Pub Date : 2025-02-01DOI: 10.1016/j.segy.2025.100174
Upeksha Caldera , Andreas Mühlbauer , Mai ElSayed , Arman Aghahosseini , Christian Breyer
About 95% of Egypt is desert and 5% of the land is inhabited by more than 95% of the population. Aim of this research is to show how Egypt can make use of its plentiful renewable resources, available land area, and access to the sea, to establish cost-effective afforestation irrigated with renewable energy-based seawater desalination for land degradation mitigation. This carbon dioxide removal opportunity offers to sequester up to 0.37 GtCO2 annually at an average CO2 sequestration cost of 155 €/tCO2 by mid-century. By 2100, a total of 34 GtCO2 is estimated to be sequestered in an area of 132,500 km2. The CO2 sequestration costs decrease from 420 €/tCO2 in 2030, at the start of the project, to about 80 €/tCO2 by 2100. Regions with cooler climate and closer to the coastline, such as the North Western region of Egypt, offer the least cost CO2 sequestration with values as low as 40–50 €/tCO2 by 2070. The low cost of renewable electricity, especially solar photovoltaics, and the increasing sequestration rate of trees as they mature drive down costs. This research highlights how Egypt can use afforestation with renewable energy-based desalination to sequester CO2 while combatting land degradation and yielding economic benefits.
{"title":"Costs and benefits of afforestation with renewable electricity-based desalination: Case study for Egypt","authors":"Upeksha Caldera , Andreas Mühlbauer , Mai ElSayed , Arman Aghahosseini , Christian Breyer","doi":"10.1016/j.segy.2025.100174","DOIUrl":"10.1016/j.segy.2025.100174","url":null,"abstract":"<div><div>About 95% of Egypt is desert and 5% of the land is inhabited by more than 95% of the population. Aim of this research is to show how Egypt can make use of its plentiful renewable resources, available land area, and access to the sea, to establish cost-effective afforestation irrigated with renewable energy-based seawater desalination for land degradation mitigation. This carbon dioxide removal opportunity offers to sequester up to 0.37 GtCO<sub>2</sub> annually at an average CO<sub>2</sub> sequestration cost of 155 €/tCO<sub>2</sub> by mid-century. By 2100, a total of 34 GtCO<sub>2</sub> is estimated to be sequestered in an area of 132,500 km<sup>2</sup>. The CO<sub>2</sub> sequestration costs decrease from 420 €/tCO<sub>2</sub> in 2030, at the start of the project, to about 80 €/tCO<sub>2</sub> by 2100. Regions with cooler climate and closer to the coastline, such as the North Western region of Egypt, offer the least cost CO<sub>2</sub> sequestration with values as low as 40–50 €/tCO<sub>2</sub> by 2070. The low cost of renewable electricity, especially solar photovoltaics, and the increasing sequestration rate of trees as they mature drive down costs. This research highlights how Egypt can use afforestation with renewable energy-based desalination to sequester CO<sub>2</sub> while combatting land degradation and yielding economic benefits.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"17 ","pages":"Article 100174"},"PeriodicalIF":5.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154610","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}
Pub Date : 2025-02-01DOI: 10.1016/j.segy.2025.100173
Seyed Shahabaldin Tohidi, Henrik Madsen, Davide Calì, Tobias K.S. Ritschel
Renewable Energy Sources play a key role in smart energy systems. To achieve 100% renewable energy, utilizing the flexibility potential on the demand side becomes the cost-efficient option to balance the grid. However, it is not trivial to exploit these available capacities and flexibility options profitably. The amount of available flexibility is a complex and time-varying function of the price signal and weather forecasts. In this work, we use a Flexibility Function to represent the relationship between the price signal and the demand and investigate optimization problems for the price signal computation. Consequently, this study considers the higher and lower levels in the hierarchy from the markets to appliances, households, and districts. This paper investigates optimal price generation via the Flexibility Function and studies its employment in controller design for demand-side management, its capability to provide ancillary services for balancing throughout the Smart Energy Operating System, and its effect on the physical level performance. Sequential and simultaneous approaches for computing the price signal, along with various cost functions are analyzed and compared. Simulation results demonstrate the generated price/penalty signal and its employment in a model predictive controller.
