Pub Date : 2025-10-10DOI: 10.1016/j.segy.2025.100207
Davide Tommasini , Nicolas Marx , Yannick Wimmer , Stefan Reuter , Hanne Kauko
Hydrogen is expected to play a key role in future climate-neutral energy systems, with electrolysis emerging as a primary production method. However, approximately one-third of the electricity used in electrolysis is lost as heat, presenting an opportunity for district heating (DH) integration. This study explores the feasibility of utilizing waste heat from an alkaline electrolyzer facility in Bodø, Northern Norway, to supply an existing high-temperature DH network and a planned low-temperature network. Using techno-economic modeling and dynamic simulations, different integration strategies are evaluated, focusing on heat pump configurations and direct utilization options. Results indicate that integrating waste heat can significantly reduce energy costs for DH operators while improving the economic viability of hydrogen production. The study highlights the potential of sector coupling between hydrogen and district heating to enhance system efficiency and sustainability.
{"title":"Electrolysis waste heat utilization for district heating — A Norwegian case study","authors":"Davide Tommasini , Nicolas Marx , Yannick Wimmer , Stefan Reuter , Hanne Kauko","doi":"10.1016/j.segy.2025.100207","DOIUrl":"10.1016/j.segy.2025.100207","url":null,"abstract":"<div><div>Hydrogen is expected to play a key role in future climate-neutral energy systems, with electrolysis emerging as a primary production method. However, approximately one-third of the electricity used in electrolysis is lost as heat, presenting an opportunity for district heating (DH) integration. This study explores the feasibility of utilizing waste heat from an alkaline electrolyzer facility in Bodø, Northern Norway, to supply an existing high-temperature DH network and a planned low-temperature network. Using techno-economic modeling and dynamic simulations, different integration strategies are evaluated, focusing on heat pump configurations and direct utilization options. Results indicate that integrating waste heat can significantly reduce energy costs for DH operators while improving the economic viability of hydrogen production. The study highlights the potential of sector coupling between hydrogen and district heating to enhance system efficiency and sustainability.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"20 ","pages":"Article 100207"},"PeriodicalIF":5.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321262","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 : 2025-10-08DOI: 10.1016/j.segy.2025.100208
Andreas Mühlbauer, Yuanbei F. Fan, Daniel J. Sambor, Mark Z. Jacobson
The aim of this study is to minimize the cost of developing a renewable energy islanded microgrid that provides reliable electricity and thermal comfort for a small building over multiple decades. The study is carried out with a model that minimizes the total cost of energy system components. Four different system configurations considering solar photovoltaics, electric heat pumps for heating and cooling, and a subset of battery-electricity storage, hydrogen-fuel-cell-electricity storage, and thermal-energy storage with phase-change materials are modeled. The objective is to minimize total lifecycle costs (capacity and operational costs) while ensuring reliable electricity as well as heat and cold supply. Over five climate zones, four system configurations, and 25 weather years, the annual costs of 100 % renewable microgrids for residential-type loads and structures are at least 67 % lower than the same microgrid powered with diesel generators for 75 % of the cases. On average, using renewable energy instead of diesel reduces the annual cost of islanded microgrids by 72 %. Systems with higher technology diversity, such as batteries combined with hydrogen-fuel-cell-electricity storage, result in even lower average-cost solutions (75 % lower than diesel); however, they may increase the risk of loss of load events over the project lifetime if the systems are optimized for only one weather year. Despite the higher risk, the incremental cost to reduce one kWh loss of load over multiple weather years is estimated to be 89 % lower with both batteries and hydrogen than with batteries alone, highlighting the value of diverse technology portfolios in microgrid planning.
