[Paper Review] Smart Charging and Parking of Plug-in Hybrid Electric Vehicles in Microgrids Considering Renewable Energy Sources

Zheming Liang, Yuanxiong Guo|arXiv (Cornell University)|Jul 15, 2018

Electric Vehicles and Infrastructure3 citations

TL;DR

This paper proposes a two-stage stochastic optimization framework for smart charging and parking of plug-in hybrid electric vehicles (PHEVs) in microgrids with renewable energy sources (RES) and combined heat and power (CHP) units. By modeling uncertainties in RES output and PHEV charging behavior through scenario-based stochastic programming and transforming the problem into a large-scale mixed-integer linear program, the approach minimizes average operating costs while ensuring microgrid reliability, with simulations showing a 15-20% cost reduction compared to deterministic methods under varying solar penetration levels.

ABSTRACT

With the current trend of transforming a centralized power system into a decentralized one for efficiency, reliability, and environment reasons, the concept of microgrid that integrates a variety of distributed energy resources (DERs) on the distribution network is gaining popularity. In this paper, we investigate the smart charging and parking of plug-in hybrid electric vehicles (PHEVs) in microgrids with renewable energy sources (RES), such as solar panels, in grid-connected mode. To address the uncertainties associated with RES power output and PHEVs charging condition in the microgrid, we propose a two-stage scenario-based stochastic optimization approach with the objective of providing a proper scheduling for parking and charging of PHEVs that minimizes the average total operating cost while maintaining the reliability of the microgrid. A case study is conducted to show the effectiveness of the proposed approach. Extensive simulation results show that the microgrid can minimize the operating cost and ensure its reliability.

Motivation & Objective

• To address the challenge of integrating PHEVs into microgrids without overloading the distribution system.
• To manage uncertainties in renewable energy output and PHEV charging behavior in grid-connected microgrids.
• To minimize the average total operating cost of the microgrid while maintaining reliability.
• To provide a controllable, flexible energy management strategy for microgrids with distributed energy resources (DERs).
• To evaluate the effectiveness of the proposed stochastic approach using real-world datasets and realistic system parameters.

Proposed method

• A two-stage scenario-based stochastic optimization model is formulated to handle uncertainties in RES power output and PHEV charging conditions.
• The model uses scenario generation and reduction techniques to represent stochastic variables, capturing the variability of solar power and PHEV availability.
• The two-stage stochastic program is transformed into a large-scale mixed-integer linear program (MILP) for efficient solution.
• The energy management system schedules PHEV charging and parking using binary variables to represent vehicle availability at charging stations.
• Constraints model PHEV battery dynamics, including state-of-charge limits, charging/discharging efficiency, and rate limits.
• The objective function minimizes total operating cost, including fuel, electricity purchase, and battery degradation costs, while ensuring power and heat balance.

Experimental results

Research questions

• RQ1 How does the proposed stochastic approach compare to a deterministic approach in minimizing microgrid operating costs under uncertain RES and PHEV conditions?
• RQ2 What impact does the flexibility of deferrable load scheduling have on the average operating cost of the microgrid?
• RQ3 How does increasing solar power penetration affect the cost-effectiveness and reliability of the microgrid energy management system?
• RQ4 To what extent can the integration of PHEVs with smart charging reduce peak load and system operating costs in a microgrid?
• RQ5 Can the proposed framework maintain microgrid reliability while minimizing total operating costs under diverse stochastic scenarios?

Key findings

• The stochastic approach reduces average operating costs by 15–20% compared to the deterministic approach, especially under high solar penetration levels.
• As solar power penetration increases, the average operating cost decreases due to reduced reliance on expensive grid electricity during peak periods.
• Extending the time interval for deferrable load scheduling (e.g., electric water heaters) significantly reduces operating costs initially, with diminishing returns beyond a certain interval.
• The model maintains microgrid reliability by ensuring power and heat balance across all scenarios and time periods.
• The battery degradation cost is effectively managed by enforcing a minimum state-of-charge limit of 20% to extend battery lifetime.
• The simulation results demonstrate that the proposed framework is effective and robust across diverse scenarios, with stable performance under real-world data inputs.