Skip to main content
SpringerLink
Log in

Introduction

  • Chapter
  • First Online:
Data Analytics-Based Demand Profiling and Advanced Demand Side Management for Flexible Operation of Sustainable Power Networks

Part of the book series: Springer Theses ((Springer Theses))

  • 317 Accesses

Abstract

This chapter introduces the two main areas of the research presented in this thesis, with some theoretical aspects. It provides an overview of the past work in these areas, with respect to both research and real-world practices. After identifying some of the gaps in previous research on the topic, the main aims and objectives are defined, followed by extracting the main contributions of this thesis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Electric Power Research Institute (2016) Electric power system flexibility: challenges and opportunities. Electric Power Research Institute, Palo Alto, California

    Google Scholar 

  2. Kang C, Wang Y, Xue Y, Mu G, Liao R (2018) Big data analytics in China’s electric power industry: modern information, communication technologies, and millions of smart meters. IEEE Power Energ Mag 16:54–65

    Article  Google Scholar 

  3. Li F, Li R, Zhang Z, Dale M, Tolley D, Ahokangas P (2018) Big data analytics for flexible energy sharing: accelerating a low-carbon future. IEEE Power Energ Mag 16:35–42

    Article  Google Scholar 

  4. Samarakoon K, Ekanayake J, Jenkins N (2013) Reporting available demand response. IEEE Trans Smart Grid 4:1842–1851

    Article  Google Scholar 

  5. Woolf M, Ustinova T, Ortega E, O’Brien H, Djapic P, Strbac G (2014) Distributed generation and demand side response. Report A7 for the “Low Carbon London” LCNF project: Imperial College London

    Google Scholar 

  6. Pipattanasomporn M, Kuzlu M, Rahman S, Teklu Y (2014) Load profiles of selected major household appliances and their demand response opportunities. IEEE Trans Smart Grid 5:742–750

    Article  Google Scholar 

  7. Abrahamse W, Darby S, McComas K (2018) Communication is key: how to discuss energy and environmental issues with consumers. IEEE Power Energ Mag 16:29–34

    Article  Google Scholar 

  8. Digest of United Kingdom Energy Statistics (2015) Department of Energy and Climate Change, [Online]. Available https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/450302/DUKES_2015.pdf

  9. Claessens BJ, Vrancx P, Ruelens F (2018) Convolutional neural networks for automatic state-time feature extraction in reinforcement learning applied to residential load control. IEEE Trans Smart Grid 9:3259–3269

    Article  Google Scholar 

  10. Brooks A, Lu E, Reicher D, Spirakis C, Weihl B (2010) Demand dispatch. IEEE Power Energ Mag 8:20–29

    Article  Google Scholar 

  11. “CEER Status Review on European Regulatory Approaches Enabling Smart Grids Solutions (“Smart Regulation”)” Council of European Energy Regulators 2014, [Online]. Available https://www.ceer.eu/documents/104400/-/-/f83fc0d2-bff9-600b-3e0f-14eccad7a8d8

  12. Jalali A, Aldeen M (2017) Modified modal analysis approach for distribution power systems. In: 2017 IEEE PES innovative smart grid technologies conference Europe (ISGT-Europe), Torino, Italy, pp 1–6

    Google Scholar 

  13. Zhu Y (2016) Ranking of power system loads based on their influence on power system stability. First Year Transfer Report. The University of Manchester

    Google Scholar 

  14. Preparing UK Electricity Networks for Electric Vehicles (2018) [Online]. Available https://es.catapult.org.uk/wp-content/uploads/2018/07/Preparing-UK-Electricity-Networks-for-Electric-Vehicles-FINAL.pdf

  15. “Future Energy Scenarios,” National Grid (2018) [Online]. Available http://fes.nationalgrid.com/media/1363/fes-interactive-version-final.pdf

  16. Xu Y (2015) Probabilistic estimation and prediction of the dynamic response of the demand at bulk supply points. Ph.D. thesis, School of Electrical and Electronic Engineering, University of Manchester

    Google Scholar 

  17. Makarov YV, Hill DJ, Milanovic JV (1997) Effect of load uncertainty on small disturbance stability margins in open-access power systems. In: Proceedings of the Thirtieth Hawaii international conference on system sciences, 1997, pp 648–657