{"title":"Optimal price signal generation for demand-side energy management","authors":"Seyed Shahabaldin Tohidi, Henrik Madsen, Davide Calì, Tobias K.S. Ritschel","doi":"10.1016/j.segy.2025.100173","DOIUrl":"10.1016/j.segy.2025.100173","url":null,"abstract":"<div><div>Renewable Energy Sources play a key role in smart energy systems. To achieve 100% renewable energy, utilizing the flexibility potential on the demand side becomes the cost-efficient option to balance the grid. However, it is not trivial to exploit these available capacities and flexibility options profitably. The amount of available flexibility is a complex and time-varying function of the price signal and weather forecasts. In this work, we use a Flexibility Function to represent the relationship between the price signal and the demand and investigate optimization problems for the price signal computation. Consequently, this study considers the higher and lower levels in the hierarchy from the markets to appliances, households, and districts. This paper investigates optimal price generation via the Flexibility Function and studies its employment in controller design for demand-side management, its capability to provide ancillary services for balancing throughout the Smart Energy Operating System, and its effect on the physical level performance. Sequential and simultaneous approaches for computing the price signal, along with various cost functions are analyzed and compared. Simulation results demonstrate the generated price/penalty signal and its employment in a model predictive controller.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"17 ","pages":"Article 100173"},"PeriodicalIF":5.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154607","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}
Pub Date : 2025-02-01DOI: 10.1016/j.segy.2024.100170
Maximilian Roth, Stephan Harmuth, Stephan Rinderknecht
Decentralized energy systems like microgrids can be a favourable alternative to the centralized organisation of the current energy supply system. An important problem regarding microgrids is the optimisation of their operational strategy, although most works assume time-independent behaviour of microgrid components in optimisation models. Therefore, the goal of this study is the integration of the dynamic behaviour of a Gas Stirling engine combined heat and power plant and an auxiliary gas boiler in the operational planning problem of a hybrid microgrid. A dynamic optimisation model is developed and compared to a static and approximated model based on the results of three different test scenarios. The simulation results show that the integration of dynamic component characteristics has a significant impact on the operational strategy. The resulting setpoints for the control of the components are able to consider the transient behaviour of these components and therefore their real behaviour is more accurately represented within the optimisation problem. However, the integration of the dynamic modelling approaches leads to more difficult optimisation problems which require more computational effort. The approximation to a convex quadratic problem represents a good compromise between computation time and setpoint accuracy.
{"title":"Integration of dynamic CHPP and gas boiler behaviour into the convex planning problem for the optimised operation of multimodal microgrids","authors":"Maximilian Roth, Stephan Harmuth, Stephan Rinderknecht","doi":"10.1016/j.segy.2024.100170","DOIUrl":"10.1016/j.segy.2024.100170","url":null,"abstract":"<div><div>Decentralized energy systems like microgrids can be a favourable alternative to the centralized organisation of the current energy supply system. An important problem regarding microgrids is the optimisation of their operational strategy, although most works assume time-independent behaviour of microgrid components in optimisation models. Therefore, the goal of this study is the integration of the dynamic behaviour of a Gas Stirling engine combined heat and power plant and an auxiliary gas boiler in the operational planning problem of a hybrid microgrid. A dynamic optimisation model is developed and compared to a static and approximated model based on the results of three different test scenarios. The simulation results show that the integration of dynamic component characteristics has a significant impact on the operational strategy. The resulting setpoints for the control of the components are able to consider the transient behaviour of these components and therefore their real behaviour is more accurately represented within the optimisation problem. However, the integration of the dynamic modelling approaches leads to more difficult optimisation problems which require more computational effort. The approximation to a convex quadratic problem represents a good compromise between computation time and setpoint accuracy.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"17 ","pages":"Article 100170"},"PeriodicalIF":5.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155048","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}
Pub Date : 2024-11-19DOI: 10.1016/j.segy.2024.100165
Luigi Moccia
We present an optimization model of an energy district in South Italy that supplies baseload electricity and hydrogen services. The district is sized such that a nuclear reactor could provide these services. We define scenarios for 2050 to explore the system effects of discount rate sensitivity, vetoes on technologies, and cost uncertainties. We address the following issues relevant to decarbonization in South Italy: land-based wind and solar vs. exclusive solar rooftop, extra cost of a veto on nuclear, conservative assumptions on future storage technology and the role of pumped hydro storage, lack of low-cost geological storage of hydrogen and the industrial competitiveness of this carrier, and the methanation synergy with the agroforestry sector. Our results quantify the high system cost of vetoes on land-based wind and solar. Nuclear may enter the optimal mix only with a veto against onshore wind and a hypothesis of equal project risk, hence an equal discount rate, with renewables. Scenarios with land-based wind and solar obtain low-cost hydrogen and thus allow industrial uses for this carrier. The methanation synergy with the agroforestry sector does not offer a system cost advantage but improves the district’s configuration. The extra cost of full decarbonization relative to unregulated fossil gas is small with land-based wind and solar, and significant with vetoes to these technologies.
