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A Review of Smart Metering for Future Chinese Grids
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Energy Procedia 00www.sciencedirect.com (2018) 000–000 Available online Available online atatwww.sciencedirect.com Energy Procedia 00 (2018) 000–000
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www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 152 Energy Procedia 00(2018) (2017)1194–1199 000–000 www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, CUE2018-Applied Energy and Forum 2018: Low carbon and Applied Energy Symposium andSymposium Forum 2018: Low carbon cities and urbancities energy systems, CUE2018, 5–7June 2018, Shanghai, China CUE2018, 5–7June 2018, Shanghai, China urban energy systems, 5–7 June 2018, Shanghai, China
A Review of Smart Metering for Future Chinese Grids The 15thof International Symposium for on District Heating and Cooling A Review Smart Metering Future Chinese Grids
Yikuai Wangab* , Huadong Qiuaa, Ying Tuaa, Qiang Liuaa, Yi Dingbb, Weifeng Wangaa ab* Yikuai Wang , Huadong Qiu , Ying , Qiang Liuheat , Yi Ding , Weifeng Wang Assessing the feasibility ofTuusing the demand-outdoor a
State Grid Zhejiang Electrical Power Co., LTD, No. 8 Road Huanglong, Hangzhou, 310000, China
Zhejiang University, Zheda, Hangzhou, 310000, China 310000, State Grid Zhejiang Electrical Co., LTD, No. 8 Road Huanglong, Hangzhou, China temperature function forPower a No.38Road long-term district heat demand forecast Zhejiang University, No.38Road Zheda, Hangzhou, 310000, China a
b b
I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
Abstract a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b
Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Smart metering solutions are crucial to the future power grid infrastructure. In order to develop next generation of c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Smart metering are crucial theresearch future power grid infrastructure. In order to develop next generation of metering system insolutions Chinese smart grid, to this investigates smart metering in industrial perspectives, focusing metering system in Chinese smart grid, this research investigates smart metering in industrial perspectives, focusing on mainstream international metering technologies. Especially, to meet with the time and qualification requirements on the mainstream international metering technologies. to meet with the time and qualification requirements of start-up period of developing spot market, theEspecially, study applies economic mathematical method to estimate smart of the start-up period of developing spot market, the study applies economic mathematical method to estimate smart Abstract metering updating project. metering updating project.
District heating are All commonly addressed in the literature as one of the most effective solutions for decreasing the Copyright © 2018 networks Elsevier Ltd. rights reserved. Copyright © gas 2018emissions Elsevier Ltd. Ltd. All rights reserved. greenhouse fromAll therights building sector. These systems require investments which are returned through heat Copyright © 2018 Elsevierunder reserved. Selection and peer-review responsibility of the scientific committee ofhigh Applied Energy Symposium and Forum 2018:theLow Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, Selection and peer-review under responsibility of the scientific committee of Applied Energy Symposium and Forum 2018: Low carbon energy CUE2018. Forum cities 2018:and Lowurban carbon citiessystems, and urban energy systems. prolonging return period. carbon cities the andinvestment urban energy systems, CUE2018. The main scope of this Smart papergrid; is toPower assessmarkets the feasibility of using the heat demand – outdoor temperature function for heat demand Keywords:Smart metering; forecast. The metering; district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 Keywords:Smart Smart grid; Power markets buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were 1.renovation Introduction with results from a dynamic heat demand model, previously developed and validated by the authors. 1.compared Introduction The that environment when only weather is considered, the margin of the errorsituation could beon acceptable forThe someapproaches applications In results recent showed centuries, issueschange such as global warming threat the earth. (the error annual demand was lowerissues than 20% for all weather scenarios considered). However, after introducing renovation Inpeople recentinexploit centuries, such as influence global warming threat the On situation on side, the earth. The approaches that and environment consume energy have huge on environment. the other the demand of global scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). that people and So consume energy have on environment. On thesector other side, the demand of global energy still exploit increases. in order to suit forhuge the influence future development, the energy is urgent for reforms. As The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the energy stillbyincreases. So inEnergy order to suit for the future development, the energy sector iscontinues urgent forapace, reforms. As published International Agency (IEA)[1], low-carbon generation technologies among decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and published by International Energy Agency (IEA)[1], low-carbon generation technologies continues apace, among which are led by wind turbine and solar photovoltaic (PV). It is anticipated that the summary share of wind and solar renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the which arescenarios). ledgeneration by wind photovoltaic (PV). It20% is anticipated the summary share of China wind and solar in the global grows fromsolar 5% at present to almost until 2040.that Among allfor thethe countries, plays an coupled Theturbine values and suggested could be used to modify the function parameters scenarios considered, and inimprove the global generation grows from 5% at present to almost 20% until 2040. Among all the countries, China plays an the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +86 15088681889; fax: +86 0571 51102144. Cooling. *
