Advances in Applied Energy最新文献

{"title":"Reviewing the complexity of endogenous technological learning for energy system modeling","authors":"Johannes Behrens ,&nbsp;Elisabeth Zeyen ,&nbsp;Maximilian Hoffmann ,&nbsp;Detlef Stolten ,&nbsp;Jann M. Weinand","doi":"10.1016/j.adapen.2024.100192","DOIUrl":"10.1016/j.adapen.2024.100192","url":null,"abstract":"<div><div>Energy system components like renewable energy technologies or electrolyzers are subject to decreasing investment costs driven by technological progress. Various methods have been developed in the literature to capture model-endogenous technological learning. This review demonstrates the non-linear relationship between investment costs and production volume, resulting in non-convex optimization problems and discuss concepts to account for technological progress. While iterative solution methods tend to find future energy system designs that rely on suboptimal technology mixes, exact solutions leading to global optimality are computationally demanding. Most studies omit important system aspects such as sector integration, or a detailed spatial, temporal, and technological resolution to maintain model solvability, which likewise distorts the impact of technological learning. This can be improved by the application of methods such as temporal or spatial aggregation, decomposition methods, or the clustering of technologies. This review reveals the potential of those methods and points out important considerations for integrating endogenous technological learning. We propose a more integrated approach to handle computational complexity when integrating technological learning, that aims to preserve the model's feasibility. Furthermore, we identify significant gaps in current modeling practices and suggest future research directions to enhance the accuracy and utility of energy system models.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100192"},"PeriodicalIF":13.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586498","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":"Toward global rooftop PV detection with Deep Active Learning","authors":"Matthias Zech ,&nbsp;Hendrik-Pieter Tetens ,&nbsp;Joseph Ranalli","doi":"10.1016/j.adapen.2024.100191","DOIUrl":"10.1016/j.adapen.2024.100191","url":null,"abstract":"<div><div>It is crucial to know the location of rooftop PV systems to monitor the regional progress toward sustainable societies and to ensure the integration of decentralized energy resources into the electricity grid. However, locations of PV are often unknown, which is why a large number of studies have proposed variants of Deep Learning to detect PV panels in remote sensing data using supervised Deep Learning. However, these methods are based on annotating datasets and therefore often require relabeling when fine-tuned or extended to a different region. Recent advances in Deep Active Learning offer the opportunity to significantly reduce the number of required annotated images by intelligently selecting the images to label next based on their informative value for the model. In this study, we compare different Deep Active Learning algorithms using a variety of datasets from different regions and compare different model training variants. In the simulations, the entropy-based acquisition function shows the highest performance with only 3% of the data needed in case-imbalanced data, while remaining simple to implement. We believe that Deep Active Learning provides an elegant solution to maintain high model accuracy while reducing annotation effort substantially. This facilitates the development of generalizable models for worldwide rooftop PV detection.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100191"},"PeriodicalIF":13.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of mixed-integer linear formulations for framework-based energy system models","authors":"Maximilian Hoffmann ,&nbsp;Bruno U. Schyska ,&nbsp;Julian Bartels ,&nbsp;Tristan Pelser ,&nbsp;Johannes Behrens ,&nbsp;Manuel Wetzel ,&nbsp;Hans Christian Gils ,&nbsp;Chuen-Fung Tang ,&nbsp;Marius Tillmanns ,&nbsp;Jan Stock ,&nbsp;André Xhonneux ,&nbsp;Leander Kotzur ,&nbsp;Aaron Praktiknjo ,&nbsp;Thomas Vogt ,&nbsp;Patrick Jochem ,&nbsp;Jochen Linßen ,&nbsp;Jann M. Weinand ,&nbsp;Detlef Stolten","doi":"10.1016/j.adapen.2024.100190","DOIUrl":"10.1016/j.adapen.2024.100190","url":null,"abstract":"<div><div>Optimization-based frameworks for energy system modeling such as TIMES, ETHOS.FINE, or PyPSA have emerged as important tools to outline a cost-efficient energy transition. Consequently, numerous reviews have compared the capabilities and application cases of established energy system optimization frameworks with respect to their model features or adaptability but widely neglect the frameworks’ underlying mathematical structure. This limits their added value for users who not only want to use models but also program them themselves.</div><div>To address this issue, we follow a hybrid approach by not only reviewing 63 optimization-based frameworks for energy system modeling with a focus on their mathematical implementation but also conducting a meta-review of 68 existing literature reviews.