{"title":"Optimization of baseload electricity and hydrogen services by renewables for a nuclear-sized district in South Italy","authors":"Luigi Moccia","doi":"10.1016/j.segy.2024.100165","DOIUrl":"10.1016/j.segy.2024.100165","url":null,"abstract":"<div><div>We present an optimization model of an energy district in South Italy that supplies baseload electricity and hydrogen services. The district is sized such that a nuclear reactor could provide these services. We define scenarios for 2050 to explore the system effects of discount rate sensitivity, vetoes on technologies, and cost uncertainties. We address the following issues relevant to decarbonization in South Italy: land-based wind and solar <em>vs.</em> exclusive solar rooftop, extra cost of a veto on nuclear, conservative assumptions on future storage technology and the role of pumped hydro storage, lack of low-cost geological storage of hydrogen and the industrial competitiveness of this carrier, and the methanation synergy with the agroforestry sector. Our results quantify the high system cost of vetoes on land-based wind and solar. Nuclear may enter the optimal mix only with a veto against onshore wind and a hypothesis of equal project risk, hence an equal discount rate, with renewables. Scenarios with land-based wind and solar obtain low-cost hydrogen and thus allow industrial uses for this carrier. The methanation synergy with the agroforestry sector does not offer a system cost advantage but improves the district’s configuration. The extra cost of full decarbonization relative to unregulated fossil gas is small with land-based wind and solar, and significant with vetoes to these technologies.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"17 ","pages":"Article 100165"},"PeriodicalIF":5.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706105","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}
Pub Date : 2024-11-13DOI: 10.1016/j.segy.2024.100166
K. Lepiksaar , G.-M. Kajandi , S. Sukumaran , I. Krupenski , T. Kirs , A. Volkova
Using solar energy is one way to integrate sustainable, clean and non-combustional energy to energy mix. In electricity sector, the share of solar energy has significantly grown over the last decade because of increased public awareness, declining costs and government incentives. However, the adoption of solar energy in heating and cooling sector is relatively new. There is a visible relation between solar energy production curves and cooling energy consumption curves which indicates that using solar energy in cooling sector would be efficient way to use solar energy. Still, the utilisation of solar heat for district cooling remains a grey area. In district heating and cooling sector, the use of solar energy in Estonia has been modest so far, although there is a significant solar energy potential. Hence, Tallinn district heating and cooling system has been chosen as a case study to investigate how solar energy can be used most beneficially and efficiently. In this regard, three main integration scenarios with respect to the different technical configurations and energy transformations are analysed. It was observed that the proposed solar park could generate 27.58 GWh thermal energy per annum. The share of useful solar energy (or solar fraction) reached more than 98.5 %, when TES integration is considered. From the analysed scenarios, it can be concluded that integration of TES is highly important to tap solar heat to the fullest. The seasonal match between load and generation contributed to higher share of solar energy for district cooling than district heating. This study is expected to be useful reference material for project developers, investors and policy makers.