E-mail address:author. [email protected] Corresponding Tel.: +86 15088681889; fax: +86 0571 51102144. E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection peer-review under responsibility the scientific 1876-6102and Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urbanand energy systems, under CUE2018. Selection peer-review responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, CUE2018.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems. 10.1016/j.egypro.2018.09.158
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Yikuai Wang et al. / Energy Procedia 152 (2018) 1194–1199 Yikuai Wang et al./ Energy Procedia 00 (2018) 000–000
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important role in the renewable energy market. As published by National Energy Administration of China, in 2017, the electricity consumption of the whole society supported by grids is 6307.7 billion kW·h. In the near future, with the deregulated power markets evolution, the Chinese grids face new challenges in energy metering and monitoring. As the predominant infrastructure in smart grids, the smart metering is fundamental to the bull generations, renewable energy, and power markets. As a result, the architectural design of metering system for future smart grid and power market is crucial for China now. This review targets to investigate smart metering from industrial perspectives, under the current reforms of electricity spot markets[2]. Methodology is mainly through the quantitative analysis of applicability of the metering updating project. Nomenclature AMI Automated Meter Infrastructure AMR Automated Meter Reading IoT Internet of Things DC Direct Current CHP Combine Heat and Power PV Solar Photovoltaic HAN Home Area Network GIS Geographic Information System NPW Net Present Worth MARR Minimum Attractive Rate of Return MACRS Modified Accelerated Cost of Recovery System DSM Demand Side Management 1.1. International mainstream metering systems AMR appears after widely use of electronic meters. This metering technology replaces periodic manual work for energy (consumption or generation) collection. By one-way communication from meter to station, AMR makes it possible to remote control the electricity supply and simple cut-off, match accounts to offer bills with time-of-use tariff, and obtain rough load profile. Over the years, the AMR now is capable for short term interval (1 hour or less) data collection, on-request reading and multi-commodities reading. While the AMR comes across bottle neck when it is now in high demand of building more interoperable, connected, intelligent metering networks. In recent decades, metering system is developing from AMR to AMI, which already widely spread out in countries like US, UK, and Australia[2][3]. The most significant promotion of AMI is the two-way communication. The purpose of the AMI is to achieve the following: • Support power market trading with real-time (5 min to 1 h) data • Activate the distributed renewables and prosumers • Support other smart grid application and business enablement. 1.2. Top issues of energy metering in China Current or upcoming issues of energy metering in China mainly lie in the deregulated market reform, low-carbon technologies and end-user demands. Metering for spot market needs to be highly supported. Zhejiang Province is among the first trials of spot markets in China. Taking this province as a case, metering system is challenged by the spot markets under planning. Though in retailed side, smart meters have already covered more than 99% of end users, the most important problem is that the qualification of these installed meters should be modified. Especially, problems remain in 1) the frequency of interval metering data in spot market is much higher than the present; 2) the requirement of metering installation clock
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should be enhanced, which calls for accuracy as high as in seconds;3) balancing the financial cost and requirement of spot markets is crucial. Metering for low-carbon energy technologies is to be standardized. New energy technologies in generation, transmission, and distribution network propose challenges to the modern metering measures and standards. Net metering is a typical positive policy for green energy prosumers. This approach encourages distributed renewables to generate power by compensating green energy generation amount from consumption. Another potential trend, DC metering and dispatching network are to be re-defined, for the reason that there are more and more DC situations appearing in current grid, e.g. VSC-HVDC, PV, wind turbine, battery storage, DC charging for EVs[5]. Metering for end user scenarios is to be integrated. Energy users need more than single electricity consumption metering. Reliable energy supply is the premise for industrial production. Metering with multi-commodities reading is in highly demand, like in CHP system. By monitoring outage detection, energy flow and energy quality, this factory can get sooner fault diagnosis and more predictable restoration. This can help data central collection and analysis. When PV generation is predictable enough, return of investment have more likelihood of business intelligence. Furthermore, cost control is not only about energy saving to reach efficiency, but also operating energy utilities plan at right time in proper order. As the same principal, in other typical scenarios like household energy also rely on customer behavior[6]. Therefore, metering data analysis can support decision-making. 2. Issues Analysis In this part, firstly, it is illustrated that the components in a typical metering system. Then an engineering economics method, NPW is introduced to evaluate the cost and effect of smart meter update project. After these, business enablement is discussed to make more use of the expensive and mass roll-out of smart meters. 2.1. Technology architecture
