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Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based

Arul Rajagopalan1, Karthik Nagarajan2, Mohit Bajaj3,4,5

  • 1Centre for Smart Grid Technologies, School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, 600 127, India. arulphd@yahoo.co.in.

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|July 8, 2024
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Summary
This summary is machine-generated.

This study introduces a new energy management strategy (EMS) using the SaCryStAl algorithm to optimize microgrids with renewable energy sources and plug-in hybrid electric vehicles (PHEVs). The algorithm effectively reduces operating costs and environmental impact across various scenarios.

Keywords:
Electric vehiclesEnergy managementIterative map-based self-adaptive crystal structure algorithmMicrogridOptimal schedulingRenewable energy sourcesSolar photovoltaicWind power

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Environmental Science

Background:

  • Plug-in hybrid electric vehicles (PHEVs) offer a solution to energy and environmental challenges.
  • Integrating PHEVs into the distribution grid can significantly improve its flexibility.
  • Microgrids with renewable energy sources (RESs) require efficient energy management strategies.

Purpose of the Study:

  • To propose an innovative energy management strategy (EMS) for microgrids using the self-adaptive crystal structure algorithm (SaCryStAl).
  • To optimize multi-objective scheduling for microgrids incorporating various RESs and PHEVs.
  • To minimize microgrid operating costs and environmental impacts.

Main Methods:

  • Development of an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) for microgrid EMS.
  • Multi-objective optimization considering nonlinear constraints and fuzzy logic for decision-making.
  • Simulation of three scenarios: microgrid with all RESs, rated wind power operation, and PHEV integration in different charging modes.

Main Results:

  • The SaCryStAl algorithm demonstrated superior performance in reducing operation costs and emissions compared to other optimization methods.
  • Optimal cost and emissions were achieved in scenario 1 (177.29 €ct, 469.92 kg) and scenario 2 (112.02 €ct, 196.15 kg).
  • In scenario 3, SaCryStAl achieved optimal costs of 319.9301 €ct (uncoordinated), 160.9827 €ct (coordinated), and 128.2815 €ct (smart charging).

Conclusions:

  • The proposed SaCryStAl algorithm is highly effective for optimizing microgrid operation with RESs and PHEVs.
  • SaCryStAl significantly outperforms traditional and other advanced evolutionary optimization algorithms.
  • The strategy offers a viable solution for cost-effective and environmentally friendly microgrid management.