{"title":"Optimizing solar energy integration in Tallinn's district heating and cooling systems","authors":"K. Lepiksaar , G.-M. Kajandi , S. Sukumaran , I. Krupenski , T. Kirs , A. Volkova","doi":"10.1016/j.segy.2024.100166","DOIUrl":"10.1016/j.segy.2024.100166","url":null,"abstract":"<div><div>Using solar energy is one way to integrate sustainable, clean and non-combustional energy to energy mix. In electricity sector, the share of solar energy has significantly grown over the last decade because of increased public awareness, declining costs and government incentives. However, the adoption of solar energy in heating and cooling sector is relatively new. There is a visible relation between solar energy production curves and cooling energy consumption curves which indicates that using solar energy in cooling sector would be efficient way to use solar energy. Still, the utilisation of solar heat for district cooling remains a grey area. In district heating and cooling sector, the use of solar energy in Estonia has been modest so far, although there is a significant solar energy potential. Hence, Tallinn district heating and cooling system has been chosen as a case study to investigate how solar energy can be used most beneficially and efficiently. In this regard, three main integration scenarios with respect to the different technical configurations and energy transformations are analysed. It was observed that the proposed solar park could generate 27.58 GWh thermal energy per annum. The share of useful solar energy (or solar fraction) reached more than 98.5 %, when TES integration is considered. From the analysed scenarios, it can be concluded that integration of TES is highly important to tap solar heat to the fullest. The seasonal match between load and generation contributed to higher share of solar energy for district cooling than district heating. This study is expected to be useful reference material for project developers, investors and policy makers.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"17 ","pages":"Article 100166"},"PeriodicalIF":5.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706104","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}
Pub Date : 2024-11-01DOI: 10.1016/j.segy.2024.100167
Vladimir Z. Gjorgievski, Natasa Markovska, Brian Vad Mathiesen, Neven Duić
As a significant challenge to sustainable development, climate changes require prompt and coordinated action based on a holistic approach for decarbonizing the energy system. In this framework, accounting for the sectoral interdependencies of the energy system, and their interactions with water and environmental systems is essential. The 18th SDEWES Conference in Dubrovnik, held in September 2023, served as a platform that offers experts the opportunity to exchange ideas on state-of-the-art research on the topic. This special issue of Smart Energy highlights peer-reviewed papers from the conference, covering diverse topics such as the energy-water nexus, innovative funding models for district heating, planning of thermal energy storage, and machine learning-based monitoring for HVAC appliances. These contributions highlight the importance of pursuing an integrated analysis of energy systems and provide valuable insights relevant to spearheading the energy transition.
{"title":"Fostering sustainable development of energy, water and environment through a smart energy framework","authors":"Vladimir Z. Gjorgievski, Natasa Markovska, Brian Vad Mathiesen, Neven Duić","doi":"10.1016/j.segy.2024.100167","DOIUrl":"10.1016/j.segy.2024.100167","url":null,"abstract":"<div><div>As a significant challenge to sustainable development, climate changes require prompt and coordinated action based on a holistic approach for decarbonizing the energy system. In this framework, accounting for the sectoral interdependencies of the energy system, and their interactions with water and environmental systems is essential. The 18th SDEWES Conference in Dubrovnik, held in September 2023, served as a platform that offers experts the opportunity to exchange ideas on state-of-the-art research on the topic. This special issue of Smart Energy highlights peer-reviewed papers from the conference, covering diverse topics such as the energy-water nexus, innovative funding models for district heating, planning of thermal energy storage, and machine learning-based monitoring for HVAC appliances. These contributions highlight the importance of pursuing an integrated analysis of energy systems and provide valuable insights relevant to spearheading the energy transition.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"16 ","pages":"Article 100167"},"PeriodicalIF":5.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167927","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}
Pub Date : 2024-11-01DOI: 10.1016/j.segy.2024.100164
Valentin Kaisermayer , Daniel Muschick , Martin Horn , Gerald Schweiger , Thomas Schwengler , Michael Mörth , Richard Heimrath , Thomas Mach , Michael Herzlieb , Markus Gölles
Retrofitting buildings with predictive control strategies can reduce their energy demand and improve thermal comfort by considering their thermal inertia and future weather conditions. A key challenge is minimizing additional infrastructure, such as sensors and actuators, while ensuring user comfort at all times. This study focuses on retrofitting with intelligent software, incorporating the users’ feedback directly into the control loop. We propose a predictive control strategy using an optimization-based energy management system (EMS) to control thermal zones in an office building. It uses a physically motivated grey-box model to predict and adjust thermal demand, with individual zones modelled using an RC-approach and parameter estimation handled by an unscented Kalman filter (UKF). This reduces deployment effort as the parameters are learned from historical data. The objective function ensures user comfort, penalizes undesirable behaviour and minimizes heating and cooling costs. An internal comfort model, automatically calibrated with user feedback by another UKF, further improves system performance. The practical case study is an office building at the ”Innovation District Inffeld”. Operation of the system for one year yielded significant results compared to conventional control. Thermal comfort was improved by 12% and thermal energy consumption for heating and cooling was reduced by about 35%.