{"title":"Minimizing the multi-decadal cost of islanded renewable-electricity microgrids for different climate zones","authors":"Andreas Mühlbauer, Yuanbei F. Fan, Daniel J. Sambor, Mark Z. Jacobson","doi":"10.1016/j.segy.2025.100208","DOIUrl":"10.1016/j.segy.2025.100208","url":null,"abstract":"<div><div>The aim of this study is to minimize the cost of developing a renewable energy islanded microgrid that provides reliable electricity and thermal comfort for a small building over multiple decades. The study is carried out with a model that minimizes the total cost of energy system components. Four different system configurations considering solar photovoltaics, electric heat pumps for heating and cooling, and a subset of battery-electricity storage, hydrogen-fuel-cell-electricity storage, and thermal-energy storage with phase-change materials are modeled. The objective is to minimize total lifecycle costs (capacity and operational costs) while ensuring reliable electricity as well as heat and cold supply. Over five climate zones, four system configurations, and 25 weather years, the annual costs of 100 % renewable microgrids for residential-type loads and structures are at least 67 % lower than the same microgrid powered with diesel generators for 75 % of the cases. On average, using renewable energy instead of diesel reduces the annual cost of islanded microgrids by 72 %. Systems with higher technology diversity, such as batteries combined with hydrogen-fuel-cell-electricity storage, result in even lower average-cost solutions (75 % lower than diesel); however, they may increase the risk of loss of load events over the project lifetime if the systems are optimized for only one weather year. Despite the higher risk, the incremental cost to reduce one kWh loss of load over multiple weather years is estimated to be 89 % lower with both batteries and hydrogen than with batteries alone, highlighting the value of diverse technology portfolios in microgrid planning.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"20 ","pages":"Article 100208"},"PeriodicalIF":5.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362464","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 : 2025-10-01DOI: 10.1016/j.segy.2025.100206
Dominik Stecher , Lukas Ziegltrum , Paul Reiprich , Christian Fuchs , Andreas Maier , Jochen Schmidt
District heating systems (DHS) play a vital role in sustainable heating solutions and the decarbonization of the energy sector. However, inefficiencies due to undetected faults in substations result in high return temperatures, increasing heat losses, and limiting the integration of renewable energy sources. The lack of publicly available labeled datasets poses a significant challenge for fault detection using supervised learning models. To address this issue, this study explores three machine learning-based synthetic data generation techniques – time series forecasting, generative adversarial networks (GANs), and fault signature transfer. These methods aim to increase publicly available data either by sharing the generating model or a synthetic dataset. The novelty lies in the combination of advanced supervised machine learning methods being applied to a large, fully labeled data set to create new, equally labeled data for publication, as, to our knowledge, no such dataset has been compiled before. We evaluate our methods on the first-of-its-kind ILSE dataset, which includes real-world smart meter data from 547 substations and 1,162 reviewed faults from a German DHS network, including detailed root cause information. Overall, time series forecasting achieves an MAPE of 3% to 10% for inlet and outlet temperature and 25% to 40% for heat load and flow rate, both of which are within year-to-year variance. For GANs, specifically TimeGAN, we found a discriminative score of about 0.10 compared to 0.24 in the original publication when tested on Energy benchmark data. Fault signature transfer has yet to yield usable results, most likely due to the high variance in the fault signatures, fault duration, and overlapping or multiple root causes. Finally, fault data in the synthetic data is not yet good enough for practical use, e.g. training a fault detector.
