- home
- Advanced Search
- Energy Research
- Open Access
- Closed Access
- Restricted
- 9. Industry and infrastructure
- Applied Energy
- Energy Research
- Open Access
- Closed Access
- Restricted
- 9. Industry and infrastructure
- Applied Energy
description Publicationkeyboard_double_arrow_right Article , Journal 2017 United KingdomPublisher:Elsevier BV Funded by:UKRI | Whole Systems Energy Mode...UKRI| Whole Systems Energy Modelling Consortium (WholeSEM)Authors: Ameli, H; Strbac; Qadrdan;handle: 10044/1/48612
The electricity system balancing is becoming increasingly challenging due to the integration of Renewable Energy Sources (RES). At the same time, the dependency of electricity network on gas supply system is expected to increase, as a result of employing flexible gas generators to support the electricity system balancing. Therefore the capability of the gas supply system to deliver gas to generators under a range of supply and demand scenarios is of a great importance. As potential solutions to improve security of gas and electricity supply, this paper investigates benefits of employing flexible multi-directional compressor stations as well as adopting a fully integrated approach to operate gas and electricity networks. A set of case studies for a GB gas and electricity networks in 2030 have been defined to quantify the value of an integrated operation paradigm versus sequential operation of gas and electricity networks. The results indicate there are significant overall system benefits (up to 65% in extreme cases) to be gained from integrated optimization of gas and electricity systems, emphasizing the important role of gas network infrastructure flexibility in efficiently accommodating the expected expansion of intermittent RES in future power systems.
Imperial College Lon... arrow_drop_down Imperial College London: SpiralArticle . 2017License: CC BYFull-Text: http://hdl.handle.net/10044/1/48612Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2017.05.132&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 47 citations 47 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
visibility 6visibility views 6 download downloads 66 Powered bymore_vert Imperial College Lon... arrow_drop_down Imperial College London: SpiralArticle . 2017License: CC BYFull-Text: http://hdl.handle.net/10044/1/48612Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2017.05.132&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:Elsevier BV Authors: Kaibo Wang; Jia Wang;Weiwei Shao;
Chao Mei; +3 AuthorsWeiwei Shao
Weiwei Shao in OpenAIREKaibo Wang; Jia Wang;Weiwei Shao;
Chao Mei; Jiahong Liu; Ding Xiangyi; Zejin Li;Weiwei Shao
Weiwei Shao in OpenAIREAbstract Green infrastructure (GI) is a low-carbon solution for urban rainwater management. Hydrological processes and the corresponding emissions of greenhouse gas (GHG) during rainfall events are optimized by GI when the latter is compared with a traditional urban drainage system. This study establishes an city-scale quantitative analysis, based on hydrological processes, with which to assess the contribution of GIs to low-carbon urban drainage systems and cities. The emission factor method is applied to measure GHG emissions. Attributable sources of emissions are wastewater treatment plants and wastewater and rainwater pumps. The amount and rate of change in GHG emissions were selected as indicators of the impacts of GI-based urban drainage systems and a case study was conducted in Dongying, China, based on 48 hydrological scenarios from 1970 to 2017. The amount of annual GHG emissions decreased by 3752.5 to 26238.9 tons of CO2 equivalent at an average of 10677.3 tons/a. The rate of annual GHG emissions decreased by 25.9–68.7% with an average reduction of 45.9%. An S-shaped logistic curve fit the data, indicated that annual rainfall is non-linearly and positively correlated with both the amount and rate of annual GHG emissions mitigated. The probability of benefits to GHG emissions in the 48 hydrological scenarios is analyzed based on a Pearson type III distribution curve. These findings can provide information that local authorities can use to guide policies towards their goals of applying GIs to mitigate GHG emissions in the urban drainage system.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2020.115686&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 21 citations 21 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2020.115686&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020 SwitzerlandPublisher:Elsevier BV Authors:Kai Fang;
