Efficient Grid-Connected EV Charging | #sciencefather #researchawards #phenomenalogical #EnergyManagementSystem #SmartCharging

Integrated Optimization and Dynamic Pricing for PV-EV Charging Stations Using Hybrid Energy Storage Under Temperature Variability

1. Introduction

With the growing adoption of electric vehicles (EVs), the need for efficient, sustainable, and grid-friendly charging infrastructure has become paramount. Integrating renewable energy sources, such as photovoltaic (PV) systems, and deploying energy storage solutions has shown significant promise. However, environmental factors—especially temperature—greatly influence system performance, including PV efficiency, battery capacity, and EV charging demand. This study proposes a comprehensive optimization framework that incorporates temperature variations into dynamic pricing and charging strategies for enhanced operational efficiency.


2. System Architecture Overview

The proposed system includes a PV-powered EV charging station integrated with a hybrid energy storage setup composed of supercapacitors and lithium-ion batteries. A coordinated control mechanism manages:

  • Real-time energy flows

  • Demand response based on pricing signals

  • Temperature-influenced performance fluctuations

Key Components:

  • PV panels with temperature-sensitive efficiency

  • Lithium-ion batteries (energy-dense, slow response)

  • Supercapacitors (power-dense, fast response)

  • Bidirectional EV-grid interfaces for discharging

3. Impact of Temperature on System Performance

3.1 PV Output Degradation

  • PV panel efficiency decreases with rising temperature.

  • A temperature coefficient (−0.4%/°C) quantifies output losses.

3.2 Battery Storage Efficiency

  • Lithium-ion battery capacity and efficiency decline at high or low temperatures.

  • SOC management is affected, influencing energy availability and pricing dynamics.

3.3 EV Charging Behavior

  • EV energy demand varies with temperature due to battery heating/cooling needs and altered driving efficiency.

4. Peak-Valley Dynamic Pricing Strategy

4.1 Time-of-Use (TOU) Adaptation

  • Traditional TOU pricing fails under high EV penetration.

  • New pricing model based on peak-valley difference better aligns price with grid stress.

4.2 Pricing Model Description

  • Higher prices during high-demand periods

  • Incentives for off-peak usage to flatten load curves

  • Temperature adjustments influence price to reflect system stress and PV yield

5. Multi-Objective Optimization Approach

5.1 Optimization Goals

Using Multi-Objective Particle Swarm Optimization (MOPSO) to simultaneously:

  • Minimize grid load fluctuations

  • Maximize aggregator profits

  • Reduce user charging costs

5.2 Constraints and Variables

  • Energy balance

  • Battery SOC range

  • PV power limits

  • Temperature-dependent performance parameters

6. Role of Hybrid Energy Storage

6.1 Supercapacitor Functionality

  • Handles fast power fluctuations

  • Mitigates PV intermittency

6.2 Lithium-Ion Battery Role

  • Stores excess energy for longer durations

  • Supports off-peak demand and price smoothing

6.3 Coordination Strategy

  • Hierarchical control to dispatch storage based on real-time needs and efficiency

7. Simulation and Results

7.1 Setup and Scenarios

  • Various temperature profiles simulated (hot, moderate, cold)

  • Different EV penetration levels analyzed

7.2 Key Findings

  • Charging costs reduced by up to 15%

  • Grid peak-valley difference lowered by 20–30%

  • Aggregator profits increased through optimized arbitrage

8. Conclusion and Future Work

Temperature-aware charging strategies offer significant potential to improve grid stability, economic returns, and user satisfaction in PV-powered EV charging stations. Future studies may integrate real-time weather forecasting, AI-driven load prediction, and vehicle-to-grid (V2G) coordination to further enhance system performance.

#EVChargingOptimization #MultiObjectiveOptimization #DynamicPricing #HybridEnergyStorage  #MOPSO #EnergyManagementSystem #SmartCharging

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