Smart Energy最新文献

{"title":"Fostering sustainable development of energy, water and environment through a smart energy framework","authors":"Vladimir Z. Gjorgievski,&nbsp;Natasa Markovska,&nbsp;Brian Vad Mathiesen,&nbsp;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}

{"title":"Predictive building energy management with user feedback in the loop","authors":"Valentin Kaisermayer ,&nbsp;Daniel Muschick ,&nbsp;Martin Horn ,&nbsp;Gerald Schweiger ,&nbsp;Thomas Schwengler ,&nbsp;Michael Mörth ,&nbsp;Richard Heimrath ,&nbsp;Thomas Mach ,&nbsp;Michael Herzlieb ,&nbsp;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}

{"title":"Optimal energy management in smart energy systems: A deep reinforcement learning approach and a digital twin case-study","authors":"Dhekra Bousnina ,&nbsp;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}

{"title":"Economic viability of decentralised battery storage systems for single-family buildings up to cross-building utilisation","authors":"Albert Hiesl,&nbsp;Jasmine Ramsebner,&nbsp;Reinhard Haas","doi":"10.1016/j.segy.2024.100160","DOIUrl":"10.1016/j.segy.2024.100160","url":null,"abstract":"&lt;div&gt;&lt;div&gt;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}

{"title":"The impact of offshore energy hub and hydrogen integration on the Faroe Island’s energy system","authors":"Elisabeth Andreae ,&nbsp;Marianne Petersen ,&nbsp;Iva Ridjan Skov ,&nbsp;Frederik Dahl Nielsen ,&nbsp;Shi You ,&nbsp;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}

{"title":"The cost of CO2 emissions abatement in a micro energy community in a Belgian context","authors":"Lucas Verleyen ,&nbsp;Javier Arroyo ,&nbsp;Lieve Helsen","doi":"10.1016/j.segy.2024.100162","DOIUrl":"10.1016/j.segy.2024.100162","url":null,"abstract":"<div><div>This paper investigates and quantifies the benefits and the cost of CO₂ emissions abatement of a Micro Energy Community (MEC) in a tiny residential cluster of three houses in a Belgian context. A simulation-based comparative analysis is performed of two individual and two MEC concepts, i.e. a reference scenario, an individual electrification scenario, an electricity sharing scenario, and an energy community scenario. A deterministic dynamic white-box modelling approach, including optimal control, is used, considering heat for space heating and domestic hot water, and electricity for electrical appliances. The energy system that achieves the greatest emission reduction at the lowest cost, has a collective heat pump and a photovoltaic installation sized based on the plug load demand. This system reduces the annual CO₂ emissions by 11.48 tons at a cost of 179 EUR/ton CO₂ considering 2021 dynamic retail electricity prices with a median of 251 EUR/MWh and a fixed retail gas price of 64 EUR/MWh. However, the results are strongly dependent on the gas-electricity price ratio. The highest value of the retail gas price in 2021 leads to a negative CO₂ emissions abatement cost of 154 EUR/ton CO₂, putting energy community concepts on the radar for a cost-effective energy transition.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"16 ","pages":"Article 100162"},"PeriodicalIF":5.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442026","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":"Fault detection for district heating substations: Beyond three-sigma approaches","authors":"Chris Hermans,&nbsp;Jad Al Koussa,&nbsp;Tijs Van Oevelen,&nbsp;Dirk Vanhoudt","doi":"10.1016/j.segy.2024.100159","DOIUrl":"10.1016/j.segy.2024.100159","url":null,"abstract":"<div><div>The topic of this paper is fault detection for district heating substations, which is an important enabler for the transition towards fourth-generation district heating systems. Classical fault detection approaches are often based on anomaly detection, commonly making the implicit assumption that the errors between the measurements and the predictions made by the baseline model are i.i.d. and following an underlying Gaussian distribution. Our analysis shows that this does not hold up in the field, showing clear seasonality in the error over time. We propose to replace the Gaussian error model by a quantile regression model in order to provide a more nuanced fault threshold, conditioned on time and other input variables. Additionally, we observed that properly training the baseline model comes with its own challenges due to this time dependency, which we propose to resolve by employing an ensemble of models, trained on different periods of time. We demonstrate our method on unlabelled operational data obtained from a Swedish district heating operator to illustrate its use in the field. In addition, we validate it on labelled data from our residential lab setup, testing a variety of common faults.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"16 ","pages":"Article 100159"},"PeriodicalIF":5.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421646","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":"Current and emerging waste-to-energy technologies: A comparative study with multi-criteria decision analysis","authors":"Shivaraj Chandrakant Patil ,&nbsp;Corinna Schulze-Netzer ,&nbsp;Magnus Korpås","doi":"10.1016/j.segy.2024.100157","DOIUrl":"10.1016/j.segy.2024.100157","url":null,"abstract":"<div><div>In response to the rise in waste crisis and the possibility of energy utilization from waste, there has been increasing interest in waste-to-energy (WtE) conversion technologies, which requires intense scientific attention. There are diverse WtE technologies that apply to different waste types and require multidisciplinary decision support. The paper applies a Multi-criteria Decision Analysis (MCDA) tool to compare their economic, technological, socio-cultural, and environmental aspects to help identify the most promising choice. The comparison used in this study concerns four widely used technologies: Incineration (INC), Anaerobic Digestion (AD), Gasification (GAS), and Pyrolysis (PYR), and one emerging WtE conversion technology, Hydro-thermal Carbonization (HTC). The Comparison criteria are divided into four main criteria and fifteen sub-criteria. The Analytical Hierarchy Process (AHP) model was implemented using ’SuperDecisions’ software to make pairwise comparisons of identified criteria and to rank the WtE technology alternatives. Thirty-two international studies were shortlisted to gather data and provide input into the AHP model. The results show that the environmental factors are prioritized with a priority vector of 0.56. Further, the study concludes that the most suitable WtE technology, based on chosen parameters, is AD, followed by HTC, INC, and PYR with the priority vectors of 0.348, 0.201, 0.162, and 0.148, respectively, provided applicability. The emerging technology, HTC, is found to be the second most suitable technology. Further, the results represent the hierarchy structure arranged so that the main components are divided into sub-components with alternatives at the structure’s base, and the ’SuperDecisions’ model based on this hierarchy can be used in the future to find suitable WtE technology for a specific city with the necessary input for identified main and sub-criteria. This research not only provides a structured comparison of WtE technologies but also offers a scalable AHP framework that can be adapted for specific municipal contexts in future studies. By addressing the diverse needs of decision-makers across different regions, our model contributes to a more nuanced understanding of WtE technology selection and lays the groundwork for incorporating local policies and regulations in subsequent research phases.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"16 ","pages":"Article 100157"},"PeriodicalIF":5.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666955224000273/pdfft?md5=d09ea75c7df06468cfc89c10a57bc0bd&pid=1-s2.0-S2666955224000273-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315407","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 effect of brief in-survey product experience on preferences for smart energy technologies","authors":"Stepan Vesely,&nbsp;Gloria Amaris,&nbsp;Christian A. Klöckner","doi":"10.1016/j.segy.2024.100155","DOIUrl":"10.1016/j.segy.2024.100155","url":null,"abstract":"<div><p>Survey research on the adoption of smart energy technologies is growing rapidly, generating important knowledge about factors on the consumer side that may help facilitate transition towards sustainable energy systems. However, much of this research uses survey measures to elicit consumer preferences without explicit consideration of whether and how in-survey experience with the technologies affects preference estimates. For this reason, we experimentally test (for the first time) whether brief in-survey product experience, mainly in the form of additional time spent deliberating about relevant products, influences stated consumer preferences for smart energy monitoring and management apps. Findings obtained in our first experiment conducted in the United Kingdom suggest modest effects of in-survey product experience on consumer preferences, where consumer preferences can be both strengthened or weakened depending on the type of in-survey product experience. These findings are, however, not replicated in our second experiment conducted in Spain. The Spanish experiment, nonetheless, suggests that brief in-survey product experience can help participants make more reasoned choices better reflecting their environmental concern and income constraints. Our results point to possible ways how to improve the reliability of stated preference surveys by providing respondents with adequate in-survey experience with unfamiliar products.</p></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"16 ","pages":"Article 100155"},"PeriodicalIF":5.4,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266695522400025X/pdfft?md5=13e902ec5ea4db2e6cd8085030831554&pid=1-s2.0-S266695522400025X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242720","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":"Presentation of a distributed testing infrastructure for joint experiments across multiple remote laboratories for robust development of new district heating concepts","authors":"Lilli Frison ,&nbsp;Urs Gumbel ,&nbsp;Simone Steiger ,&nbsp;Herbert Sinnesbichler ,&nbsp;Benedikt Ahrens ,&nbsp;Dennis Lottis ,&nbsp;Matthias Wecker ,&nbsp;Anna Marie Cadenbach","doi":"10.1016/j.segy.2024.100152","DOIUrl":"10.1016/j.segy.2024.100152","url":null,"abstract":"<div><p>4th-generation district heating networks confront numerous challenges such as integrating decentralized renewable energy sources, bidirectional heat transfer, new storage concepts, low-temperature operation, custom heat supply, data management, and advanced control strategies. Laboratory and hardware-in-the-loop testing offer a safe, cost-effective environment for testing and validating these innovations. This paper presents a framework for joint experiments in multiple remote laboratories, enhancing the testing of district heating system components. This distributed testbed enhances the efficiency of testing by utilizing existing equipment and expertise from various laboratories, thereby reducing costs and time and allowing for more scenarios to test. It targets manufacturers, grid operators, and research institutions, facilitating collaborative lab work for technology testing before field deployment. This approach allows for diverse test scenarios, considering component interactions across different locations without identical hardware or software. The framework's efficacy is shown in a proof-of-concept with a low-temperature district heating network integrated across four Fraunhofer Institutes. An initial experiment connects a test building and a ground-source heat pump physically existing in different labs with emulated models of a district heating network and a geothermal source. Results from a three-week operation validate the framework's performance.</p></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"15 ","pages":"Article 100152"},"PeriodicalIF":5.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666955224000224/pdfft?md5=61b738d44dc521f74a546417b15f3c25&pid=1-s2.0-S2666955224000224-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992700","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}