Cited by 19Smart Grid Energy ManagementElectric Power System OptimizationElectric Vehicles and Infrastructure

Technical, Economic, Social and Regulatory Feasibility Evaluation of Dynamic Distribution Tariff Designs

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Christensen, Kristoffer, Ma, Zheng, Jørgensen, Bo Nørregaard·2021·Energies

Abstract

The increasing number of distributed energy resources in the distribution grids creates the risk of grid congestion and the high cost of grid expansion. The implementation of the dynamic distribution grid tariffs can potentially avoid grid congestion. Meanwhile, the design and implementation of any distribution tariff need to consider and match the regional/national requirements. However, there is no sufficient evaluation method available to review and evaluate the feasibility of the dynamic distribution tariffs. Therefore, this paper introduces a feasibility evaluation method with four dimensions of technical, economic, social, and regulatory to review dynamic distribution tariffs. The literature on dynamic distribution tariffs is collected, and 29 dynamic distribution tariffs are selected and further categorized into five attributes of rationale, cost drivers, dynamics, events, and active demand. The evaluation results show that the time-of-use tariff is the most feasible dynamic distribution tariff, and the review of a proposed future distribution tariff model in Denmark verifies the evaluation method and results. The developed feasibility evaluation method for dynamic distribution tariffs can ensure the design and implementation of a dynamic distribution tariff to be feasible and applicable in a region.

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  • The aggregator approach suits DSOs operating under constraints that limit the adoption of fully dynamic tariffs.

    “From a regulatory and operational standpoint, the aggregator approach suits DSOs operating under constraints that limit the adoption of fully dynamic tariffs [4,5].”

    From A Multi-Agent, Laxity-Based Aggregation Strategy for Cost-Effective Electric Vehicle Charging and Local Transformer Overload Prevention (2025) · cites this paper as [5]

    The Introduction and §3.4 (page numbers unavailable) explain that DSO tariffs must meet regulatory requirements and that a real-time pricing scheme may fail the transparency requirement. The paper therefore supports the existence of constraints on dynamic tariffs, but it does not say that an aggregator approach suits DSOs facing those constraints; its mention of aggregators concerns forecast parameters. (LLM, conf=0.84)

