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2020 | OriginalPaper | Chapter

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

Authors : Mario Džamarija, Guillaume Leclercq, Miguel Marroquin, Manita Herman

Published in: TSO-DSO Interactions and Ancillary Services in Electricity Transmission and Distribution Networks

Publisher: Springer International Publishing

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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.

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previous chapter TSO-DSO Interaction and Acquisition of Ancillary Services from Distribution
next chapter ICT Requirements in a Smart Grid Environment
Footnotes
1
For the sake of the example, only upward flexibility is illustrated. Prices are expressed in cur/MWh.
 
2
This minimum price can even be negative for upward flexibility.
 
3
This does not pretend to be an exhaustive list, but a selection of useful ways to cooperate.
 
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
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
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
PQ capability diagram characterizes active and reactive power limits of a generator.
 
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
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
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.
 
Literature
1.
2.
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)CrossRef
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)CrossRef
5.
D.R. Biggar, M.R. Hesamzadeh, The Economics of Electricity Markets (John Wiley & Sons, Chichester, 2014)
6.
D. Kirschen, G. Strbac, Fundamentals of Power System Economics (John Wiley & Sons, Chichester, 2004)CrossRef
7.
G. Leclercq, M. Pavesi, T. Gueuning et al., Network and market models. SmartNet project, D2.2 (2019)
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)
9.
10.
11.
12.
P. Holmberg, E. Lazarczyk, Congestion management in electricity: Nodal, zonal and discriminatory pricing. IFN Working Paper No. 915 (2012)
13.
14.
T. Brown, S. Newell, D. Oates, K. Spees, International review of demand response mechanisms (2015)., 83
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)
16.
17.
H. Gerard, E. Rivero, D. Six, Basic schemes for TSO-DSO coordination and ancillary services provision. SmartNet project, D1.3 (2016)
18.
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
20.
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)CrossRef
22.
S.Ø. Ottesen, A. Tomasgard, S.E. Fleten, Prosumer bidding and scheduling in electricity markets. Energy 94, 828–843 (2016)CrossRef
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)CrossRef
24.
J. Camargo, F. Spiessens, C. Hermans, A network flow model for price-responsive control of deferrable load profiles. Energies 11(3), 613 (2018)CrossRef
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)CrossRef
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)
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)CrossRef
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)CrossRef
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)CrossRef
30.
M. Gonzalez Vaya, Optimizing the Electricity Demand of Electric Vehicles: Creating Value Through Flexibility, PhD thesis, ETH Zurich (2015)
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
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)MathSciNetCrossRef
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)CrossRef
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
35.
P. Koponen et al., Toolbox for aggregator of flexible demand. 2012 IEEE International Energy Conference and Exhibition (ENERGYCON), Florence (2012), pp. 623–628
36.
37.
38.
39.
40.
41.
42.
Metadata
Title
Modeling of Complex Systems Including Transmission, Distribution, Aggregation, Ancillary Services Markets
Authors
Mario Džamarija
Guillaume Leclercq
Miguel Marroquin
Manita Herman
Copyright Year
2020
Book
TSO-DSO Interactions and Ancillary Services in Electricity Transmission and Distribution Networks

Print ISBN: 978-3-030-29202-7

Electronic ISBN: 978-3-030-29203-4

Copyright Year: 2020

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