Applied Energy最新文献

{"title":"High temperature heat pump with dual uses of cooling and heating for industrial applications","authors":"Yixiu Dong ,&nbsp;Hatef Madani ,&nbsp;Xiaoxue Kou ,&nbsp;Ruzhu Wang","doi":"10.1016/j.apenergy.2024.124962","DOIUrl":"10.1016/j.apenergy.2024.124962","url":null,"abstract":"<div><div>The temperature difference between evaporating and condensing side of cascade high temperature heat pump (CHTHP) can be large. However, its heating coefficient of performance (COP) is not ideal due to the performance attenuation brought by large temperature lift. If both heating and cooling sides can be utilized, the whole COP will be greatly improved. In this work, a CHTHP prototype is established, along with three application scenarios, specifically dairy processing, liquor processing, and deep dehumidification, which simultaneously have cooling and heating demands consistent with the operating range of the unit. The experimental results indicate that the CHTHP prototype can supply cooling as low as 2 °C and heating up to 120 °C with comprehensive COP over 2.58, being more than 45.8 % higher than single heating system, showing impressive performance in combined cooling and heating (CCH) for industrial processes. Through the joint investigation of heat pump and application scenarios, it is revealed that the comprehensive performance of CHTHP can surpass conventional approach of using two separate heat pumps to provide cooling and heating respectively when the ratio of heating to cooling demand is high. In addition, the performance of CCH system can be further enhanced by optimizing corresponding process parameters in different scenarios. Based on the excellent performance of CHTHP in CCH and its practical industrial applications, this work will maximize the effectiveness of high temperature heat pump in the electrificaiton of industrial thermal energy consumption.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124962"},"PeriodicalIF":10.1,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coordinated DSO-VPP operation framework with energy and reserve integrated from shared energy storage: A mixed game method","authors":"Mohan Lin ,&nbsp;Jia Liu ,&nbsp;Zao Tang ,&nbsp;Yue Zhou ,&nbsp;Biao Jiang ,&nbsp;Pingliang Zeng ,&nbsp;Xinghua Zhou","doi":"10.1016/j.apenergy.2024.125006","DOIUrl":"10.1016/j.apenergy.2024.125006","url":null,"abstract":"<div><div>Virtual power plants (VPPs) contribute to the flexibility and economy of distributed system by leveraging integrated distributed renewable resources, optimizing energy production and consumption patterns, and facilitating dynamic grid management. However, challenges arise when multi-VPPs coordinated operate, including complex spatial and temporal correlation characteristics and conflicting interests in multi-agent decision-making. Shared energy storage (SES), as a product of the sharing economy, can be more flexible to help VPPs consume power generation from distributed renewable resources. Hence, focusing on the complementary problems and conflicts of interest between VPPs and improving the utilization of distributed renewable resources, this paper proposes an energy-reserve coordinated optimization model led by the distribution system operator (DSO) and involves the participation of both multi-VPPs and SES. Firstly, a Stackelberg-cooperative mixed game (SC-mixed game) framework is proposed for the DSO-VPP system, which leverages the DSO as the leader. The transactional electricity price between DSO and VPPs is optimized via the Stackelberg game, and the operation strategies for multi-VPPs can be calculated by the Cooperative game. Besides, an energy-reserve model is proposed for SES, which considers six reserve modes for further exploring the reserve potential of SES and guarantee the reserve provision ability. Additionally, a tailored alternating direction method of multipliers (ADMM), integrating a bisection method, is proposed to solve the SC-mixed game model efficiently. Finally, several case studies are conducted to validate the effectiveness of the proposed model.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 125006"},"PeriodicalIF":10.1,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transparent enhancement of active distribution network through FCA-based blind decomposition","authors":"Xing He ,&nbsp;Zhuangyan Zhang ,&nbsp;Qian Ai ,&nbsp;Zenan Ling ,&nbsp;Yuezhong Tang ,&nbsp;Robert Qiu","doi":"10.1016/j.apenergy.2024.124776","DOIUrl":"10.1016/j.apenergy.2024.124776","url":null,"abstract":"<div><div>Transparency is crucial for decision-making within an active distribution network (ADN). To enhance ADN’s transparency, this study develops a novel Blind Decomposition of Composite Events (BDCE) approach rooted in Free Component Analysis (FCA), with a detailed exploration of its related theorems, algorithms, and deductions. Notably, FCA employs non-commutative matrix variables instead of scalar variables, establishing a natural connection to Random Matrix Theory (RMT). By incorporating RMT, FCA-BDCE effectively utilizes spatial–temporal correlation—a matrix-derived spectrum statistic; it allows for the filtration of locally independent noises, such as individual-level measurement error, ubiquitous white noise, while retaining globally influential signals across some specified spatial–temporal span. This capability is particularly valuable when gaining insight into the complex ADN, a landscape with significant diversity and uncertainty. In general, our approach is model-free, theory-guided, and unsupervised, making it particularly suitable for ADN. A comprehensive case study validates the practical effectiveness of our FCA-BDCE approach, demonstrating its superiority over ICA-BDCE.