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1. Enhancing Distribution Grid Resilience to Power Outages Using Electric Vehicles in Residential Microgrids

   https://doi.org/10.1109/NAPS52732.2021.9654570  

   Simental, Orlando Quezada ; Mandal, Paras ; Galvan, Eric ( November 2021 , 2021 North American Power Symposium (NAPS))

   The transition to electric vehicles (EVs) is underway globally and EVs are expected to become more widely adopted in the coming years. One of the main characteristics of EVs is that they are not only seen as mean for transportation but also potentially as a flexible energy storage resource in vehicle-to-grid (V2G) applications. This paper proposes a resilience analysis on the feasibility of using EVs for power restoration and supply of residential networked microgrids (MGs) experiencing a power outage due to extreme weather. In order to evaluate the effectiveness of utilizing EVs as a backup power supply during an outage, various case studies are presented considering different scenarios and resilience metrics. Test results demonstrate that EVs can satisfy the energy requirements of a residential household for more than 6 hours but, also provide power to the distribution grid through MG aggregation. 

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2. Power Loss-Aware Transactive Microgrid Coalitions under Uncertainty

   https://doi.org/10.3390/en13215782  

   Sadeghi, Mohammad ; Mollahasani, Shahram ; Erol-Kantarci, Melike ( November 2020 , Energies)

   null (Ed.)

   Peer-to-peer energy trading within microgrid (MG) communities emerges as a key enabler of the future transactive distribution system and the transactive electricity market. Energy trading within MGs refers to the idea that the surplus energy of one MG can be used to satisfy the demand of another MG or a group of MGs that form an MG community. These communities can be dynamically established through time, based on the variations of demand and supply of the interconnected MGs. In many modern MGs, Electric Vehicles (EVs) have been considered as a viable storage option due to their ease of use (plug-and-play) and their growing adoption rates by drivers. On the other hand, the dynamic nature of EVs escalates the uncertainty in the transactive distribution system. In this paper, we study the problem of energy trading among MGs and EVs with the aim of power loss minimization where there is uncertainty. We propose a novel Bayesian Coalition Game (BCG) based algorithm, which allows the MGs and EVs to reduce the overall power loss by allowing them to form coalitions intelligently. The proposed scheme is compared with a conventional coalitional game theory-based approach and a Q-learning based approach. Our results show significant improvement over other compared techniques. 

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3. Vehicle-to-Vehicle Charging Coordination over Information Centric Networking

   https://doi.org/10.1109/LCN53696.2022.9843207  

   Thompson, Robert ; Ismail, Muhammad ; Shannigrahi, Susmit ( September 2022 , IEEE LCN)

   Cities around the world are increasingly promoting electric vehicles (EV) to reduce and ultimately eliminate greenhouse gas emissions. A huge number of EVs will put unprecedented stress on the power grid. To efficiently serve the increased charging load, these EVs need to be charged in a coordinated fashion. One promising coordination strategy is vehicle-to-vehicle (V2V) charging coordination, enabling EVs to sell their surplus energy in an ad-hoc, peer to peer manner. This paper introduces an Information Centric Networking (ICN)-based protocol to support ad-hoc V2V charging coordination (V2V-CC). Our evaluations demonstrate that V2V-CC can provide added flexibility, fault tolerance, and reduced communication latency than a conventional centralized cloud based approach. We show that V2V-CC can achieve a 93% reduction in protocol completion time compared to a conventional approach. We also show that V2V-CC also works well under extreme packet loss, making it ideal for V2V charging coordination. 

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4. Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate

   https://doi.org/10.1038/s41467-022-32018-4  

   Feng, Kairui ; Ouyang, Min ; Lin, Ning ( December 2022 , Nature Communications)

   Abstract Tropical cyclones (TCs) have caused extensive power outages. The impacts of TC-caused blackouts may worsen in the future as TCs and heatwaves intensify. Here we couple TC and heatwave projections and power outage and recovery process analysis to investigate how TC-blackout-heatwave compound hazard risk may vary in a changing climate, with Harris County, Texas as an example. We find that, under the high-emissions scenario RCP8.5, long-duration heatwaves following strong TCs may increase sharply. The expected percentage of Harris residents experiencing at least one longer-than-5-day TC-blackout-heatwave compound hazard in a 20-year period could increase dramatically by a factor of 23 (from 0.8% to 18.2%) over the 21 st century. We also reveal that a moderate enhancement of the power distribution network can significantly mitigate the compound hazard risk. Thus, climate adaptation actions, such as strategically undergrounding distribution network and developing distributed energy sources, are urgently needed to improve coastal power system resilience. 

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5. CatCharger: Deploying In-motion Wireless Chargers in a Metropolitan Road Network via Categorization and Clustering of Vehicle Traffic

   https://doi.org/10.1109/JIOT.2021.3121756  

   Yan, Li ; Shen, Haiying ; Zhao, Juanjuan ; Xu, Chengzhong ; Luo, Feng ; Qiu, Chenxi ; Zhang, Zhe ; Mahmud, Shohaib ( October 2021 , IEEE Internet of Things Journal)

   In metropolitan areas with heavy transit demands, electric vehicles (EVs) are expected to be continuously driving without recharging downtime. Wireless Power Transfer (WPT) provides a promising solution for in-motion EV charging. Nevertheless, previous works are not directly applicable for the deployment of in-motion wireless chargers due to their different charging characteristics. The challenge of deploying in-motion wireless chargers to support the continuous driving of EVs in a metropolitan road network with the minimum cost remains unsolved. We propose CatCharger to tackle this challenge. By analyzing a metropolitan-scale dataset, we found that traffic attributes like vehicle passing speed, daily visit frequency at intersections (i.e., landmarks) and their variances are diverse, and these attributes are critical to in-motion wireless charging performance. Driven by these observations, we first group landmarks with similar attribute values using the entropy minimization clustering method, and select candidate landmarks from the groups with suitable attribute values. Then, we use the Kernel Density Estimator (KDE) to deduce the expected vehicle residual energy at each candidate landmark and consider EV drivers’ routing choice behavior in charger deployment. Finally, we determine the deployment locations by formulating and solving a multi-objective optimization problem, which maximizes vehicle traffic flow at charger deployment positions while guaranteeing the continuous driving of EVs at each landmark. Trace-driven experiments demonstrate that CatCharger increases the ratio of driving EVs at the end of a day by 12.5% under the same deployment cost. 

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