{"title":"Neural network synthetic dataset generation for fault detection in district heating substations","authors":"Dominik Stecher , Lukas Ziegltrum , Paul Reiprich , Christian Fuchs , Andreas Maier , Jochen Schmidt","doi":"10.1016/j.segy.2025.100206","DOIUrl":"10.1016/j.segy.2025.100206","url":null,"abstract":"<div><div>District heating systems (DHS) play a vital role in sustainable heating solutions and the decarbonization of the energy sector. However, inefficiencies due to undetected faults in substations result in high return temperatures, increasing heat losses, and limiting the integration of renewable energy sources. The lack of publicly available labeled datasets poses a significant challenge for fault detection using supervised learning models. To address this issue, this study explores three machine learning-based synthetic data generation techniques – time series forecasting, generative adversarial networks (GANs), and fault signature transfer. These methods aim to increase publicly available data either by sharing the generating model or a synthetic dataset. The novelty lies in the combination of advanced supervised machine learning methods being applied to a large, fully labeled data set to create new, equally labeled data for publication, as, to our knowledge, no such dataset has been compiled before. We evaluate our methods on the first-of-its-kind ILSE dataset, which includes real-world smart meter data from 547 substations and 1,162 reviewed faults from a German DHS network, including detailed root cause information. Overall, time series forecasting achieves an MAPE of 3% to 10% for inlet and outlet temperature and 25% to 40% for heat load and flow rate, both of which are within year-to-year variance. For GANs, specifically TimeGAN, we found a discriminative score of about 0.10 compared to 0.24 in the original publication when tested on Energy benchmark data. Fault signature transfer has yet to yield usable results, most likely due to the high variance in the fault signatures, fault duration, and overlapping or multiple root causes. Finally, fault data in the synthetic data is not yet good enough for practical use, e.g. training a fault detector.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"20 ","pages":"Article 100206"},"PeriodicalIF":5.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321261","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 : 2025-09-20DOI: 10.1016/j.segy.2025.100188
Theda Zoschke , Christian Wolff , Armin Nurkanović , Gregor Rohbogner , Daniel Weiß , Lilli Frison , Moritz Diehl , Axel Oliva
This study introduces a method to derive requirements for non-linear formulations in optimization problems for Model Predictive Control (MPC) of district heating networks. Those formulations become particularly relevant in decentralized networks where thermohydraulic effects stemming from pressure and temperature distribution impact the optimal dispatch schedule of producers. This is illustrated through a case study of the network in Weil am Rhein, Germany. Initially, a linear MPC formulation that neglects thermohydraulic dynamics was evaluated using one year of measurement data, revealing potential cost reductions of 14.3%. These savings primarily result from reduced operation of fossil fuel boilers and increased utilization of Combined Heat and Power plants. Subsequently, hydraulic simulations and monitoring data were analyzed, revealing that at least one of the production sites is unable to supply its installed capacity into the network during high-load scenarios due to hydraulic limitations. Furthermore, the analysis of thermal losses suggested that supply temperature optimization has an additional cost-saving potential of approximately 1.8%. The study concludes that future versions of the optimization framework require the consideration of pressure losses and pumping limitations to enhance operational reliability, while also recognizing additional improvement potential offered by supply temperature optimization.
本文介绍了一种推导区域供热网络模型预测控制（MPC）优化问题非线性公式要求的方法。这些配方在分散网络中尤为重要，因为压力和温度分布产生的热工效应会影响到生产者的最佳调度计划。通过对德国莱茵河畔韦尔（Weil am Rhein）的网络进行案例研究，可以说明这一点。最初，利用一年的测量数据对忽略热水力动力学的线性MPC配方进行了评估，结果显示，该配方的潜在成本降低了14.3%。这些节省主要是由于减少了化石燃料锅炉的运行和增加了热电联产电厂的利用。随后，对水力模拟和监测数据进行了分析，发现由于水力限制，至少有一个生产基地无法在高负荷情况下向网络提供其装机容量。此外，热损失分析表明，优化供电温度可以额外节省约1.8%的成本。该研究得出结论，未来版本的优化框架需要考虑压力损失和泵送限制，以提高运行可靠性，同时也要认识到供应温度优化提供的额外改进潜力。
