goals, especially as Europe seeks to diversify energy sources and invest in hydrogen and hydrogen-derived fuels. To achieve these objectives, Denmark and other Northern European countries aim to establish large-scale offshore energy hubs. This study examines two key aspects: (1) optimal infrastructure for connecting energy hubs to the mainland, and (2) the most advantageous export strategy, focusing on hydrogen or hydrogen-derived Sustainable Aviation Fuel (e-SAF) for the German market. The techno-economic analysis evaluates High Voltage Direct Current (HVDC) cables and hydrogen pipelines, assessing the Levelized Cost of Hydrogen (LCOH) for offshore and onshore electrolysis. Sensitivity analyses explore the economic benefits of utilizing excess heat in district heating. Using EnergyPLAN, a system-level assessment compares a decarbonized Danish energy system in 2045 with scenarios involving hydrogen and e-SAF exports. Results show that onshore electrolysis with HVDC is preferable for distances under 350 km, leveraging excess heat utilization for district heating and lower infrastructure costs. Hydrogen pipelines are advantageous only for greater distances. Utilizing excess heat from onshore electrolysis can reduce the LCOH by 10–30 %, depending on the selling price and utilization of heat. Direct hydrogen exports become profitable when hydrogen prices exceed 2.7 €/kg (80 €/MWh), while e-SAF export is favourable above 170 €/MWh (1.65 €/litre), aligning with fossil jet fuel competitiveness under carbon taxation. These findings underscore Denmark's potential to lead in renewable energy exports while bolstering European energy security and decarbonisation.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"21 ","pages":"Article 100224"},"PeriodicalIF":5.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1016/j.segy.2025.100225
Nguyen Quoc Minh, Tran Van Dai, Pham Minh Hoang, Pham Hong Hai, Le Thi Minh Chau
With the rapid expansion of renewable energy sources (RES), battery energy storage systems (BESS) have become essential for ensuring grid stability, reliability, and operational efficiency. However, optimizing BESS sizing is a complex challenge that requires balancing economic, technical, and environmental factors while considering the long-term impact of battery degradation and replacement costs. This study presents a comprehensive optimization model for determining the optimal capacity of BESS within a microgrid, explicitly incorporating battery aging effects and associated lifecycle costs. The proposed model employs a mixed-integer linear programming (MILP) approach to minimize the total system cost, which includes investment, operation, and replacement expenses, while ensuring that load demand is met, RES integration is maximized, and system reliability is maintained. By considering the degradation of BESS performance over time, the model provides a more accurate estimation of long-term economic and technical feasibility. Simulation results validate the effectiveness of the model in optimizing BESS sizing and installation costs. Additionally, the study evaluates different BESS technologies and examines the impact of various factors, such as RES penetration and emission costs on overall system performance. The findings offer valuable insights into developing cost-effective BESS operation schedules and management strategies, contributing to improved energy efficiency and sustainability in microgrid applications.
{"title":"A mixed-integer linear programming model for BESS sizing optimization considering aging effects and emission costs","authors":"Nguyen Quoc Minh, Tran Van Dai, Pham Minh Hoang, Pham Hong Hai, Le Thi Minh Chau","doi":"10.1016/j.segy.2025.100225","DOIUrl":"10.1016/j.segy.2025.100225","url":null,"abstract":"<div><div>With the rapid expansion of renewable energy sources (RES), battery energy storage systems (BESS) have become essential for ensuring grid stability, reliability, and operational efficiency. However, optimizing BESS sizing is a complex challenge that requires balancing economic, technical, and environmental factors while considering the long-term impact of battery degradation and replacement costs. This study presents a comprehensive optimization model for determining the optimal capacity of BESS within a microgrid, explicitly incorporating battery aging effects and associated lifecycle costs. The proposed model employs a mixed-integer linear programming (MILP) approach to minimize the total system cost, which includes investment, operation, and replacement expenses, while ensuring that load demand is met, RES integration is maximized, and system reliability is maintained. By considering the degradation of BESS performance over time, the model provides a more accurate estimation of long-term economic and technical feasibility. Simulation results validate the effectiveness of the model in optimizing BESS sizing and installation costs. Additionally, the study evaluates different BESS technologies and examines the impact of various factors, such as RES penetration and emission costs on overall system performance. The findings offer valuable insights into developing cost-effective BESS operation schedules and management strategies, contributing to improved energy efficiency and sustainability in microgrid applications.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"21 ","pages":"Article 100225"},"PeriodicalIF":5.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1016/j.segy.2025.100223
