Advances in Applied Energy最新文献

{"title":"Data sharing in energy systems","authors":"Jianxiao Wang ,&nbsp;Feng Gao ,&nbsp;Yangze Zhou ,&nbsp;Qinglai Guo ,&nbsp;Chin-Woo Tan ,&nbsp;Jie Song ,&nbsp;Yi Wang","doi":"10.1016/j.adapen.2023.100132","DOIUrl":"10.1016/j.adapen.2023.100132","url":null,"abstract":"<div><p>Big data has been advocated as a dominant driving force to unleash the great waves of the next-generation industrial revolution. While the ever-increasing proliferation of heterogeneous data contributes to a more sustainable energy system, considerable challenges remain for breaking down the barrier of data sharing across monopolistic sectors and fully exploiting data asset value in a trustworthy environment. Here, we focus on a global aspiration and interest regarding the challenges, techniques, and prospects of data sharing in energy systems. In this paper, a conceptual framework for data sharing is designed, in which we introduce the commodity attribute of data assets and explain the bottlenecks of data trading. Two critical issues, i.e., right confirmation and privacy protection, are then systematically reviewed, which provide a fundamental guarantee for credible data openness. A detailed data market is conceived by elaborating on market bids, data asset valuation and pricing strategy, and game-based clearing. Finally, we conduct a discussion about some low-hanging fruit of data sharing in energy systems.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"10 ","pages":"Article 100132"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48270544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactions between hybrid power plant development and local transmission in congested regions","authors":"Julie Mulvaney Kemp,&nbsp;Dev Millstein,&nbsp;James Hyungkwan Kim,&nbsp;Ryan Wiser","doi":"10.1016/j.adapen.2023.100133","DOIUrl":"10.1016/j.adapen.2023.100133","url":null,"abstract":"<div><p>Hybrid power plants, namely those consisting of variable renewable energy (VRE) generators and energy storage in the same location, are growing in popularity and interact differently with the electrical grid than either component would individually. We investigate plant-grid dynamics in highly congested regions to determine whether stand-alone VRE, stand-alone storage, and hybrid VRE-plus-storage plants will reduce or increase the need for nearby transmission. The focus on congested regions offers empirical insight into future grid conditions, as VRE penetration continues to grow. Near congested load centers, we find that hybrid, stand-alone VRE and stand-alone storage plants each reduce transmission value, defined in terms of production costs. On the other hand, in congested areas with high VRE penetration, stand-alone storage and VRE generators have opposing effects, decreasing and increasing the need for transmission, respectively. Importantly, whether or not a hybrid plant’s optimal operation increases or decreases local transmission value depends on the plant’s technological specifications (i.e., lowering degradation costs of battery cycling reduces transmission value) and regulatory environment (i.e., allowing a hybrid to utilize grid charging reduces transmission value). Therefore, technological advances in energy storage and policy decisions will influence which variation of these results are realized. We also assess the financial implications of transmission expansion on hybrid and stand-alone plants. In VRE-rich areas, we find that wind plants stand to gain significantly more from transmission expansion than do solar plants, with a typical energy market revenue increase equal to that from hybridizing with four hours worth of storage. Results are based on real-time nodal price data and location-specific solar and wind generation profiles for 2018–2021 at 23 existing wind and solar plant locations in the United States that experience congestion patterns representative of regions with either high VRE penetration or high demand.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"10 ","pages":"Article 100133"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49593431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High resolution modeling and analysis of cryptocurrency mining’s impact on power grids: Carbon footprint, reliability, and electricity price","authors":"Ali Menati ,&nbsp;Xiangtian Zheng ,&nbsp;Kiyeob Lee ,&nbsp;Ranyu Shi ,&nbsp;Pengwei Du ,&nbsp;Chanan Singh ,&nbsp;Le Xie","doi":"10.1016/j.adapen.2023.100136","DOIUrl":"https://doi.org/10.1016/j.adapen.2023.100136","url":null,"abstract":"<div><p>Blockchain technologies are considered one of the most disruptive innovations of the last decade, enabling secure decentralized trust-building. However, in recent years, with the rapid increase in the energy consumption of blockchain-based computations for cryptocurrency mining, there have been growing concerns about their sustainable operation in electric grids. This paper investigates the tri-factor impact of such large loads on carbon footprint, grid reliability, and electricity market price in the Texas grid. We release open-source high-resolution data to enable high-resolution modeling of influencing factors such as location and flexibility. We reveal that the per-megawatt-hour carbon footprint of cryptocurrency mining loads across locations can vary by as much as 50% of the crude system average estimate. We show that the flexibility of mining loads can significantly mitigate power shortages and market disruptions that can result from the deployment of mining loads. These findings suggest policymakers to facilitate the participation of large mining facilities in wholesale markets and require them to provide mandatory demand response.