Smart Energy最新文献

{"title":"Comparative analysis of optimisation models for control of hydrogen multi-energy systems","authors":"B. Riederer,&nbsp;P. Kury,&nbsp;K. Lichtenegger,&nbsp;D. Muschick,&nbsp;V. Kaisermayer,&nbsp;C. Albert,&nbsp;F. Radner,&nbsp;W. Siegl,&nbsp;M. Gölles","doi":"10.1016/j.segy.2026.100262","DOIUrl":"10.1016/j.segy.2026.100262","url":null,"abstract":"<div><div>Decarbonisation of energy systems increasingly relies on integrating various energy carriers as part of a broader sector-coupling strategy. Especially in the industrial and transport sector, hydrogen has become increasingly important, offering flexibility across several domains. As energy systems grow more interconnected, the need for smart supervisory-level control increases. However, hydrogen components introduce unique operational challenges due to their intrinsic non-linear behaviour. This work contributes to developing such supervisory control strategies, supporting the transition to climate-neutral, smart multi-energy systems.</div><div>Therefore, an existing model predictive control framework for multi-energy systems is extended to hydrogen subsystems. This expanded framework enables coordinated control of hydrogen flows alongside other energy carriers, focusing on operational optimisation and strategic component use. Various modelling strategies for pressure-mass flow relationships are presented, ranging from linear to piecewise-affine (PWA) linearised and non-linear formulations. These are compared in simulation scenarios for load profiles and system constraints, with quantitative accuracy metrics against reference data from the National Institute of Standards and Technology (NIST).</div><div>The paper shows that non-linear models require careful reformulation to achieve computational tractability, yet they do not provide improved accuracy over PWA models: the Redlich–Kwong equation of state introduces a systematic bias exceeding that of a 4-segment PWA approximation of NIST data. Linear models such as the ideal gas law severely underestimate pressure levels, leading to over-production and component damage risks. Thus, PWA models offer the best compromise between speed, accuracy, and safety, making them the recommended modelling approach for real-time supervisory control.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"23 ","pages":"Article 100262"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148737887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Updating the MULTINODE add-on for EnergyPLAN to model energy systems with high spatial resolution: A case study of the mainland Portugal electrical system","authors":"Carlos Santos Silva,&nbsp;Joana Patrício","doi":"10.1016/j.segy.2026.100261","DOIUrl":"10.1016/j.segy.2026.100261","url":null,"abstract":"<div><div>The increasing complexity of energy systems transitioning towards decarbonization requires advances in planning tools, particularly in terms of spatial and temporal resolution. This study applies EnergyPLAN, together with its MULTINODE add-on, to model energy systems at high spatial resolution. We revise the original MULTINODE equations to better represent cross–border flows and introduce a post–processing procedure that improves the dispatch of thermal generation, hydro reservoirs and pumped storage. We also use PyPSA to cluster the national transmission grid and estimate representative internal inter-regional transfer capacities.</div><div>The approach is applied to mainland Portugal’s 2023 electrical system, comparing results at the national, 2-region, and 5-region (NUTS 2) scales and performing sensitivity analyses on the most critical methodological assumptions. Results show that the NUTS 2 configuration significantly improves model accuracy by more faithfully reproducing the dispatch of all technologies, reducing deviations from real-world data to below 16%. Higher spatial resolution also reveals important regional dynamics, such as Lisbon’s structural import dependency and Alentejo’s suitability for developing green-hydrogen hubs. However, challenges remain regarding the availability of granular data and the estimation of internal transmission limits.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"23 ","pages":"Article 100261"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148636724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Managing solar abundance through smart household flexibility: A socio-technical assessment of Australia's Solar Sharer Offer","authors":"Alireza Ganjovi,&nbsp;Saman A. Gorji","doi":"10.1016/j.segy.2026.100269","DOIUrl":"10.1016/j.segy.2026.100269","url":null,"abstract":"<div><div>Australia's rapid growth in rooftop photovoltaic generation is creating periods of low operational demand, negative wholesale prices and renewable-electricity surplus. Tariff-based demand response may help align household consumption with these periods, but its contribution depends on resource capability, availability, accessibility and delivered response. This study develops a socio-technical assessment of Australia's Solar Sharer Offer, which provides eligible households with a 3-h zero-price electricity window. The framework integrates a Flexibility Access Assessment with household load-shifting, aggregate participation, bill-impact and sensitivity analyses, supported by empirical plausibility benchmarking. Five stylised household configurations provide 0-12 kWh of flexible energy per event. Delivering a 263 MW demand increase for 3 h