Evaluating the use of a Net-Metering mechanism in microgrids to reduce power generation costs with a swarm-intelligent algorithm

C.G. Marcelino, G.M.C. Leite, E.F. Wanner, S. Jiménez-Fernández, S. Salcedo-Sanz

Research output: Contribution to journalArticlepeer-review


The micro-generation of electricity arises as a clean and efficient alternative to provide electrical power. However, the unpredictability of wind and solar radiation poses a challenge to attend load demand, while maintaining a stable operation of the microgrids (MGs). This paper proposes the modeling and optimization, using a swarm-intelligent algorithm, of a hybrid MG system (HMGS) with a Net-Metering compensation policy. Using real industrial and residential data from a Spanish region, a HMGS with a generic ESS is used to analyze the influence of four different Net-Metering compensation levels regarding costs, percentage of renewable energy sources (RESs), and LOLP. Furthermore, the performance of two ESSs, Lithium Titanate Spinel (Li4Ti5O 12 (LTO)) and Vanadium redox flow batteries (VRFB), is assessed in terms of the final $/kWh costs provided by the MG. The results obtained indicate that the Net-Metering policy reduces the surplus from over 14% to less than 0.5% and increases RESs participation in the MG by more than 10%. Results also show that, in a yearly projection, a MG using a VRFB system with a 25% compensation policy can yield more than 100000$ dollars of savings, when compared to a MG using a LTO system without Net-Metering.

Original languageEnglish
Article number126317
Number of pages16
Early online date8 Dec 2022
Publication statusPublished - 1 Mar 2023

Bibliographical note

Copyright © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Acknowledgments & Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skfłodowska-Curie grant agreement No 754382. This research has
been partially supported by Ministerio de Economía 𝑦 Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P) and by Comunidad de Madrid, PROMINT-CM project (grant No. P2018/EMT-4366). The authors thank UAH, UFRJ and CEFET-MG for the infrastructure used to conduct this work, and Brazilian research agencies: CAPES (Finance Code 001) and CNPq for support.


  • Microgrid systems
  • Net-Metering
  • Renewable sources
  • Swarm evolutionary optimization


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