Tyler A. Hansen , Ben Hinchliffe, Elizabeth J. Wilson
Offshore wind development in Europe's North Sea and the United States East Coast is central to decarbonization, but achieving ambitious deployment targets requires fundamentally changing how projects connect to the grid. While point-to-point (“radial”) connections enabled early growth, sector expansion to hundreds of gigawatts is increasingly costly, socially and environmentally disruptive, and dependent on extensive onshore reinforcements. Coordinated offshore transmission systems—where multiple projects share offshore and onshore transmission assets—show enormous promise to address these challenges and enable smarter energy systems, yet real-world implementation has proved slow and fragmented. Only a single project, integrating under 1 GW of offshore wind, has been completed. To examine this gap, this paper focuses on the institutional contexts that shape implementation, conducting a comparative case study of six jurisdictions—Denmark, Belgium, and the United Kingdom in the North Sea; and New Jersey, New York, and Massachusetts in the United States. Drawing on a literature review of conceptual and modeling studies and document analysis of policy, regulatory, and planning materials, this research compares the theoretical promise of coordinated transmission with real-world implementation efforts. The analysis shows that while implementation efforts are underway in all six jurisdictions, accelerating and scaling them requires addressing three intersecting institutional challenges: (1) developing coordinated offshore transmission systems amid fragmented authority and without an established regional plan, (2) legitimizing and incentivizing large upfront investments for benefits that are delayed and dispersed, and (3) building resilience amidst political and macroeconomic shocks. The paper concludes with a research agenda to explore potential solutions.
{"title":"Bridging theory and practice: Building a coordinated offshore grid in the North Sea and United States Eastern Seaboard","authors":"Tyler A. Hansen , Ben Hinchliffe, Elizabeth J. Wilson","doi":"10.1016/j.segy.2025.100223","DOIUrl":"10.1016/j.segy.2025.100223","url":null,"abstract":"<div><div>Offshore wind development in Europe's North Sea and the United States East Coast is central to decarbonization, but achieving ambitious deployment targets requires fundamentally changing how projects connect to the grid. While point-to-point (“radial”) connections enabled early growth, sector expansion to hundreds of gigawatts is increasingly costly, socially and environmentally disruptive, and dependent on extensive onshore reinforcements. Coordinated offshore transmission systems—where multiple projects share offshore and onshore transmission assets—show enormous promise to address these challenges and enable smarter energy systems, yet real-world implementation has proved slow and fragmented. Only a single project, integrating under 1 GW of offshore wind, has been completed. To examine this gap, this paper focuses on the institutional contexts that shape implementation, conducting a comparative case study of six jurisdictions—Denmark, Belgium, and the United Kingdom in the North Sea; and New Jersey, New York, and Massachusetts in the United States. Drawing on a literature review of conceptual and modeling studies and document analysis of policy, regulatory, and planning materials, this research compares the theoretical promise of coordinated transmission with real-world implementation efforts. The analysis shows that while implementation efforts are underway in all six jurisdictions, accelerating and scaling them requires addressing three intersecting institutional challenges: (1) developing coordinated offshore transmission systems amid fragmented authority and without an established regional plan, (2) legitimizing and incentivizing large upfront investments for benefits that are delayed and dispersed, and (3) building resilience amidst political and macroeconomic shocks. The paper concludes with a research agenda to explore potential solutions.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"21 ","pages":"Article 100223"},"PeriodicalIF":5.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-13DOI: 10.1016/j.segy.2025.100221
Laura Herrera-Mejía, Carlos D. Hoyos, David La Rotta, Yissel Mejía, John Castillo
Forecasting day-ahead energy prices in Colombia is challenging, as bids are shaped not only by hydrology and demand but also by strategic bidding under evolving regulatory and economic conditions. We develop a Machine Learning (ML) framework to forecast day-ahead bids of individual hydroelectric plants in the National Interconnected System (SIN), using plant-specific models that capture operational and strategic heterogeneity. To our knowledge, this is the first study to develop plant-specific ML models for Colombian hydro bidding.
The framework employs a two-stage architecture: (i) a classifier predicts whether a plant will bid at the regulatory minimum, and (ii) a regression model estimates the actual bid otherwise. Using daily data from XM (2016–2025), we benchmark multiple algorithms—including XGBoost, Random Forest, Gradient Boosting, MLP, and stacked ensembles.
