Smart Energy最新文献

{"title":"Deep learning surrogate model to explain decision variable synergies in energy system modelling","authors":"Matteo Giacomo Prina ,&nbsp;Carlo Pelizzoni ,&nbsp;Mackenzie Judson ,&nbsp;Madeleine McPherson ,&nbsp;Andrea Menapace ,&nbsp;Giampaolo Manzolini ,&nbsp;Wolfram Sparber","doi":"10.1016/j.segy.2026.100246","DOIUrl":"10.1016/j.segy.2026.100246","url":null,"abstract":"<div><div>Energy system models are powerful tools for planning the low-carbon transition. They identify optimal technology portfolios but rarely explain why certain combinations outperform others. Understanding decision variable synergies across multiple sectors remains a key gap in the literature. This study addresses that gap with a two-stage methodology applied to the Piemonte region (Italy) for 2050, using 17 decision variables and two objective functions (total annual costs and CO₂ emissions). In the first stage, an expert-based analysis of six electrification technologies is conducted using the EPLANopt model. It reveals a fundamental behavioral dichotomy: heat pumps reduce CO₂ independently of grid decarbonization, while Power-to-X technologies require an effective renewable share above approximately 50% to yield a net environmental benefit. In the second stage, a deep learning surrogate model trained on thousands EnergyPLAN simulations enables systematic sensitivity and interaction analysis across all 17 variables. Explainability artificial intelligence methods identify solar capacity and synthetic gas as the dominant variables. Pairwise analysis across all 136 variable combinations reveals a dominant PV surplus–Power-to-Gas valorisation chain. Third-order analysis shows that battery storage acts as a critical mediator between PV generation and Power-to-Gas conversion, producing emergent synergies that cannot be decomposed into pairwise effects. These findings provide a physically interpretable, data-driven basis for sequencing technology deployments in regional energy planning.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"22 ","pages":"Article 100246"},"PeriodicalIF":5.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147850455","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}

{"title":"Tensor-based modeling framework for district heating pipes","authors":"Nicholas Tedjosantoso ,&nbsp;Arne Speerforck ,&nbsp;Torben Warnecke ,&nbsp;Hans Schäfers ,&nbsp;Gerwald Lichtenberg","doi":"10.1016/j.segy.2026.100245","DOIUrl":"10.1016/j.segy.2026.100245","url":null,"abstract":"<div><div>District heating network simulation faces a fundamental computational challenge: traditional nonlinear models become intractable at large scales due to the curse of dimensionality, while linear models cannot accurately represent the nonlinear dynamics essential for district heating systems. Tensor-based methods have demonstrated effectiveness in modeling heating, ventilation, and air conditioning (HVAC) as well as local heating systems by providing a scalable compromise between accuracy and computational efficiency, yet their application to district heating networks is first described in this paper. This work applies multilinear time-invariant (MTI) modeling using a tensor-based framework for scalable representations of district heating networks.</div><div>Tensor and multilinear functions efficiently represent the governing equations and their nonlinear relationships, especially the quadratic pressure-loss relationships defined by the Darcy-Weisbach equation and nonlinear friction factors across flow regimes without causing an exponential growth in model complexity. Binary variables model discontinuous transitions between laminar and turbulent flow, maintaining computational tractability while preserving physical accuracy. The tensor structure inherently avoids the curse of dimensionality that constrains conventional approaches by factorization. Benchmarking against established models on a small network shows minimal deviations alongside considerable memory reductions, demonstrating the potential of tensor-based methods for efficient simulation and optimization of large-scale district heating networks and supporting the integration of renewable energy sources and advanced control strategies essential for modern energy-efficient systems.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"22 ","pages":"Article 100245"},"PeriodicalIF":5.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147798056","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}