Yanmei Li;Kai Fang
Kai Fang in OpenAIREQi Wen;
Jiashuo Li;
+5 AuthorsJiashuo Li
Jiashuo Li in OpenAIREKai Fang;
Yanmei Li;Kai Fang
Kai Fang in OpenAIREQi Wen;
Jiashuo Li;
Jiashuo Li
Jiashuo Li in OpenAIREQinli Lu;
Qinli Lu
Qinli Lu in OpenAIREReinout Heijungs;
Reinout Heijungs;Reinout Heijungs
Reinout Heijungs in OpenAIREKuishuang Feng;
Kuishuang Feng
Kuishuang Feng in OpenAIREXianjin Huang;
Xianjin Huang
Xianjin Huang in OpenAIREA continuous growth of international trade, especially between developing countries, has greatly increased carbon dioxide (CO2) emissions associated with energy consumption over the past two decades. Given the more intensified intraregional cooperation and trade within the Belt and Road Initiative (BRI), this study aims to trace the imbalance of CO2 embodied in trade between nations in BRI and the rest of the world, providing new insights into the drivers of emissions growth by contrasting consumption, production and technological differences-based perspectives. Results indicate that the BRI contributed to over 50% of global carbon footprint and 92% of its increase in 1995–2015. The BRI was a net exporter of trade-embodied emissions, whose technological-adjusted carbon footprint remained remarkably large due to comparatively high carbon intensity. Geographically, carbon leakage has gradually moved from China and India to other BRI countries, especially to Southeast Asia, West Asia and Africa. Technological change was the key driver of emissions reduction, followed by the change in industrial structure. The growth in final demand per capita was the most important driver for the growth of CO2 emissions in BRI. Improving carbon efficiency remains a critical step for BRI nations to slow down not only emissions growth but also carbon leakage. The paper managed to provide novel insights into the carbon leakage in BRI by contrasting the consumption, production and technological differences-based perspectives, thus being able to better inform policymakers on region-specific low-carbon transition and global climate governance.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2020.115934&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 94 citations 94 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2020.115934&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019 Hong Kong, China (People's Republic of)Publisher:Elsevier BV Authors:Huilong Wang;
Huilong Wang
Huilong Wang in OpenAIREShengwei Wang;
Shengwei Wang
Shengwei Wang in OpenAIRERui Tang;
Rui Tang
Rui Tang in OpenAIREhandle: 10397/95382
Abstract Renewable electricity generations have been growing rapidly in recent years worldwide as a result of efforts to address the global energy issue. However, it has also imposed increasing challenges on the balance of power grids due to their intermittent nature. Building sector, as the largest consumer, is expected increasingly to play an important role in providing ancillary services (AS) to relieve stress and reduce the needs of investment for power balance of the smart grids. Grid-responsive building therefore becomes a worthwhile and essential feature of buildings today and in the future. This paper provides an overview of the classification, benefits, applications as well as methods/technologies for HVAC systems in non-residential buildings to provide ancillary services, as fast responses (i.e., in the magnitude of seconds or minutes) to the needs of smart grids. In addition, the challenges of applications and potential solutions are investigated and summarized. The potential capacities of HVAC systems in the entire non-residential building sector in Hong Kong for providing frequency regulation (FR) and spinning reserve are also quantified.