</div><div>Our work reveals that the basic concept of network-based energy flow optimization has remained the same since the earliest publications in the 1970s. Thereby, the number of open-source available optimization frameworks for energy system modeling has more than doubled in the last ten years, mainly driven by the uptake of energy transition and progress in computer-aided optimization.</div><div>To go beyond a qualitative discussion, we also define the mathematical formulation for a mixed-integer optimization model comprising all the model features discussed in this work. We thereby aim to facilitate the implementation of future object-oriented frameworks and to increase the comprehensibility of existing ones for energy system modelers.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100190"},"PeriodicalIF":13.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419110","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":"Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data","authors":"Kosaku Nakano ,&nbsp;Sophia Vögler ,&nbsp;Kenji Tanaka","doi":"10.1016/j.adapen.2024.100188","DOIUrl":"10.1016/j.adapen.2024.100188","url":null,"abstract":"<div><p>The widespread adoption of electric vehicles (EVs) underscores the urgent need for innovative approaches to estimate their lithium-ion batteries’ state of health (SOH), which is crucial for ensuring safety and efficiency. This study introduces SOH-TEC, a transformer encoder-based model that processes raw time-series battery and vehicle-related data from a single EV trip to estimate the SOH. Unlike conventional methods that rely on lab-experimented battery cycle data, SOH-TEC utilizes real-world EV operation data, enhancing practical application. The model is trained and evaluated on a real-world dataset collected over nearly three years from three EVs. This dataset includes reliable SOH labels obtained through periodic constant-current full-discharge tests using a chassis dynamometer. Despite the challenges posed by noisy EV real-world data, the model shows high accuracy, with a mean absolute error of 0.72% and a root mean square error of 1.17%. Moreover, our proposed pre-training strategies with unlabeled data, particularly SOH ordinal comparison, significantly enhance the model’s performance; using only 50% of the labeled data achieves results nearly identical to those obtained with the full dataset. Self-attention map analysis reveals that the model primarily focuses on stationary or consistent driving periods to estimate SOH. While the study is constrained by a dataset featuring repetitive driving patterns, it highlights the significant potential of transformer for SOH estimation in EVs and offers valuable insights for future data collection and model development.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100188"},"PeriodicalIF":13.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266679242400026X/pdfft?md5=d77f0e82e70762f99f4b385e9253c7d3&pid=1-s2.0-S266679242400026X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164649","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":"Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling","authors":"Ao Li ,&nbsp;Fu Xiao ,&nbsp;Ziwei Xiao ,&nbsp;Rui Yan ,&nbsp;Anbang Li ,&nbsp;Yan Lv ,&nbsp;Bing Su","doi":"10.1016/j.adapen.2024.100189","DOIUrl":"10.1016/j.adapen.2024.100189","url":null,"abstract":"<div><p>Machine learning is widely recognized as a promising data-driven modeling technique for the model-based control and optimization of building energy systems. However, the generalizability of data-driven models often faces significant challenges, as the available training data from building operations usually only covers a limited range of working conditions. Active learning can proactively test unseen and informative working conditions to enrich the training set by adding new data samples, leading to improved generalization performance of data-driven models. A novel distance and information density-based sample strategy is developed that accounts for the real-time status of building operation and outdoor environment. Based on Mahalanobis distance, this strategy determines the sampling value of an unlabeled sample (unseen working condition) by assessing its similarity to both the training samples and other unlabeled samples. As collecting sufficiently representative samples can be difficult, costly, and time-consuming, a distance-based sampling cost metric is proposed to compare the efficiency of different sampling methods, considering the detrimental effects of the actively sampling process on the normal operation of building energy systems. This paper presents a comprehensive and in-depth comparison of five active learning methods, including one incorporating the distance-based sampling strategy, by conducting data experiments on the data collected from the cooling towers of a real high-rise building. The results show that active learning can effectively identify informative data samples and improve the generalization performance of data-driven models. The research outcomes are valuable for enhancing AI-enabled data-driven modeling of building energy systems with substantial decreases in costs on data sampling.