Abstract
The widespread popularity of smart meters has promoted the transition from traditional grid into modern smart grid, which is aimed to meet the rapid-growing demand for higher quality service and take up the emergence of new challenges. How to analyze, transmit and make the most of massive smart meter data to enhance the efficiency and reliability is our priority. The purpose of this paper is to conduct a detailed review to summarize and evaluate the latest advances in smart meter data analysis, privacy preserving and residential energy management. We conclude the analysis of smart meter data, protecting techniques in the process of delivering and end-uses of smart meter data application according to the flowing direction of smart meter data. Compared with other review papers, we analyze the merits and drawbacks in corresponding situations and provide readers a more detailed eyesight to the research status in modern smart grid.
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References
Momoh. Smart grid design for efficient and flexible power networks operation and control. In: 2009 IEEE/PES Power Systems Conference and Exposition, pp. 1–8. IEEE (2009)
Asghar, M.R.: Smart meter data privacy: a survey. IEEE Commun. Surv. Tutor. 19(4), 2820–2835 (2017)
Preibusch, S.: Privacy behaviors after Snowden. Commun. ACM 58(5), 48–55 (2015)
How many smart meters are installed in the United States, and who has them? https://www.eia.gov/tools/faqs/faq.php?id=108&t=3. Accessed 31 July 2017
Mohassel, R.R.: A survey on advanced metering infrastructure. Int. J. Electr. Power Energy Syst. 63, 473–484 (2014)
McDaniel, P.: Security and privacy challenges in the smart grid. IEEE Secur. Priv. 7(3), 75–77 (2009)
Molina-Markham, A.: Private memories of a smart meter. In: Proceedings of the ACM Workshop Embedded Sensors System Efficiency Building, Switzerland, pp. 61–66 (2010)
Kalogridis, G.: Elecprivacy: evaluating the privacy protection of electricity management algorithms. IEEE Trans. Smart Grid 2(4), 750–758 (2011)
Komninos, N.: Survey in smart grid and smart home security: issues, challenges and countermeasures. IEEE Commun. Surv. Tutor. 16(4), 1933–1954 (2014)
Lu, X.: Authentication and integrity in the smart grid: an empirical study in substation automation systems. Int. J. Distrib. Sens. Netw. 8(6), 175262 (2012)
Khurana, H.: Smart-grid security issues. IEEE Secur. Priv. 8(1), 81–85 (2010)
Anderson, R.J.: On the security economics of electricity metering. In: WEIS (June 2010)
Wang, W.: Cyber security in the smart grid: survey and challenges. Comput. Netw. 57(5), 1344–1371 (2013)
Sharma, K.: Performance analysis of smart metering for smart grid: an overview. Renew. Sustain. Energy Rev. 49, 720–735 (2015)
Finster, S.: Privacy-aware smart metering: a survey. IEEE Commun. Surv. Tutor. 16(3), 1732–1745 (2014)
Nguyen, H.: Distributed demand side management with energy storage in smart grid. IEEE Trans. Parallel Distrib. Syst. 26(12), 3346–3357 (2014)
Marsan, M.: Towards zero grid electricity networking: powering BSs with renewable energy sources. In: 2013 IEEE International Conference on Communications Workshops (ICC), pp. 596–601. IEEE (2013)
Borenstein, S.: Dynamic Pricing Advanced Metering and Demand Response in Electricity Markets. Center for the Study of Energy Markets (2002)
Han, J.: Green home energy management system through comparison of energy usage between the same kinds of home appliances. In: 2011 IEEE 15th International Symposium on Consumer Electronics (ISCE), pp. 1–4. IEEE (2011)
McArthur, S.: Multi-agent systems for power engineering applications—Part I: Concepts, approaches, and technical challenges. IEEE Trans. Power Syst. 22(4), 1743–1752 (2007)
Swan, L.: Modeling of end-use energy consumption in the residential sector: a review of modeling techniques. Renew. Sustain. Energy Rev. 13(8), 1819–1835 (2009)
Bucher, C.: Generation of domestic load profiles-an adaptive top-down approach. Proc. PMAPS 2012, 10–14 (2012)
Prudenzi, A.: Analysis of residential standby power demand control through a psychological model of demand. In: 10th International Conference on Environment and Electrical Engineering, pp. 1–4. IEEE (2011)
Muratori, M.: A highly resolved modeling technique to simulate residential power demand. Appl. Energy 107, 465–473 (2013)
Kavgic, M.: A review of bottom-up building stock models for energy consumption in the residential sector. Build. Environ. 45(7), 1683–1697 (2010)
Suganthi, L.: Energy models for demand forecasting—a review. Renew. Sustain. Energy Rev. 16(2), 1223–1240 (2012)
Davarzani, S.: A Novel Methodology for Predicting Potential Responsiveness in Residential Demand (2017)
Jazaeri, J.: Model predictive control of residential demand in low voltage network using ice storage. In: 2018 Australian & New Zealand Control Conference (ANZCC), pp. 51–55. IEEE (2018)
Barbato, A.: Forecasting the usage of household appliances through power meter sensors for demand management in the smart grid. In: 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 404–409. IEEE (2011)