Fig. 1. Typical Smart Metering System Structure
The AMI physical system consists of 3 level networks, like in Fig.1, mater station and data process for business. The AMI meter is a nuclear cell of the AMI system. The AMI meter specifications are usually mandated by governments in the functional order. The meters are required to have advanced functions such as remote interval data reading, remote connect and disconnect, advanced load control and an interface to a HAN. The HAN connects the household energy consuming devices, taking charge of household energy management. The AMI local area network includes the communications network between the AMI meter and the AMI access points. The AMI local area network includes communications modems that reside in the AMI meters (network interface cards). The AMI wide area
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network The wide area network covers the communications systems and infrastructure between the network management system in the back-office, and the access points in the field. The WAN solution includes modems for the access points, WAN communications management aspects of the access points, communications link between the data centers, tele-communications carriers, the firewalls, secure zones within the data center and the overall security of the network. The AMI network management system manages the communications network and AMI meters, including management of the communications to and from the meters, as well as device management functions such as firmware and configuration management. It can be envisaged that in a complex system such as smart grid, heterogeneous communication technologies [7] are required to meet the diverse needs of the system. Therefore, the standardization of communications for smart grid means making interfaces, messages and work flows interoperable, instead of focusing on or defining one particular technology, it is more important to achieve agreement on usage and interpretation of interfaces and messages that can seamlessly bridge different standards. The goal of communication standardization for smart grids is ensuring interoperability between different system components rather than defining these components. In the case of Victory, Australia, it is widely use multiple communication mode in each network. This measure could be used in future design for future Chinese metering communication. For example, short-range wireless such as Bluetooth or UWB could be used for the interface between meter and end customer devices, IEEE 802.15.4 (ZigBee) and IEEE 802.11 (Wi-Fi) could be used for smart meter interfaces in the home and local area network, and cellular wireless (e.g. GPRS, UMTS, or 4G technologies like 802.16m and LTE) could be used for the interface between relays and the mater stations[4]. 2.2. NPW analysis Definition of NPW is according to the time value of money. The interest rate is determined by a company. This interest rate is usually referred to as a minimum attractive rate of return, namely MARR. Then when estimating the life-cycle of the project, incomes and outcomes should be counted in a certain year. And considering the cash flow of each item, finally the net cash flow is determined[8]. Equations as below in (1) 𝑃𝑃𝑃𝑃 𝑖𝑖 =
where
%&
(()*)&
+
%-
(()*)-
+
%.
(()*).
+⋯+
%0
(()*)0
=
%2 3 456 (()*)2
(1)
An = Net cash flow at end of period (or year) n i = MARR (or cost of capital) N = Service life of the project
Depreciation rules here fit for the MACRS method. The yearly recovery or depreciation expense is determined by multiplying the asset’s depreciation base by the applicable recovery allowance percentage. Based on the above fundamental methodology, the Zhejiang case is modeled to simulate the current situation of smart meter updating for the customers of 35 kV or above levels. The investment for fixed cost is assumed to happen in 2018. The period is determined to be 10 years because of the smart meter average lifelong cycle. The unit cost of each item is according to the range of approved projects budget limit in State Grid Zhejiang Electrical Power Co., LTD. Considering all the metering installations, maintenance, depreciation, income taxes, it is obvious to have each year net cash flow results in the below Table 1. Table 1. Cash Flow Statement. End of Year Net Cash Flow
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
-50
1.1564
2.1564
1.7064
1.3464
1.1464
0.82765
0.93515
0.9339
0.9339
0.4214
Yikuai Wang et al. / Energy Procedia 152 (2018) 1194–1199 Yikuai Wang et al./ Energy Procedia 00 (2018) 000–000
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5
Fig. 2. NPW of smart meter update project required for spot market
As the result of calculation, a company can hardly make it profitable. No matter how much the MARR the firm sets, the NPW is always below Zero. This curve also shows that to balance the budget of update project, how much profit should be earned based on the smart meter update project for balancing the costs. So it is highly recommended that overall updating project is not advisable, the same as the startup measures in Australia and the US spot market. Instead, it is suggested to use net metering method to solve the problem. 