{"title":"Predictive building energy management with user feedback in the loop","authors":"Valentin Kaisermayer , Daniel Muschick , Martin Horn , Gerald Schweiger , Thomas Schwengler , Michael Mörth , Richard Heimrath , Thomas Mach , Michael Herzlieb , Markus Gölles","doi":"10.1016/j.segy.2024.100164","DOIUrl":"10.1016/j.segy.2024.100164","url":null,"abstract":"<div><div>Retrofitting buildings with predictive control strategies can reduce their energy demand and improve thermal comfort by considering their thermal inertia and future weather conditions. A key challenge is minimizing additional infrastructure, such as sensors and actuators, while ensuring user comfort at all times. This study focuses on retrofitting with intelligent software, incorporating the users’ feedback directly into the control loop. We propose a predictive control strategy using an optimization-based energy management system (EMS) to control thermal zones in an office building. It uses a physically motivated grey-box model to predict and adjust thermal demand, with individual zones modelled using an RC-approach and parameter estimation handled by an unscented Kalman filter (UKF). This reduces deployment effort as the parameters are learned from historical data. The objective function ensures user comfort, penalizes undesirable behaviour and minimizes heating and cooling costs. An internal comfort model, automatically calibrated with user feedback by another UKF, further improves system performance. The practical case study is an office building at the ”Innovation District Inffeld”. Operation of the system for one year yielded significant results compared to conventional control. Thermal comfort was improved by 12% and thermal energy consumption for heating and cooling was reduced by about 35%.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"16 ","pages":"Article 100164"},"PeriodicalIF":5.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552977","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}
Pub Date : 2024-10-16DOI: 10.1016/j.segy.2024.100163
Dhekra Bousnina , Gilles Guerassimoff
This research work introduces a novel approach to energy management in Smart Energy Systems (SES) using Deep Reinforcement Learning (DRL) to optimize the management of flexible energy systems in SES, including heating, cooling and electricity storage systems along with District Heating and Cooling Systems (DHCS). The proposed approach is applied on Meridia Smart Energy (MSE), a french demonstration project for SES. The proposed DRL framework, based on actor–critic architecture, is first applied on a Modelica digital twin that we developed for the MSE SES, and is benchmarked against a rule-based approach. The DRL agent learnt an effective strategy for managing thermal and electrical storage systems, resulting in optimized energy costs within the SES. Notably, the acquired strategy achieved annual cost reduction of at least 5% compared to the rule-based benchmark strategy. Moreover, the near-real time decision-making capabilities of the trained DRL agent provides a significant advantage over traditional optimization methods that require time-consuming re-computation at each decision point. By training the DRL agent on a digital twin of the real-world MSE project, rather than hypothetical simulation models, this study lays the foundation for a pioneering application of DRL in the real-world MSE SES, showcasing its potential for practical implementation.
这项研究工作介绍了一种新颖的智能能源系统（SES）能源管理方法，利用深度强化学习（DRL）优化智能能源系统中灵活能源系统的管理，包括供热、制冷和电力存储系统以及区域供热和制冷系统（DHCS）。所提出的方法适用于法国的 SES 示范项目 Meridia Smart Energy (MSE)。提议的 DRL 框架基于行为批判架构，首先应用于我们为 MSE SES 开发的 Modelica 数字孪生系统，并以基于规则的方法为基准。DRL 代理学习了管理热能和电力存储系统的有效策略，从而优化了 SES 的能源成本。值得注意的是，与基于规则的基准策略相比，所获得的策略实现了每年至少 5% 的成本降低。此外，与需要在每个决策点进行耗时的重新计算的传统优化方法相比，训练有素的 DRL 代理的近实时决策能力具有显著优势。通过在现实世界 MSE 项目的数字孪生而非假设的仿真模型上训练 DRL 代理，本研究为 DRL 在现实世界 MSE SES 中的开创性应用奠定了基础，展示了其实际应用的潜力。
{"title":"Optimal energy management in smart energy systems: A deep reinforcement learning approach and a digital twin case-study","authors":"Dhekra Bousnina , Gilles Guerassimoff","doi":"10.1016/j.segy.2024.100163","DOIUrl":"10.1016/j.segy.2024.100163","url":null,"abstract":"<div><div>This research work introduces a novel approach to energy management in Smart Energy Systems (SES) using Deep Reinforcement Learning (DRL) to optimize the management of flexible energy systems in SES, including heating, cooling and electricity storage systems along with District Heating and Cooling Systems (DHCS). The proposed approach is applied on Meridia Smart Energy (MSE), a french demonstration project for SES. The proposed DRL framework, based on actor–critic architecture, is first applied on a Modelica digital twin that we developed for the MSE SES, and is benchmarked against a rule-based approach. The DRL agent learnt an effective strategy for managing thermal and electrical storage systems, resulting in optimized energy costs within the SES. Notably, the acquired strategy achieved annual cost reduction of at least 5% compared to the rule-based benchmark strategy. Moreover, the near-real time decision-making capabilities of the trained DRL agent provides a significant advantage over traditional optimization methods that require time-consuming re-computation at each decision point. By training the DRL agent on a digital twin of the real-world MSE project, rather than hypothetical simulation models, this study lays the foundation for a pioneering application of DRL in the real-world MSE SES, showcasing its potential for practical implementation.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"16 ","pages":"Article 100163"},"PeriodicalIF":5.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525881","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}
