Review of Challenges and Opportunities in the Integration of Electric Vehicles to the Grid

Vishnu, Gayathry; Kaliyaperumal, Deepa; Jayaprakash, Ramprabhakar; Karthick, Alagar; Kumar Chinnaiyan, V.; Ghosh, Aritra

doi:10.3390/wevj14090259

Cited by 8 publications

(2 citation statements)

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“…Reliable real-time communication is of paramount importance for an effective MG that implements V2G technology. Successful implementation of V2G services requires efficient communication among EVs, the control center, and aggregators [46]. Inadequate communication frequently results in packet losses, communication delays, diminished precision, and financial losses, which affect the overall MG performance.…”

Section: ) Communicationmentioning

confidence: 99%

Enhancing Microgrid Operation Through Electric Vehicle Integration: A Survey

Sora,

Serban,

Petreus

2024

IEEE Access

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The integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids (MGs) has significant potential for enhancing energy resilience, addressing environmental concerns, and promoting decentralized energy systems as a global shift towards sustainable energy solutions. Therefore, this survey paper provides a comprehensive discussion on improving MG operation through EV integration. This study evaluates the status of EV integration into MGs, focusing on technological advancements, and emerging trends, while pinpointing key technical challenges and opportunities. Furthermore, this paper examines the pivotal role of EVs in participating in vehicle-to-grid (V2G) services, providing ancillary support to improve MG performance. The importance of a reliable communication infrastructure for information exchange between EV, EV charging stations (EVCSs), and MG has been emphasized for the effective implementation of V2G services. This discussion extends to the contributions of EVs to primary, secondary, and tertiary MG controls. The paper also analyzes the integration of EVs into AC and DC MGs and further proposes configurations for both MG cases. Finally, the paper concludes by providing recommendations for future research to unlock the full potential of EV contributions to MG performance, thereby contributing to the ongoing advancement of sustainable and resilient energy systems. The key findings of this work include solutions for MG voltage and frequency regulation implemented through EV bidirectional converter power flow control, EV charger configurations for integration into AC and DC, EV contributions in improving the MG's operational resilience and adaptability, and the noteworthy challenges arising from V2G implementation in such systems.INDEX TERMS electric vehicle charging, microgrids, renewable energy sources, vehicle-to-grid

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Section: ) Communicationmentioning

confidence: 99%

Enhancing Microgrid Operation Through Electric Vehicle Integration: A Survey

Sora,

Serban,

Petreus

2024

IEEE Access

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“…The objective of EV scheduling is to be able to participate collectively in demand response programs (DRs) used to balance the supply and demand of electricity in the smart grid [36]. We considered V2G-enabled EVs (Vehicle to Grid) [37] that can respond to grid operator signals by modifying their energy consumption or generation patterns. We formalize a DR program as follows:…”

Section: Deep Q-learning For Ev Energy Schedulingmentioning

confidence: 99%

Deep Q-Learning-Based Smart Scheduling of EVs for Demand Response in Smart Grids

Chifu,

Cioara,

Pop

et al. 2024

Applied Sciences

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Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid equipment and energy balance due to increased power demands and the timing of their usage. In our view, grid management should leverage on EV scheduling flexibility to support local network balancing through active participation in demand response programs. In this paper, we propose a model-free solution, leveraging deep Q-learning to schedule the charging and discharging activities of EVs within a microgrid to align with a target energy profile provided by the distribution system operator. We adapted the Bellman equation to assess the value of a state based on specific rewards for EV scheduling actions and used a neural network to estimate Q-values for available actions and the epsilon-greedy algorithm to balance exploitation and exploration to meet the target energy profile. The results are promising, showing the effectiveness of the proposed solution in scheduling the charging and discharging actions for a fleet of 30 EVs to align with the target energy profile in demand response programs, achieving a Pearson coefficient of 0.99. This solution also demonstrates a high degree of adaptability in effectively managing scheduling situations for EVs that involve dynamicity, influenced by various state-of-charge distributions and e-mobility features. Adaptability is achieved solely through learning from data without requiring prior knowledge, configurations, or fine-tuning.

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