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"10 ","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An adaptive deep-learning load forecasting framework by integrating transformer and domain knowledge","authors":"Jiaxin Gao ,&nbsp;Yuntian Chen ,&nbsp;Wenbo Hu ,&nbsp;Dongxiao Zhang","doi":"10.1016/j.adapen.2023.100142","DOIUrl":"10.1016/j.adapen.2023.100142","url":null,"abstract":"<div><p>Electrical energy is essential in today's society. Accurate electrical load forecasting is beneficial for better scheduling of electricity generation and saving electrical energy. In this paper, we propose an adaptive deep-learning load forecasting framework by integrating Transformer and domain knowledge (Adaptive-TgDLF). Adaptive-TgDLF introduces the deep-learning model Transformer and adaptive learning methods (including transfer learning for different locations and online learning for different time periods), which captures the long-term dependency of the load series, and is more appropriate for realistic scenarios with scarce samples and variable data distributions. Under the theory-guided framework, the electrical load is divided into dimensionless trends and local fluctuations. The dimensionless trends are considered as the inherent pattern of the load, and the local fluctuations are considered to be determined by the external driving forces. Adaptive learning can cope with the change of load in location and time, and can make full use of load data at different locations and times to train a more efficient model. Cross-validation experiments on different districts show that Adaptive-TgDLF is approximately 16% more accurate than the previous TgDLF model and saves more than half of the training time. Adaptive-TgDLF with 50% weather noise has the same accuracy as the previous TgDLF model without noise, which proves its robustness. We also preliminarily mine the interpretability of Transformer in Adaptive-TgDLF, which may provide future potential for better theory guidance. Furthermore, experiments demonstrate that transfer learning can accelerate convergence of the model in half the number of training epochs and achieve better performance, and online learning enables the model to achieve better results on the changing load.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"10 ","pages":"Article 100142"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42365066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increasing electrical reserve provision in districts by exploiting energy flexibility of buildings with robust model predictive control","authors":"Felix Bünning ,&nbsp;Philipp Heer ,&nbsp;Roy S. Smith ,&nbsp;John Lygeros","doi":"10.1016/j.adapen.2023.100130","DOIUrl":"10.1016/j.adapen.2023.100130","url":null,"abstract":"<div><p>Due to their thermal inertia, buildings equipped with electric heating and cooling systems can help to stabilize the electricity grid by shifting their load in time, and can thus facilitate energy flexible urban energy systems with the right control system in place. Because of minimum capacity requirements, they can often only participate in demand response schemes, such as secondary frequency reserves through aggregation. Such an aggregation could also take the form of entire district heating and cooling systems with connected buildings that are supplied by large-scale heat pumps and chillers. However, there is a lack of studies investigating the control of such configurations, both in simulation and in application. We present a two-level control scheme based on robust Model Predictive Control with affine policies to offer frequency reserves with a district system, where we exploit the thermal inertia of buffer storage tanks and a subset of the connected buildings. We leverage data-driven model generation methods to overcome the bottleneck of physics-based building modeling. In a numerical case study based on one-year historical data of a real system, we compare the approach to a situation where only the buffer storage is used for flexibility and demonstrate that the reserves offered increase substantially if the inertia of a subset of the connected buildings is also exploited. Furthermore, we validate the control approach in a first-of-its-kind experiment on the actual system, where we show that reserves can be offered by the district system without compromising the comfort in the connected buildings.