Pub Date : 2024-10-09DOI: 10.1016/j.segy.2024.100160
Albert Hiesl, Jasmine Ramsebner, Reinhard Haas
Due to the great cost decrease of photovoltaics as well as battery storage, especially in the segment of decentralised home storage, the number of grid-connected battery-supported photovoltaic systems being installed in recent years is steadily increasing. However, the scientific community has intensively discussed that lithium-based battery storage systems cannot yet be operated economically in most cases. This paper addresses the level to which the cost of lithium battery storage needs to decrease in order to be economically viable. For this purpose, the economic viability of battery storage systems in single-family buildings, multi-apartment buildings and across-buildings is analysed on the basis of a linear optimisation model and the method of the internal rate of return. The utilisation of the storage system is optimised for different battery and photovoltaic capacities on the basis of generation and consumption. The internal rate of return method is used to compare the savings resulting from the reduced consumption from the electricity grid with the investment costs and the operation and maintenance costs. In order to be able to estimate the influence of the most important parameters a sensitivity analysis is also carried out. The analysis concludes that, depending on the combination of capacities of photovoltaics, battery storage and in relation to the load profile, the battery storage costs would have to drop by at least 85% in order to generate a certain predefined return over a depreciation period of 25 years. Furthermore, the more different load profiles can be covered directly with photovoltaic electricity, e.g. in a multi-apartment building or across buildings, the less electricity needs to be stored and this reduces the benefit and the utilisation of the battery storage and therefore the specific investment costs must further decrease. Another conclusion that emerges from the sensitivity analysis is that the electricity price and the spread between the electricity price and the feed-in tariff have the greatest influence on the investment costs and profitability. Due to limited space for photovoltaics and simultaneously high consumption, self-consumption is already quite high with cross-building utilisation and can no longer be increased to the necessary extent by the battery storage system, which is why the investment costs must also be lower. The novelty of this paper lies in particular in the fact that it deals with the target costs of battery storage systems in various scenarios for certain rates of return. The analyses in this paper are intended to provide a deeper understanding of the framework conditions for the economic operation of a battery storage system in the aforementioned scenarios. However, this paper does not take into account alternative sources of income other than savings on grid consumption. The possibility of time-variable (grid) tariffs, for example, is also not considered in detail in this paper and
{"title":"Economic viability of decentralised battery storage systems for single-family buildings up to cross-building utilisation","authors":"Albert Hiesl, Jasmine Ramsebner, Reinhard Haas","doi":"10.1016/j.segy.2024.100160","DOIUrl":"10.1016/j.segy.2024.100160","url":null,"abstract":"<div><div>Due to the great cost decrease of photovoltaics as well as battery storage, especially in the segment of decentralised home storage, the number of grid-connected battery-supported photovoltaic systems being installed in recent years is steadily increasing. However, the scientific community has intensively discussed that lithium-based battery storage systems cannot yet be operated economically in most cases. This paper addresses the level to which the cost of lithium battery storage needs to decrease in order to be economically viable. For this purpose, the economic viability of battery storage systems in single-family buildings, multi-apartment buildings and across-buildings is analysed on the basis of a linear optimisation model and the method of the internal rate of return. The utilisation of the storage system is optimised for different battery and photovoltaic capacities on the basis of generation and consumption. The internal rate of return method is used to compare the savings resulting from the reduced consumption from the electricity grid with the investment costs and the operation and maintenance costs. In order to be able to estimate the influence of the most important parameters a sensitivity analysis is also carried out. The analysis concludes that, depending on the combination of