Alzate, E.: A high-resolution smart home power demand model and future impact on load profile in Germany. In: 2014 IEEE International Conference on Power and Energy (PECon), pp. 53–58. IEEE (2014)
Shaikh, P.: A review on optimized control systems for building energy and comfort management of smart sustainable buildings. Renew. Sustain. Energy Rev. 34, 409–429 (2014)
Vega, A.: Modeling for home electric energy management: a review. Renew. Sustain. Energy Rev. 52, 948–959 (2015)
Fischer, D.: Model for electric load profiles with high time resolution for German households. Energy Build. 92, 170–179 (2015)
Gottwalt, S.: Demand side management—a simulation of household behavior under variable prices. Energy Policy 39(12), 8163–8174 (2011)
Muratori, M.: Impact of uncoordinated plug-in electric vehicle charging on residential power demand. Nat. Energy 3(3), 193–201 (2018)
Ge, Y.: Domestic electricity load modelling by multiple Gaussian functions. Energy Build. 126, 455–462 (2016)
Wu, Z.: Real-time scheduling of residential appliances via conditional risk-at-value. IEEE Trans. Smart Grid 5(3), 1282–1291 (2014)
Chavali, P.: A distributed algorithm of appliance scheduling for home energy management system. IEEE Trans. Smart Grid 5(1), 282–290 (2014)
McLoughlin, F.: The generation of domestic electricity load profiles through Markov chain modelling. Euro-Asian J. Sustain. Energy Dev. Policy 3, 12 (2010)
You, Y.: Energy management strategy for smart meter privacy and cost saving. IEEE Trans. Inf. Forens. Secur. 16, 1522–1537 (2020)
Kriechbaumer, T.: BLOND, a building-level office environment dataset of typical electrical appliances. Sci. Data 5(1), 1–14 (2018)
Chicco, G.: Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy 42(1), 68–80 (2012)
Sousa, J.C.: Load forecasting based on neural networks and load profiling. In: 2009 IEEE Bucharest PowerTech, pp. 1–8. IEEE (2009)
Salehkalaibar, S.: Hypothesis testing for privacy of smart meters with side information. IEEE Trans. Smart Grid 10(2), 2059–2067 (2017)
Varodayan, D.: Smart meter privacy using a rechargeable battery: minimizing the rate of information leakage. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1932–1935. IEEE (2011)
Kement, C.: Comparative analysis of load-shaping-based privacy preservation strategies in a smart grid. IEEE Trans. Indust. Inf. 13(6), 3226–3235 (2017)
Zainab, A.: Distributed tree-based machine learning for short-term load forecasting with apache spark. IEEE Access 9, 57372–57384 (2021)
Syed, D.: Deep learning-based short-term load forecasting approach in smart grid with clustering and consumption pattern recognition. IEEE Access 9, 54992–55008 (2021)
Wang, Y.: Secondary forecasting based on deviation analysis for short-term load forecasting. IEEE Trans. Power Syst. 26(2), 500–507 (2010)
Yadav, R.K.: A nature inspired strategy for demand side management in residential sector with smart grid environment. In: 9th International Conference System Modeling and Advancement in Research Trends (SMART), pp. 235–239. IEEE (2020)
Gaur, G., Mehta, N.: Demand side management in a smart grid environment. In: 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC). IEEE (2017)
Du, J., Lu, Y.: Model quality evaluation of advanced distribution management system based on smart grid architecture model. In: 2021 China International Conference on Electricity Distribution (CICED), pp. 688–691. IEEE (2021)
Souza, S.M.: Operation scheduling of prosumer with renewable energy sources and storage devices. In: 13th International Conference on the European Energy Market (EEM), pp. 1–5. IEEE (2016)
Mahmood, A.: Energy sharing and management for prosumers in smart grid with integration of storage system. In: 5th International Istanbul Smart Grid and Cities Congress and Fair (ICSG). IEEE (2017)
Yang, L.: Cost-effective and privacy-preserving energy management for smart meters. IEEE Trans. Smart Grid 6(1), 486–495 (2014)
Tan, O.: Privacy-cost trade-offs in demand-side management with storage. IEEE Trans. Inf. Forens. Secur. 12(6), 1458–1469 (2017)
Chen, Z.: Residential appliance DR energy management with electric privacy protection by online stochastic optimization. IEEE Trans. Smart Grid 4(4), 1861–1869 (2013)
Bu, F.: Enriching load data using micro-PMUs and smart meters. IEEE Trans. Smart Grid 12(6), 5084–5094 (2021)
Avula, R.R.: Design framework for privacy-aware demand-side management with realistic energy storage model. IEEE Trans. Smart Grid (99), 1 (2021)
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Dong, R., Hao, S., Yang, T.H., Tang, Z., Yan, Y., Chen, J. (2022). Recent Advances in Smart Meter: Data Analysis, Privacy Preservation and Applications. In: Tian, Y., Ma, T., Khan, M.K., Sheng, V.S., Pan, Z. (eds) Big Data and Security. ICBDS 2021. Communications in Computer and Information Science, vol 1563. Springer, Singapore. https://doi.org/10.1007/978-981-19-0852-1_8
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