2.3. Business enablement How to earn the extra profit from new generation of smart metering? It is about new business enablement. Smart meters infrastructure provides IoT rough data, which can generate extra profit for updating smart meter project. It needs data processing and analytics to dig further business applications[9].Business intelligence could be achieved based on AMI system. Some core capabilities have been defined, such as the GIS, metering data management, network management, asset management, outage restoration, and other chances power market. From the side of consumers or market participants[10], the integrated technologies and supply of energy is needed. The CHP relies on gas, heat, electricity combining metering to provide service [11][12]. As described in [4], data mining techniques can be used to reveal trends of personal behavior in the metering data even if data sampling rate (e.g. every 30 minutes) is relatively low. What is more, the research by[13] shows that metering data used into energy load disaggregation gives transparent insights for residential, commercial or industrial customers. Some of DSM methods dependent on smart meters to control the load [14][15][16][17]. These aspects all could be promoted to help customer get down the cost, not only in energy but also in financial result[10]. From the side of market operation, metering data helps algorithm optimization to analyze and predict the power markets. Nodal pricing can be solved by graph theory as described in[5], [18], [19], particularly when the variable renewables entry into grid in quantity. This research[13]applies Support Vector Machines (SVM) to develop smart metering for renewables generation prediction. The K-means method used in [20] is one of the most typically used partition cluster analysis methods. 3. Barriers remained Smart metering in future smart grid would face with the following issues, some of which are of high concerned by the public. • Cyber-security and privacy issues • Standards or interoperation issues New threats occur. Firstly, smart grid cyber threats have the potential to breach national security, economic stability and even physical security. Smart grid cyber-security needs to a) prevent such attacks from happening and b) have a recovery mechanism in case of potential attacks. Secondly, metering data contain mainly the consumers’ individual,
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economic, or industrial behaviors. With the metering frequency demand of power market getting higher, the data privacy is more and more of a big issue under debate. It is proposed that there are two classes of privacy protection schemes [4]: a) regulatory-based ones and b) technological-based ones. It is without doubt that both of the two schemes should be applied flexibly. 4. Conclusions This paper presents an overview investigation of mainstream smart metering, from AMR to AMI. Especially for the startup period of developing spot market, the study applies economic mathematical method to estimate smart metering updating project. It is highly suggested to use net metering method to solve the problem. Though there are still worries about the mass rollout of smart meters, it is obvious that the smart metering can help energy market participants to find new potential application in future smart grid, no matter in China or worldwide. Acknowledgements This research is supported by State Grid Zhejiang Electrical Power Co., LTD. and Zhejiang University SGOOL laboratory. References [1] IEA., World Energy Outlook 2017. Organisation for Economic Co-operation and Development, OECD, 2017. [2] D. Yu et al., “Roadmap of retail electricity market reform in China: assisting in mitigating wind energy curtailment,” IOP Conf. Ser. Earth Environ. Sci., vol. 52, no. 1, p. 012031, 2017. [3] W. PAPER, “Great Britain Smart Meter Infrastructure: Analysis of Potential Architectures,” p. 32. [4] Z. Fan et al., “Smart grid communications: Overview of research challenges, solutions, and standardization activities,” IEEE Commun. Surv. Tutor., vol. 15, no. 1, pp. 21–38, 2013. [5] C. Zhang, Q. Huang, J. Tian, L. Chen, Y. Cao, and R. Zhang, “Smart grid facing the new challenge: The management of electric vehicle charging loads,” Energy Procedia, vol. 12, pp. 98–103, 2011. [6] J. Stephenson, B. Barton, G. Carrington, D. Gnoth, R. Lawson, and P. Thorsnes, “Energy cultures: A framework for understanding energy behaviours,” Energy Policy, vol. 38, no. 10, pp. 6120–6129, 2010. [7] M. Mourshed et al., “Smart grid futures: Perspectives on the integration of energy and ICT services,” 2015. [8] Contemporary Engineering Economics (5th Edition): Chan S. Park. [9] D. Alahakoon and X. Yu, “Smart electricity meter data intelligence for future energy systems: A survey,” IEEE Trans. Ind. Inform., vol. 12, no. 1, pp. 425–436, 2016. [10] A. Roos, S. Ø. Ottesen, and T. F. Bolkesjø, “Modeling consumer flexibility of an aggregator participating in the wholesale power market and the regulation capacity market,” Energy Procedia, vol. 58, pp. 79–86, 2014. [11] “A Framework for Incorporating Demand Response of Smart Buildings into the Integrated Heat and Electricity Energy System - IEEE Journals & Magazine.”. [12] C. Shao, Y. Ding, J. Wang, and Y. Song, “Modeling and Integration of Flexible Demand in Heat and Electricity Integrated Energy System,” IEEE Trans. Sustain. Energy, vol. 9, no. 1, pp. 361–370, Jan. 2018. [13] K. Chahine et al., “Electric load disaggregation in smart metering using a novel feature extraction method and supervised classification,” Energy Procedia, vol. 6, pp. 627–632, 2011. [14] H. Hui, Y. Ding, W. Liu, Y. Lin, and Y. Song, “Operating reserve evaluation of aggregated air conditioners,” Appl. Energy, vol. 196, no. Supplement C, pp. 218–228, Jun. 2017. [15] C. Shao, Y. Ding, Y. Song, and C. Zhu, “Demand response from multiple-energy customers in integrated energy system,” in 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), 2017, pp. 1–6. [16] P. Wang, J. Y. Huang, Y. Ding, P. Loh, and L. Goel, “Demand Side Load Management of Smart Grids using intelligent trading/Metering/ Billing System,” in IEEE PES General Meeting, 2010, pp. 1–6. [17] H. Jia, W. Jin, Y. Ding, Y. Song, and D. Yu, “Multi-state time-varying reliability evaluation of smart grid with flexible demand resources utilizing Lz transform,” IOP Conf. Ser. Earth Environ. Sci., vol. 52, no. 1, p. 012011, 2017. [18] A. Jokić, M. Lazar, and P. P. van den Bosch, “Real-time control of power systems using nodal prices,” Int. J. Electr. Power Energy Syst., vol. 31, no. 9, pp. 522–530, 2009. [19] P. Staudt, F. Wegner, J. Garttner, and C. Weinhardt, “Analysis of redispatch and transmission capacity pricing on a local electricity market setup,” in 2017 14th International Conference on the European Energy Market (EEM), 2017, pp. 1–6. [20] A. Al-Wakeel and J. Wu, “K-means based cluster analysis of residential smart meter measurements,” Energy Procedia, vol. 88, pp. 754–760, 2016.
