HYBRID FUZZY LOGIC AND MACHINE LEARNING BASED CONTROLLER FOR POWER FLOW MANAGEMENT AND COMMUNICATION OPTIMIZATION IN V2G-ENABLED SMART GRIDS

Abstract

This project presents a hybrid controller integrating Fuzzy Logic Controllers (FLC) and Machine Learning (ML) to optimize power flow management and communication performance in a Vehicle-to-Grid (V2G) system within a realistic electricity distribution network. The V2G setup consists of multiple charging stations deployed across sub-feeder nodes, along with critical components such as aggregators, controllers, EVs, communication systems, and the grid infrastructure. The FLC operates at two hierarchical levels: one at the distribution grid level and the other at the charging station level, enabling smooth and efficient energy exchange. Communication utilizes the IEEE 802.11 (WiFi) protocol, where the MAC layer’s distributed coordination function (DCF) facilitates data transmission from EVs to charging station aggregators. ML models enhance communication reliability by optimizing key parameters such as bit error rates, timeouts, and retransmission delays, ensuring minimal disruptions. This ML-augmented hybrid model effectively mitigates communication failures, ensures seamless packet delivery, and maintains optimized power flow control. The approach enhances both energy management and communication resilience, ensuring robust, scalable, and efficient operations across V2G-enabled smart grids.

Specification

Description:The invention presents a hybrid controller model that integrates Fuzzy Logic Controllers (FLC) for power flow management and Machine Learning (ML) models for optimizing communication performance in a V2G-enabled smart grid. It ensures seamless energy exchange across distributed charging stations and EV fleets while dynamically switching between FLC and ML outputs based on communication quality.

1. Distribution Substation Controller and Aggregator
• Function:
Manages the overall energy distribution from the grid to subfeeders and charging stations. It determines how much power needs to be supplied or absorbed by different subfeeders based on grid conditions.
• Application:
Helps maintain grid stability by managing energy flow between the grid and distributed subfeeders, ensuring that each subfeeder receives the appropriate amount of power.

2. Subfeeder Aggregators
• Function:
Acts as an intermediary between the distribution substation and hybrid controllers. It distributes power from the grid to various charging stations connected to the subfeeder.
• Application:
Ensures balanced power distribution across multiple charging stations to avoid overloading and manage demand effectively across the grid.
3. Hybrid Controllers
• Function:
Each hybrid controller combines Fuzzy Logic Controllers (FLC) for power management and Machine Learning (ML) models for optimizing communication. It ensures smooth energy flow and predicts or compensates for communication issues.
• Application:
Plays a critical role in maintaining seamless energy management between charging stations and the grid while ensuring communication resilience under varying network conditions. The hybrid controller ensures the switch between FLC-based power control and ML-based error compensation when needed.

4. Charging Station Aggregators
• Function:
Collects and manages data from connected EV fleets and exchanges information with the hybrid controllers. It ensures that charging and discharging operations align with grid conditions and energy requirements.
• Application:
Provides real-time coordination between EV fleets and the grid by managing charging schedules to support tasks like peak shaving or valley filling.

5. Electric Vehicle Fleets (EV Fleets)
• Function:
Participates in bi-directional energy exchange, charging during off-peak hours and discharging energy back to the grid when needed to support grid stability.
• Application:
Acts as a distributed energy storage system that enhances grid stability by providing energy during peak demand and absorbing energy during surplus conditions.

6. Communication Channels
• Operational Communication Channel:
o Function: Facilitates reliable communication between distribution controllers, subfeeders, and hybrid controllers using optical fiber.
o Application: Ensures high-speed, error-free communication for operational coordination between critical components of the system.
• WiFi Communication Channel:
o Function: Connects EV fleets, charging stations, and hybrid controllers using the IEEE 802.11 MAC protocol. This channel is susceptible to bit errors, timeouts, and retransmission delays.
o Application:
Enables data exchange in real time between EV fleets and the charging stations. ML models optimize this communication by minimizing the impact of network disruptions.
, Claims:

1. Hybrid Controller Integration
A hybrid controller system for Vehicle-to-Grid (V2G) applications in smart grids, comprising Fuzzy Logic Controllers (FLC) for power flow management and Machine Learning (ML) models for communication optimization, ensuring seamless energy exchange between the grid, charging stations, and electric vehicles (EVs).
2. Fuzzy Logic-Based Power Flow Management
The use of FLC at multiple hierarchical levels, including the distribution grid level and charging station level, to manage bi-directional power flow between the grid and EV fleets, enabling efficient energy exchange even under fluctuating grid conditions.
3. Machine Learning for Communication Optimization
A method wherein ML models optimize key communication parameters, including bit error rate, timeouts, and retransmission delays, for reliable data exchange over WiFi communication channels between EV fleets, aggregators, and charging stations.
4. Adaptive Switching Mechanism
A hybrid decision-making system that dynamically switches between FLC and ML outputs based on communication quality, using the FLC for power flow control when communication is stable and ML models to predict and compensate for missing data during communication disruptions.
5. Real-Time Anomaly Detection and Prediction
The ML models provide real-time detection of communication anomalies and predict network disruptions, ensuring uninterrupted data flow and enabling proactive adjustments in energy management through the hybrid controller.
6. Scalability and Multi-Node Coordination
The system supports scalable integration across multiple charging stations and sub-feeder nodes, with hybrid controllers enabling efficient coordination between EV fleets, charging stations, and grid components.
7. Communication Resilience Using IEEE 802.11 Protocol
The system utilizes the IEEE 802.11 (WiFi) protocol for communication between EV fleets and aggregators, with ML models mitigating packet loss and retransmission delays to maintain reliable data exchange under varying network conditions.
8. Enhanced Grid Stability and Energy Efficiency
A method of maintaining grid stability by enabling peak shaving and valley filling through coordinated energy exchange between EV fleets and the grid, facilitated by the hybrid controller's optimized power flow management and resilient communication

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