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124776"},"PeriodicalIF":10.1,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint optimal sizing and operation scheduling of a power-to-gas hub based on a linear program","authors":"Filip Rukavina,&nbsp;Marijo Šundrica,&nbsp;Antonio Karneluti,&nbsp;Mario Vašak","doi":"10.1016/j.apenergy.2024.124849","DOIUrl":"10.1016/j.apenergy.2024.124849","url":null,"abstract":"<div><div>With rising challenges of greening the gas sector and decreasing wasteful curtailment of renewable energy sources, power-to-gas hubs seem to be a suitable solution for mitigation of both. Power-to-gas hubs can act as energy storage and as sector coupling solution between electricity, gas, heat, and water grids, but also agricultural markets when using biomass and/or biochar. However, the economic viability and adequate operation of such hubs is crucial in order to make a difference. The optimization procedure developed tackles the complex problem of finding the optimal structure of a power-to-gas hub and simultaneously optimizing its operation. The unique aspects of the proposed procedure include: i) finding the optimal system structure across a large number of possible technologies, processes and storage components, rather than optimizing a power-to-gas hub with a single mass/energy conversion pathway; ii) collocation of a power-to-gas hub with an existing renewable energy plant and/or an existing industrial plant; and iii) viable computational complexity while short-term intermittence of renewable sources is included. After comprehensive techno-economic modelling of the overall setup, the developed optimization procedure is used on three case studies showcasing the strengths of the procedure. The case studies show how the optimal system configuration and optimal components’ sizes change depending on prices of electricity and natural gas, and on possibility to sell digestate and/or biochar. Also, the case studies show the influence of electricity sources, or rather the intermittence of renewables, on optimal operating schedules.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124849"},"PeriodicalIF":10.1,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rethinking the role of indicators for electricity access in Latin America: Towards energy justice","authors":"Alonso Alegre-Bravo ,&nbsp;Richard C. Stedman ,&nbsp;C. Lindsay Anderson","doi":"10.1016/j.apenergy.2024.124877","DOIUrl":"10.1016/j.apenergy.2024.124877","url":null,"abstract":"<div><div>Electricity access is the primary indicator that countries use to create public policies for rural electrification. In Latin America, which has some of the highest inequality rates in the world, international organizations estimate that almost 20 million people still lack electricity access. A critical barrier to research and progress in energy justice is the lack of accurate information on the population living with inadequate electricity service. This review article examines the following in the context of Latin America: i) the definitions and methodologies for the indicator for electricity access; ii) the importance of a multidimensional indicator for electricity access that considers national governments' constraints; and iii) the failure of existing indicators in representing those living with poor electricity service conditions. This analysis finds that the shortcomings of methods for measuring indicators of electricity access make them inadequate to inform policies, and the use of the indicator may have been counterproductive in improving electricity service conditions. To significantly improve the conditions of people living with inadequate electricity service, indicators for electricity access need to provide a more accurate assessment, from the collection data process to the assessment of electricity supply conditions. Ultimately, this work aims to open a discussion in Latin America about ways that countries can better understand electricity access inequalities in the system and work towards a more just energy agenda.