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References 43 · Cited by 19

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  1. Mlecnik, E.; Parker, J.; Ma, Z.; Corchero, C.; Knotzer, A.; Pernetti, R. Policy challenges for the development of energy flexibility services. Energy Policy 2020, 137, 111147. [CrossRef] · DOI
  2. Ma, Z.; Billanes, J.D.; Kjargaard, M.B.; Jorgensen, B.N. Energy flexibility in retail buildings: From a business ecosystem perspective. In Proceedings of the 2017 4th International Conference on the European Energy Market (EEM), Dresden, Germany, 6-9 June 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1-6. [CrossRef] · DOI
  3. Ma, Z.; Sommer, S.; Jorgensen, B.N. The smart grid impact on the Danish DSOs' business model. In Proceedings of the 2016 IEEE Electrical Power and Energy Conference (EPEC), Ottawa, ON, Canada, 12-14 October 2016; pp. 1-5. [CrossRef] · DOI
  4. Howard, D.A.; Ma, Z.; Engvang, J.A.; Hagenau, M.; Jørgensen, K.L.; Olesen, J.F.; Jørgensen, B.N. Optimization of Energy Flexibility in Cooling Process for Brewery Fermentation with Multi-Agent Simulation. In Proceedings of the 6th IEEJ International Workshop on Sensing, Actuation, Motion Control, and Optimization, Shibaura Institute of Technology, Tokyo, Japan, 16 March 2020; p. TT-16. Available online: http://id.nii.ac.jp/1031/00127065/ (accessed on 13 October 2020).
  5. Christensen, K.; Ma, Z.; Demazeau, Y.; Jørgensen, B.N. Agent-based Modeling for Optimizing CO 2 Reduction in Commercial Greenhouse Production with the Implicit Demand Response. In Proceedings of the 6th IEEJ International Workshop on Sensing, Actuation, Motion Control, and Optimization (SAMCON2020), Tokyo, Japan, 14-16 March 2020; Available online: http://id.nii.ac.jp/1031/00127067/ (accessed on 13 October 2020).
  6. Ma, Z.; Prljaca, Z.; Jørgensen, B.N. The international electricity market infrastructure-insight from the nordic electricity market. In Proceedings of the 2016 13th International Conference on the European Energy Market (EEM), Porto, Portugal, 6-9 June 2016; pp. 1-5.
  7. Christensen, K.; Ma, Z.; Verbak, M.; Demazeau, Y.; Jorgensen, B.N. Agent-based Decision Making for Adoption of Smart Energy Solutions. In Proceedings of the 2019 IEEE Sciences and Humanities International Research Conference (SHIRCON), Lima, Peru, 12-15 November 2019; pp. 1-4. [CrossRef] · DOI
  8. Ma, Z.; Broe, M.; Fischer, A.; Sorensen, T.B.; Frederiksen, M.V.; Joergensen, B.N. Ecosystem Thinking: Creating Microgrid Solutions for Reliable Power Supply in India's Power System. In Proceedings of the 2019 1st Global Power, Energy and Communication Conference (GPECOM), Nevsehir, Turkey, 12-15 June 2019; pp. 392-397.
  9. Ma, Z.; Santos, A.Q.; Gamborg, F.; Nielsen, J.F.; Johannesen, J.M.; Dahl, M.; Jensen, H.; Pedersen, M.R.; Jorgensen, B.N. Solutions for Remote Island Microgrids: Discussion and analysis of Indonesia's remote island energy system. In Proceedings of the International Conference on Innovative Smart Grid Technologies (IEEE PES ISGT Asia 2018), Singapore, 22-25 May 2018; IEEE: Piscataway, NJ, USA, 2018.
  10. Ma, Z.; Bloch-Hansen, K.; Buck, J.W.; Hansen, A.K.; Henriksen, L.J.; Thielsen, C.F.; Santos, A.Q.; Jorgensen, B.N. Peer-to-Peer Trading Solution for Microgrids in Kenya. In Proceedings of the 2018 IEEE PES/IAS PowerAfrica Conference-River Club, Cape Town, South Africa, 25-29 June 2018; IEEE: Piscataway, NJ, USA, 2018.
  11. O'Connell, N.; Wu, Q.; Ostergaard, J.; Nielsen, A.H.; Cha, S.T.; Ding, Y. Electric Vehicle (EV) charging management with dynamic distribution system tariff. In Proceedings of the 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, Manchester, UK, 5-7 December; pp. 1-7. [CrossRef] · DOI
  12. Ding, Y.; Li, Y.; Pineda, S.; Østergaard, J.; Jin, T. The impact of dynamic electricity tariff on long-run incremental cost. In Proceedings of the 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA, 22-26 July 2012; pp. 1-5. [CrossRef] · DOI
  13. Gu, Y.; Xie, J.; Chen, X.; Yu, K.; Chen, Z.; Li, Z. Dynamic tariff method for congestion management in distribution networks. In Proceedings of the 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China, 26-28 November 2017; pp. 1-6. [CrossRef] · DOI