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"10 ","pages":"Article 100130"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42896130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-triggered coordination of distributed renewable generators for frequency restoration in islanded microgrids: A low communication and computation strategy","authors":"Yulin Chen ,&nbsp;Donglian Qi ,&nbsp;Hongxun Hui ,&nbsp;Shaohua Yang ,&nbsp;Yurun Gu ,&nbsp;Yunfeng Yan ,&nbsp;Yi Zheng ,&nbsp;Jiangfeng Zhang","doi":"10.1016/j.adapen.2023.100128","DOIUrl":"10.1016/j.adapen.2023.100128","url":null,"abstract":"<div><p>Microgrid provides a promising solution to consume more distributed renewable energies. To coordinate the increasingly developed distributed renewable generators in a high flexibility and high efficiency way, distributed event-triggered mechanisms have been widely investigated in the literature to reduce the communication requirement and hence improve the control performance of microgrids. However, most of the event-triggered mechanisms mandate continuous calculation of complicated triggering conditions, which may in turn impose the computation burden of the controller and increase additional energy cost. To this end, this paper presents a distributed self-triggered control strategy for the frequency restoration in islanded microgrids with the aid of a linear clock. With this self-triggered solution, each distributed generator’s controller decides its own control and communication actions based on monitoring the linear clock, which excludes continuous calculation of any triggering conditions. Thus, the communication and computation costs can be reduced simultaneously. Moreover, Zeno behavior can be naturally excluded by the above design. The results of theoretical analysis and simulations show that the proposed distributed self-triggered control scheme can effectively coordinate distributed renewable generators with very low communication and computation requirements. Therefore, this research can improve the coordination efficiency of microgrids greatly, which is very useful for guiding the efficient operation of large-scale distributed renewable generators.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"10 ","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42280575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical metal requirement for clean energy transition: A quantitative review on the case of transportation electrification","authors":"Chunbo Zhang ,&nbsp;Jinyue Yan ,&nbsp;Fengqi You","doi":"10.1016/j.adapen.2022.100116","DOIUrl":"10.1016/j.adapen.2022.100116","url":null,"abstract":"<div><p>The clean energy transition plays an essential role in achieving climate mitigation targets. As for the transportation sector, battery and fuel cell electric vehicles (EVs) have emerged as a key solution to reduce greenhouse gasses from transportation emissions. However, the rapid uptake of EVs has triggered potential supply risks of critical metals (e.g., lithium, nickel, cobalt, platinum group metals (PGMs), etc.) used in the production of lithium-ion batteries and fuel cells. Material flow analysis (MFA) has been widely applied to assess the demand for critical metals used in transportation electrification on various spatiotemporal scales. This paper presents a quantitative review and analysis of 78 MFA research articles on the critical metal requirement of transportation electrification. We analyzed the characteristics of the selected studies regarding their geographical and temporal scopes, transportation sectors, EV categories, battery technologies, materials, and modeling approaches. Based on the global forecasts in those studies, we compared the annual and cumulative global requirements of the four metals that received the most attention: lithium, nickel, cobalt, and PGMs. Although major uncertainties exist, most studies indicate that the annual demand for these four metals will continue to increase and far exceed their production capacities in 2021. Global reserves of these metals may meet their cumulative demand in the short-term (2020–2030) and medium-term (2020–2050) but are insufficient for the long-term (2020–2100) needs. Then, we summarized the proposed policy implications in these studies. Finally, we discuss the main findings from the four aspects: environmental and social implications of deploying electric vehicles, whether or not to electrify heavy-duty vehicles, opportunities and challenges in recycling, and future research direction.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"9 ","pages":"Article 100116"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41370203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient AI-based Control of Semi-closed Greenhouses Leveraging Robust Optimization in Deep Reinforcement Learning","authors":"Akshay Ajagekar ,&nbsp;Neil S. Mattson ,&nbsp;Fengqi You","doi":"10.1016/j.adapen.2022.100119","DOIUrl":"10.1016/j.adapen.2022.100119","url":null,"abstract":"<div><p>As greenhouses are being widely adopted worldwide, it is important to improve the energy efficiency of the control systems while accurately regulating their indoor climate to realize sustainable agricultural practices for food production. In this work, we propose an artificial intelligence (AI)-based control framework that combines deep reinforcement learning techniques to generate insights into greenhouse operation combined with robust optimization to produce energy-efficient controls by