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124877"},"PeriodicalIF":10.1,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TimeGPT in load forecasting: A large time series model perspective","authors":"Wenlong Liao ,&nbsp;Shouxiang Wang ,&nbsp;Dechang Yang ,&nbsp;Zhe Yang ,&nbsp;Jiannong Fang ,&nbsp;Christian Rehtanz ,&nbsp;Fernando Porté-Agel","doi":"10.1016/j.apenergy.2024.124973","DOIUrl":"10.1016/j.apenergy.2024.124973","url":null,"abstract":"<div><div>Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer vision and natural language processing, this paper aims to discuss the potential of large time series models in load forecasting with scarce historical data. Specifically, the large time series model is constructed as a time series generative pre-trained transformer (TimeGPT), which is trained on massive and diverse time series datasets consisting of 100 billion data points (e.g., finance, transportation, banking, web traffic, weather, energy, healthcare, etc.). Then, the scarce historical load data is used to fine-tune the TimeGPT, which helps it to adapt to the data distribution and characteristics associated with load forecasting. Simulation results show that TimeGPT outperforms the popular benchmarks for load forecasting on several real datasets with scarce training samples, particularly for short look-ahead times. However, it cannot be guaranteed that TimeGPT is always superior to benchmarks for load forecasting with scarce data, since the performance of TimeGPT may be affected by the distribution differences between the load data and the training data. In practical applications, operators can divide the historical data into a training set and a validation set, and then use the validation set loss to decide whether TimeGPT is the best choice for a specific dataset.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124973"},"PeriodicalIF":10.1,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A parametric, control-integrated and machine learning-enhanced modeling method of demand-side HVAC systems in industrial buildings: A practical validation study","authors":"Dezhou Kong ,&nbsp;Yu Hong ,&nbsp;Yimin Yang ,&nbsp;Tingyue Gu ,&nbsp;Yude Fu ,&nbsp;Yihang Ye ,&nbsp;Weihao Xi ,&nbsp;Zhiang Zhang","doi":"10.1016/j.apenergy.2024.124971","DOIUrl":"10.1016/j.apenergy.2024.124971","url":null,"abstract":"<div><div>The development of high-tech manufacturing in recent years has promoted the rapid growth of industrial cleanrooms. The strict requirements for indoor environment control in cleanrooms lead to huge cooling energy requirements, which has given rise to research on energy modeling of cooling systems relevant to the industrial sector. Efficient and accurate modeling of demand-side Heating, Ventilation and Air-conditioning (HVAC) systems can significantly enhance water-side and air-side control strategies, thereby improving overall energy efficiency. However, existing grey-box methods usually only consider independent air-side or room models in industrial scenarios, and the inefficient handling of missing facility data makes it difficult to achieve optimal model performance. Meanwhile, there is also a lack of systematic investigation on the performance of black-box and grey-box methods in industrial demand-side HVAC modeling scenarios. In this study, based on a real industrial park, a demand-side HVAC system model was established using both the Modelica-based method and pure data-driven method to predict the cooling load under summer conditions. By using a machine learning approach, the true value of the missing internal heat gain is inferred instead of using a fixed value. The model results after Genetic Algorithm (GA) calibration show that in the case of small sample size, the prediction error of the machine learning-enhanced Modelica-based model is reduced by about 34.5 % compared with the original modelica-based model. Compared with the data-driven model with hyperparameter tuning, the reduction is about 38.8 %/21.3 % in the case of small/large sample size, respectively. Then the reasons for the differences in the performance of these modeling methods are compared and discussed in detail. The framework of machine learning-enhanced modeling method proposed in this study can also be applied to other industrial demand-side HVAC system modeling scenarios with missing on-site facility data, ultimately achieving more efficient industrial production through optimal control strategies for water-side systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124971"},"PeriodicalIF":10.1,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of predictive, optimization, and smart control strategies for hydrogen-based building heating systems","authors":"Amirreza Kaabinejadian ,&nbsp;Artur Pozarlik ,&nbsp;Canan Acar","doi":"10.1016/j.apenergy.2024.124994","DOIUrl":"10.1016/j.apenergy.2024.124994","url":null,"abstract":"<div><div>The use of energy in the built environment contributes to over one-third of the world's carbon emissions. To reduce that effect, two primary solutions can be adopted, i.e. (i) renovation of old buildings and (ii) increasing the renewable energy penetration. This review paper focuses on the latter. Renewable energy sources typically have an intermittent nature. In other words, it is not guaranteed that these sources can be harnessed on