Skip to main content
SpringerLink
Log in
Book cover

TSO-DSO Interactions and Ancillary Services in Electricity Transmission and Distribution Networks pp 25–59Cite as

Modeling of Complex Systems Including Transmission, Distribution, Aggregation, Ancillary Services Markets

  • Chapter
  • First Online:

Abstract

With an increased deployment of DERs, and a supporting distribution grid ICT infrastructure in place, the flexibility potential of these resources can be utilized to provide services both locally and for overall system – to a mutual benefit of DER owners and system operator. In order to achieve this there is a need to coordinate TSOs and DSOs, as discussed in Chap. 2, as well as to implement new market architectures, as detailed in this chapter, so as to manage flexibility offers from DERs. For the most part, this chapter provides mathematical models of different market framework components: aggregation of flexibility offers from DERs, ancillary services (AS) market architecture, market arbitrage, transmission, and distribution network models. In addition, this chapter discusses the computational complexity aspects of the market clearing algorithm and, in context of this, how the key AS market parameters, as well as the choice of the TSO-DSO coordination scheme, impact its tractability.

This is a preview of subscription content, 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   119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   159.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    For the sake of the example, only upward flexibility is illustrated. Prices are expressed in cur/MWh.

  2. 2.

    This minimum price can even be negative for upward flexibility.

  3. 3.

    This does not pretend to be an exhaustive list, but a selection of useful ways to cooperate.

  4. 4.

    For example, in the USA, the minimum required size of generation as well as demand response to access the market is 100 kW [20].

  5. 5.

    Aggregators’ bidding strategies will not only depend on costs but also on the gain opportunities offered by the present market session, depending on a guess on the bids from other subjects and on an estimation of the market power owned by the bidder. However, for simplicity we are not going to consider such aspects, which would require a much more complex modeling approach (e.g., game theory-based models).

  6. 6.

    The baseline can be obtained from the previous market, i.e., day-ahead, intraday, or the previous, i.e., t − 1, AS market clearing.

  7. 7.

    PQ capability diagram characterizes active and reactive power limits of a generator.

  8. 8.

    Here, we consider thermostatically controlled devices, such as air-conditioning and heaters, as well as time-shiftable devices (i.e., controlled by a timer) like washing machines and dishwashers.

  9. 9.

    In the literature it is referred to as the curtailable bid, which has a single price for a range between two quantities, and the market operator can accept any value between these two quantities.

  10. 10.

    For simplicity we omit the detailed equation for each component of the flexibility cost equation. A comprehensive explanation, with equations, can be found in deliverables D2.1 [41] and D2.2 [42], of the SmartNet project.

References

  1. REN21, Renewables global status report 2012 (Jun 2012). [Online]. Available: http://www.ren21.net/. Accessed 26 Sept 2018

  2. Energy technology perspectives 2012: Executive summary. [Online]. Available: http://www.iea.org/publications/freepublications/. Accessed 26 Sept 2018

  3. C.J. Barnhart, M. Dale, A.R. Brandt, S.M. Benson, The energetic implications of curtailing versus storing solar- and wind-generated electricity. Energy Environ. Sci. 6, 2804–2810 (2013)

    Article  Google Scholar 

  4. F. Martín-Martínez, A. Sánchez-Miralles, M. Rivier, Prosumers’ optimal DER investments and DR usage for thermal and electrical loads in isolated microgrids. Electr. Power Syst. Res. 140, 473 (2016)

    Article  Google Scholar 

  5. D.R. Biggar, M.R. Hesamzadeh, The Economics of Electricity Markets (John Wiley & Sons, Chichester, 2014)

    Google Scholar 

  6. D. Kirschen, G. Strbac, Fundamentals of Power System Economics (John Wiley & Sons, Chichester, 2004)

    Book  Google Scholar 

  7. G. Leclercq, M. Pavesi, T. Gueuning et al., Network and market models. SmartNet project, D2.2 (2019)

    Google Scholar 

  8. J. Merino, I. Gómez, E. Turienzo, C. Madina, Ancillary service provision by RES and DSM connected at distribution level in the future power system. SmartNet project, D2.1 (2016)

    Google Scholar 

  9. K. De Vos, N. Stevens, O. Devolder, et al., Dynamic dimensioning approach for operating reserves: Proof of concept in Belgium. Energy Policy 124, 272–285 (2018). https://doi.org/10.1016/j.enpol.2018.09.031