Across plants, the framework achieves average F1-scores above 0.90 for classification, and regression errors in the range MAE = 1.5–3.0 COP/kWh (normalized by regulatory floor). Tercile classification accuracy exceeds 70% for key strategic plants such as Guavio and San Carlos, indicating strong ordinal predictive skill even under volatile conditions. The models perform particularly well for plants with stable bidding patterns, while highly intermittent plants (e.g. Porce II & III) remain harder to anticipate.
These results demonstrate that ML methods can capture mid-term gradients and relative positioning of competitor bids, providing actionable insights for market operators and traders. Future extensions should integrate contextual data—portfolio composition, regulatory signals, or sentiment indicators—to improve forecast robustness in behaviorally complex markets.
{"title":"Forecasting day-ahead hydropower bids in the colombian electricity market: A two-stage machine learning framework","authors":"Laura Herrera-Mejía, Carlos D. Hoyos, David La Rotta, Yissel Mejía, John Castillo","doi":"10.1016/j.segy.2025.100221","DOIUrl":"10.1016/j.segy.2025.100221","url":null,"abstract":"<div><div>Forecasting day-ahead energy prices in Colombia is challenging, as bids are shaped not only by hydrology and demand but also by strategic bidding under evolving regulatory and economic conditions. We develop a Machine Learning (ML) framework to forecast day-ahead bids of individual hydroelectric plants in the National Interconnected System (SIN), using plant-specific models that capture operational and strategic heterogeneity. To our knowledge, this is the first study to develop plant-specific ML models for Colombian hydro bidding.</div><div>The framework employs a two-stage architecture: (i) a classifier predicts whether a plant will bid at the regulatory minimum, and (ii) a regression model estimates the actual bid otherwise. Using daily data from XM (2016–2025), we benchmark multiple algorithms—including XGBoost, Random Forest, Gradient Boosting, MLP, and stacked ensembles.</div><div>Across plants, the framework achieves average F1-scores above 0.90 for classification, and regression errors in the range MAE = 1.5–3.0 COP/kWh (normalized by regulatory floor). Tercile classification accuracy exceeds 70% for key strategic plants such as Guavio and San Carlos, indicating strong ordinal predictive skill even under volatile conditions. The models perform particularly well for plants with stable bidding patterns, while highly intermittent plants (e.g. Porce II & III) remain harder to anticipate.</div><div>These results demonstrate that ML methods can capture mid-term gradients and relative positioning of competitor bids, providing actionable insights for market operators and traders. Future extensions should integrate contextual data—portfolio composition, regulatory signals, or sentiment indicators—to improve forecast robustness in behaviorally complex markets.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"21 ","pages":"Article 100221"},"PeriodicalIF":5.0,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-06DOI: 10.1016/j.segy.2025.100218
Kai Droste, Luca Döring, Jonas Klingebiel, Rahul Karuvingal, Dirk Müller
In this study, we present a methodological framework to identify the spatial position and heat demand of District Heating Networks (DHNs) for residential buildings in Germany by utilizing the census dataset for 2022. A clustering approach is used to locate DHNs and further datasets are included to derive and evaluate additional characteristics. A top-down calibration is applied to match the heat demand of the identified networks with validation data for 2022 from the Federal Statistical Office of Germany.
In total, 8,684 areas of presumed DHNs could be identified in Germany. 90 % of the demand is covered by about 10.87 % of the DHNs. The resulting dataset is published under the name District Heating Networks Dataset from Clustered Census Data (AixDHN)1 on GitHub.
To demonstrate the application of the AixDHN dataset, a use case is presented in which the potential of Shallow Geothermal Collectors (SGCs) for supplying the identified district heating grids is evaluated. The use case reveals a technical potential of approximately 15.31 TWh/a for SGCs in Germany, supplying the heat for domestic buildings connected to the DHNs.
The AixDHN dataset provides a spatially detailed, transparent basis and enables the evaluation of the potential of further renewable heat sources for integration into DHNs. The methodology’s strengths lie in its robust, generalized, and systematic approach, which makes it applicable throughout Germany without any further research on a regional level. It may act as a tool to support the planning and transformation of the heating sector towards renewable sources as well as municipal heat planning on a broad scope.
When cooling applications are addressed alongside heating, the economic viability of cold DHNs can be significantly improved, particularly enabling new applications in mixed-use areas that remain attractive even with lower heat densities.