    Google Scholar 

  18. Carpaneto E, Chicco G (2008) Probabilistic characterisation of the aggregated residential load patterns. Gener Transm Distrib IET 2:373–382

    Article  Google Scholar 

  19. Canizares CA (2002) Voltage stability assessment: concepts, practices and tools. IEEE/PES power system stability subcommittee special publication

    Google Scholar 

  20. Al Abri R, El-Saadany EF, Atwa YM (2013) Optimal placement and sizing method to improve the voltage stability margin in a distribution system using distributed generation. IEEE Trans Power Syst 28:326–334

    Article  Google Scholar 

  21. Hedayati H, Nabaviniaki SA, Akbarimajd A (2008) A method for placement of DG units in distribution networks. IEEE Trans Power Delivery 23:1620–1628

    Article  Google Scholar 

  22. Ettehadi M, Ghasemi H, Vaez-Zadeh S (2013) Voltage stability-based DG placement in distribution networks. IEEE Trans Power Delivery 28:171–178

    Article  Google Scholar 

  23. IEEE-PES Task Force on Microgrid Stability Analysis and Modeling (2018) Microgrid stability definitions, analysis, and modeling

    Google Scholar 

  24. Pecan Street Inc. Dataport (2017) [Online]. Available http://www.pecanstreet.org/

  25. Gerbec D, Gasperic S, Gubina F (2003) Determination and allocation of typical load profiles to the eligible consumers. In: 2003 IEEE Bologna Power Tech conference proceedings, vol 1. Italy, p 5

    Google Scholar 

  26. Load Profiles and Their Use in Electricity Settlement, Elexon2013, [Online]. Available https://www.elexon.co.uk/wp-content/uploads/2013/11/load_profiles_v2.0_cgi.pdf

  27. Kirschen DS (2003) Demand-side view of electricity markets. IEEE Trans Power Syst 18:520–527

    Article  Google Scholar 

  28. Urquhart AJ, Thomson M (2015) Impacts of demand data time resolution on estimates of distribution system energy losses. IEEE Trans Power Syst 30:1483–1491

    Article  Google Scholar 

  29. Chicco G, Napoli R, Piglione F (2006) Comparisons among clustering techniques for electricity customer classification. IEEE Trans Power Syst 21:933–940

    Article  Google Scholar 

  30. Chicco G (2015) A Multi-faceted view on the characterisation of electrical demand. Invited talk at The University of Manchester

    Google Scholar 

  31. Rigoni V, Ochoa LF, Chicco G, Navarro-Espinosa A, Gozel T (2015) Representative residential LV feeders: a case study for the North West of England. IEEE Trans Power Syst PP:1–13

    Google Scholar 

  32. Sajjad IA, Chicco G, Napoli R (2016) Definitions of demand flexibility for aggregate residential loads. IEEE Trans Smart Grid 7:2633–2643

    Article  Google Scholar 

  33. Kong W, Xu Y, Dong Z, Hill DJ, Ma J, Lu C (2015) An extended prototypical smart meter architecture for demand side management. In: 2015 IEEE 13th international conference on industrial informatics (INDIN), pp 1008–1013

    Google Scholar 

  34. Reinhardt A, Baumann P, Burgstahler D, Hollick M, Chonov H, Werner M, Steinmetz R (2012) On the accuracy of appliance identification based on distributed load metering data. In: Sustainable internet and ICT for sustainability (SustainIT). Pisa, Italy, pp 1–9

    Google Scholar 

  35. Ledva GS, Du Z, Balzano L, Mathieu JL (2018) Disaggregating load by type from distribution system measurements in real time. In: Energy markets and responsive grids. Springer, pp 413–437

    Google Scholar 

  36. Srinivasan D, Ng WS, Liew AC (2006) Neural-network-based signature recognition for harmonic source identification. IEEE Trans Power Delivery 21:398–405

    Article  Google Scholar 

  37. Kolter JZ, Johnson MJ (2011) REDD: a public data set for energy disaggregation research. In: Workshop on data mining applications in sustainability (SIGKDD). San Diego, CA, pp 59–62

