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Data-driven optimization for microgrid control under distributed energy resource variability.

Akhilesh Mathur1, Ruchi Kumari1, V P Meena2,3

  • 1Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India.

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|May 11, 2024
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Summary

This study optimizes hybrid Microgrid operation using a priority-based cost function and Grey Wolf Optimization (GWO). The method effectively manages uncertainties for cost-efficient, reliable energy in grid-connected and stand-alone modes.

Keywords:
Grey-Wolf optimizationJaya algorithmK-mean clusteringMicrogridsMonte Carlo simulationOptimal schedulingProbability distribution functionStochastic process

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Area of Science:

  • Electrical Engineering
  • Renewable Energy Systems
  • Optimization Techniques

Background:

  • Smart grids benefit from renewable energy integration for resilience and clean electricity.
  • The intermittent nature of renewable sources necessitates advanced scheduling strategies for Microgrids.
  • Optimizing hybrid systems with photovoltaic (PV), wind, and controllable distributed generators (CDGs) is crucial for both grid-connected and stand-alone operations.

Purpose of the Study:

  • To develop a priority-based cost optimization function for hybrid Microgrids.
  • To incorporate uncertainties of intermittent parameters into the scheduling methodology.
  • To minimize the overall operational cost of Microgrids through advanced optimization.

Main Methods:

  • A priority-based cost optimization function was developed, including operating, emission, battery, grid energy exchange, and load shedding costs.
  • Monte Carlo simulations generated multiple scenarios for uncertain parameters, which were then reduced using k-means clustering.
  • The Grey Wolf Optimization (GWO) algorithm was employed to minimize the developed cost function, validated on the CIGRE test network.

Main Results:

  • The GWO algorithm demonstrated effectiveness in minimizing the Microgrid's cost function under various scenarios.
  • Different priorities assigned to sub-objectives yielded distinct operational outcomes, showcasing the flexibility of the proposed method.
  • The GWO approach proved superior to Jaya and Particle Swarm Optimization (PSO) algorithms in optimizing the Microgrid's cost.

Conclusions:

  • The proposed priority-based cost optimization strategy effectively manages uncertainties in hybrid Microgrids.
  • The Grey Wolf Optimization algorithm provides a robust and efficient method for optimizing Microgrid operations.
  • The methodology ensures reliable and cost-effective energy supply in both grid-connected and stand-alone Microgrid configurations.