  14. Haendel, M.; Stute, J. Grid Expansion Costs Considering Different Price Control Strategies of Power-to-X Options Based on Dynamic Tariffs at the Low-Voltage Level. In Proceedings of the 2019 16th International Conference on the European Energy Market (EEM), Ljubljana, Slovenia, 18-20 September 2019; pp. 1-6. [CrossRef] · DOI
  15. Huang, S.; Wu, Q. Dynamic Tariff-Subsidy Method for PV and V2G Congestion Management in Distribution Networks. IEEE Trans. Smart Grid 2019, 10, 5851-5860. [CrossRef] · DOI
  16. Huang, S.; Wu, Q.; Cheng, L.; Liu, Z. Optimal Reconfiguration-Based Dynamic Tariff for Congestion Management and Line Loss Reduction in Distribution Networks. IEEE Trans. Smart Grid 2016, 7, 1295-1303. [CrossRef] · DOI
  17. Huang, S.; Wu, Q.; Cheng, L.; Liu, Z.; Zhao, H. Uncertainty Management of Dynamic Tariff Method for Congestion Management in Distribution Networks. IEEE Trans. Power Syst. 2016, 31, 4340-4347. [CrossRef] · DOI
  18. Huang, S.; Wu, Q.; Nielsen, A.H.; Zhao, H.; Liu, Z. Long term incentives for residential customers using dynamic tariff. In Proceedings of the 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Brisbane, Australia, 15-18 November 2015; pp. 1-5. [CrossRef] · DOI
  19. Huang, S.; Wu, Q.; Shahidehpour, M.; Liu, Z. Dynamic Power Tariff for Congestion Management in Distribution Networks. IEEE Trans. Smart Grid 2019, 10, 2148-2157. [CrossRef] · DOI
  20. Huang, S.; Wu, Q.; Zhao, H.; Li, C. Distributed Optimization-Based Dynamic Tariff for Congestion Management in Distribution Networks. IEEE Trans. Smart Grid 2017, 10, 184-192. [CrossRef] · DOI
  21. Khan, G.M.; Arshad, R.; Khan, N.M. Efficient Prediction of Dynamic Tariff in Smart Grid Using CGP Evolved Artificial Neural Networks. In Proceedings of the 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico, 18-21 December 2017; pp. 493-498. [CrossRef] · DOI
  22. Kumar, M.S.; Srinivasan, S.; Subathra, B. Demand Response Program for Shiftable Modes in Variable Tariff Zones of an Utility. In Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 13-15 May 2020; pp. 1044-1049. [CrossRef] · DOI
  23. Oldewurtel, F.; Ulbig, A.; Morari, M.; Andersson, G. Building control and storage management with dynamic tariffs for shaping demand response. In Proceedings of the 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, Manchester, UK, 5-7 December 2011; pp. 1-8. [CrossRef] · DOI
  24. Pereyra-Zamora, F.H.; Tahan, C.M.V.; Kagan, N.; Moreira, H.L. An Infrastructure of Dynamic Tariff Management and Demand Response applied to Smart Grids using Renewable Energy Resources and Energy Storage Systems. In Proceedings of the 2019 IEEE PES Innovative Smart Grid Technologies Conference-Latin America (ISGT Latin America), Gramado City, Brazil, 15-18 September 2019; pp. 1-6. [CrossRef] · DOI
  25. Rasmussen, T.B.; Wu, Q.; Huang, S. Real time emulation of dynamic tariff for congestion management in distribution networks. In Proceedings of the 2016 Asian Conference on Energy, Power and Transportation Electrification (ACEPT), Singapore, 25-27 October 2016; pp. 1-6. [CrossRef] · DOI
  26. Ribeiro, C.; Pinto, T.; Faria, P.; Ramos, S.; Vale, Z.; Baptista, J.; Soares, J.; Navarro-Caceres, M.; Corchado, J.M. Dynamic electricity tariff definition based on market price, consumption and renewable generation patterns. In Proceedings of the 2018 Clemson University Power Systems Conference (PSC), Charleston, SC, USA, 4-7 September 2018; Institute of Electrical and Electronics Engineers (IEEE): Piscataway, NJ, USA, 2018; pp. 1-5.
  27. Huang, S.; Wu, Q.; Liu, Z.; Zhao, H. Sensitivity analysis of dynamic tariff method for congestion management in distribution networks. In Proceedings of the 2015 IEEE Power & Energy Society General Meeting, Denver, CO, USA, 26-30 July 2015; pp. 1-6. [CrossRef] · DOI
  28. Shen, F.; Huang, S.; Wu, Q.; Repo, S.; Xu, Y.; Ostergaard, J. Comprehensive Congestion Management for Distribution Networks Based on Dynamic Tariff, Reconfiguration, and Re-Profiling Product. IEEE Trans. Smart Grid 2018, 10, 4795-4805. [CrossRef] · DOI
  29. Ulbig, A.; Andersson, G. Towards variable end-consumer electricity tariffs reflecting marginal costs: A benchmark tariff. In Proceedings of the 2010 7th International Conference on the European Energy Market, Madrid, Spain, 23-25 June 2010; pp. 1-6. [CrossRef] · DOI
  30. Lutz, O.; Hollinger, R.; Olavarria, V.; Wittwer, C. Time-optimized dynamic two-step tariffs for CHP operation. In Proceedings of the International ETG Congress 2017, Bonn, Germany, 28-29 November 2017; pp. 1-6.