ScienceDirect ScienceDirect
Energy Procedia 00www.sciencedirect.com (2018) 000–000 Available online Available online atatwww.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 152 Energy Procedia 00(2018) (2017)1194–1199 000–000 www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, CUE2018-Applied Energy and Forum 2018: Low carbon and Applied Energy Symposium andSymposium Forum 2018: Low carbon cities and urbancities energy systems, CUE2018, 5–7June 2018, Shanghai, China CUE2018, 5–7June 2018, Shanghai, China urban energy systems, 5–7 June 2018, Shanghai, China
A Review of Smart Metering for Future Chinese Grids The 15thof International Symposium for on District Heating and Cooling A Review Smart Metering Future Chinese Grids
Yikuai Wangab* , Huadong Qiuaa, Ying Tuaa, Qiang Liuaa, Yi Dingbb, Weifeng Wangaa ab* Yikuai Wang , Huadong Qiu , Ying , Qiang Liuheat , Yi Ding , Weifeng Wang Assessing the feasibility ofTuusing the demand-outdoor a
State Grid Zhejiang Electrical Power Co., LTD, No. 8 Road Huanglong, Hangzhou, 310000, China
Zhejiang University, Zheda, Hangzhou, 310000, China 310000, State Grid Zhejiang Electrical Co., LTD, No. 8 Road Huanglong, Hangzhou, China temperature function forPower a No.38Road long-term district heat demand forecast Zhejiang University, No.38Road Zheda, Hangzhou, 310000, China a
b b
I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
Abstract a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b
Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Smart metering solutions are crucial to the future power grid infrastructure. In order to develop next generation of c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Smart metering are crucial theresearch future power grid infrastructure. In order to develop next generation of metering system insolutions Chinese smart grid, to this investigates smart metering in industrial perspectives, focusing metering system in Chinese smart grid, this research investigates smart metering in industrial perspectives, focusing on mainstream international metering technologies. Especially, to meet with the time and qualification requirements on the mainstream international metering technologies. to meet with the time and qualification requirements of start-up period of developing spot market, theEspecially, study applies economic mathematical method to estimate smart of the start-up period of developing spot market, the study applies economic mathematical method to estimate smart Abstract metering updating project. metering updating project.
District heating are All commonly addressed in the literature as one of the most effective solutions for decreasing the Copyright © 2018 networks Elsevier Ltd. rights reserved. Copyright © gas 2018emissions Elsevier Ltd. Ltd. All rights reserved. greenhouse fromAll therights building sector. These systems require investments which are returned through heat Copyright © 2018 Elsevierunder reserved. Selection and peer-review responsibility of the scientific committee ofhigh Applied Energy Symposium and Forum 2018:theLow Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, Selection and peer-review under responsibility of the scientific committee of Applied Energy Symposium and Forum 2018: Low carbon energy CUE2018. Forum cities 2018:and Lowurban carbon citiessystems, and urban energy systems. prolonging return period. carbon cities the andinvestment urban energy systems, CUE2018. The main scope of this Smart papergrid; is toPower assessmarkets the feasibility of using the heat demand – outdoor temperature function for heat demand Keywords:Smart metering; forecast. The metering; district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 Keywords:Smart Smart grid; Power markets buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were 1.renovation Introduction with results from a dynamic heat demand model, previously developed and validated by the authors. 1.compared Introduction The that environment when only weather is considered, the margin of the errorsituation could beon acceptable forThe someapproaches applications In results recent showed centuries, issueschange such as global warming threat the earth. (the error annual demand was lowerissues than 20% for all weather scenarios considered). However, after introducing renovation Inpeople recentinexploit centuries, such as influence global warming threat the On situation on side, the earth. The approaches that and environment consume energy have huge on environment. the other the demand of global scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). that people and So consume energy have on environment. On thesector other side, the demand of global energy still exploit increases. in order to suit forhuge the influence future development, the energy is urgent for reforms. As The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the energy stillbyincreases. So inEnergy order to suit for the future development, the energy sector iscontinues urgent forapace, reforms. As published International Agency (IEA)[1], low-carbon generation technologies among decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and published by International Energy Agency (IEA)[1], low-carbon generation technologies continues apace, among which are led by wind turbine and solar photovoltaic (PV). It is anticipated that the summary share of wind and solar renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the which arescenarios). ledgeneration by wind photovoltaic (PV). It20% is anticipated the summary share of China wind and solar in the global grows fromsolar 5% at present to almost until 2040.that Among allfor thethe countries, plays an coupled Theturbine values and suggested could be used to modify the function parameters scenarios considered, and inimprove the global generation grows from 5% at present to almost 20% until 2040. Among all the countries, China plays an the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +86 15088681889; fax: +86 0571 51102144. Cooling. *
