Towards Stochastic Optimization-Based Electric Vehicle Penetration in a Novel Archipelago Microgrid

Area of Science:

• Cyber-Physical Systems (CPS)
• Internet of Things (IoT)
• Renewable Energy Systems

Background:

• Islanded Micro-Grids (IMG) offer advantages by isolating from main power grids, preventing disturbances.
• Integrating Electric Vehicles (EVs) into micro-grids can reduce CO2 emissions and operational expenses.
• Managing the inherent uncertainty of Renewable Energy Sources (RES) is a key challenge in micro-grid operation.

Purpose of the Study:

• To propose a novel self-sufficient Cyber-Physical System (CPS) named 'archipelago micro-grid' integrating power grids and IoT.
• To develop a two-stage Stochastic Optimal Penetration (SOP) model for optimizing EV integration in micro-grids.
• To minimize system-wide CO2 emissions and operational costs while addressing RES uncertainties.

Main Methods:

• Formalized the optimal EV penetration as a two-stage Stochastic Optimal Penetration (SOP) model.
• Utilized Monte Carlo-based methods to capture uncertainties from RES (wind, solar) and load demand.
• Developed two EV deployment schemes: Unlimited Coordinated Scheme (UCS) and Limited Coordinated Scheme (LCS).

Main Results:

• The proposed SOP model effectively minimizes CO2 emissions and operational costs in islanded micro-grids.
• Simulation results on a modified 9-bus system demonstrate the effectiveness of both UCS and LCS schemes.
• The strategy successfully reduced environmental pollution and operational expenses in the studied micro-grid configurations.

Conclusions:

• The 'archipelago micro-grid' concept, powered by IoT and optimized EV integration, presents a viable solution for sustainable energy management.
• The SOP model and proposed scheduling schemes provide a robust framework for handling RES variability in micro-grids.
• This approach significantly contributes to reducing carbon footprints and operational costs in isolated power systems.