63 research outputs found
Real-Time Bi-directional Electric Vehicle Charging Control with Distribution Grid Implementation
As electric vehicle (EV) adoption is growing year after year, there is no
doubt that EVs will occupy a significant portion of transporting vehicle in the
near future. Although EVs have benefits for environment, large amount of
un-coordinated EV charging will affect the power grid and degrade power
quality. To alleviate negative effects of EV charging load and turn them to
opportunities, a decentralized real-time control algorithm is developed in this
paper to provide optimal scheduling of EV bi-directional charging. To evaluate
the performance of the proposed algorithm, numerical simulation is performed
based on real-world EV user data, and power flow analysis is carried out to
show how the proposed algorithm improve power grid steady state operation. .
The results show that the implementation of proposed algorithm can effectively
coordinate bi-directional charging by 30% peak load shaving, more than 2% of
voltage drop reduction, and 40% transmission line current decrease
Distributed Optimal Vehicle Grid Integration Strategy with User Behavior Prediction
With the increasing of electric vehicle (EV) adoption in recent years, the
impact of EV charging activities to the power grid becomes more and more
significant. In this article, an optimal scheduling algorithm which combines
smart EV charging and V2G gird service is developed to integrate EVs into power
grid as distributed energy resources, with improved system cost performance.
Specifically, an optimization problem is formulated and solved at each EV
charging station according to control signal from aggregated control center and
user charging behavior prediction by mean estimation and linear regression. The
control center collects distributed optimization results and updates the
control signal, periodically. The iteration continues until it converges to
optimal scheduling. Experimental result shows this algorithm helps fill the
valley and shave the peak in electric load profiles within a microgrid, while
the energy demand of individual driver can be satisfied.Comment: IEEE PES General Meeting 201
Two-Tier Prediction of Solar Power Generation with Limited Sensing Resource
This paper considers a typical solar installations scenario with limited
sensing resources. In the literature, there exist either day-ahead solar
generation prediction methods with limited accuracy, or high accuracy short
timescale methods that are not suitable for applications requiring longer term
prediction. We propose a two-tier (global-tier and local-tier) prediction
method to improve accuracy for long term (24 hour) solar generation prediction
using only the historical power data. In global-tier, we examine two popular
heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN).
In local-tier, the global-tier results are adaptively updated using real-time
analytical residual analysis. The proposed method is validated using the UCLA
Microgrid with 35kW of solar generation capacity. Experimental results show
that the proposed two-tier prediction method achieves higher accuracy compared
to day-ahead predictions while providing the same prediction length. The
difference in the overall prediction performance using either weighted k-NN
based or NN based in the global-tier are carefully discussed and reasoned. Case
studies with a typical sunny day and a cloudy day are carried out to
demonstrate the effectiveness of the proposed two-tier predictions
Mesh Network for RFID and Electric Vehicle Monitoring in Smart Charging Infrastructure
With an increased number of plug-in electric vehicles (PEVs) on the roads, PEV charging infrastructure is gaining an ever-more important role in simultaneously meeting the needs of drivers and those of the local distribution grid. However, the current approach to charging is not well suited to scaling with the PEV market. If PEV adoption continues, charging infrastructure will have to overcome its current shortcomings such as unresponsiveness to grid constraints, low degree of autonomy, and high cost, in order to provide a seamless and configurable interface from the vehicle to the power grid. Among the tasks a charging station will have to accomplish will be PEV identification, charging authorization, dynamic monitoring, and charge control. These will have to be done with a minimum of involvement at a maximum of convenience for a user. The system proposed in this work allows charging stations to become more responsive to grid constraints and gain a degree of networked autonomy by automatically identifying and authorizing vehicles, along with monitoring and controlling all charging activities via an RFID mesh network consisting of charging stations and in-vehicle devices. The proposed system uses a ZigBee mesh network of in-vehicle monitoring devices which simultaneously serve as active RFID tags and remote sensors. The system outlined lays the groundwork for intelligent charge-scheduling by providing access to vehicle’s State of Charge (SOC) data as well as vehicle/driver IDs, allowing a custom charging schedule to be generated for a particular driver and PEV. The approach presented would allow PEV charging to be conducted effectively while observing grid constraints and meeting the needs of PEV drivers
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