    Google Scholar 

  38. Kelly J, Knottenbelt W (2015) Neural nilm: deep neural networks applied to energy disaggregation. In: Proceedings of the 2nd ACM international conference on Embedded Systems for Energy-Efficient Built Environments, Seoul, South Korea, pp 55–64

    Google Scholar 

  39. Xu Y, Milanović JV (2015) Artificial-intelligence-based methodology for load disaggregation at bulk supply point. IEEE Trans Power Syst 30:795–803

    Article  Google Scholar 

  40. Liang J, Ng SK, Kendall G, Cheng JW (2010) Load signature study—part II: disaggregation framework, simulation, and applications. IEEE Trans Power Delivery 25:561–569

    Article  Google Scholar 

  41. Tang X, Hasan KN, Milanović JV, Bailey K, Stott SJ (2018) Estimation and validation of characteristic load profile through smart grid trials in a medium voltage distribution network. IEEE Trans Power Syst 33:1848–1859

    Article  Google Scholar 

  42. Collin AJ, Tsagarakis G, Kiprakis AE, McLaughlin S (2014) Development of low-voltage load models for the residential load sector. IEEE Trans Power Syst 29:2180–2188

    Article  Google Scholar 

  43. Aristidou P, Valverde G, Van Cutsem T (2017) Contribution of distribution network control to voltage stability: a case study. IEEE Trans Smart Grid 8:106–116

    Article  Google Scholar 

  44. Kundur P, Balu NJ, Lauby MG (1994) Power system stability and control, vol 7. McGraw-Hill, New York

    Google Scholar 

  45. Tang X, Milanović JV (2017) Assessment of the impact of demand side management on power system small signal stability. In: 2017 IEEE Manchester PowerTech, pp 1–6

    Google Scholar 

  46. Milanović JV, Xu Y (2015) Methodology for estimation of dynamic response of demand using limited data. IEEE Trans Power Syst 30:1288–1297

    Article  Google Scholar 

  47. d. Silva APA, Ferreira C, d. Souza ACZ, Lambert-Torres G (1997) A new constructive ANN and its application to electric load representation. IEEE Trans Power Syst 12:1569–1575

    Google Scholar 

  48. Modelling and aggregation of loads in flexible power networks (2014) CIGRE WG C4.605 (566). ISBN: 978-2-85873-261-6

    Google Scholar 

  49. Aghaei J, Alizadeh MI, Abdollahi A, Barani M (2016) Allocation of demand response resources: toward an effective contribution to power system voltage stability. IET Gener Transm Distrib 10:4169–4177

    Article  Google Scholar 

  50. Albadi MH, El-Saadany EF (2008) A summary of demand response in electricity markets. Electr Power Syst Res 78:1989–1996

    Article  Google Scholar 

  51. Logenthiran T, Srinivasan D, Shun TZ (2012) Demand side management in smart grid using heuristic optimization. IEEE Trans Smart Grid 3:1244–1252

    Article  Google Scholar 

  52. Agnetis A, Dellino G, De Pascale G, Innocenti G, Pranzo M, Vicino A (2011) Optimization models for consumer flexibility aggregation in smart grids: the ADDRESS approach. In: 2011 IEEE first international workshop on smart grid modeling and simulation (SGMS). Brussels, Belgium, pp 96–101

    Google Scholar 

  53. Christakou K (2016) A unified control strategy for active distribution networks via demand response and distributed energy storage systems. Sustain Energ Grids Netw 6:1–6

    Article  Google Scholar 

  54. Hayes BP (2013) Distributed generation and demand side management: applications to transmission system operation. Ph.D. Thesis. University of Edinburgh

    Google Scholar 

  55. Cobelo I (2005) Active control of distribution networks. Ph.D. Thesis, The University of Manchester

    Google Scholar 

  56. Aggregators—Barriers and External Impacts, OFGEM (2016) [Online]. Available https://www.ofgem.gov.uk/publications-and-updates/aggregators-barriers-and-external-impacts-report-pa-consulting

  57. Zhong H, Xia Q, Kang C, Ding M, Yao J, Yang S (2015) An efficient decomposition method for the integrated dispatch of generation and load. IEEE Trans Power Syst 30:2923–2933