    Article  Google Scholar 

  10. ENTSO-E, Balancing and ancillary services markets. https://www.entsoe.eu/about/market/#balancing-and-ancillary-services-markets. Accessed 1 Jun 2019

  11. M. Caramanis, E. Ntakou, W.W. Hogan, et al., Co-optimization of power and reserves in dynamic T&D power markets with nondispatchable renewable generation and distributed energy resources. Proc. IEEE 104, 807–836 (2016). https://doi.org/10.1109/JPROC.2016.2520758

    Article  Google Scholar 

  12. P. Holmberg, E. Lazarczyk, Congestion management in electricity: Nodal, zonal and discriminatory pricing. IFN Working Paper No. 915 (2012)

    Google Scholar 

  13. F. Geth, R. D’Hulst, D. Van Hertem, Convex power flow models for scalable electricity market modelling. CIRED – Open Access Proc. J. 2017, 989–993 (2017). https://doi.org/10.1049/oap-cired.2017.0325

    Article  Google Scholar 

  14. T. Brown, S. Newell, D. Oates, K. Spees, International review of demand response mechanisms (2015)., 83

    Google Scholar 

  15. S.F. Tierney, T. Schatzki, R. Mukerji, Uniform-Pricing Versus Pay-as-Bid in Wholesale Electricity Markets: Does it Make a Difference? (ISO, New York, 2008)

    Google Scholar 

  16. A. Papavasiliou, Analysis of distribution locational marginal prices. IEEE Trans. Smart Grid 9, 4872–4882 (2017). https://doi.org/10.1109/TSG.2017.2673860

    Article  Google Scholar 

  17. H. Gerard, E. Rivero, D. Six, Basic schemes for TSO-DSO coordination and ancillary services provision. SmartNet project, D1.3 (2016)

    Google Scholar 

  18. H. Gerard, E.I. Rivero Puente, D. Six, Coordination between transmission and distribution system operators in the electricity sector: A conceptual framework. Util. Policy 50, 40–48 (2018). https://doi.org/10.1016/j.jup.2017.09.011

    Article  Google Scholar 

  19. A. Papavasiliou, I. Mezghani, Coordination schemes for the integration of transmission and distribution system operations. 2018 Power Systems Computation Conference (PSCC), IEEE (2018), pp 1–7

    Google Scholar 

  20. A. Levitt, Comparing RTO market framework rules in DER contex (Aug 2016). [Online]. Available: http://www.pjm.com/~/media/committees-groups/committees/mrc/20160824-special/20160824-item-02-der-rto-benchmarking.ashx. Accessed 26 Sept 2018

  21. M. Parvania, M. Fotuhi-Firuzabad, M. Shahidehpour, Optimal demand response aggregation in wholesale electricity markets. IEEE Trans. Smart Grid 4(4), 1957–1965 (2013)

    Article  Google Scholar 

  22. S.Ø. Ottesen, A. Tomasgard, S.E. Fleten, Prosumer bidding and scheduling in electricity markets. Energy 94, 828–843 (2016)

    Article  Google Scholar 

  23. M. Vasirani, S. Ossowski, Smart consumer load balancing: State of the art and an empirical evaluation in the Spanish electricity market. Artif. Intell. Rev. 39(1), 81–95 (2013)

    Article  Google Scholar 

  24. J. Camargo, F. Spiessens, C. Hermans, A network flow model for price-responsive control of deferrable load profiles. Energies 11(3), 613 (2018)

    Article  Google Scholar 

  25. N. Ruiz, B. Claessens, J. Jimeno, J.A. López, D. Six, Residential load forecasting under a demand response program based on economic incentives. Int. Trans. Electr. Energy Syst. 25(8), 1436–1451 (2015)

    Article  Google Scholar 

  26. S. Iacovella, F. Ruelens, P. Vingerhoets, B. Claessens, G. Deconinck, Cluster control of heterogeneous thermostatically controlled loads using tracer devices. IEEE Trans. Smart Grid 8(2), 528–536 (2017)

    Google Scholar 

  27. J. Saez-Gallego, J.M. Morales, M. Zugno, H. Madsen, A data-driven bidding model for a cluster of price-responsive consumers of electricity. IEEE Trans. Power Syst. 31(6), 5001–5011 (2016)

    Article  Google Scholar 

  28. O. Corradi, H. Ochsenfeld, H. Madsen, P. Pinson, Controlling electricity consumption by forecasting its response to varying prices. IEEE Trans. Power Syst. 28(1), 421–429 (2013)