E-mail address:author. [email protected] Corresponding Tel.: +86 15088681889; fax: +86 0571 51102144. E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection peer-review under responsibility the scientific 1876-6102and Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urbanand energy systems, under CUE2018. Selection peer-review responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, CUE2018.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems. 10.1016/j.egypro.2018.09.158
2
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important role in the renewable energy market. As published by National Energy Administration of China, in 2017, the electricity consumption of the whole society supported by grids is 6307.7 billion kW·h. In the near future, with the deregulated power markets evolution, the Chinese grids face new challenges in energy metering and monitoring. As the predominant infrastructure in smart grids, the smart metering is fundamental to the bull generations, renewable energy, and power markets. As a result, the architectural design of metering system for future smart grid and power market is crucial for China now. This review targets to investigate smart metering from industrial perspectives, under the current reforms of electricity spot markets[2]. Methodology is mainly through the quantitative analysis of applicability of the metering updating project. Nomenclature AMI Automated Meter Infrastructure AMR Automated Meter Reading IoT Internet of Things DC Direct Current CHP Combine Heat and Power PV Solar Photovoltaic HAN Home Area Network GIS Geographic Information System NPW Net Present Worth MARR Minimum Attractive Rate of Return MACRS Modified Accelerated Cost of Recovery System DSM Demand Side Management 1.1. International mainstream metering systems AMR appears after widely use of electronic meters. This metering technology replaces periodic manual work for energy (consumption or generation) collection. By one-way communication from meter to station, AMR makes it possible to remote control the electricity supply and simple cut-off, match accounts to offer bills with time-of-use tariff, and obtain rough load profile. Over the years, the AMR now is capable for short term interval (1 hour or less) data collection, on-request reading and multi-commodities reading. While the AMR comes across bottle neck when it is now in high demand of building more interoperable, connected, intelligent metering networks. In recent decades, metering system is developing from AMR to AMI, which already widely spread out in countries like US, UK, and Australia[2][3]. The most significant promotion of AMI is the two-way communication. The purpose of the AMI is to achieve the following: • Support power market trading with real-time (5 min to 1 h) data • Activate the distributed renewables and prosumers • Support other smart grid application and business enablement. 1.2. Top issues of energy metering in China Current or upcoming issues of energy metering in China mainly lie in the deregulated market reform, low-carbon technologies and end-user demands. Metering for spot market needs to be highly supported. Zhejiang Province is among the first trials of spot markets in China. Taking this province as a case, metering system is challenged by the spot markets under planning. Though in retailed side, smart meters have already covered more than 99% of end users, the most important problem is that the qualification of these installed meters should be modified. Especially, problems remain in 1) the frequency of interval metering data in spot market is much higher than the present; 2) the requirement of metering installation clock
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should be enhanced, which calls for accuracy as high as in seconds;3) balancing the financial cost and requirement of spot markets is crucial. Metering for low-carbon energy technologies is to be standardized. New energy technologies in generation, transmission, and distribution network propose challenges to the modern metering measures and standards. Net metering is a typical positive policy for green energy prosumers. This approach encourages distributed renewables to generate power by compensating green energy generation amount from consumption. Another potential trend, DC metering and dispatching network are to be re-defined, for the reason that there are more and more DC situations appearing in current grid, e.g. VSC-HVDC, PV, wind turbine, battery storage, DC charging for EVs[5]. Metering for end user scenarios is to be integrated. Energy users need more than single electricity consumption metering. Reliable energy supply is the premise for industrial production. Metering with multi-commodities reading is in highly demand, like in CHP system. By monitoring outage detection, energy flow and energy quality, this factory can get sooner fault diagnosis and more predictable restoration. This can help data central collection and analysis. When PV generation is predictable enough, return of investment have more likelihood of business intelligence. Furthermore, cost control is not only about energy saving to reach efficiency, but also operating energy utilities plan at right time in proper order. As the same principal, in other typical scenarios like household energy also rely on customer behavior[6]. Therefore, metering data analysis can support decision-making. 2. Issues Analysis In this part, firstly, it is illustrated that the components in a typical metering system. Then an engineering economics method, NPW is introduced to evaluate the cost and effect of smart meter update project. After these, business enablement is discussed to make more use of the expensive and mass roll-out of smart meters. 2.1. Technology architecture
Fig. 1. Typical Smart Metering System Structure
