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Optimization of smart energy systems based on response time and energy storage losses

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  • Andiappan, Viknesh

Abstract

Smart grids contain flexible smart energy systems to cater to users' energy demands. Energy systems in smart grid operations must be agile and have quick response times to adjust operations toward demand-side changes. However, technologies operating within smart energy systems tend to have unique (i.e., fast or slow) response times. This poses a complex decision-making problem for smart energy systems. Several studies have evaluated smart energy systems. Unfortunately, no work explicitly considered response times of energy systems in smart grid operations. Moreover, previous studies assumed pre-defined energy storage technologies in their analysis without considering technology selection and did not factor in storage losses. This work aims to present a generic optimization model that optimizes the selection of technologies in energy system operations for a smart grid while factoring in technology response time and energy storage losses. An illustrative example was solved to demonstrate the impact of response time requirements on the optimal technology selection in smart energy systems.

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  • Andiappan, Viknesh, 2022. "Optimization of smart energy systems based on response time and energy storage losses," Energy, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:energy:v:258:y:2022:i:c:s0360544222017145
    DOI: 10.1016/j.energy.2022.124811
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    1. Marcelino, C.G. & Leite, G.M.C. & Wanner, E.F. & Jiménez-Fernández, S. & Salcedo-Sanz, S., 2023. "Evaluating the use of a Net-Metering mechanism in microgrids to reduce power generation costs with a swarm-intelligent algorithm," Energy, Elsevier, vol. 266(C).
    2. Zheng Li & Ruoyao Tang & Hanbin Qiu & Linwei Ma, 2023. "Smart Energy Urban Agglomerations in China: The Driving Mechanism, Basic Concepts, and Indicator Evaluation," Sustainability, MDPI, vol. 15(15), pages 1-23, August.

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