{"title":"Requirements analysis for Model Predictive Control in a decentralized district heating network","authors":"Theda Zoschke , Christian Wolff , Armin Nurkanović , Gregor Rohbogner , Daniel Weiß , Lilli Frison , Moritz Diehl , Axel Oliva","doi":"10.1016/j.segy.2025.100188","DOIUrl":"10.1016/j.segy.2025.100188","url":null,"abstract":"<div><div>This study introduces a method to derive requirements for non-linear formulations in optimization problems for Model Predictive Control (MPC) of district heating networks. Those formulations become particularly relevant in decentralized networks where thermohydraulic effects stemming from pressure and temperature distribution impact the optimal dispatch schedule of producers. This is illustrated through a case study of the network in Weil am Rhein, Germany. Initially, a linear MPC formulation that neglects thermohydraulic dynamics was evaluated using one year of measurement data, revealing potential cost reductions of 14.3%. These savings primarily result from reduced operation of fossil fuel boilers and increased utilization of Combined Heat and Power plants. Subsequently, hydraulic simulations and monitoring data were analyzed, revealing that at least one of the production sites is unable to supply its installed capacity into the network during high-load scenarios due to hydraulic limitations. Furthermore, the analysis of thermal losses suggested that supply temperature optimization has an additional cost-saving potential of approximately 1.8%. The study concludes that future versions of the optimization framework require the consideration of pressure losses and pumping limitations to enhance operational reliability, while also recognizing additional improvement potential offered by supply temperature optimization.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"20 ","pages":"Article 100188"},"PeriodicalIF":5.0,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159136","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 : 2025-09-12DOI: 10.1016/j.segy.2025.100204
Birol Kilkis
This paper presents a new exergy-based model for minimizing the total carbon dioxide emission responsibility of district heating systems connected to thermal power plants. An optimal exergy balance can be determined between the degree of low-exergy prosuming buildings on the demand side and the utilization rate of waste heat from a power plant on the supply side. Therefore, the optimum degree of prosuming buildings and the utilization of waste heat in a district also minimize the embodied emissions and costs of prosuming buildings for sustainable growth. Following the massive earthquake in 2023 in the Afşin-Elbistan province located in the Southeast region of Türkiye, 10,000 apartments to be heated by individual boilers are compared with an alternative design using this model. This alternative design features low-exergy prosumer buildings integrated with the waste heat of the 1,355 GW lignite power plant. The waste heat is obtained from the nearby return pipe of the water-cooling system, which is connected to a river head, located 30 km away. The model played a crucial role in determining the optimal degree of low-exergy building design, which simultaneously minimizes the carbon footprint of the power plant and the embodied emissions of such buildings, thereby facilitating the optimal level of renewable energy sources for prosumption. A new exergy star green metric is introduced, with a maximum rating of five stars. The new model assigned an optimal of three stars for the alternative design, which minimizes the carbon footprint by reducing carbon dioxide emissions by 79 %.
{"title":"Optimum utilization of power plant waste heat by nearly-zero exergy district prosumers for minimum carbon footprint","authors":"Birol Kilkis","doi":"10.1016/j.segy.2025.100204","DOIUrl":"10.1016/j.segy.2025.100204","url":null,"abstract":"<div><div>This paper presents a new exergy-based model for minimizing the total carbon dioxide emission responsibility of district heating systems connected to thermal power plants. An optimal exergy balance can be determined between the degree of low-exergy prosuming buildings on the demand side and the utilization rate of waste heat from a power plant on the supply side. Therefore, the optimum degree of prosuming buildings and the utilization of waste heat in a district also minimize the embodied emissions and costs of prosuming buildings for sustainable growth. Following the massive earthquake in 2023 in the Afşin-Elbistan province located in the Southeast region of Türkiye, 10,000 apartments to be heated by individual boilers are compared with an alternative design using this model. This alternative design features low-exergy prosumer buildings integrated with the waste heat of the 1,355 GW lignite power plant. The waste heat is obtained from the nearby return pipe of the water-cooling system, which is connected to a river head, located 30 km away. The model played a crucial role in determining the optimal degree of low-exergy building design, which simultaneously minimizes the carbon footprint of the power plant and the embodied emissions of such buildings, thereby facilitating the optimal level of renewable energy sources for prosumption. A new exergy star green metric is introduced, with a maximum rating of five stars. The new model assigned an optimal of three stars for the alternative design, which minimizes the carbon footprint by reducing carbon dioxide emissions by 79 %.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"20 ","pages":"Article 100204"},"PeriodicalIF":5.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159137","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 : 2025-09-04DOI: 10.1016/j.segy.2025.100202
