Abstract
The purpose of the chapter is to describe models and methodologies for the integration of the distributed energy resources (DERs) in LV distribution networks, using multi-agent systems (MAS). In the MAS approach, an aggregator is used to coordinate the behaviour of independent agents in order to elaborate strategies so that the total load demand processed (ESSs and active loads) does not cause contingencies in the network (i.e. not exceeding a defined voltage limit). The proposed strategies allow exploiting the potential of energy storages, supporting the grid operation (e.g. absorbing the surplus energy produced by PV and supplying energy during peak periods), reducing substation transformers and line loading. Application examples from real word will be illustrated to highlight the effectiveness of the aggregation of the resources (AD, EVs and ESSs), in providing grid services, supporting the DSO in the operation of the distribution network.
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References
European Commission, A Clean Planet for All a European Strategic Long-Term Vision for a Prosperous, Modern, Competitive and Climate Neutral Economy, 28/11/2018 [Online]. https://ec.europa.eu/clima/sites/clima/files/docs/pages/com_2018_733_en.pdf Last access: 8/03/2019
European Commission, Directive of the European Parliament and of the Council on common rules for the internal market in electricity (COM (2016) 864 final), Brussels, 23/2/2017
X. Wang, A. Palazoglu, N.H. El-Farra, Operational optimization and demand response of hybrid renewable energy systems. Appl. Energy 143, 324–335 (2015)
O. Erdinc, N.G. Paterakis, T.D.P. Mendes, A.G. Bakirtzis, J. P. S. Catalão, Smart household operation considering bi-directional EV and ESS utilization by real-time pricing-based DR. IEEE Trans. Smart Grid 6(3), 1281–1291 (2015)
F.S. Gazijahania, J. Salehi, Game theory based profit maximization model for microgrid aggregators with presence of EDRP using information gap decision theory. IEEE Syst. J. 99, 1–9 (2018)
J. Soares, H. Morais, T. Sousa, Z. Vale, P. Faria, Day-ahead resource scheduling including demand response for electric vehicles. IEEE Trans. Smart Grid 4(1), 596–605 (2013)
ADDRESS Project. [Online]. http://www.addressfp7.org/ Last access: 8/03/2019
R.J. Bessa, M.A. Matos, The role of an aggregator agent for EV in the electricity market, in Proceedings of 7th MEDPOWER, Ayia Napa, Cyprus 7–10 November (2010)
F. Samadi Gazijahania, J. Salehi, Integrated DR and reconfiguration scheduling for optimal operation of microgrids using Hong’s point estimate method. Int. J. Electr. Power Energy Syst. 99, 481–492 (2018)
F. Samadi Gazijahania, J. Salehi, Reliability constrained two-stage optimization of multiple renewable-based microgrids incorporating critical energy peak pricing demand response program using robust optimization approach. Energy 161, 999–1015 (2018)
H. Yang, T. Xiong, J. Qiu, D. Qiu, Z. Yang Dong, Optimal operation of DES/CCHP based regional multi-energy prosumer with demand response. Appl. Energy 167, 353–365 (2016)
A.L. Dimeas, N.D. Hatziargyriou, Operation of a multiagent system for microgrid control. IEEE Trans. Power Systems 20, 1447 (2005)
J.E.L. Karfopoulos, N.D. Hatziargyriou, A multi-agent system for controlled charging of a large population of electric vehicles. IEEE Trans. Power Syst 28(2), 1196–1204 (2012)
L. Gan, U. Topcu, S.H. Low, Optimal decentralized protocol for electric vehicle charging. IEEE Trans. Power Syst. 28(2), 940 (2013)
K. Mets, R. D’hulst, C. Develder, Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid. J. Commun. Netw. 14(6), 672 (2012)
B. Asare-Bediako, W.L. Kling, P.F. Ribeiro, Multi-agent system architecture for smart home energy management and optimization, in 4th IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), October 6–9, Copenhagen (2013)
M. Biabani, M. A. Golkar, A. Sajadi, Operation of a multi-agent system for load management in smart power distribution system, in Proceedings of 11th International Conference on Environment and Electrical Engineering (EEEIC), Venice, 18–25 May (2012)