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100189"},"PeriodicalIF":13.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000271/pdfft?md5=0f782334db1159003891c4cf1c77159d&pid=1-s2.0-S2666792424000271-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A probabilistic model for real-time quantification of building energy flexibility","authors":"Binglong Han ,&nbsp;Hangxin Li ,&nbsp;Shengwei Wang","doi":"10.1016/j.adapen.2024.100186","DOIUrl":"10.1016/j.adapen.2024.100186","url":null,"abstract":"<div><p>Buildings have great energy flexibility potential to manage supply-demand imbalance in power grids with high renewable penetration. Accurate and real-time quantification of building energy flexibility is essential not only for engaging buildings in electricity and grid service markets, but also for ensuring the reliable and optimal operation of power grids. This paper proposes a probabilistic model for rapidly quantifying the aggregated flexibility of buildings under uncertainties. An explicit equation is derived as the analytical solution of a commonly used second-order building thermodynamic model to quantify the flexibility of individual buildings, eliminating the need of time-consuming iterative and finite difference computations. A sampling-based uncertainty analysis is performed to obtain the distribution of aggregated building flexibility, considering major uncertainties comprehensively. Validation tests are conducted using 150 commercial buildings in Hong Kong. The results show that the proposed model not only quantifies the aggregated flexibility with high accuracy, but also dramatically reduces the computation time from 3605 s to 6.7 s, about 537 times faster than the existing probabilistic model solved numerically. Moreover, the proposed model is 8 times faster than the archetype-based model and achieves significantly higher accuracy.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100186"},"PeriodicalIF":13.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000246/pdfft?md5=5103629f1a558de886b8f7db3d5993e4&pid=1-s2.0-S2666792424000246-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077117","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":"Planning reliable wind- and solar-based electricity systems","authors":"Tyler H. Ruggles ,&nbsp;Edgar Virgüez ,&nbsp;Natasha Reich ,&nbsp;Jacqueline Dowling ,&nbsp;Hannah Bloomfield ,&nbsp;Enrico G.A. Antonini ,&nbsp;Steven J. Davis ,&nbsp;Nathan S. Lewis ,&nbsp;Ken Caldeira","doi":"10.1016/j.adapen.2024.100185","DOIUrl":"10.1016/j.adapen.2024.100185","url":null,"abstract":"<div><p>Resource adequacy, or ensuring that electricity supply reliably meets demand, is more challenging for wind- and solar-based electricity systems than fossil-fuel-based ones. Here, we investigate how the number of years of past weather data used in designing least-cost systems relying on wind, solar, and energy storage affects resource adequacy. We find that nearly 40 years of weather data are required to plan highly reliable systems (e.g., zero lost load over a decade). In comparison, this same adequacy could be attained with 15 years of weather data when additionally allowing traditional dispatchable generation to supply 5 % of electricity demand. We further observe that the marginal cost of improving resource adequacy increased as more years, and thus more weather variability, were considered for planning. Our results suggest that ensuring the reliability of wind- and solar-based systems will require using considerably more weather data in system planning than is the current practice. However, when considering the potential costs associated with unmet electricity demand, fewer planning years may suffice to balance costs against operational reliability.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100185"},"PeriodicalIF":13.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000234/pdfft?md5=a1828cdb491a3fbda3e3e4a773b1bcba&pid=1-s2.0-S2666792424000234-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048529","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 potential of radiative cooling enhanced photovoltaic systems in China","authors":"Maoquan Huang ,&nbsp;Hewen Zhou ,&nbsp;G.H. Tang ,&nbsp;Mu Du ,&nbsp;Qie Sun","doi":"10.1016/j.adapen.2024.100184","DOIUrl":"10.1016/j.adapen.2024.100184","url":null,"abstract":"<div><p>Soaring solar cell temperature hindered photovoltaic (PV) efficiency, but a novel radiative cooling (RC) cover developed in this study offered a cost-effective solution. Using a randomly particle-doping structure, the radiative cooling cover achieved a high “sky window” emissivity of 95.3% while maintaining a high solar transmittance of 94.8%. The RC-PV system reached a peak power output of 147.6 W/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>. A field study to explore its potential in various provinces in China revealed significant efficiency improvements, with yearly electricity outputs surpassing those of ordinary PV systems by a relative improvement of 2.78%–3.72%. The largest increases were observed under clear skies and in dry, cool climates, highlighting the potential of RC-PV systems under real weather and environmental conditions. This work provided the theoretical foundation for designing scalable radiative cooling films for PV systems, unlocking the full potential of solar energy.