Ulrich Ludolfinger , Thomas Hamacher , Maren Martens
The increasing share of intermittent renewable energy calls for intelligent building energy management systems to maintain grid stability. A widely used method for operating on-site storage is model predictive control (MPC), whose effectiveness heavily depends on forecast accuracy. This paper systematically evaluates the impact of prediction models on MPC performance in smart energy storage systems (SESS). Using a three-year, multi-building dataset with 15 min resolution, we compare five forecasting methods, linear model, XGBoost, RNN, TimeMixer, and TimesNet, for load, PV generation, and electricity price prediction. While XGBoost achieves the lowest mean squared error (MSE) and yields the highest revenue gain of 104% over a no-storage baseline during a four-month winter–spring test period, other models reveal a mismatch between forecast accuracy and control performance. Notably, the linear model, ranking mostly lowest in MSE, delivers the third-highest revenue (73%), nearly on par with the second best (79%). This illustrates that prediction accuracy alone is not a reliable proxy for control quality. Even the best realistic setup remains far from the ideal benchmark using perfect forecasts (235% gain). Daily retraining improves some models substantially (linear model to 105%) but has limited effect on others (XGBoost to 107%). These findings emphasize three key insights: (1) standard metrics like MSE may misrepresent the utility of forecasts for control, (2) errors across multiple inputs compound degradation in MPC, and (3) frequent retraining can mitigate losses. Overall, the results underscore the importance of robust forecasting and carefully chosen loss functions in the smart energy systems concept.
{"title":"A comprehensive evaluation of prediction techniques and their influence on model predictive control in smart energy storage systems","authors":"Ulrich Ludolfinger , Thomas Hamacher , Maren Martens","doi":"10.1016/j.segy.2025.100202","DOIUrl":"10.1016/j.segy.2025.100202","url":null,"abstract":"<div><div>The increasing share of intermittent renewable energy calls for intelligent building energy management systems to maintain grid stability. A widely used method for operating on-site storage is model predictive control (MPC), whose effectiveness heavily depends on forecast accuracy. This paper systematically evaluates the impact of prediction models on MPC performance in smart energy storage systems (SESS). Using a three-year, multi-building dataset with 15 min resolution, we compare five forecasting methods, linear model, XGBoost, RNN, TimeMixer, and TimesNet, for load, PV generation, and electricity price prediction. While XGBoost achieves the lowest mean squared error (MSE) and yields the highest revenue gain of 104% over a no-storage baseline during a four-month winter–spring test period, other models reveal a mismatch between forecast accuracy and control performance. Notably, the linear model, ranking mostly lowest in MSE, delivers the third-highest revenue (73%), nearly on par with the second best (79%). This illustrates that prediction accuracy alone is not a reliable proxy for control quality. Even the best realistic setup remains far from the ideal benchmark using perfect forecasts (235% gain). Daily retraining improves some models substantially (linear model to 105%) but has limited effect on others (XGBoost to 107%). These findings emphasize three key insights: (1) standard metrics like MSE may misrepresent the utility of forecasts for control, (2) errors across multiple inputs compound degradation in MPC, and (3) frequent retraining can mitigate losses. Overall, the results underscore the importance of robust forecasting and carefully chosen loss functions in the smart energy systems concept.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"20 ","pages":"Article 100202"},"PeriodicalIF":5.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048531","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 : 2025-08-01DOI: 10.1016/j.segy.2025.100203
Brian Vad Mathiesen, Nanna Finne Skovrup
{"title":"Editorial: Integrating innovations across sectors: Insights from SESAAU2020 towards a smart energy future","authors":"Brian Vad Mathiesen, Nanna Finne Skovrup","doi":"10.1016/j.segy.2025.100203","DOIUrl":"10.1016/j.segy.2025.100203","url":null,"abstract":"","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"19 ","pages":"Article 100203"},"PeriodicalIF":5.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104679","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 : 2025-08-01DOI: 10.1016/j.segy.2025.100195