ENTSO-E, ENTSO-G, “TYNDP 2018 Scenario Report: Main Report”. [Online]. https://docstore.entsoe.eu/Documents/TYNDP%20documents/TYNDP2018/Scenario_Report_2018_Final.pdf Last access: 8/03/2019
StoRES project. [Online]. https://stores.interreg-med.eu/ Last access: 8/03/2019
S.D.J. McArthur et al., Multi-agent systems for power engineering applications—Part I: Concepts, approaches, and technical challenges, IEEE Trans. Power Syst. Vol. 22, No. 4, November 2007 1743-1752
M. Wooldridge, An introduction to MultiAgent system (Wiley, Hoboken, 2002)
M. Nikraz, G. Caire, P.A. Bahri, A Methodology for the Analysis and Design of Multi-Agent Systems Using JADE [Online]. http://jade.tilab.com/doc/tutorials/JADE_methodology_website_version.pdf. Last access: 8/03/2019
JADE Programmer’s Guide. [Online]. http://jade.tilab.com/doc/programmersguide.pdf Last access: 08/03/2019
The FIPA website. [Online]. http://www.fipa.org/ Last access: 08/03/2019
R.C. Dougan, T.E. McDermott, An open source platform for collaborating on smart grid research, in 2011 IEEE, Power and Energy Society General Meeting (2011)
http://sourceforge.net/projects/electricdss. Last access: 8/03/2019
Z. Ma, D. Callaway, I. Hiskens, Decentralised charging control for large population of plug-in electric vehicles, in Proceedings of 49th IEEE CDC Conference, Yokohama, Japan (2010)
S. Mocci, N. Natale, S. Ruggeri, F. Pilo, Multi-agent control system for increasing hosting capacity in active distribution networks with EV, in Proceedings of Energycon IEEE International Energy Conference 2014, Dubrovnik, 13–16 May (2014)
Mathworks website [Online]. https://it.mathworks.com/help/optim/ug/quadprog.html. Last access: 08/03/2019
S. Mocci, N. Natale, F. Pilo, S. Ruggeri, Demand side integration in LV smart grids with multi-agent control system. Electr. Power Syst. Res. 125, 23–33 (2015)
S. Mocci, N. Natale, F. Pilo, S. Ruggeri, Multi-agent control system to coordinate optimal demand response actions in active distribution networks, in Proceedings of MEDPOWER 2014, Athens, 2–5 November (2014)
S. Mocci, N. Natale, F. Pilo, S. Ruggeri, Multi-agent control system to coordinate optimal EV charging and demand response actions in active distribution networks, in Proceedings of 3rd Renewable Power Generation Conference, Naples, 24–25 September (2014)
W. Kempton, J. Tomić, Vehicle to grid power fundamentals: calculating capacity and net revenue. J. Power Sources 144, 268 (2005)
S. Mocci, N. Natale, F. Pilo, S. Ruggeri, Exploiting distributed energy storage to increase network hosting capacity with a multi-agent control system, in 2016 AEIT International Annual Conference (2016)
A. Bracale, R. Caldon, M. Coppo, D. Dal Canto, R. Langella, G. Petretto, F. Pilo, G. Pisano, D. Proto, S. Ruggeri, S. Scalari, R. Turri, Active management of distribution networks with the ATLANTIDE models, in Proceedings of 8th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2012, Cagliari, 1–3 October (2012)
A. Bracale, R. Caldon, G. Celli, M. Coppo, D. Dal Canto, R. Langella, G. Petretto, F. Pilo, G. Pisano, D. Proto, S. Scalari, R. Turri, Analysis of the Italian distribution system evolution through reference networks, in Proceedings of IEEE PES Innovative Smart Grid Technologies (ISGT) Europe Conference, Berlin, 14–17 Oct (2012)
S. Mocci, N. Natale, F. Pilo, S. Ruggeri, Multi-agent control system for the exploitation of vehicle to grid in active LV networks, in CIRED Workshop, 14–15 June (2016)
S. Russell, P. Norvig, Artificial intelligence: a modern approach, Prentice Hall, ISBN D-IH-IQBSOS-E (1995)
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Mocci, S., Ruggeri, S. (2020). Demand Side Integration in the Operation of LV Smart Grids. In: Nojavan, S., Zare, K. (eds) Demand Response Application in Smart Grids. Springer, Cham. https://doi.org/10.1007/978-3-030-32104-8_8
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