hedging against associated uncertainties. The proposed control strategy is capable of learning from historical greenhouse climate trajectories while adapting to current climatic conditions and disturbances like time-varying crop growth and outdoor weather. We evaluate the performance of the proposed AI-based control strategy against state-of-the-art model-based and model-free approaches like certainty-equivalent model predictive control, robust model predictive control (RMPC), and deep deterministic policy gradient. Based on the computational results obtained for the tomato crop's greenhouse climate control case study, the proposed control technique demonstrates a significant reduction in energy consumption of 57% over traditional control techniques. The AI-based control framework also produces robust controls that are not overly conservative, with an improvement in deviation from setpoints of over 26.8% as compared to the baseline control approach RMPC.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"9 ","pages":"Article 100119"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44864813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A general model for comprehensive electrical characterization of photovoltaics under partial shaded conditions","authors":"Fuxiang Li ,&nbsp;Wentao Dong ,&nbsp;Wei Wu","doi":"10.1016/j.adapen.2022.100118","DOIUrl":"10.1016/j.adapen.2022.100118","url":null,"abstract":"<div><p>Partial shading condition (PSC) causes underperformance, unreliability, and fire risks in photovoltaic (PV) systems. Accurate estimation of PV behaviors is crucial to fundamental understanding and further mitigation. However, current modeling methods lack full consideration of the physical behaviors, system complexities, and shading pattern diversities, ending in coarse and simple analysis. Herein, an innovative modeling approach with high-performance algorithms is proposed to address these challenges simultaneously. Based on rigorous analysis, physics models considering the reverse-biased behaviors, the system complexities, and shading pattern diversities, are developed at the cell, module, and array levels, respectively. Then, a strict and progressive validation via measurement data is conducted to justify the effectiveness of the developed method. The method is valid for mainstream PV technologies in the market and can predict cell behaviors and module electrical characteristics perfectly. Notably, the proposed method is more computationally efficient than Simulink when simulating the same PV array. Lastly, to demonstrate its exclusive advantages, two case studies are conducted. The localized power dissipation can be quantified. The observed energy loss justifies the necessity of reverse biased behaviors and high-resolution simulation. This method can be coded in any development environment, providing an efficient and comprehensive tool to analyze PV systems.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"9 ","pages":"Article 100118"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43737089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden: Trading and forecast verification","authors":"O. Lindberg ,&nbsp;D. Lingfors ,&nbsp;J. Arnqvist ,&nbsp;D. van der Meer ,&nbsp;J. Munkhammar","doi":"10.1016/j.adapen.2022.100120","DOIUrl":"10.1016/j.adapen.2022.100120","url":null,"abstract":"<div><p>This paper presents a first step in the field of probabilistic forecasting of co-located wind and photovoltaic (PV) parks. The effect of aggregation is analyzed with respect to forecast accuracy and value at a co-located park in Sweden using roughly three years of data. We use a fixed modelling framework where we post-process numerical weather predictions to calibrated probabilistic production forecasts, which is a prerequisite when placing optimal bids in the day-ahead market. The results show that aggregation improves forecast accuracy in terms of continuous ranked probability score, interval score and quantile score when compared to wind or PV power forecasts alone. The optimal aggregation ratio is found to be 50%–60% wind power and the remainder PV power. This is explained by the aggregated time series being smoother, which improves the calibration and produces sharper predictive distributions, especially during periods of high variability in both resources, i.e., most prominently in the summer, spring and fall. Furthermore, the daily variability of wind and PV power generation was found to be anti-correlated which proved to be beneficial when forecasting the aggregated time series. Finally, we show that probabilistic forecasts of co-located production improve trading in the day-ahead market, where the more accurate and sharper forecasts reduce balancing costs. In conclusion, the study indicates that co-locating wind and PV power parks can improve probabilistic forecasts which, furthermore, carry over to electricity market trading. The results from the study should be generally applicable to other co-located parks in similar climates.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"9 ","pages":"Article 100120"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47902255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}