  31. Lutz, O.; Olavarria, V.; Hollinger, R.; Wittwer, C.; Koch, B. Dynamic tariff design for a robust smart grid concept: An analysis of global vs. local incentives. In Proceedings of the 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Turin, Italy, 26-29 September 2017; pp. 1-6. [CrossRef] · DOI
  32. Malatji, E.M. The use of Dynamic Tariff by The Utilities to Counter act The Influence of Renewable Energy Sources. In Proceedings of the 2019 7th International Conference on Smart Grid (icSmartGrid), Newcastle, Australia, 9-11 December 2019; pp. 103-107. [CrossRef] · DOI
  33. Fischer, D.; Stephen, B.; Flunk, A.; Kreifels, N.; Lindberg, K.B.; Wille-Haussmann, B.; Owens, E.H. Modeling the Effects of Variable Tariffs on Domestic Electric Load Profiles by Use of Occupant Behavior Submodels. IEEE Trans. Smart Grid 2016, 8, 2685-2693. [CrossRef] · DOI
  34. Philippou, N.; Hadjipanayi, M.; Makrides, G.; Efthymiou, V.; Georghiou, G.E.; Nikolas, P. Effective dynamic tariffs for price-based Demand Side Management with grid-connected PV systems. In Proceedings of the 2015 IEEE Eindhoven PowerTech, Eindhoven, The Netherlands, 29 June-2 July 2015; pp. 1-5. [CrossRef] · DOI
  35. Pires, G.; Saraiva, J.; Nunes, J.; Pinto, R.B.; Fidalgo, J. Dynamic Network Tariffs: Are they the Most Efficient Way to Match Peak Consumption and Network Incremental Costs? In Proceedings of the CIRED Workshop, Helsinki, Finland, 14-15 June 2016; pp. 1-4. [CrossRef] · DOI
  36. Skytte, K.; Bergaentzle, C.; Soysal, E.R.; Olsen, O.J. Design of grid tariffs in electricity systems with variable renewable energy and power to heat. In Proceedings of the 2017 14th International Conference on the European Energy Market (EEM), Dresden, Germany, 6-9 June 2017; pp. 1-7. [CrossRef] · DOI
  37. Saraiva, J.T.; Fidalgo, J.N.; Pinto, R.B.; Soares, R.; Afonso, J.S.; Pires, G. Implementation of dynamic tariffs in the Portuguese electricity system-Preliminary results of a Cost-Benefit Analysis. In Proceedings of the 2016 13th International Conference on the European Energy Market (EEM), Porto, Portugal, 6-9 June 2016; pp. 1-5. [CrossRef] · DOI
  38. Tonge, K.; Mane, V.; Burad, S.; Urkunde, V.; Aghav, K. Demand based Variable Electricity Tariff Meter. In Proceedings of the 2020 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 28-30 July 2020; pp. 1452-1455. [CrossRef] · DOI
  39. Verma, S.K.; Shandilya, A. Variable tariff energy meter with automatic power flow control. In Proceedings of the 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India, 1-2 August 2017; pp. 58-61. [CrossRef] · DOI
  40. Santos, A.Q.; Ma, Z.; Olsen, C.G.; Jørgensen, B.N. Framework for Microgrid Design Using Social, Economic, and Technical Analysis. Energies 2018, 11, 2832. [CrossRef] · DOI
  41. Deshpande, A.; Guestrin, C.; Madden, S.R.; Hellerstein, J.M.; Hong, W. Model-driven data acquisition in sensor networks. In Proceedings of the Thirtieth International Conference on Very Large Data Bases-Volume 30, Toronto, ON, Canada, 31 August-3 September 2004.
  42. Ma, Z.; Schultz, M.J.; Christensen, K.; Værbak, M.; Demazeau, Y.; Jørgensen, B.N. The Application of Ontologies in Multi-Agent Systems in the Energy Sector: A Scoping Review. Energies 2019, 12, 3200. [CrossRef] · DOI
  43. Ma, Z. Business ecosystem modeling-the hybrid of system modeling and ecological modeling: An application of the smart grid. Energy Inform. 2019, 2, 1-24. [CrossRef] · DOI · Paper page

Authors
Christensen, K., Ma, Z., & Jørgensen, B. N.
Affiliations
  • University of Southern Denmark (DK)
  • Center for Energy Informatics, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
Corresponding author
Christensen, Kristoffer
Venue
Energies
Publisher
MDPI AG
Published
2021-05-15
Volume
14
Issue
10
Pages
2860
ISSN
1996-1073 (ISSN-L)
OpenAlex
W3163780794
Cited by
19
References
43
Citations by year
2026: 2 · 2025: 4 · 2024: 2 · 2023: 5 · 2022: 3 · 2021: 3
FWCI
0.96
Type
journal article
Language
en
Open access
Locations
  • Energies (publishedVersion, cc-by)
  • University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Innovationsfonden (8090-00069B)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
Keywords
feasibility assessment, time-of-use pricing, distributed energy resources, grid congestion, active demand, energy regulation, electricity pricing, Denmark

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