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A Smart Grid Framework for Optimally Integrating Supply-Side, Demand-Side and Transmission Line Management Systems

Author

Listed:
  • Chukwuka Monyei

    (School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Westville Campus, Private Bag X54001, Durban 4000, South Africa)

  • Serestina Viriri

    (School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Westville Campus, Private Bag X54001, Durban 4000, South Africa)

  • Aderemi Adewumi

    (School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Westville Campus, Private Bag X54001, Durban 4000, South Africa)

  • Innocent Davidson

    (Department of Electrical Power Engineering, Durban University of Technology, Durban 4001, South Africa)

  • Daniel Akinyele

    (Department of Electrical and Computer Engineering, Elizade University, Ilara-Mokin P.M.B. 002, Ondo State, Nigeria)

Abstract

A coordinated centralized energy management system (ConCEMS) is presented in this paper that seeks to integrate for optimal grid operation—the supply side energy management system (SSEMS), home energy management system (HEMS) and transmission line management system (TLMS). ConCEMS in ensuring the optimal operation of an IEEE 30-bus electricity network harmonizes the individual objective function of SSEMS, HEMS and TLMS to evolve an optimal dispatch of participating demand response (DR) loads that does not violate transmission line ampacity limits (TLMS constraint) while minimizing consumer cost (HEMS constraint) and supply side operations cost (SSEMS constraint). An externally constrained genetic algorithm (ExC-GA) that is influenced by feedback from TLMS is also presented that intelligently varies the dispatch time of participating DR loads to meet the individual objective functions. Hypothetical day ahead dynamic pricing schemes (Price1, Price2 and Price3) have also been adopted alongside an existing time of use (Price0) pricing scheme for comparison and discussion while a dynamic thermal line rating (DTLR) algorithm has also been incorporated to dynamically compute power limits based on real time associated data.

Suggested Citation

  • Chukwuka Monyei & Serestina Viriri & Aderemi Adewumi & Innocent Davidson & Daniel Akinyele, 2018. "A Smart Grid Framework for Optimally Integrating Supply-Side, Demand-Side and Transmission Line Management Systems," Energies, MDPI, vol. 11(5), pages 1-27, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1038-:d:142932
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    References listed on IDEAS

    as
    1. Monyei, C.G. & Adewumi, A.O., 2017. "Demand Side Management potentials for mitigating energy poverty in South Africa," Energy Policy, Elsevier, vol. 111(C), pages 298-311.
    2. Karunanithi, K. & Saravanan, S. & Prabakar, B.R. & Kannan, S. & Thangaraj, C., 2017. "Integration of Demand and Supply Side Management strategies in Generation Expansion Planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 966-982.
    3. Monyei, Chukwuka G. & Adewumi, Aderemi O. & Obolo, Michael O. & Sajou, Barka, 2018. "Nigeria's energy poverty: Insights and implications for smart policies and framework towards a smart Nigeria electricity network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1582-1601.
    4. Welsch, Manuel & Bazilian, Morgan & Howells, Mark & Divan, Deepak & Elzinga, David & Strbac, Goran & Jones, Lawrence & Keane, Andrew & Gielen, Dolf & Balijepalli, V.S.K. Murthy & Brew-Hammond, Abeeku , 2013. "Smart and Just Grids for sub-Saharan Africa: Exploring options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 336-352.
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