{"title":"Scenario-based energy system planning for Samoa's clean energy transition to 2050","authors":"Nicollo Moeono-Alaiasa ,&nbsp;Xavier Rixhon ,&nbsp;Francesco Baldi ,&nbsp;Martin Atkins ,&nbsp;Michael Walmsley ,&nbsp;Tupuivao Junior Vaiaso","doi":"10.1016/j.segy.2026.100244","DOIUrl":"10.1016/j.segy.2026.100244","url":null,"abstract":"<div><div>Pacific Small Island Developing States (PSIDS) face complex energy transition challenges arising from geographic isolation, fossil fuel dependency, land constraints, and climate vulnerability. The study conducts a techno-economic assessment of Samoa's energy system using EnergyScope Typical Days. This study stress-tests the main transition challenges through scenario analysis. Five scenarios spanning 2025–2050 are examined: a Reference Scenario, Increasing Indigenous Primary Energy Supply, Natural Disaster Shock, Emissions-Constrained Decarbonization, and Innovation Shift.</div><div>Results show that under the Innovation Shift scenario, Samoa can achieve over 85% renewable energy in total primary supply by 2050 while accommodating a 45% increase in electricity demand. Biofuels and solar photovoltaics account for up to 38% and 30% of total supply, respectively. Battery energy storage and electric vehicles enhance system resilience under disaster conditions, with up to 819 MWh of distributed EV storage required to meet 10% of national demand. Emissions-constrained pathways demonstrate escalating marginal abatement costs, rising from $470 USD/tCO_2e in 2030 to over $3000 USD/tCO_2e by 2050. The Innovation Shift scenario highlights a system characterised by full renewable electricity, extensive electrification, and sector-coupling through biogas and hydrogen, showing that Samoa's long-term transition is shaped more by strategic design choices than by absolute cost values.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"22 ","pages":"Article 100244"},"PeriodicalIF":5.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147798125","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}

{"title":"Modelling battery waste heat recovery for sector-coupled power-heat systems in district heating planning","authors":"Niklas Marius Denter,&nbsp;Jiahe Chu,&nbsp;Matthias Huber,&nbsp;Tobias Baldauf","doi":"10.1016/j.segy.2026.100248","DOIUrl":"10.1016/j.segy.2026.100248","url":null,"abstract":"<div><div>This contribution addresses a methodological gap in energy system modelling by developing an optimisation approach that endogenously integrates battery waste heat into sector-coupled electricity and district heating systems. With increasing shares of variable renewable electricity, battery storage plays a growing role in providing flexibility, while simultaneously generating low-temperature waste heat that is typically neglected in planning models.</div><div>The proposed framework extends existing optimisation approaches by representing batteries not only as electricity storage units but also as potential low-temperature heat sources. In contrast to predefined waste heat profiles, thermal energy is generated endogenously as a direct byproduct of battery charging and discharging operations. Battery dispatch is optimised in response to electricity price signals and system constraints, such that waste heat availability is directly linked to operational behaviour and capacity sizing decisions. Recoverable waste heat can be utilised directly, upgraded via heat pumps, or stored based on techno-economic trade-offs.</div><div>The approach is implemented within the open-source energy system model Calliope and applied to a real-world district heating planning project in eastern Germany. Results show that endogenous waste heat integration substantially alters optimal system design, increasing battery and photovoltaic capacities by 54.22 % and 39.53 %, respectively, while reducing cumulative heat pump capacity by 1.9 %. In contrast, the impact on net heat generation costs remains moderate, with maximum savings of up to 0.45 ct/kWh in the best-case scenario, which corresponds to annual cost savings of approximately 43.800 €.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"22 ","pages":"Article 100248"},"PeriodicalIF":5.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147850417","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}

