Internet‐of‐Things technology and applications for clean energy systems
Energy Conversion and Economics(2021)
Abstract
As a critical technology for clean and sustainable energy transition, Internet of Things (IoT) is becoming increasingly popular for its use in extending connectivity into multiple energy resources. Based on the heterogeneous networking integration of devices, IoT has the potential of achieve seamless management of various facilities, thus enabling real-time optimisation of supply chains and dynamic response to energy system dispatch. In addition, IoT can help improve the visibility and controllability of distributed energy resources and leverage flexible loads via extensive connections among numerous devices. This special issue has received wide attention from the research community. The five papers selected for publication in this issue are briefly introduced as follows. In ‘Architecture and function analysis of integrated energy service stations considering cyber–physical integration’, Liu et al. proposed integrated energy service stations (IESSs), which comprise substations, integrated multi-energy conversion stations, data centres, communication base stations and other functional units. Two feasible schemes were then designed to realise the construction of IESSs, including entity IESSs which require refined planning and construction, and virtual IESSs which involve transformation based on existing substation resources. The motivation and practical implementation for constructing IESSs are discussed. Finally, future research interests regarding IESSs are summarised. In ‘A survey on policies, modelling and security of cyber–physical systems in smart grids’, Wang et al. provided an overview of the policy drivers for and barriers to the implementation of cyber–physical systems (CPSs). With the widespread deployment of behind-the-metre distributed energy resources (DERs), there is an increasing demand to model hardware, software and their interactions in a smart grid environment. This paper reviewed the modelling and applications of an intelligent CPS for a decentralised energy system. The integration of DERs and the supportive infrastructure can cause a modern power system to become more vulnerable to external threats such as terrorist attacks and therefore less reliable as a secure system. The latest progress in CPS implementation was summarised considering critical infrastructure identification and protection as well as risk assessment and methods for mitigating cyber threats and attacks. In ‘Strategic PMU placement to alleviate power system vulnerability against cyber attacks’, Khare et al. presented a strategic phasor measurement unit (PMU) placement scheme to reduce cyber vulnerability of power systems to cyber attacks. A multi-stage PMU placement strategy was developed to alleviate power system vulnerability to possible false data injection attacks, where forward dynamic programming was used to distribute the capital cost of PMUs over a certain period. "The authors also proposed an index to quantify the vulnerability of the nodes of a grid to false data injection attacks. This index could be useful in selecting and prioritising an optimal set of candidate buses for PMU placement in a specific deployment stage. In ‘Consensus-based decentralized energy trading for distributed energy resources’, Wang et al. proposed a fully decentralised transactive energy management method using a consensus-based algorithm. A virtual pool was designed for prosumers to trade energy and exchange information with the support of IoT technologies. The consensus-based algorithm enables prosumers to obtain an optimal energy schedule independently but in a coordinated manner without revealing personal data. Practical data were used to perform simulations and validate the proposed algorithm, which demonstrated both the efficiency and effectiveness of the consensus-based decentralised transactive energy management strategy. In ‘Hybrid clustering-based bad data detection of PMU measurements’, Zhu et al. introduced the objective of bad PMU data detection and presented an illustrative bad data instance. Three clustering methods, including linear regression, density-based spatial clustering of applications with noise (DBSCAN), and Gaussian mixture models (GMM), were combined for bad PMU data detection. A statistical analysis and bound modification of data clustering were performed to further improve detection accuracy. The proposed hybrid clustering-based bad data detection method is unsupervised and can be applied to online bad PMU data detection over a short computational period. The guest editors would like to thank all the authors and reviewers for their excellent contributions to this special issue of Energy Conversion & Economics. The Editors-in-Chief and Editorial Office of Energy Conversion & Economics are also recognised for their support throughout the editorial process. Kai Strunz received the Dr.-Ing. degree (summa cum laude) from Saarland University, Saarbrücken, Germany, in 2001. He was with Brunel University in London from 1995 to 1997. From 1997 to 2002, he was with the Division Recherche et Dévelopment of Electricité de France in Paris. From 2002 to 2007, he was Assistant Professor of electrical engineering with the University of Washington, Seattle, USA. Since 2007, he has been a Professor for Sustainable Electric Networks and Sources of Energy with TU Berlin in Germany. Dr. Strunz was the Chairman of the Conference IEEE PES Innovative Smart Grid Technologies Europe in 2012. He is the chair of the IEEE PES Subcommittee “Distributed Energy Resources” and the co-chair of the IEEE Working Group “Dynamic Performance and Modeling of HVDC Systems and Power Electronics for Transmission Systems”. He received the IEEE PES Prize Paper Award in 2015, the Journal of Emerging and Selected Topics in Power Electronics First Prize Paper Award 2015, and the 2020 best paper award in the field of electric machines and drives by IEEE Transactions on Energy Conversion. On behalf of the Intergovernmental Panel on Climate Change (IPCC), he acted as the Review Editor for the Special Report on Renewable Energy Sources and Climate Change Mitigation. Xuanyuan Wang received the B.S. degree, M.A.Sc. degree and Ph.D. Degree in electrical engineering from Xi'an Jiaotong University, China, the University of Waterloo, Canada, and Tsinghua University, China, respectively. From 2001 to 2003, she was a Research Assistant with the Power/Energy System Laboratory, University of Waterloo, and an Electrical Engineer of Honeywell, Canada. From 2004 to 2010, She worked in Commercial and System Operations in ERCOT, USA. From 2010 to 2021 She worked for the State Grid Corporation of China and was the former Executive Director of Jibei Power Exchange. She is currently the Director General of Technology and Innovation Division, SGCC Jibei Electric Power Company, China. Her main research interests include electricity market, power system operations, demand-side response, power electronics, and renewable energy. Qinglai Guo (Senior Member, IEEE) was born on March 6, 1979, in Jilin City, Jilin Province, China. He received the BS degree from the Department of Electrical Engineering, Tsinghua University, Beijing, China, in 2000, and the PhD degree from Tsinghua University in 2005, where he is currently a professor. His special fields of interest include smart grids, cyber–physical systems, and electrical power control centre applications. He is currently a TCPC of Energy Internet Coordinating Committee of IEEE PES, the co-chair of IEEE PES Work Group on “Energy Internet,” IEEE PES Task Force on “Cyber–Physical Interdependence for Power System Operation and Control,” and IEEE PES Task Force on “Voltage Control for Smart Grid.” He is an editorial member of IEEE Transactions on Power Systems, Renewable and Sustainable Energy Reviews, and IEEE Transactions on Smart Grid. He is currently an IET Fellow and a CIGRE member, and he is involved in five workgroups of these organizations. Le Xie (Fellow, IEEE) received the BE degree in electrical engineering from Tsinghua University, Beijing, China, in 2004, the MS degree in engineering sciences from Harvard University, Cambridge, MA, USA, in 2005, and the PhD degree from the Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA, in 2009. He is currently a professor with the Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA. His research interests include modelling and control of large-scale complex systems, smart grids application with renewable energy resources, and electricity markets. Song Zhang (Senior Member, IEEE) is an R&D Lead Analyst at ISO New England and a senior member of the IEEE. He is a certified AWS Solution Architect and the lead of multiple cloud computing projects at ISO New England. He is also Chair of IEEE PES Task Force on Cloud Computing for Power Grid and Chair of IEEE PES Springfield Chapter. Before joining the ISO, he was a Power System Engineer at GE Grid Solutions from 2014 to 2017. Dr. Zhang received his Ph.D. degree in Electrical Engineering from Arizona State University. His research interest includes power system operation, power system analysis, power system stability and control, cloud computing, big data and synchrophasor technology. Xin Fang (Senior Member, IEEE) received the BS degree from the Huazhong University of Science and Technology, China, in 2009, the MS degree from the China Electric Power Research Institute, China, in 2012, and the PhD degree from the University of Tennessee (UT), Knoxville, TN, USA, in 2016. Currently, he is a researcher with the National Renewable Energy Laboratory (NREL). His research interests include power system planning and optimization, electricity market operation considering renewable energy integration, and demand response. Jianxiao Wang (Member, IEEE) received his BS and PhD degrees in electrical engineering from Tsinghua University, Beijing, China, in 2014 and 2019. He was a visiting student researcher at Stanford University, CA, USA. He is currently an assistant professor in the School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China. He was awarded as Forbes China 30 Under 30, Outstanding Young Talent by Chinese Renewable Energy Society, Beijing Outstanding Young Talent by the Beijing Government and so forth. His research interests include smart grid operation and planning, hydrogen and storage technology, transportation and energy systems integration, electricity market and data analytics.
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Key words
clean energy systems,internet‐of‐things
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