{"title":"A methodological framework for identifying District Heating Networks in Germany by utilizing the census data","authors":"Kai Droste, Luca Döring, Jonas Klingebiel, Rahul Karuvingal, Dirk Müller","doi":"10.1016/j.segy.2025.100218","DOIUrl":"10.1016/j.segy.2025.100218","url":null,"abstract":"<div><div>In this study, we present a methodological framework to identify the spatial position and heat demand of District Heating Networks (DHNs) for residential buildings in Germany by utilizing the census dataset for 2022. A clustering approach is used to locate DHNs and further datasets are included to derive and evaluate additional characteristics. A top-down calibration is applied to match the heat demand of the identified networks with validation data for 2022 from the Federal Statistical Office of Germany.</div><div>In total, 8,684<!--> <!-->areas of presumed DHNs could be identified in Germany. 90<!--> <!-->% of the demand is covered by about 10.87<!--> <!-->% of the DHNs. The resulting dataset is published under the name District Heating Networks Dataset from Clustered Census Data (AixDHN)<span><span><sup>1</sup></span></span> on GitHub.</div><div>To demonstrate the application of the AixDHN dataset, a use case is presented in which the potential of Shallow Geothermal Collectors (SGCs) for supplying the identified district heating grids is evaluated. The use case reveals a technical potential of approximately 15.31<!--> <!-->TWh/a for SGCs in Germany, supplying the heat for domestic buildings connected to the DHNs.</div><div>The AixDHN dataset provides a spatially detailed, transparent basis and enables the evaluation of the potential of further renewable heat sources for integration into DHNs. The methodology’s strengths lie in its robust, generalized, and systematic approach, which makes it applicable throughout Germany without any further research on a regional level. It may act as a tool to support the planning and transformation of the heating sector towards renewable sources as well as municipal heat planning on a broad scope.</div><div>When cooling applications are addressed alongside heating, the economic viability of cold DHNs can be significantly improved, particularly enabling new applications in mixed-use areas that remain attractive even with lower heat densities.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"21 ","pages":"Article 100218"},"PeriodicalIF":5.0,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1016/j.segy.2025.100219
Mohamed Mostafa , Arman Ashabi , Andriy Hryshchenko , Ken Bruton , Dominic T.J. O'Sullivan
Industrial waste heat-to-heat recovery directly repurposes thermal energy for on-site duties, avoiding conversion losses and fuel consumption. This paper presents a decision-support tool that screens and ranks thirteen heat-to-heat technologies: indirect heat exchangers, recuperators/regenerators, heat pipes, thermal storage, air preheaters, heat pumps, absorption heat pumps, and mechanical vapour recompression, using uniform criteria weights, performance, economic, and environmental indicators. The framework filters options based on temperature class and industrial application, normalises datasets, and uses a weighted-sum multi-criteria analysis with visual heatmaps to clarify trade-offs. For a rubber facility with medium-temperature exhaust and low-temperature demand, the tool shows that heat pumps and conventional exchangers are preferable due to their readiness, low integration risk, and minimal CAPEX/OPEX in cost-focused scenarios. Technical emphasis elevates heat-upgrading options, as heat pumps improve recoverable temperature lift and site coverage, but require higher investment. Environmental emphasis prioritises heat pumps and long-lived equipment, achieving the largest CO2-intensity reductions per delivered kilowatt-hour of useful heat. Results highlight that optimal selections depend strongly on stakeholder priorities and boundary conditions. Conventional exchangers deliver fast, low-risk savings; upgrading technologies unlock deeper decarbonisation where a temperature gap exists. The framework enables auditable, site-specific short-listing, clarifies the rationale behind rankings, and supports early engagement between process owners and integrators. The results show a 20.8 % annual reduction in useful energy demand. Savings narrow during demand peaks and decline during extended periods of low load.