    Article  Google Scholar 

  58. Muratori M, Rizzoni G (2016) Residential demand response: dynamic energy management and time-varying electricity pricing. IEEE Trans Power Syst 31:1108–1117

    Article  Google Scholar 

  59. Zhong H, Xie L, Xia Q (2013) Coupon incentive-based demand response: theory and case study. IEEE Trans Power Syst 28:1266–1276

    Article  Google Scholar 

  60. North American Electric Reliability Corporation (2013) 2011 Demand response availability report, March 2013

    Google Scholar 

  61. Levi V (2018) Demand response mechanisms and network support services. Presentation at the University of Manchester

    Google Scholar 

  62. Callaway DS, Hiskens IA (2011) Achieving controllability of electric loads. Proc IEEE 99:184–199

    Article  Google Scholar 

  63. Steg L, Shwom R, Dietz T (2018) What drives energy consumers?: engaging people in a sustainable energy transition. IEEE Power Energ Mag 16:20–28

    Article  Google Scholar 

  64. van der Werff E, Thogersen J, de Bruin WB (2018) Changing household energy usage: the downsides of incentives and how to overcome them. IEEE Power Energ Mag 16:42–48

    Article  Google Scholar 

  65. De Paola A, Angeli D, Strbac G (2017) Convergence and optimality of a new iterative price-based scheme for distributed coordination of flexible loads in the electricity market. In: 2017 IEEE 56th annual conference on Decision and Control (CDC), Melbourne, Australia, pp 1386–1393

    Google Scholar 

  66. Elghitani F, Zhuang W (2018) Aggregating a large number of residential appliances for demand response applications. IEEE Trans Smart Grid 9:5092–5100

    Article  Google Scholar 

  67. Du P, Lu N (2011) Appliance commitment for household load scheduling. IEEE Trans Smart Grid 2:411–419

    Article  Google Scholar 

  68. The integrated grid—realizing the full value of central and distributed energy resources, EPRI (2014) [Online]. Available https://www.energy.gov/sites/prod/files/2015/03/f20/EPRI%20Integrated%20Grid021014.pdf

  69. Xiang Y, Junyong L, Wei Y, Huang C (2015) Active energy management strategies for active distribution system. J Mod Power Syst Clean Energ 3:533–543

    Article  Google Scholar 

  70. Wang D, Jia H, Wang C, Lu N, Fan M, Miao W, Liu Z (2014) Performance evaluation of controlling thermostatically controlled appliances as virtual generators using comfort-constrained state-queueing models. IET Gener Transm Distrib 8:591–599

    Article  Google Scholar 

  71. Medina J, Muller N, Roytelman I (2010) Demand response and distribution grid operations: opportunities and challenges. IEEE Trans Smart Grid 1:193–198

    Article  Google Scholar 

  72. Ma O, Cheung K, Olsen DJ, Matson N, Sohn MD, Rose CM, Dudley JH, Goli S, Kiliccote S, Cappers P (2016) Demand response and energy storage integration study. National Renewable Energy Lab. (NREL), Golden, CO (United States)

    Google Scholar 

  73. Kiliccote S, Olsen D, Sohn MD, Piette MA (2016) Characterization of demand response in the commercial, industrial, and residential sectors in the United States. Wiley Interdisc Rev Energ Environ 5:288–304

    Google Scholar 

  74. Joe-Wong C, Sen S, Sangtae H, Mung C (2012) Optimized day-ahead pricing for smart grids with device-specific scheduling flexibility. IEEE J Sel Areas Commun 30:1075–1085

    Article  Google Scholar 

  75. Kirschen DS, Rosso A, Juan M, Ochoa LF (2012) Flexibility from the demand side. In: Power and energy society general meeting, 2012. IEEE, San Diego, CA, USA, pp 1–6

    Google Scholar 

  76. Wang D, Parkinson S, Miao W, Jia H, Crawford C, Djilali N (2012) Online voltage security assessment considering comfort-constrained demand response control of distributed heat pump systems. Appl Energ 96:104–114