Hong Kong Polytechni... arrow_drop_down Hong Kong Polytechnic University: PolyU Institutional Repository (PolyU IR)Article . 2022License: CC BY NC NDFull-Text: http://hdl.handle.net/10397/95382Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2019.04.159&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 103 citations 103 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert Hong Kong Polytechni... arrow_drop_down Hong Kong Polytechnic University: PolyU Institutional Repository (PolyU IR)Article . 2022License: CC BY NC NDFull-Text: http://hdl.handle.net/10397/95382Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2019.04.159&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Journal , Research 2020 Germany, Australia, AustraliaPublisher:Elsevier BV Funded by:[no funder available]Authors:Gunther Glenk;
Stefan Reichelstein; Stefan Reichelstein;Gunther Glenk
Gunther Glenk in OpenAIREStephen Comello;
Stephen Comello
Stephen Comello in OpenAIREComprehensive global decarbonization will require that transportation services cease to rely on fossil fuels. Here we develop a generic life-cycle cost model to address two closely related questions central to the emergence of sustainable transportation: (i) the utilization rates (hours of operation) that rank-order alternative drivetrains in terms of their cost, and (ii) the cost-efficient share of clean energy drivetrains in a vehicle fleet of competing drivetrains. Calibrating our model framework in the context of urban transit buses, we examine how the comparison between diesel and battery-electric buses varies with the specifics of the duty cycle (route). We find that even for less favorable duty cycles, battery-electric buses will entail lower life-cycle costs once utilization rates exceed 20% of the annual hours. Yet, the current economics of that particular application still calls for a one-third share of diesel drivetrains in a cost-efficient fleet.
MAnnheim DOCument Se... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.2139/ssrn.3716750&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu34 citations 34 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!
more_vert MAnnheim DOCument Se... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.2139/ssrn.3716750&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:Elsevier BV Authors:Wenqiang Sun;
Qiang Wang; Yue Zhou;Wenqiang Sun
Wenqiang Sun in OpenAIREJianzhong Wu;
Jianzhong Wu
Jianzhong Wu in OpenAIREIntegrated analysis and optimization of material and energy flows in the iron and steel industry have drawn considerable interest from steelmakers, energy engineers, policymakers, financial firms, and academic researchers. Numerous publications in this area have identified their great potential to bring significant benefits and innovation. Although much technical work has been done to analyze and optimize material and energy flows, there is a lack of overview of material and energy flows of the iron and steel industry. To fill this gap, this work first provides an overview of different steel production routes. Next, the modelling, scheduling and interrelation regarding material and energy flows in the iron and steel industry are presented by thoroughly reviewing the existing literature. This study selects eighty publications on the material and energy flows of steelworks, from which a map of the potential of integrating material and energy flows for iron and steel sites is constructed. The paper discusses the challenges to be overcome and the future directions of material and energy flow research in the iron and steel industry, including the fundamental understandings of flow mechanisms, the dynamic material and energy flow scheduling and optimization, the synergy between material and energy flows, flexible production processes and flexible energy systems, smart steel manufacturing and smart energy systems, and revolutionary steelmaking routes and technologies.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2020.114946&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 170 citations 170 popularity Top 1% influence Top 10% impulse Top 0.1% Powered by BIP!
download 284download downloads 284 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2020.114946&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2016Publisher:Elsevier BV Abstract A scientific evaluation of the energy efficiency and CO2 emission performance of the thermal power industry could not only provide valuable information for reducing energy consumption and carbon emissions but also serve as a tool to estimate the effectiveness of relevant policy reforms. Considering the opposite effects of energy conservation and carbon emission reduction on generation cost, this study respectively measures the energy and CO2 emission performance of the thermal power industries in China’s 30 provincial administrative regions during the period 2005–2012 from both static and dynamic perspectives. We implement the bootstrap method for the directional distance function to correct the possible estimate bias and test the significance of productivity changes where the weak disposability of undesirable outputs is also integrated. The empirical analysis leads to the following conclusions. The bootstrapping results could provide us with much valuable information because the initial estimates might result from sampling noise rather than reveal the real variations. In addition, some differences do exist between the energy and CO2 emission performance of China’s thermal power industry. Furthermore, technological progress is the main driving force for energy and CO2 emission productivity improvement and it works better for the former.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2015.02.066&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 82 citations 82 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2015.02.066&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2012Publisher:Elsevier BV Authors:Jinyue Yan;