capacities of photovoltaics, battery storage and in relation to the load profile, the battery storage costs would have to drop by at least 85% in order to generate a certain predefined return over a depreciation period of 25 years. Furthermore, the more different load profiles can be covered directly with photovoltaic electricity, e.g. in a multi-apartment building or across buildings, the less electricity needs to be stored and this reduces the benefit and the utilisation of the battery storage and therefore the specific investment costs must further decrease. Another conclusion that emerges from the sensitivity analysis is that the electricity price and the spread between the electricity price and the feed-in tariff have the greatest influence on the investment costs and profitability. Due to limited space for photovoltaics and simultaneously high consumption, self-consumption is already quite high with cross-building utilisation and can no longer be increased to the necessary extent by the battery storage system, which is why the investment costs must also be lower. The novelty of this paper lies in particular in the fact that it deals with the target costs of battery storage systems in various scenarios for certain rates of return. The analyses in this paper are intended to provide a deeper understanding of the framework conditions for the economic operation of a battery storage system in the aforementioned scenarios. However, this paper does not take into account alternative sources of income other than savings on grid consumption. The possibility of time-variable (grid) tariffs, for example, is also not considered in detail in this paper and ","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"16 ","pages":"Article 100160"},"PeriodicalIF":5.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421645","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}
Pub Date : 2024-10-05DOI: 10.1016/j.segy.2024.100161
Elisabeth Andreae , Marianne Petersen , Iva Ridjan Skov , Frederik Dahl Nielsen , Shi You , Henrik W. Bindner
This study explores the integration of offshore wind energy and hydrogen production into the Faroe Islands’ energy system to support decarbonisation efforts, particularly focusing on the maritime sector. The EnergyPLAN model is used to simulate the impact of incorporating green hydrogen, produced via electrolysis, within a closed energy system. The study evaluates different configurations of hydrogen production and their feasibility focusing on electrolyser technologies and placement options (in-turbine, platform-based, and shoreline). The hydrogen produced is intended for ammonia production, replacing 11% of the fossil fuels used in maritime transport by 2030. Results indicate that integrating hydrogen with offshore wind energy can reduce fossil fuel reliance and carbon dioxide emissions. The in-turbine electrolyser setup offers the cost-effective placement option, while the platform setup is the most expensive. Among the three electrolyser technologies evaluated (alkaline, solid oxide and proton exchange membrane), the alkaline electrolyser results in the lowest overall system cost. The findings provide insights into the potential for renewable energy systems in a small island context and contribute to a broader understanding of green hydrogen’s role in energy transitions.
{"title":"The impact of offshore energy hub and hydrogen integration on the Faroe Island’s energy system","authors":"Elisabeth Andreae , Marianne Petersen , Iva Ridjan Skov , Frederik Dahl Nielsen , Shi You , Henrik W. Bindner","doi":"10.1016/j.segy.2024.100161","DOIUrl":"10.1016/j.segy.2024.100161","url":null,"abstract":"<div><div>This study explores the integration of offshore wind energy and hydrogen production into the Faroe Islands’ energy system to support decarbonisation efforts, particularly focusing on the maritime sector. The EnergyPLAN model is used to simulate the impact of incorporating green hydrogen, produced via electrolysis, within a closed energy system. The study evaluates different configurations of hydrogen production and their feasibility focusing on electrolyser technologies and placement options (in-turbine, platform-based, and shoreline). The hydrogen produced is intended for ammonia production, replacing 11% of the fossil fuels used in maritime transport by 2030. Results indicate that integrating hydrogen with offshore wind energy can reduce fossil fuel reliance and carbon dioxide emissions. The in-turbine electrolyser setup offers the cost-effective placement option, while the platform setup is the most expensive. Among the three electrolyser technologies evaluated (alkaline, solid oxide and proton exchange membrane), the alkaline electrolyser results in the lowest overall system cost. The findings provide insights into the potential for renewable energy systems in a small island context and contribute to a broader understanding of green hydrogen’s role in energy transitions.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"16 ","pages":"Article 100161"},"PeriodicalIF":5.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421647","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}
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