    Article  Google Scholar 

  77. Varaiya PP, Wu FF, Bialek JW (2011) Smart operation of smart grid: risk-limiting dispatch. Proc IEEE 99:40–57

    Article  Google Scholar 

  78. Hao H, Sanandaji BM, Poolla K, Vincent TL (2015) Potentials and economics of residential thermal loads providing regulation reserve. Energ Policy 79:115–126

    Article  Google Scholar 

  79. Kladnik B, Gubina A, Artac G, Nagode K, Kockar I (2011) Agent-based modeling of the demand-side flexibility. In: 2011 IEEE power and energy society general meeting. Detroit, MI, USA, pp 1–8

    Google Scholar 

  80. Vivekananthan C, Mishra Y, Ledwich G, Fangxing L (2014) Demand response for residential appliances via customer reward scheme. IEEE Trans Smart Grid 5:809–820

    Article  Google Scholar 

  81. Bhattarai BP, Bak-Jensen B, Mahat P, Pillai JR (2013) Voltage controlled dynamic demand response. In: IEEE PES ISGT Europe 2013. Copenhagen, Denmark, pp 1–5

    Google Scholar 

  82. Ballanti A, Ochoa LF (2015) Initial assessment of voltage-led demand response from UK residential loads. In: 2015 IEEE power & energy society innovative smart grid technologies conference (ISGT), pp 1–5

    Google Scholar 

  83. Gottwalt S, Gärttner J, Schmeck H, Weinhardt C (2017) Modeling and valuation of residential demand flexibility for renewable energy integration. IEEE Trans Smart Grid 8:2565–2574

    Article  Google Scholar 

  84. Moreno JAF, García AM, Marín AG, Lázaro EG, Bel CA (2004) An integrated tool for assessing the demand profile flexibility. IEEE Trans Power Syst 19:668–675

    Article  Google Scholar 

  85. Molina A, Gabaldon A, Fuentes J, Canovas F (2000) Approach to multivariable predictive control applications in residential HVAC direct load control. In: 2000 Power engineering society summer meeting (Cat. No. 00CH37134). Seattle, WA, USA, pp 1811–1816

    Google Scholar 

  86. Xu Y, Milanović JV (2016) Day-ahead prediction and shaping of dynamic response of demand at bulk supply points. IEEE Trans Power Syst 31:3100–3108

    Article  Google Scholar 

  87. Lof P-A, Smed T, Andersson G, Hill D (1992) Fast calculation of a voltage stability index. IEEE Trans Power Syst 7:54–64

    Article  Google Scholar 

  88. Yao M, Mathieu JL, Molzahn DK (2017) Using demand response to improve power system voltage stability margins. In: PowerTech, 2017. IEEE, Manchester, pp 1–6

    Google Scholar 

  89. Greene S, Dobson I, Alvarado FL (1997) Sensitivity of the loading margin to voltage collapse with respect to arbitrary parameters. IEEE Trans Power Syst 12:262–272

    Article  Google Scholar 

  90. Ghahremani E, Kamwa I (2013) Optimal placement of multiple-type FACTS devices to maximize power system loadability using a generic graphical user interface. IEEE Trans Power Syst 28:764–778

    Article  Google Scholar 

  91. Mansour Y (1990) Voltage stability of power systems: concepts, analytical tools, and industry experience. IEEE special publication [Online]. Available https://resourcecenter.ieee-pes.org/technical-publications/tutorial-papers/PESTP283.html

  92. Ajjarapu V (ed) (2007) Continuation power flow. In: Computational techniques for voltage stability assessment and control. Springer US, Boston, MA, pp 49–116

    Google Scholar 

  93. Dong Y, Xie X, Shi W, Zhou B, Jiang Q (2018) Demand-response-based distributed preventive control to improve short-term voltage stability. IEEE Trans Smart Grid 9:4785–4795

    Article  Google Scholar 

  94. Weckx S, Hulst RD, Driesen J (2015) Primary and secondary frequency support by a multi-agent demand control system. IEEE Trans Power Syst 30:1394–1404

    Article  Google Scholar 

  95. Rebours YG, Kirschen DS, Trotignon M, Rossignol S (2007) A survey of frequency and voltage control ancillary services—part I: technical features. IEEE Trans Power Syst 22:350–357

