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Optimized Demand Side Management for Refrigeration: Modeling and Case Study Insights from Kenya

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  • Josephine Nakato Kakande

    (Chair of Electrical Energy Technology—Sustainable Energy Concepts (EET-NEK), Faculty of Computer Science, Electrical Engineering and Mathematics, Paderborn University, Pohlweg 55, 33098 Paderborn, Germany
    Department of Electrical and Computer Engineering, Makerere University, Kampala P.O. Box 7062, Uganda)

  • Godiana Hagile Philipo

    (Chair of Electrical Energy Technology—Sustainable Energy Concepts (EET-NEK), Faculty of Computer Science, Electrical Engineering and Mathematics, Paderborn University, Pohlweg 55, 33098 Paderborn, Germany
    Department of Material, Energy, Water and Environmental Sciences, The Nelson Mandela African Institution of Science and Technology, Arusha P.O. Box 447, Tanzania)

  • Stefan Krauter

    (Chair of Electrical Energy Technology—Sustainable Energy Concepts (EET-NEK), Faculty of Computer Science, Electrical Engineering and Mathematics, Paderborn University, Pohlweg 55, 33098 Paderborn, Germany)

Abstract

According to the International Institute of Refrigeration (IIR), 20% of worldwide electricity consumption is for refrigeration, with domestic refrigeration appliances comprising a fifth of this demand. As the uptake of renewable energy sources for on-grid and isolated electricity supply increases, the need for mechanisms to match demand and supply better and increase power system flexibility has led to enhanced attention on demand-side management (DSM) practices to boost technology, infrastructure, and market efficiencies. Refrigeration requirements will continue to rise with development and climate change. In this work, particle swarm optimization (PSO) is used to evaluate energy saving and load factor improvement possibilities for refrigeration devices at a site in Kenya, using a combination of DSM load shifting and strategic conservation, and based on appliance temperature evolution measurements. Refrigeration energy savings of up to 18% are obtained, and the load factor is reduced. Modeling is done for a hybrid system with grid, solar PV, and battery, showing a marginal increase in solar energy supply to the load relative to the no DSM case, while the grid portion of the load supply reduces by almost 25% for DSM relative to No DSM.

Suggested Citation

  • Josephine Nakato Kakande & Godiana Hagile Philipo & Stefan Krauter, 2025. "Optimized Demand Side Management for Refrigeration: Modeling and Case Study Insights from Kenya," Energies, MDPI, vol. 18(13), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3258-:d:1684466
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    References listed on IDEAS

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    1. Mohammad Reza Zavvar Sabegh & Chris Bingham, 2019. "Model Predictive Control with Binary Quadratic Programming for the Scheduled Operation of Domestic Refrigerators," Energies, MDPI, vol. 12(24), pages 1-20, December.
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    3. Christoforos Menos-Aikateriniadis & Ilias Lamprinos & Pavlos S. Georgilakis, 2022. "Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision," Energies, MDPI, vol. 15(6), pages 1-26, March.
    4. Banala Venkatesh & Padmini Sankaramurthy & Bharatiraja Chokkalingam & Lucian Mihet-Popa, 2022. "Managing the Demand in a Micro Grid Based on Load Shifting with Controllable Devices Using Hybrid WFS2ACSO Technique," Energies, MDPI, vol. 15(3), pages 1-25, January.
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