requires 1.315 million appliance-only households or 65,750 highly automated households under ideal conditions; with 75% resource availability and 80% response delivery, these requirements increase to approximately 2.19 million and 109,583, respectively. Using matched 2026-27 tariffs, central annual household net value ranges from -AUD 100.9 to AUD 954.2, depending on flexibility capability and whether free-period savings offset the approximately 2.62 c/kWh higher SSO charge on remaining non-free consumption. Australian trials support the plausibility of selected flexibility assumptions but do not validate voluntary Solar Sharer Offer response. The results show that tariff signals can support renewable-energy absorption, but their system value depends on delivered flexibility, coordination and equitable access to enabling technologies.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"23 ","pages":"Article 100269"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148995339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the potential for biomass savings through targeted retrofitting of district heating systems in Austria","authors":"Aadit Malla,&nbsp;Lukas Kranzl,&nbsp;Nirav Patel,&nbsp;Leopold Tobias Riedl,&nbsp;Stefan Retschitzegger,&nbsp;Christian Ramerstorfer,&nbsp;Joachim Kelz,&nbsp;Ingo Leusbrock,&nbsp;Nicolas Marx,&nbsp;Ralf-Roman Schmidt,&nbsp;Adam Buruzs","doi":"10.1016/j.segy.2026.100266","DOIUrl":"10.1016/j.segy.2026.100266","url":null,"abstract":"<div><div>Biomass district heating plants are central to renewable heat supply in rural Austria, but growing competition for limited biomass resources requires more efficient use of existing infrastructure. This study presents a techno-economic assessment of retrofit measures for the Austrian biomass district heating sector. Plants are grouped into representative clusters and evaluated for combustion optimization, passive and active flue gas condensation, and waste-heat heat pump integration under varying energy price scenarios. Results are projected into sector-wide rollout pathways to 2050. Active flue gas condensation emerges as the most broadly viable measure, adopted by 36%–84% of plants across scenarios. Heat pumps deliver the largest per-plant biomass savings but depend on waste heat availability and electricity prices. Plants near hydropower facilities achieve levelized costs approximately 13 EUR/MWh lower than grid-connected configurations. Waste-heat heat pumps also provide operational flexibility, with 14.4% of their annual electricity consumption shiftable in response to hourly price signals. The findings provide a basis for prioritizing retrofit investments and designing targeted policy support across plant types and market conditions.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"23 ","pages":"Article 100266"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148951160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smarter electric vehicle charging from Open Charge Point Protocol data: Reducing grid peaks and energy costs with tariff-aware scheduling","authors":"Md Sabbir Hossen,&nbsp;Gobbi Ramasamy,&nbsp;Ngu Eng Eng,&nbsp;Md Tanjil Sarker","doi":"10.1016/j.segy.2026.100267","DOIUrl":"10.1016/j.segy.2026.100267","url":null,"abstract":"<div><div>The increasing adoption of electric vehicles (EVs) presents significant operational challenges for charging infrastructure, particularly in managing charging demand under feeder capacity and electricity tariff constraints. This paper proposes a practical data-driven framework that integrates short-term charging load forecasting, anomaly detection, and tariff-aware scheduling using real Open Charge Point Protocol (OCPP) telemetry collected from a multi-station residential charging network. The forecasting module employs lightweight statistical models with Fourier seasonality, while an Isolation Forest identifies abnormal charging behavior and a heuristic scheduling strategy coordinates charging according to feeder limits and time-of-use electricity prices. Experimental results demonstrate that the proposed framework achieves up to 35.0% peak demand reduction and 28.0% electricity cost savings while maintaining 100% energy delivery. The forecasting component requires only 0.0247 s for model training and 0.0008 s for prediction, demonstrating its suitability for real-time deployment. The results show that practical and computationally efficient methods, when integrated using real OCPP operational data, can effectively improve EV charging management under realistic operating conditions.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"23 ","pages":"Article 100267"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148951159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A unified deep-forecasting and IGDT framework for robust operation of Multi-Energy Microgrids with hydrogen systems and electric vehicle integration","authors":"Mojtaba Hadi,&nbsp;Elhoussin Elbouchikhi,&nbsp;Zhibin Zhou,&nbsp;Abdelhakim Saim","doi":"10.1016/j.segy.2026.100265","DOIUrl":"10.1016/j.segy.2026.100265","url":null,"abstract":"<div><div>This paper proposes a forecasting-driven uncertainty management framework for a hydrogen-enabled Multi-Energy Microgrid (MEMG) integrating a 14-bus IEEE power network, a District Heating Network (DHN), and a Gas Distribution System (GDS). The framework incorporates renewable energy resources, Combined Heat and Power (CHP) unit, Power-to-Hydrogen-and-Heat (P2HH) technology, and