Abstract
This chapter illustrates the concept of multi-objective DSM in a distribution network in support of transmission network operation. The methodology builds on the results of the methodology on Advanced Demand Profiling, detailed in the previous chapter. Information about demand composition is used to model demand at each load bus of the network, facilitating that way further studies of the effect DSM may have on network performance indicators.
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References
Tang X, Milanović JV (2017) Assessment of the impact of demand side management on power system small signal stability. IEEE Manchester PowerTech 2017:1–6
CIGRE (2014) Modelling and aggregation of loads in flexible power networks. CIGRE WG C4.605 (566), ISBN: 978-2-85873-261-6
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
Kun-Yuan H, Yann-Chang H (2004) Integrating direct load control with interruptible load management to provide instantaneous reserves for ancillary services. IEEE Trans Power Syst 19:1626–1634
Deh-Chang W, Nanming C (1995) Air conditioner direct load control by multi-pass dynamic programming. IEEE Trans Power Syst 10:307–313
Kosa KM, Cates SC, Godwin SL, Coppings RJ, Speller-Henderson L (2011) Most Americans are not prepared to ensure food safety during power outages and other emergencies. Food Protect. Trends 31:428–436
Hayes BP (2013) Distributed generation and demand side management: applications to transmission system operation. PhD Thesis, University of Edinburgh
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
Demand Side Flexibility Annual Report (2016) Power Responsive, National Grid, 2016. Available: http://powerresponsive.com/wp-content/uploads/2017/01/Power-Responsive-Annual-Report-2016-FINAL.pdf
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 Energy 96:104–114
Milano F (2010) Power system modelling and scripting. Springer Science & Business Media, Berlin
Bo Z, Yi-Jia C (2005) Multiple objective particle swarm optimization technique for economic load dispatch. J Zhejiang Univ-Sci A 6:420–427
Niknam T, Narimani M, Aghaei J, Azizipanah-Abarghooee R (2012) Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET Gener Transm Distrib 6:515–527
Hassan R, Cohanim B, De Weck O, Venter G (2005) A comparison of particle swarm optimization and the genetic algorithm. In: 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, Austin, TX, USA, p 1897
Zimmerman RD, Murillo-Sánchez CE (2016) Matpower 6.0 User’s Manual
Eberhart RC, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimization. In: International conference on evolutionary programming, San Diego, CA, USA, pp 611–616
Boeringer DW, Werner DH (2004) Particle swarm optimization versus genetic algorithms for phased array synthesis. IEEE Trans Antennas Propag 52:771–779
Clerc M (2010) Particle swarm optimization, vol 93. Wiley, Hoboken
Pecan Street Inc. (2017) Dataport 2017. Available: http://www.pecanstreet.org/
Paatero JV, Lund PD (2006) A model for generating household electricity load profiles. Int J Energy Res 30:273–290
Ofgem (2009) Electricity distribution systems losses non-technical overview. Available: https://www.ofgem.gov.uk/publications-and-updates/electricity-distribution-systems-losses-non-technical-overview
Jardini J, Tahan C, Ahn S, Ferrari E (1997) Distribution transformer loading evaluation based on load profiles measurements. IEEE Trans Power Deliv 12:1766–1770
Silvente J, Kopanos GM, Pistikopoulos EN, Espuña A (2015) A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids. Appl Energy 155:485–501
Preparing UK Electricity Networks for Electric Vehicles (2018) Available: https://es.catapult.org.uk/wp-content/uploads/2018/07/Preparing-UK-Electricity-Networks-for-Electric-Vehicles-FINAL.pdf
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Ponoćko, J. (2020). Multi-objective Demand Side Management at Distribution Network Level. 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_4
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DOI: https://doi.org/10.1007/978-3-030-39943-6_4
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