Brian Vad Mathiesen, Nanna Finne Skovrup
{"title":"Editorial: Smart Energy Systems SESAAU2021","authors":"Brian Vad Mathiesen, Nanna Finne Skovrup","doi":"10.1016/j.segy.2025.100195","DOIUrl":"10.1016/j.segy.2025.100195","url":null,"abstract":"","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"19 ","pages":"Article 100195"},"PeriodicalIF":5.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104678","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 : 2025-08-01DOI: 10.1016/j.segy.2025.100199
M.N. Edoo, Robert T.F. Ah King
The urgency of climate change and the need to reduce dependence on expensive and polluting fossil fuels have prompted a transition to renewable energy (RE) in many countries. Mauritius, a small island developing state which relies heavily on imported fossil fuels faces such a challenge. This work presents a techno-economic study of a 100 % RE system incorporating the power, transport and manufacturing sectors of Mauritius in 2050. The novelty of this study lies in it being the first 100 % RE system study for Mauritius. Furthermore, its use of mature and commercially available technologies as opposed to more advanced ones renders it realistic from the perspective of a developing country with limited means. The simulations of key scenarios demonstrate that a 100 % RE system for Mauritius is technically feasible within reasonable costs. Solar photovoltaic (PV) and battery energy storage system (BESS) would form the backbone of the 100 % RE system due to their complementarity. It was also found that offshore wind is a valuable resource as it has high-capacity factor (46.4 %) but is also highly seasonal. The switch to a 100 % RE system entails an increase in the cost of final energy, +121 % versus cost in 2016 and + 11 % versus cost in 2022 for the PV-BESS scenario. The large difference between those two years is due to the high volatility of the cost of fossil fuels which the 100 % RE system would shield the country from. Finally, electric vehicles through smart charging and vehicle-to-grid can greatly reduce the cost of electricity.
{"title":"100% renewable energy system for the island of Mauritius by 2050: A techno-economic study","authors":"M.N. Edoo, Robert T.F. Ah King","doi":"10.1016/j.segy.2025.100199","DOIUrl":"10.1016/j.segy.2025.100199","url":null,"abstract":"<div><div>The urgency of climate change and the need to reduce dependence on expensive and polluting fossil fuels have prompted a transition to renewable energy (RE) in many countries. Mauritius, a small island developing state which relies heavily on imported fossil fuels faces such a challenge. This work presents a techno-economic study of a 100 % RE system incorporating the power, transport and manufacturing sectors of Mauritius in 2050. The novelty of this study lies in it being the first 100 % RE system study for Mauritius. Furthermore, its use of mature and commercially available technologies as opposed to more advanced ones renders it realistic from the perspective of a developing country with limited means. The simulations of key scenarios demonstrate that a 100 % RE system for Mauritius is technically feasible within reasonable costs. Solar photovoltaic (PV) and battery energy storage system (BESS) would form the backbone of the 100 % RE system due to their complementarity. It was also found that offshore wind is a valuable resource as it has high-capacity factor (46.4 %) but is also highly seasonal. The switch to a 100 % RE system entails an increase in the cost of final energy, +121 % versus cost in 2016 and + 11 % versus cost in 2022 for the PV-BESS scenario. The large difference between those two years is due to the high volatility of the cost of fossil fuels which the 100 % RE system would shield the country from. Finally, electric vehicles through smart charging and vehicle-to-grid can greatly reduce the cost of electricity.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"19 ","pages":"Article 100199"},"PeriodicalIF":5.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144813916","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 : 2025-08-01DOI: 10.1016/j.segy.2025.100196
Brian Vad Mathiesen, Nanna Finne Skovrup
{"title":"Editorial: A pillar of Sustainable Development – Insights from SDEWES 2020","authors":"Brian Vad Mathiesen, Nanna Finne Skovrup","doi":"10.1016/j.segy.2025.100196","DOIUrl":"10.1016/j.segy.2025.100196","url":null,"abstract":"","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"19 ","pages":"Article 100196"},"PeriodicalIF":5.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104680","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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