The AMI physical system consists of 3 level networks, like in Fig.1, mater station and data process for business. The AMI meter is a nuclear cell of the AMI system. The AMI meter specifications are usually mandated by governments in the functional order. The meters are required to have advanced functions such as remote interval data reading, remote connect and disconnect, advanced load control and an interface to a HAN. The HAN connects the household energy consuming devices, taking charge of household energy management. The AMI local area network includes the communications network between the AMI meter and the AMI access points. The AMI local area network includes communications modems that reside in the AMI meters (network interface cards). The AMI wide area
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network The wide area network covers the communications systems and infrastructure between the network management system in the back-office, and the access points in the field. The WAN solution includes modems for the access points, WAN communications management aspects of the access points, communications link between the data centers, tele-communications carriers, the firewalls, secure zones within the data center and the overall security of the network. The AMI network management system manages the communications network and AMI meters, including management of the communications to and from the meters, as well as device management functions such as firmware and configuration management. It can be envisaged that in a complex system such as smart grid, heterogeneous communication technologies [7] are required to meet the diverse needs of the system. Therefore, the standardization of communications for smart grid means making interfaces, messages and work flows interoperable, instead of focusing on or defining one particular technology, it is more important to achieve agreement on usage and interpretation of interfaces and messages that can seamlessly bridge different standards. The goal of communication standardization for smart grids is ensuring interoperability between different system components rather than defining these components. In the case of Victory, Australia, it is widely use multiple communication mode in each network. This measure could be used in future design for future Chinese metering communication. For example, short-range wireless such as Bluetooth or UWB could be used for the interface between meter and end customer devices, IEEE 802.15.4 (ZigBee) and IEEE 802.11 (Wi-Fi) could be used for smart meter interfaces in the home and local area network, and cellular wireless (e.g. GPRS, UMTS, or 4G technologies like 802.16m and LTE) could be used for the interface between relays and the mater stations[4]. 2.2. NPW analysis Definition of NPW is according to the time value of money. The interest rate is determined by a company. This interest rate is usually referred to as a minimum attractive rate of return, namely MARR. Then when estimating the life-cycle of the project, incomes and outcomes should be counted in a certain year. And considering the cash flow of each item, finally the net cash flow is determined[8]. Equations as below in (1) 𝑃𝑃𝑃𝑃 𝑖𝑖 =
where
%&
(()*)&
+
%-
(()*)-
+
%.
(()*).
+⋯+
%0
(()*)0
=
%2 3 456 (()*)2
(1)
An = Net cash flow at end of period (or year) n i = MARR (or cost of capital) N = Service life of the project
Depreciation rules here fit for the MACRS method. The yearly recovery or depreciation expense is determined by multiplying the asset’s depreciation base by the applicable recovery allowance percentage. Based on the above fundamental methodology, the Zhejiang case is modeled to simulate the current situation of smart meter updating for the customers of 35 kV or above levels. The investment for fixed cost is assumed to happen in 2018. The period is determined to be 10 years because of the smart meter average lifelong cycle. The unit cost of each item is according to the range of approved projects budget limit in State Grid Zhejiang Electrical Power Co., LTD. Considering all the metering installations, maintenance, depreciation, income taxes, it is obvious to have each year net cash flow results in the below Table 1. Table 1. Cash Flow Statement. End of Year Net Cash Flow
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
-50
1.1564
2.1564
1.7064
1.3464
1.1464
0.82765
0.93515
0.9339
0.9339
0.4214
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Fig. 2. NPW of smart meter update project required for spot market
As the result of calculation, a company can hardly make it profitable. No matter how much the MARR the firm sets, the NPW is always below Zero. This curve also shows that to balance the budget of update project, how much profit should be earned based on the smart meter update project for balancing the costs. So it is highly recommended that overall updating project is not advisable, the same as the startup measures in Australia and the US spot market. Instead, it is suggested to use net metering method to solve the problem. 2.3. Business enablement How to earn the extra profit from new generation of smart metering? It is about new business enablement. Smart meters infrastructure provides IoT rough data, which can generate extra profit for updating smart meter project. It needs data processing and analytics to dig further business applications[9].Business intelligence could be achieved based on AMI system. Some core capabilities have been defined, such as the GIS, metering data management, network management, asset management, outage restoration, and other chances power market. From the side of consumers or market participants[10], the integrated technologies and supply of energy is needed. The CHP relies on gas, heat, electricity combining metering to provide service [11][12]. As described in [4], data mining techniques can be used to reveal trends of personal behavior in the metering data even if data sampling rate (e.g. every 30 minutes) is relatively low. What is more, the research by[13] shows that metering data used into energy load disaggregation gives transparent insights for residential, commercial or industrial customers. Some of DSM methods dependent on smart meters to control the load [14][15][16][17]. These aspects all could be promoted to help customer get down the cost, not only in energy but also in financial result[10]. From the side of market operation, metering data helps algorithm optimization to analyze and predict the power markets. Nodal pricing can be solved by graph theory as described in[5], [18], [19], particularly when the variable renewables entry into grid in quantity. This research[13]applies Support Vector Machines (SVM) to develop smart metering for renewables generation prediction. The K-means method used in [20] is one of the most typically used partition cluster analysis methods. 