demand. Thus, complement solutions should be considered to use renewable energy sources efficiently. Hydrogen is recognized as a potential solution. It can be used to store excess energy or be directly exploited to generate thermal energy. Throughout this review, various research papers focusing on hydrogen-based heating systems were reviewed, analyzed, and classified from different perspectives. Subsequently, articles related to machine learning models, optimization algorithms, and smart control systems, along with their applications in building energy management were reviewed to outline their potential contributions to reducing energy use, lowering carbon emissions, and improving thermal comfort for occupants. Furthermore, research gaps in the use of these smart strategies in residential hydrogen heating systems were thoroughly identified and discussed. The presented findings indicate that the semi-decentralized hydrogen-based heating systems hold significant potential. First, these systems can control the thermal demand of neighboring homes through local substations; second, they can reduce reliance on power and gas grids. Furthermore, the model predictive control and reinforcement learning approaches outperform other control systems ensuring energy comfort and cost-effective energy bills for residential buildings.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124994"},"PeriodicalIF":10.1,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EnergyNet: A modality-aware attention fusion network for building energy efficiency classification","authors":"Shuang Dai,&nbsp;Matt Eames,&nbsp;Raffaele Vinai,&nbsp;Voicu Ion Sucala","doi":"10.1016/j.apenergy.2024.124888","DOIUrl":"10.1016/j.apenergy.2024.124888","url":null,"abstract":"<div><div>In the face of rising global energy demands, precise classification of building energy efficiency is critical for advancing sustainable energy practices. Traditional classification methods have been limited by their inability to effectively integrate diverse data types. Additionally, the valuable environmental information visible in building street view images has been consistently overlooked, leading to less comprehensive evaluations. This study introduces EnergyNet, an innovative framework designed to synergistically fuse multimodal data, including the environmental context that has previously been underutilized. The framework employs a state-of-the-art dual-branch architecture with a modality-aware attention mechanism to optimize the interpretation and fusion of both visual and textual data. Comparative experiments on real-world data demonstrate that EnergyNet substantially improves upon existing models, achieving an accuracy rate of 87.22% and an F1 score improvement of 5.39% over the best-performing benchmarks. The proven generalization capacity of the framework across different geographical regions highlights its potential as a scalable and effective solution for enhancing global energy efficiency measures.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124888"},"PeriodicalIF":10.1,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time Error Compensation Transfer Learning with Echo State Networks for Enhanced Wind Power Prediction","authors":"Yingqin Zhu ,&nbsp;Yue Liu ,&nbsp;Nan Wang ,&nbsp;ZhaoZhao Zhang ,&nbsp;YuanQiang Li","doi":"10.1016/j.apenergy.2024.124893","DOIUrl":"10.1016/j.apenergy.2024.124893","url":null,"abstract":"<div><div>Accurate wind power forecasting is essential for efficient energy management and grid stability, enabling energy providers to balance supply and demand, optimize renewable energy integration, reduce operational costs, and enhance power grid reliability. The Echo State Network (ESN) is widely used for modeling nonlinear dynamic systems due to its simple and rapid training process. However, ESNs can be prone to system errors, leading to inaccurate models when handling high-order nonlinear complexities. To overcome this, we developed the Error Compensation Transfer Learning Echo State Network (ETL-ESN), which combines a computing layer based on ESN and a compensation layer using transfer learning. Our model identifies error auto-correlation as a key factor that increases variance in ESN predictions, and addresses this with an error compensation layer to reduce system errors. We further leverage transfer learning to prevent overfitting within the error domain. Extensive experiments using real-world wind power data demonstrate that the ETL-ESN model reduces training time from 65 s to 2 s compared to LSTM, while lowering MAE by up to 95%. The ETL-ESN achieves a 95% to 98% improvement in prediction accuracy across different turbines compared to traditional models. The code and datasets used in this study are available at GitHub repository <span><span>https://github.com/zhuyingqin/Error-Transfer-ESN</span><svg><path></path></svg></span> for further research and replication.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124893"},"PeriodicalIF":10.1,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}