    Article  Google Scholar 

  29. J. Saez-Gallego, J.M. Morales, Short-term forecasting of price-responsive loads using inverse optimization. IEEE Trans. Smart Grid 9(5), 4805–4814 (2018)

    Article  Google Scholar 

  30. M. Gonzalez Vaya, Optimizing the Electricity Demand of Electric Vehicles: Creating Value Through Flexibility, PhD thesis, ETH Zurich (2015)

    Google Scholar 

  31. G. Petretto et al., Representative distribution network models for assessing the role of active distribution systems in bulk ancillary services markets. 2016 Power Systems Computation Conference (PSCC), Genoa (2016), pp. 1–7

    Google Scholar 

  32. R. Carmona, M. Coulon, D. Schwarz, Electricity price modeling and asset valuation: A multi-fuel structural approach. Math. Financ. Econ. 7(2), 167–202 (2013)

    Article  MathSciNet  Google Scholar 

  33. D. Pudjianto, C. Ramsay, G. Strbac, Virtual power plant and system integration of distributed energy resources. IET Renew. Power Gener. 1(1), 10–16 (2007)

    Article  Google Scholar 

  34. C. Kieny, B. Berseneff, N. Hadjsaid, Y. Besanger, J. Maire, On the concept and the interest of virtual power plant: Some results from the European project Fenix. IEEE Power & Energy Society general meeting, Calgary, AB (2009), pp. 1–6

    Google Scholar 

  35. P. Koponen et al., Toolbox for aggregator of flexible demand. 2012 IEEE International Energy Conference and Exhibition (ENERGYCON), Florence (2012), pp. 623–628

    Google Scholar 

  36. APEX-Spot, Intraday market with delivery on the French TSO zone. [Online]. Available: http://www.epexspot.com/en/product-info/intradaycontinuous. Accessed 6 Mar 2018

  37. Gestor Mercati Energetici (GME), Spot electricity market in Italy. [Online]. Available: http://www.mercatoelettrico.org/en/mercati/mercatoelettrico/mpe.aspx. Accessed 6 Mar 2018

  38. OMIE, Intraday market in Iberian Peninsular (Spain and Portugal). [Online]. Available: http://www.omie.es/en/home/markets-and-products/electricity-market/our-electricity-markets/intraday-market. Accessed 6 Mar 2018

  39. Hungarian Power Exchange Intraday Market Workshop (Nov 2016). [Online]. Available: https://docplayer.net/35666822-Hupx-intraday-market-workshop.html. Accessed 26 Sept 2018

  40. Polish Power Exchange (POLPX) indices for the Polish Day-Ahead and Intraday Market. [Online]. Available: https://wyniki.tge.pl/en/wyniki/euroindex/description/. Accessed 6 Mar 2018

  41. SmartNet, Aggregation models. Deliverable D2.1 (May 2018). [Online]. Available: http://smartnet-project.eu/wp-content/uploads/2018/05/D2.1_20180524_V1.0.pdf. Accessed 28 Feb 2019

  42. SmartNet, Network and market models. Deliverable D2.2 (Feb 2019). [Online]. Available: http://smartnet-project.eu/wp-content/uploads/2019/02/2019215113154_D2.2_20190215_V1.0.pdf. Accessed 28 Feb 2019

Download references

Author information

Authors and Affiliations

  1. Danmarks Tekniske Universitet, Kongens Lyngby, Denmark

    Mario Džamarija

  2. N-SIDE S.A., Louvain-la-Neuve, Belgium

    Guillaume Leclercq

  3. Nuestra Nueva Energia Sl, Alicante, Spain

    Miguel Marroquin & Manita Herman

Authors
  1. Mario Džamarija
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guillaume Leclercq
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miguel Marroquin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manita Herman
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Energy System Development Department, Ricerca sul Sistema Energetico, Milano, Italy

    Gianluigi Migliavacca

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

Džamarija, M., Leclercq, G., Marroquin, M., Herman, M. (2020). Modeling of Complex Systems Including Transmission, Distribution, Aggregation, Ancillary Services Markets. In: Migliavacca, G. (eds) TSO-DSO Interactions and Ancillary Services in Electricity Transmission and Distribution Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-29203-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-29203-4_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29202-7

  • Online ISBN: 978-3-030-29203-4

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation