Dynamic Stability and Power Quality Optimization of Grid-Connected Industrial Microgrids Integrating Renewable Energy, Battery Storage, and Electric Vehicle Charging Loads

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@article{1720085,
      author = {Md Sabiruzzaman, Md Sadik Hassan Arik, Md Ahsan Habib},
      title = {Dynamic Stability and Power Quality Optimization of Grid-Connected Industrial Microgrids Integrating Renewable Energy, Battery Storage, and Electric Vehicle Charging Loads},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {9},
      pages = {668-683},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1720085.pdf},
      abstract = {The rapid electrification of industrial facilities, coupled with the proliferation of distributed renewable generation, battery energy storage systems (BESS), and electric vehicle (EV) charging infrastructure, has fundamentally altered the operating characteristics of grid-connected industrial microgrids. These systems must now maintain dynamic stability and acceptable power quality under highly variable generation and load profiles, including intermittent solar and wind output, fast battery charge/discharge transients, and stochastic, high-power EV charging demand. This paper presents a comprehensive modeling, control, and optimization framework for the dynamic stability and power quality enhancement of such microgrids. A detailed small-signal state-space model is developed to capture the interactions among photovoltaic (PV) inverters, wind energy conversion systems, BESS converters, EV charging stations, and the point of common coupling (PCC) with the utility grid. A hierarchical control architecture is proposed, comprising primary adaptive droop and virtual inertia control, secondary frequency/voltage restoration, and tertiary economic and power-quality-aware dispatch. A coordinated vehicle-to-grid (V2G) charging/discharging strategy is integrated with a shunt active power filter (SAPF) to mitigate harmonics, voltage sag/swell, and unbalance introduced by nonlinear EV charger loads. The overall problem is cast as a multi-objective optimization that simultaneously minimizes frequency and voltage deviations, total harmonic distortion (THD), and operational cost, solved using a hybrid particle swarm optimization (PSO) and model predictive control (MPC) approach. Simulation case studies on a representative 13-bus industrial microgrid, incorporating high EV charging penetration and renewable variability, demonstrate that the proposed framework reduces frequency deviation by over 62%, voltage THD by more than 70%, and settling time of transient disturbances by approximately 55% relative to conventional droop-based control. The results confirm that coordinated multi-layer control combined with active power quality conditioning and intelligent EV load management can substantially improve both the dynamic security and power quality of renewable-rich industrial microgrids.},
      keywords = {Industrial Microgrid, Dynamic Stability, Power Quality, Renewable Energy Integration, Battery Energy Storage, Electric Vehicle Charging, Vehicle-To-Grid, Active Power Filter, Model Predictive Control, Hierarchical Control},
      month = {March},
      doi = {https://doi.org/10.64388/IREV7I9-1720085}
  }