Chengshan Wang; Jianhui Wang;Jinyue Yan
Jinyue Yan in OpenAIREAntonio J. Conejo;
Antonio J. Conejo
Antonio J. Conejo in OpenAIRESmart grids, renewable energy integration, and climate change mitigation - Future electric energy systems
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2012.03.014&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 69 citations 69 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2012.03.014&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:Elsevier BV Authors:Zhongtuo Shi;
Zhongtuo Shi
Zhongtuo Shi in OpenAIREWei Yao;
Zhouping Li;
Zhouping Li
Zhouping Li in OpenAIRELingkang Zeng;
+4 AuthorsLingkang Zeng
Lingkang Zeng in OpenAIREZhongtuo Shi;
Zhongtuo Shi
Zhongtuo Shi in OpenAIREWei Yao;
Zhouping Li;
Zhouping Li
Zhouping Li in OpenAIRELingkang Zeng;
Lingkang Zeng
Lingkang Zeng in OpenAIREYifan Zhao;
Yifan Zhao
Yifan Zhao in OpenAIRERunfeng Zhang;
Yong Tang;Runfeng Zhang
Runfeng Zhang in OpenAIREJinyu Wen;
Jinyu Wen
Jinyu Wen in OpenAIREAbstract Smart grid is the new trend for clean, sustainable, efficient and reliable energy generation, delivery and use. To ensure stable and secure operation is essential for the smart grid, which needs effective stability analysis and control. As the smart grid has evolved through a growing scale of interconnection, increasing integration of renewable energy, widespread operation of direct current power transmission systems, and liberalization of electricity markets, the stability characteristics of it are much more complex than the past. Due to these changes, conventional stability analysis and control approaches have a series of drawbacks in terms of speed, effectiveness and economy. On the contrary, the emerging artificial intelligence (AI) techniques provide powerful and promising tools for stability analysis and control in smart grids and have attracted growing attention. This paper aims to give a comprehensive and clear picture of recent advances in this research area. First, we present a general overview of AI, including its definitions, history and state-of-the-art methodologies. And then, this paper gives a comprehensive review of its applications to security assessment, stability assessment, fault diagnosis, and stability control in smart grids. These applications have achieved impressive results. Nevertheless, we also identify some major challenges these applications face in practice: high requirements on data, imbalanced learning, interpretability of AI, difficulties in transfer learning, the robustness of AI to communication quality, and the robustness against attack or adversarial examples. Furthermore, we provide suggestions for potential important future investigation directions to overcome these challenges and bridge the gap between research and practice.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2020.115733&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 174 citations 174 popularity Top 1% influence Top 1% impulse Top 0.1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2020.115733&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:Elsevier BV Abstract The current power systems are undergoing a rapid transition towards their more active, flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in many domains, e.g., integration of various distributed renewable energy sources, cyberspace security, demand-side management, and decision-making of system planning and operation. The fulfillment of advanced functionalities in the smart grid firmly relies on the underlying information and communication infrastructure, and the efficient handling of a massive amount of data generated from various sources, e.g., smart meters, phasor measurement units, and various forms of sensors. In this paper, a comprehensive survey of over 200 recent publications is conducted to review the state-of-the-art practices and proposals of machine learning techniques and discuss the trend in a wide range of smart grid application domains. This study demonstrates the increasing interest and rapid expansion in the use of machine learning techniques to successfully address the technical challenges of the smart grid from various aspects. It is also revealed that some issues still remain open and worth further research efforts, such as the high-performance data processing and analysis for intelligent decision-making in large-scale complex multi-energy systems, lightweight machine learning-based solutions, and so forth. Moreover, the future perspectives of utilizing advanced computing and communication technologies, e.g., edge computing, ubiquitous internet of things and 5G wireless networks, in the smart grid are also highlighted. To the best of our knowledge, this is the first review of machine learning-driven solutions covering almost all the smart grid application domains. Machine learning will be one of the major drivers of future smart electric power systems, and this study can provide a preliminary foundation for further exploration and development of related knowledge and insights.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2020.115237&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 250 citations 250 popularity Top 0.1% influence Top 1% impulse Top 0.1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2020.115237&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu