Smart Energy最新文献

{"title":"Predictive building energy management with user feedback in the loop","authors":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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}

{"title":"Analyzing the flexibility potential of bus fleet operators in Germany","authors":"","doi":"10.1016/j.segy.2024.100153","DOIUrl":"10.1016/j.segy.2024.100153","url":null,"abstract":"<div><p>The transition to smart energy systems is a crucial component for ensuring sustainability and reducing carbon emissions. Electrification is a key factor in achieving these goals, with the transport sector being an integral part of the equation. The integration of the transport sector with the electricity sector will facilitate a reduction in carbon emissions. This paper assesses the potential of electric bus depots to function as smart energy infrastructures. Analyzing the energetic system flexibility of the electrified public transport system is at the core. Previous studies emphasize the importance of identifying and managing the optimal operation strategies of electrified transport to achieve system flexibility. This work concentrates on Germany as a reference market for balancing and electricity markets at the center of the EU. The flexibility potential of a bus fleet with 80 electric buses is analyzed under optimal participation in the short-term electricity and balancing market. The bus fleet operator acts as a storage systems aggregator, which combines mobile and stationary storages to enhance energy flexibility. The study measures the potential contribution for the stability of the electricity grid in Germany. The additional battery degradation that arises with the provision of balancing services is part of the economic equation. The analysis is based on historical data from 2020, 2021, and 2022 and investigates hypothetically lower and higher demand for balancing energy in the load-frequency control area of Germany and Denmark. The paper concludes by demonstrating the feasibility of the electrified bus depot as an integral component of smart energy systems. These findings contribute to a better understanding of the electrification of transport, sector integration, and the role of infrastructures in achieving smart energy systems and showcases the attractiveness of this business model.</p></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666955224000236/pdfft?md5=5cca1fe3b5a579bd9f8678e43e379289&pid=1-s2.0-S2666955224000236-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006301","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}