Plug-in Electric Vehicles (PEVs) to simultaneously satisfy electricity, heat, and gas demands while enhancing operational flexibility. Unlike conventional IGDT-based energy management approaches that rely on predefined nominal demand profiles, the proposed framework integrates CNN–LSTM demand forecasting with IGDT-based optimization by using the forecasted demand as the nominal operating profile for uncertainty-aware scheduling. A two-stage methodology is developed. First, a hybrid CNN–LSTM model is employed to forecast electricity, heat, and gas demands. Subsequently, Information Gap Decision Theory (IGDT) is used to manage forecasting-driven uncertainty through risk-averse and risk-seeking decision-making strategies. The forecasting model is implemented in Python, while the operational optimization problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) model and solved in GAMS. Simulation results demonstrate satisfactory forecasting performance, with sMAPE values below 15% for all energy carriers. The results further show that, compared with the risk-neutral strategy, the risk-averse strategy increases total cost, electricity transaction cost, and emission cost by 28%, 51%, and 46%, respectively, in exchange for enhanced robustness against uncertainty. Conversely, the risk-seeking strategy reduces the total cost and emission cost by 40% and 85%, respectively, while the electricity transaction balance shifts from a net cost to a net revenue due to increased electricity exports under favorable operating conditions. These findings demonstrate the effectiveness of the proposed forecasting-driven IGDT framework in balancing economic performance and operational robustness in hydrogen-enabled multi-energy microgrids under uncertainty.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"23 ","pages":"Article 100265"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148951161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power-to-X for green fuels: Case study of the Bornholm Energy Island using techno-economic optimisation under different power supply scenarios","authors":"Johannes Giehl,&nbsp;Kacper Rokosz,&nbsp;Dana Hentschel,&nbsp;Lucia Ciprian,&nbsp;Jens Weibezahn","doi":"10.1016/j.segy.2026.100264","DOIUrl":"10.1016/j.segy.2026.100264","url":null,"abstract":"<div><div>The transition to climate-neutral fuels requires scalable production concepts integrated with renewable energy systems. This paper presents a techno-economic analysis of e-methanol production in an offshore energy hub, applying a Mixed-Integer Linear Programming investment and dispatch model to the Bornholm energy island. The model optimises infrastructure investment and operation while representing variable electrolyser efficiency, water and carbon dioxide supply, by-product revenues, and storage integration. Competitiveness is assessed through the levelized cost of methanol (LCOM) and the required carbon price relative to fossil alternatives.</div><div>Results show that electricity cost and carbon intensity are the dominant drivers of competitiveness. In the Status Quo, cost-minimal grid-based operation results in carbon dioxide emissions exceeding those of fossil methanol, preventing competitiveness through carbon pricing. LCOM amounts to 1013–1432 €/t. Stronger reliance on local renewable energy improves emissions but increases costs due to higher infrastructure and flexibility requirements. By 2045, cost reductions lower LCOM to 817–942 €/t. The required carbon price results in 327–464 €/t carbon dioxide equivalents and remains above projected emission allowances prices.</div><div>Side-product revenues reduce the LCOM but do not change overall competitiveness. The results reveal a structural trade-off between cost efficiency and emission reduction in offshore energy hubs. Long-term viability depends on continued reductions in technology costs, further decarbonisation of the power system, enhanced system flexibility, and policy instruments beyond carbon pricing. Future research should expand the geographical scope of analysis beyond Denmark in order to evaluate the influence of differing regulatory environments and market structures on techno-economic performance.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"23 ","pages":"Article 100264"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148951162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic feasibility of high-temperature electrothermal storage for European industrial heat","authors":"Emilija Cheshmedjievska,&nbsp;Elena Todorova,&nbsp;Vladimir Z. Gjorgievski,&nbsp;Snezana Cundeva,&nbsp;Natasa Markovska","doi":"10.1016/j.segy.2026.100268","DOIUrl":"10.1016/j.segy.2026.100268","url":null,"abstract":"<div><div>Industrial steam accounts for a substantial share of European industrial energy use and greenhouse gas emissions. Electrification via high-temperature electrothermal storage (HTES) is becoming a valuable technology for its decarbonization. Yet whether HTES pays off in practice depends on market conditions that vary substantially across Europe. This study develops an optimization framework that co-determines HTES sizing and operation as a supplement to gas boilers, such that the gas-electricity split is determined endogenously. The model is applied to industrial steam production in Finland, France, Germany, the Netherlands, and Spain, which together account for roughly 59% of EU27 industrial steam demand. Performance is assessed through a range of technical, economic and