{"title":"A decision support tool for waste heat to heat recovery technologies in industrial sectors","authors":"Mohamed Mostafa , Arman Ashabi , Andriy Hryshchenko , Ken Bruton , Dominic T.J. O'Sullivan","doi":"10.1016/j.segy.2025.100219","DOIUrl":"10.1016/j.segy.2025.100219","url":null,"abstract":"<div><div>Industrial waste heat-to-heat recovery directly repurposes thermal energy for on-site duties, avoiding conversion losses and fuel consumption. This paper presents a decision-support tool that screens and ranks thirteen heat-to-heat technologies: indirect heat exchangers, recuperators/regenerators, heat pipes, thermal storage, air preheaters, heat pumps, absorption heat pumps, and mechanical vapour recompression, using uniform criteria weights, performance, economic, and environmental indicators. The framework filters options based on temperature class and industrial application, normalises datasets, and uses a weighted-sum multi-criteria analysis with visual heatmaps to clarify trade-offs. For a rubber facility with medium-temperature exhaust and low-temperature demand, the tool shows that heat pumps and conventional exchangers are preferable due to their readiness, low integration risk, and minimal CAPEX/OPEX in cost-focused scenarios. Technical emphasis elevates heat-upgrading options, as heat pumps improve recoverable temperature lift and site coverage, but require higher investment. Environmental emphasis prioritises heat pumps and long-lived equipment, achieving the largest CO<sub>2</sub>-intensity reductions per delivered kilowatt-hour of useful heat. Results highlight that optimal selections depend strongly on stakeholder priorities and boundary conditions. Conventional exchangers deliver fast, low-risk savings; upgrading technologies unlock deeper decarbonisation where a temperature gap exists. The framework enables auditable, site-specific short-listing, clarifies the rationale behind rankings, and supports early engagement between process owners and integrators. The results show a 20.8 % annual reduction in useful energy demand. Savings narrow during demand peaks and decline during extended periods of low load.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"21 ","pages":"Article 100219"},"PeriodicalIF":5.0,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1016/j.segy.2025.100220
Henrik Rognes, Felipe Van de Sande Araujo, Pedro Crespo del Granado, Ehsan Nokandi
Renewable power producers’ commitments in the day-ahead (DA) market may face uncertainties and deviations, necessitating swift adjustments in the intraday timeframe. This paper introduces a novel daily capacity-based market, the “Pre-contracting Flexibility Market” (or simply “PreFlex”). PreFlex enables intermittent generation units, specifically Wind Power Producers (WPPs), to contract Distributed Energy Resources (DERs) for flexibility. This enhances the WPP’s ability to counter potential deviations, primarily addressed during the intraday stage. The PreFlex market operates in two steps: first, before the closure of the DA market, the WPPs contract flexibility capacity from DERs through aggregators; second, in parallel with the intraday market, the WPPs activate the acquired capacity to offset predictive generation errors. Using two-stage stochastic optimization models, a comprehensive analysis has been conducted within the Norwegian power market context. The results demonstrate significant benefits for the WPPs by reducing the exposure to imbalance penalties and intraday market illiquidity risks. In addition, from the independent system operator’s perspective, the analysis indicates that introducing the PreFlex market reduces median system imbalances. However, the total costs of procuring and maintaining balancing reserves would not be considerably reduced, as sufficient reserves are needed to cover peak imbalances, which will not be affected by the PreFlex market.
{"title":"PreFlex — A new marketplace for distributed flexibility providers hedging wind producers: A case study of the Norwegian electricity market","authors":"Henrik Rognes, Felipe Van de Sande Araujo, Pedro Crespo del Granado, Ehsan Nokandi","doi":"10.1016/j.segy.2025.100220","DOIUrl":"10.1016/j.segy.2025.100220","url":null,"abstract":"<div><div>Renewable power producers’ commitments in the day-ahead (DA) market may face uncertainties and deviations, necessitating swift adjustments in the intraday timeframe. This paper introduces a novel daily capacity-based market, the “Pre-contracting Flexibility Market” (or simply “PreFlex”). PreFlex enables intermittent generation units, specifically Wind Power Producers (WPPs), to contract Distributed Energy Resources (DERs) for flexibility. This enhances the WPP’s ability to counter potential deviations, primarily addressed during the intraday stage. The PreFlex market operates in two steps: first, before the closure of the DA market, the WPPs contract flexibility capacity from DERs through aggregators; second, in parallel with the intraday market, the WPPs activate the acquired capacity to offset predictive generation errors. Using two-stage stochastic optimization models, a comprehensive analysis has been conducted within the Norwegian power market context. The results demonstrate significant benefits for the WPPs by reducing the exposure to imbalance penalties and intraday market illiquidity risks. In addition, from the independent system operator’s perspective, the analysis indicates that introducing the PreFlex market reduces median system imbalances. However, the total costs of procuring and maintaining balancing reserves would not be considerably reduced, as sufficient reserves are needed to cover peak imbalances, which will not be affected by the PreFlex market.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"21 ","pages":"Article 100220"},"PeriodicalIF":5.0,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.segy.2025.100215
Lukas Richter , Volker Lenz , Martin Dotzauer , Joachim Seifert
{"title":"Coordinated energy systems in decentralized districts: Evaluating the cellular approach for improved grid stability and renewable integration","authors":"Lukas Richter , Volker Lenz , Martin Dotzauer , Joachim Seifert","doi":"10.1016/j.segy.2025.100215","DOIUrl":"10.1016/j.segy.2025.100215","url":null,"abstract":"","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"20 ","pages":"Article 100215"},"PeriodicalIF":5.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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