EDA‐based optimized global control for PV inverters in distribution grids

  1. Valizadeh, Hamed 1
  2. Cañadillas, David 2
  3. Guerrero‐Lemus, Ricardo 2
  4. Kleissl, Jan 1
  5. González‐Díaz, Benjamín 2
  1. 1 Center for Energy Research Department of Mechanical and Aerospace Engineering University of California, San Diego La Jolla California USA
  2. 2 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

Revista:
IET Renewable Power Generation

ISSN: 1752-1416 1752-1424

Ano de publicación: 2021

Volume: 15

Número: 2

Páxinas: 382-396

Tipo: Artigo

DOI: 10.1049/RPG2.12031 GOOGLE SCHOLAR

Outras publicacións en: IET Renewable Power Generation

Resumo

Operating distribution grids is increasingly challenging due to the increasing penetration ofphotovoltaic systems. To address these challenges, modern photovoltaic inverters includefeatures for local control, which sometimes lead to suboptimal results. Improved commu-nication infrastructure and photovoltaic inverters favour global control strategies, whichreceive information from all the systems in the grid. An estimation of distribution algo-rithm is used to optimize a global control strategy that minimizes active power curtailmentand use of reactive power of the photovoltaic inverters, while maintaining voltage stabil-ity. Optimized global control outperforms every other local control evaluated in terms ofapparent energy used for control (9.9% less usage compared to the second best alternativein all scenarios studied) and ranks second in terms of voltage stability (with a 0.14% oftotal time outside the voltage limits). Two new indicators to compare control strategies areproposed, and optimized global control strategy ranks best for both efficiency index (0.98)and average apparent power use (0.48 kVA).

Referencias bibliográficas

  • 10.1002/pip.2331
  • 10.1109/TSG.2016.2625314
  • 10.1109/TSTE.2017.2733258
  • 10.1049/joe.2018.8386
  • 10.1002/pip.1204
  • Alizadeh S.M. et al.:The impact of X/R ratio on voltage stability in a distribution network penetrated by wind farms. In2016 Australasian Universities Power Engineering Conference (AUPEC) Brisbane Australia Sep.2016 pp.1–6 https://doi.org/10.1109/AUPEC.2016.7749289
  • Ochi T. et al.:The development and the application of fast decoupled load flow method for distribution systems with high R/X ratios lines. In2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT) Feb.2013 pp.1–6 https://doi.org/10.1109/ISGT.2013.6497842
  • 10.1109/JPHOTOV.2011.2174821
  • 10.1049/iet-gtd.2016.0409
  • 10.1109/TSTE.2014.2300934
  • Martí P. et al.:Distributed reactive power control methods to avoid voltage rise in grid‐connected photovoltaic power generation systems. In2013 IEEE International Symposium on Industrial Electronics May2013 pp.1–6 https://doi.org/10.1109/ISIE.2013.6563803
  • 10.1109/TSG.2012.2225851
  • Masoum M.A.S. et al.:Optimal placement of hybrid PV‐wind systems using genetic algorithm. In2010 Innovative Smart Grid Technologies (ISGT) Jan.2010 pp.1–5 https://doi.org/10.1109/ISGT.2010.5434746
  • 10.1016/j.egypro.2018.09.132
  • L.jing Hu et al.:Capacity optimization of wind /PV/storage power system based on simulated annealing‐particle swarm optimization. In2018 37th Chinese Control Conference (CCC) Jul. 2018 pp.2222–2227 doi:10.23919/ChiCC.2018.8482706
  • 10.1016/j.egypro.2019.04.010
  • 10.1016/j.eswa.2012.01.159
  • 10.1007/978-1-4615-1539-5
  • 10.1109/TSG.2011.2151888
  • Qiu Y. et al.:Network optimization based on genetic algorithm and estimation of distribution algorithm. In2008 International Conference on Computer Science and Software Engineering Dec. 2008 pp.1058–1061 https://doi.org/10.1109/CSSE.2008.1511
  • Pelikan M.:Analysis of estimation of distribution algorithms and genetic algorithms on NK landscapes. InProceedings of the 10th Annual Conference on Genetic and Evolutionary Computation ‐ GECCO ’08 Atlanta GA USA 2008 p.1033 https://doi.org/10.1145/1389095.1389287
  • 10.1109/TIE.2009.2013749
  • 10.1016/j.eswa.2013.09.049
  • 10.1016/j.ijpe.2014.12.010
  • 10.1016/j.asoc.2019.02.045
  • 10.1016/j.jpdc.2018.02.009
  • 10.1016/j.trc.2018.12.008
  • (2016), Common Functions for Smart Inverters
  • Seuss J. et al.:Analysis of PV advanced inverter functions and setpoints under time series simulation. SAND2016‐4856 1259558 May2016.https://doi.org/10.2172/1259558
  • 10.1109/TSG.2014.2380642
  • 10.1109/TSG.2014.2321748
  • 10.1109/TSG.2014.2300146
  • 10.1186/1756-0381-1-6
  • Chen Y. et al.:Solving deceptive problems using a genetic algorithm with reserve selection. In2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Jun.2008 pp.884–889 https://doi.org/10.1109/CEC.2008.4630900
  • 10.1109/TPWRS.2017.2760011
  • 10.1109/TSTE.2013.2292828
  • 10.1109/3468.508827