    Article  Google Scholar 

  96. Molina-Garcia A, Bouffard F, Kirschen DS (2011) Decentralized demand-side contribution to primary frequency control. IEEE Trans Power Syst 26:411–419

    Article  Google Scholar 

  97. Bayat M, Sheshyekani K, Rezazadeh A (2015) A unified framework for participation of responsive end-user devices in voltage and frequency control of the smart grid. IEEE Trans Power Syst 30:1369–1379

    Article  Google Scholar 

  98. Electricity distribution systems losses non-technical overview, Ofgem (2009) [Online]. Available https://www.ofgem.gov.uk/publications-and-updates/electricity-distribution-systems-losses-non-technical-overview

  99. Hu W, Chen Z, Bak-Jensen B, Hu Y (2014) Fuzzy adaptive particle swarm optimisation for power loss minimisation in distribution systems using optimal load response. IET Gener Transm Distrib 8:1–10

    Article  Google Scholar 

  100. Deilami S, Masoum AS, Moses PS, Masoum MA (2011) Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile. IEEE Trans Smart Grid 2:456–467

    Article  Google Scholar 

  101. Heffner G, Goldman C, Kirby B, Kintner-Meyer M (2007) Loads providing ancillary services: review of international experience. Lawrence Berkeley National Laboratory Technical Report, LBNL-62701, ORNL/TM-2007/060, PNNL-16618

    Google Scholar 

  102. Hummon M, Palchak D, Denholm P, Jorgenson J, Olsen DJ, Kiliccote S, Matson N, Sohn M, Rose C, Dudley J (2013) Grid integration of aggregated demand response: part 2, modeling demand response in a production cost model. National Renewable Energy Laboratory

    Google Scholar 

  103. Olsen DJ (2013) Grid integration of aggregated demand response, part 1: load availability profiles and constraints for the western interconnection. Lawrence Berkeley National Laboratory [Online]. Available https://cloudfront.escholarship.org/dist/prd/content/qt6ps4r3xp/qt6ps4r3xp.pdf

  104. Cappers P, MacDonald J, Goldman C (2013) Market and policy barriers for demand response providing ancillary services in US markets. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    Google Scholar 

  105. Customer Load Active System Services, Electricity North West (2014) [Online]. Available https://www.enwl.co.uk/globalassets/innovation/class/class-documents/class-closedown-report-master.pdf

  106. Yao J, Zhengyu W, Jiang C, Zhang Y (2015) Dispatch and bidding strategy of active distribution network in energy and ancillary services market. J Mod Power Syst Clean Energ 3:565–572

    Article  Google Scholar 

  107. Strbac G, Gan CK, Aunedi M, Stanojevic V, Djapic P, Dejvises J, Mancarella P, Hawkes A, Pudjianto D, Le Vine S (2010) Benefits of advanced smart metering for demand response based control of distribution networks. ENA/SEDG/Imperial College report on Benefits of Advanced Smart Metering (Version 2.0). Energy Networks Association, London

    Google Scholar 

  108. Assessment of industrial load for demand response across U.S. regions of the Western interconnect. Oak Ridge National Laboratory, 2013

    Google Scholar 

  109. Demand side flexibility annual report 2016—power responsive, National Grid (2016) [Online]. Available http://powerresponsive.com/wp-content/uploads/2017/01/Power-Responsive-Annual-Report-2016-FINAL.pdf

  110. Liang J, Ng SKK, Kendall G, Cheng JWM (2010) Load signature study—part I: basic concept, structure, and methodology. IEEE Trans Power Delivery 25:551–560

    Article  Google Scholar 

  111. Richardson I, Thomson M, Infield D, Clifford C (2010) Domestic electricity use: a high-resolution energy demand model. Energ Build 42:1878–1887

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK

    Dr. Jelena Ponoćko

Authors
  1. Dr. Jelena Ponoćko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jelena Ponoćko .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ponoćko, J. (2020). Introduction. In: Data Analytics-Based Demand Profiling and Advanced Demand Side Management for Flexible Operation of Sustainable Power Networks. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-030-39943-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-39943-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39942-9

  • Online ISBN: 978-3-030-39943-6

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation