Institution：1.Xuji Group Corporation, Haidian District, Beijing 100085, P.R.China
2.Aalborg University, Aalborg DK-9100, Denmark
3.CSEE UK Branch, Birmingham B15 2TT, UK
received master degree from North China Electric Power University in power system and automation in 2005. He is working in R&D center of Xuji Corporation in Beijing. His research interests include integrated energy, wind power and renewable energy.
received master degree from Shandong University in power system and automation in 2003. He is working in R&D center of Xuji Corporation in Beijing. His research interests include micro-grid, power system automation, integrated energy and renewable energy.
received master degree from Sichuan University in power system and automation in 2002. She is working in R&D center of Xuji Corporation in Beijing. Her research interests include power storage, micro-grid, integrated energy and renewable energy.
received his bachelor and master degrees in control engineering in 2012 and 2015 from Hohai University, China. From 2015 to 2017, he was a research associate at R&D Center of State Grid Xuji Group Corporation, China. Since 2018, he has been a Ph.D. student at the Department of Energy Technology, Aalborg University, Aalborg,
Denmark. His research interest includes distributed generation, integrated energy system and relay protection.
received bachelor degree from Shanghai University Of Electric Power in power system and automation in 2015. He is working in R&D center of Xuji Corporation in Beijing. His research interests include integrated energy, renewable energy and intelligent substation.
received his bachelor degree from Northeastern University, China in 1982 and a Ph.D. degree from the Queen’s University of Belfast, UK in 1988, respectively. From 1989 to 1997, he was with the Power and Energy Group, University of Bath, UK. From 1998 to 2012, he has been with ALSTOM Grid Automation, where he was responsible for new technology
development and international research collaboration. Recently, he joined the State Grid Corporation of China (SGCC) as a specially invited expert. He has been a visiting professor at the State Power System Lab of Tsinghua University since 2001, and he has been an honorary dean of the School of Electrical Engineering of Changsha University of Science and Technology since 2008. He is the author or co-author of a few hundred technical publications and more than 30 patents.
A unified model for diagnosing energy usage abnormalities
in regional integrated energy service systems
An integrated energy service company in an industrial park or commercial building is responsible for managing all energy sources in their local region, including electricity, water, gas, heating, and cooling. To reduce energy wastage and increase energy utilization, it is necessary to perform efficiency analyses and diagnoses on integrated energy systems (IESs). However, the integrated energy data necessary for energy efficiency analyses and diagnoses come from a wide variety of instruments, each of which uses different transmission protocols and data formats. This makes it challenging to handle energy-flow data in a unified manner. Thus, we have constructed a unified model for diagnosing energy usage abnormalities in IESs. Using this model, the data are divided into working days and non-working days, and benchmark values are calculated after the data have been weighted to enable unified analysis of several types of energy data. The energy-flow data may then be observed, managed, and compared in all aspects to monitor sudden changes in energy usage and energy wastage. The abnormal data identified and selected by the unified model are then subjected to big-data analysis using technical management tools, enabling the detection of user problems such as abnormalities pertaining to acquisition device, metering, and energy usage. This model facilitates accurate metering of energy data and improves energy efficiency. The study has significant implications in terms of fulfilling the energy saving.
Integrated energy services, Energy efficiency analysis, Energy usage diagnosis, Energy usage abnormalities, Unified model.
Fig. 1 Block diagram of the IES data processing procedure
Fig. 2 Unified model for diagnosing energy usage abnormalities
Fig. 3 The energy usage abnormalities display screen