3. Barriers remained Smart metering in future smart grid would face with the following issues, some of which are of high concerned by the public. • Cyber-security and privacy issues • Standards or interoperation issues New threats occur. Firstly, smart grid cyber threats have the potential to breach national security, economic stability and even physical security. Smart grid cyber-security needs to a) prevent such attacks from happening and b) have a recovery mechanism in case of potential attacks. Secondly, metering data contain mainly the consumers’ individual,
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economic, or industrial behaviors. With the metering frequency demand of power market getting higher, the data privacy is more and more of a big issue under debate. It is proposed that there are two classes of privacy protection schemes [4]: a) regulatory-based ones and b) technological-based ones. It is without doubt that both of the two schemes should be applied flexibly. 4. Conclusions This paper presents an overview investigation of mainstream smart metering, from AMR to AMI. Especially for the startup period of developing spot market, the study applies economic mathematical method to estimate smart metering updating project. It is highly suggested to use net metering method to solve the problem. Though there are still worries about the mass rollout of smart meters, it is obvious that the smart metering can help energy market participants to find new potential application in future smart grid, no matter in China or worldwide. Acknowledgements This research is supported by State Grid Zhejiang Electrical Power Co., LTD. and Zhejiang University SGOOL laboratory. References [1] IEA., World Energy Outlook 2017. Organisation for Economic Co-operation and Development, OECD, 2017. [2] D. Yu et al., “Roadmap of retail electricity market reform in China: assisting in mitigating wind energy curtailment,” IOP Conf. Ser. Earth Environ. Sci., vol. 52, no. 1, p. 012031, 2017. [3] W. PAPER, “Great Britain Smart Meter Infrastructure: Analysis of Potential Architectures,” p. 32. [4] Z. Fan et al., “Smart grid communications: Overview of research challenges, solutions, and standardization activities,” IEEE Commun. Surv. Tutor., vol. 15, no. 1, pp. 21–38, 2013. [5] C. Zhang, Q. Huang, J. Tian, L. Chen, Y. Cao, and R. Zhang, “Smart grid facing the new challenge: The management of electric vehicle charging loads,” Energy Procedia, vol. 12, pp. 98–103, 2011. [6] J. Stephenson, B. Barton, G. Carrington, D. Gnoth, R. Lawson, and P. Thorsnes, “Energy cultures: A framework for understanding energy behaviours,” Energy Policy, vol. 38, no. 10, pp. 6120–6129, 2010. [7] M. Mourshed et al., “Smart grid futures: Perspectives on the integration of energy and ICT services,” 2015. [8] Contemporary Engineering Economics (5th Edition): Chan S. Park. [9] D. Alahakoon and X. Yu, “Smart electricity meter data intelligence for future energy systems: A survey,” IEEE Trans. Ind. Inform., vol. 12, no. 1, pp. 425–436, 2016. [10] A. Roos, S. Ø. Ottesen, and T. F. Bolkesjø, “Modeling consumer flexibility of an aggregator participating in the wholesale power market and the regulation capacity market,” Energy Procedia, vol. 58, pp. 79–86, 2014. [11] “A Framework for Incorporating Demand Response of Smart Buildings into the Integrated Heat and Electricity Energy System - IEEE Journals & Magazine.”. [12] C. Shao, Y. Ding, J. Wang, and Y. Song, “Modeling and Integration of Flexible Demand in Heat and Electricity Integrated Energy System,” IEEE Trans. Sustain. Energy, vol. 9, no. 1, pp. 361–370, Jan. 2018. [13] K. Chahine et al., “Electric load disaggregation in smart metering using a novel feature extraction method and supervised classification,” Energy Procedia, vol. 6, pp. 627–632, 2011. [14] H. Hui, Y. Ding, W. Liu, Y. Lin, and Y. Song, “Operating reserve evaluation of aggregated air conditioners,” Appl. Energy, vol. 196, no. Supplement C, pp. 218–228, Jun. 2017. [15] C. Shao, Y. Ding, Y. Song, and C. Zhu, “Demand response from multiple-energy customers in integrated energy system,” in 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), 2017, pp. 1–6. [16] P. Wang, J. Y. Huang, Y. Ding, P. Loh, and L. Goel, “Demand Side Load Management of Smart Grids using intelligent trading/Metering/ Billing System,” in IEEE PES General Meeting, 2010, pp. 1–6. [17] H. Jia, W. Jin, Y. Ding, Y. Song, and D. Yu, “Multi-state time-varying reliability evaluation of smart grid with flexible demand resources utilizing Lz transform,” IOP Conf. Ser. Earth Environ. Sci., vol. 52, no. 1, p. 012011, 2017. [18] A. Jokić, M. Lazar, and P. P. van den Bosch, “Real-time control of power systems using nodal prices,” Int. J. Electr. Power Energy Syst., vol. 31, no. 9, pp. 522–530, 2009. [19] P. Staudt, F. Wegner, J. Garttner, and C. Weinhardt, “Analysis of redispatch and transmission capacity pricing on a local electricity market setup,” in 2017 14th International Conference on the European Energy Market (EEM), 2017, pp. 1–6. [20] A. Al-Wakeel and J. Wu, “K-means based cluster analysis of residential smart meter measurements,” Energy Procedia, vol. 88, pp. 754–760, 2016.