environmental parameters. The results indicate a range of outcomes: Finland reaches complete electrification with a payback below two years and 88% CO₂ reduction compared to gas, while Germany's high electricity prices limit the HTES to cover 36% of demand with a payback approaching nine years and only 18% emission reduction. France, the Netherlands, and Spain occupy intermediate positions. Levelized cost of heat ranges from 39 €/MWh in France to 87 €/MWh in Germany. Across the studied markets, HTES most often emerges as a complement to existing gas boilers rather than a replacement, and sensitivity analysis confirms that viability is most fragile where baseline economics are tight. These findings demonstrate that the viability of HTES-based electrification is highly context-dependent, with market conditions emerging as the dominant factor shaping both economic performance and decarbonization potential.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"23 ","pages":"Article 100268"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Upgrade of a virtual 5th generation district heating and cooling network through optimal control","authors":"Anna Dell’Isola,&nbsp;Lieve Helsen","doi":"10.1016/j.segy.2026.100263","DOIUrl":"10.1016/j.segy.2026.100263","url":null,"abstract":"<div><div>District heating and cooling (DHC) networks, particularly Fifth Generation (5GDHC), effectively reduce building energy use and greenhouse gas emissions by integrating low-grade thermal energy sources at neutral temperature levels. However, the operation of these networks is not yet optimised. A key challenge is the integration and control of multiple distributed heat and cold sources, with pumping energy being crucial at neutral temperatures. Rule-based control sequences are conventionally used to manage these networks, whereas more advanced strategies like optimal control can act as a system integrator, facilitating the transition to a cost-effective decarbonised heating and cooling sector. This paper delves deeper into control strategies for a virtual 5GDHC network, comparing current-practice rule-based control with white-box optimal control approaches through dynamic simulations. A sensitivity analysis evaluates system sizing strategies and their impact on control and performance. Results show that optimal control significantly improves thermal comfort, particularly during transitional and cooling seasons, while reducing energy use by almost 50%. These benefits are achieved by optimising network temperatures, utilising anticipatory control, and leveraging the buildings’ thermal inertia and the different building loads. Additionally, optimal control enables substantial component size reductions by exploiting system flexibility during operation and thus acting as an effective system integrator.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"23 ","pages":"Article 100263"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148737881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of heat pumps on the electricity load: Evaluation of large sets of operational data including the simulation of future situations","authors":"M. Krause ,&nbsp;N. Gerhardt ,&nbsp;M. Huang ,&nbsp;S. Herkel ,&nbsp;P.J. Girón Cruz ,&nbsp;M. Zapf","doi":"10.1016/j.segy.2026.100255","DOIUrl":"10.1016/j.segy.2026.100255","url":null,"abstract":"<div><div>The expected ramp-up of heat pumps and electromobility as part of the energy transition will result in an increasing overall load on the electricity grid. To take this into account for grid planning, grid operators typically use simultaneity factors or simultaneity functions to determine the expected peak load. In case of decentralized heat pumps, the electricity load correlates strongly with the thermal demand for heating and domestic hot water. Reduced efficiency at low temperatures affects the electricity load additionally, the operation of heating elements at low outside temperatures implies a further challenge for the grid. In addition to the peak load, the daily profile of the electricity consumption of heat pumps is of major relevance for electricity balancing management. Typically, electricity providers use temperature-dependent standard load profiles for electricity consumers. However, the number of active customers, who will adjust their consumption depending on electricity prices or the availability of (renewable) generation capacity and grid capacity, will significantly increase in the future. Hereby, flexible operation will affect both the maximum peak load and the distribution of electricity consumption over the days.</div><div>To assess all these aspects, a comprehensive study has been performed using a large set of operational data derived from more than 6000 installed heat pumps over up to 2.5 years as well as from scientific monitoring projects in Germany. To support the correlations derived from the operational data e.g. at very low temperatures, simulation models have been developed according to respective buildings, system designs and operational behaviours. To evaluate the impact of flexible heat pumps, the operation has been simulated and optimized with respect to flexible tariffs for future heat pumps, considering electro-mobility as well as renewable energy market penetration.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"22 ","pages":"Article 100255"},"PeriodicalIF":5.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167522","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}