{"title":"Increasing grid hosting capacity through non-firm connections and aggregator mediated flexibility trades","authors":"Paul E. Seifert ,&nbsp;Sigurd Bjarghov ,&nbsp;Steffen J. Bakker","doi":"10.1016/j.segy.2026.100247","DOIUrl":"10.1016/j.segy.2026.100247","url":null,"abstract":"<div><div>Industrial electrification efforts and the expansion of renewable generation, combined with lengthy grid infrastructure permitting processes, have created extensive queues for connections across European electricity systems, limiting the ability to connect new customers under existing market structures. This paper develops a coordinated flexibility trading mechanism, operated by an independent aggregator, that mediates trades between customers and the grid operator. The mechanism is formulated as a two-stage optimisation model, including a day-ahead scheduling stage followed by a flexibility market that coordinates capacity and energy trades under network constraints. Using a case study of an industrial distribution network with six customers, we show that coordinated flexibility trading can reduce load shedding and allow for additional non-firm connections without grid reinforcements by reallocating capacity rights and sourcing of flexibility. In network-wide coordination scenarios, the mechanism reduces total system costs by up to 15% compared to local-only coordination, with outcomes depending on the congestion type and magnitude. Our framework demonstrates that existing grid infrastructure can accommodate substantially higher connection capacity through coordinated flexibility mechanisms, offering a practical intermediate solution between costly grid expansion and nodal market efficiency.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"22 ","pages":"Article 100247"},"PeriodicalIF":5.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147850454","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}

{"title":"A decision support tool for waste heat to heat recovery technologies in industrial sectors","authors":"Mohamed Mostafa ,&nbsp;Arman Ashabi ,&nbsp;Andriy Hryshchenko ,&nbsp;Ken Bruton ,&nbsp;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 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.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"21 ","pages":"Article 100219"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","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}

{"title":"Stochastic net load optimization in distributed integrated energy systems - A forecast based scheduling approach","authors":"Jan-Simon Telle,&nbsp;Patrik Schönfeldt,&nbsp;Sunke Schlüters,&nbsp;Benedikt Hanke,&nbsp;Karsten von Maydell","doi":"10.1016/j.segy.2026.100228","DOIUrl":"10.1016/j.segy.2026.100228","url":null,"abstract":"<div><div>The increasing integration of decentralized and volatile producers and consumers across sectors (electricity, heating and mobility) introduces significant operational challenges for distributed energy systems. This work presents a systematic stochastic optimization approach for generating day-ahead operation schedules in sector-integrated energy systems based on probabilistic net load forecasts, for local applications that require low data, low computing power and enable a high level of data security. The proposed method enables more robust decision-making under uncertainty, overcoming the limitations of deterministic point forecasts and optimizations or the requirements for large amounts of data.</div><div>The approach is demonstrated using the energy system of a logistics facility, focusing on the electrical preconditioning of refrigerated trailers under varying daily preconditioning frequencies. The study outlines how probabilistic net load forecasts can be transformed into representative scenarios and implemented as stochastic net load inputs within the energy system model.</div><div>A comparative analysis between the scenario-based stochastic optimization and three deterministic optimization variants highlights the advantages of the proposed approach. The stochastic method achieves up to 25 % lower total operating costs and reduces daily peak power exceedances by 30 to 66 % compared to deterministic scheduling. Furthermore, the analysis of regret costs indicates average daily reductions between 21 % and 69 %, depending on the number of reefers to be preconditioned, demonstrating enhanced robustness, cost efficiency, and operational reliability under forecast uncertainty.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"21 ","pages":"Article 100228"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396096","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}

{"title":"PreFlex — A new marketplace for distributed flexibility providers hedging wind producers: A case study of the Norwegian electricity market","authors":"Henrik Rognes,&nbsp;Felipe Van de Sande Araujo,&nbsp;Pedro Crespo del Granado,&nbsp;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":"2026-02-01","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}

{"title":"Bridging theory and practice: Building a coordinated offshore grid in the North Sea and United States Eastern Seaboard","authors":"Tyler A. Hansen ,&nbsp;Ben Hinchliffe,&nbsp;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":"2026-02-01","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}

{"title":"A methodological framework for identifying District Heating Networks in Germany by utilizing the census data","authors":"Kai Droste,&nbsp;Luca Döring,&nbsp;Jonas Klingebiel,&nbsp;Rahul Karuvingal,&nbsp;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>¹</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":"2026-02-01","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}