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100184"},"PeriodicalIF":13.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000222/pdfft?md5=0a129a711e045cc4c3d88283b1dec009&pid=1-s2.0-S2666792424000222-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848674","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":"Impact of forecasting on energy system optimization","authors":"Florian Peterssen ,&nbsp;Marlon Schlemminger ,&nbsp;Clemens Lohr ,&nbsp;Raphael Niepelt ,&nbsp;Richard Hanke-Rauschenbach ,&nbsp;Rolf Brendel","doi":"10.1016/j.adapen.2024.100181","DOIUrl":"10.1016/j.adapen.2024.100181","url":null,"abstract":"<div><p>Linear programs are frequently employed to optimize national energy system models, which are used to find a minimum-cost energy system. For the operation, they assume perfect forecasting of the weather and demands over the whole optimization horizon and can therefore perfectly fit the energy systems’ design and operation. Therefore, they will yield lower costs than any real energy system that only has partial forecasting available. We compare linear programming with a priority list, a heuristic operation strategy which uses no forecasting at all, in a model of a climate-neutral German energy system. We find a 28% more expensive energy system under the priority list. Optimizing the same energy system model with both strategies envelopes the cost and design of any energy system that has partial forecasting. We demonstrate this by incorporating some rudimentary forecasting into a modified priority list, which actually reduces the gap to 22%. This is thus an approach to find an upper bound for how much a linear program possibly underestimates the costs of a real energy system in Germany in regard to imperfect forecasting. We also find that the two approaches differ mainly in the dimensioning and operation of energy storage. The priority list yields 63% less batteries, 73% less thermal storage and 54% more hydrogen storage. The use of renewables and other components in the system is very similar.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100181"},"PeriodicalIF":13.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000192/pdfft?md5=fbba7e83b4274182667c200c1582b508&pid=1-s2.0-S2666792424000192-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636813","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":"Techno–Economic Modeling and Safe Operational Optimization of Multi-Network Constrained Integrated Community Energy Systems","authors":"Ze Hu ,&nbsp;Ka Wing Chan ,&nbsp;Ziqing Zhu ,&nbsp;Xiang Wei ,&nbsp;Weiye Zheng ,&nbsp;Siqi Bu","doi":"10.1016/j.adapen.2024.100183","DOIUrl":"10.1016/j.adapen.2024.100183","url":null,"abstract":"<div><p>The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the concept and modeling of ICES still remain unclear, and operational optimization of ICES is hindered by the physical constraints of heterogeneous integrated energy networks. This paper, therefore, provides an overview of the state-of-the-art concepts for techno–economic modeling of ICES by establishing a Multi-Network Constrained ICES (MNC-ICES) model. The proposed model underscores the diverse energy devices at community and consumer levels and multiple networks for power, gas, and heat in a privacy-protection manner, providing a basis for practical network-constrained community operation tools. The corresponding operational optimization in the proposed model is formulated into a constrained Markov decision process (C-MDP) and solved by a Safe Reinforcement Learning (RL) approach. A novel Safe RL algorithm, Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3), is developed to solve the C-MDP. By optimizing operations and maintaining network safety simultaneously, the proposed PD-TD3 method provides a solid backup for the ICESO and has great potential in real-world implementation. The non-convex modeling of MNC-ICES and the optimization performance of PD-TD3 is demonstrated in various scenarios. Compared with benchmark approaches, the proposed algorithm merits training speed, higher operational profits, and lower violations of multi-network constraints. Potential beneficiaries of this work include ICES operators and residents who could be benefited from improved ICES operation efficiency, as well as reinforcement learning researchers and practitioners who could be inspired for safe RL applications in real-world industry.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100183"},"PeriodicalIF":13.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000210/pdfft?md5=cd536c4a02a001e229c11a6ef7a1a59a&pid=1-s2.0-S2666792424000210-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141711958","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}