Integrated Coordination of Electric Vehicle Operations and Renewable Energy Generation in a Microgrid

This paper designs a microgrid energy controller capable of creating a charging or discharging schedule for electric vehicles (EVs), aiming at leveraging the integration of renewable energy and shaving the peak load in the microgrid. Dynamically activated on each time slot to cope with the prediction error for the power consumption and the renewable energy generation, the controller calculates the number of EVs to charge or make discharge first. Then, a greedy algorithm-based scheduler selects EVs according to the expected energy potential during their stays. The potential is the integral of a supply-demand margin function from the current time to the expected departure time. A simulator is implemented for performance evaluation, comparing with uncoordinated scheduling, according to the number of EVs as well as the behavior of energy load and production. The experiment result shows that the proposed scheme can reduce the energy waste by 16.9 %, cut down the microgrid-level energy insufficiency by 12.2 %, and enhance the amount of electricity supplied to EVs by 37.3 %, respectively, for given parameter setting

Renewable energy allocation based on maximum flow modelling within a microgrid

This paper designs an energy allocation scheme based on maximum flow modeling for a microgrid containing renewable energy generators and consumer facilities. Basically, the flow graph consists of a set of nodes representing consumers or generators as well as a set of weighted links representing the amount of energy generation, consumer-side demand, and transmission cable capacity. The main idea lies in that a special node is added to account for the interaction with the main grid and that two-pass allocation is executed. In the first pass, the maximum flow solver decides the amount of the insufficiency and thus how much to purchase from the main grid. The second pass runs the flow solver again to fill the energy lack and calculates the surplus of renewable energy generation. The performance measurement result obtained from a prototype implementation shows that the generated energy is stably distributed over multiple consumers until the energy generation reaches the maximum link capacity

Design of a Monitoring-combined Siting Scheme for Electric Vehicle Chargers

This paper designs a siting scheme for public electric vehicle chargers based on a genetic algorithm working on charger monitoring streams. The monitoring-combined allocation scheme runs on a long-term basis, iterating the process of collecting data, analyzing demand, and selecting candidates. The analysis of spatio-temporal archives, acquired from the fast chargers currently in operation, focuses on the per-charger hot hour and proximity effect to justify demand balancing in geographic cluster level. It leads to the definition of a fitness function representing the standard deviation of per-charger load and cluster-by-cluster distribution. In a chromosome, each binary integer is associated with a candidate and its static fields include the index to the cluster to which it is belonging. The performance result obtained from a prototype implementation reveals that the proposed scheme can stably distribute the charging load with an addition of a new charger, achieving the reduction of standard deviation from 8.7 % to 4.7 % in the real-world scenario

Service Time Analysis For Electric Vehicle Charging Infrastructure

This paper analyzes electric vehicle charging patterns in Jeju City, taking advantage of open software such as MySQL, Hadoop, and R, as well as open data obtained from the real-time charger monitoring system currently in operation. Main observation points lie in average service time, maximum service time, and the number of transactions, while we measure the effect of both temporal and spatial factors to them. According to the analysis result, the average service time is almost constant for all parameters. The charging time of 88.7 % transactions ranges from 10 to 40 minutes, while abnormally long transactions occupy just 3.4 % for fast chargers. The day-by-day difference in the number of charging transactions is 28.6 % at maximum, while Wednesday shows the largest number of transactions. Additionally, geographic information-based analysis tells that the charging demand is concentrated in those regions having many tourist attractions and administrative offices. With this analysis, it is possible to predict when a charger will be idle and allocate it to another service such as V2G or renewable energy integration

Price effect analysis and pre-reseravtion scheme on electric vehicle charging networks

This paper investigates the price effect to the charging demand coming from electric vehicles and then evaluates the performance of a pre-reservation mechanism using the real-life demand patterns. On the charging network in Jeju city, the occupancy rates for 3 price groups, namely, free, medium-price, and expensive chargers, are separated almost evenly by about 9.0 %, while a set of chargers dominates the charging demand during hot hours. The virtual pre-reservation scheme matches electric vehicles to a time slot of a charger so as not only to avoid intolerable waiting time in charging stations systematically but also to increase the revenue of service providers, taking into account both bidding levels specified by electric vehicles and preference criteria defined by chargers. The performance analysis results obtained by prototype implementation show that the proposed pre-reservation mechanism improves the revenue of service providers by up to 9.5 % and 42.9 %, compared with the legacy FCFS and reservation-less walk-in schemes for the given performance parameter sets

Taste Visualization Technique for Online Food Shopping Malls: Development and Application

The main goal of this study is to visualize tastes of foods and agro-products to reduce the uncertainty of purchasing of them in online shopping malls. To accomplish the goal, we select two Korean traditional foods; Kimchi and Gochujang, develop the criteria for visualization of their tastes, choose about ten products for each food that are on sale in the online shopping malls, make visualization tables and apply them to the real shopping mall. And then, we contrast the customer satisfaction, purchasing pattern, and sales of both before and after applying the taste visualization tables

Data Analysis for Solar Energy Generation in a University Microgrid

This paper presents a data acquisition process for solar energy generation and then analyzes the dynamics of its data stream, mainly employing open software solutions such as Python, MySQL, and R. For the sequence of hourly power generations during the period from January 2016 to March 2017, a variety of queries are issued to obtain the number of valid reports as well as the average, maximum, and total amount of electricity generation in 7 solar panels. The query result on all-time, monthly, and daily basis has found that the panel-by panel difference is not so significant in a university-scale microgrid, the maximum gap being 7.1% even in the exceptional case. In addition, for the time series of daily energy generations, we develop a neural network-based trace and prediction model. Due to the time lagging effect in forecasting, the average prediction error for the next hours or days reaches 27.6%. The data stream is still being accumulated and the accuracy will be enhanced by more intensive machine learning

PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels

The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than ever before. The goods in the P2E MMORPGs can be directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn via blockchain networks. Unlike traditional in-game goods, once they had been written to the blockchains, P2E goods cannot be restored by the game operation teams even with chargeback fraud such as payment fraud, cancellation, or refund. To tackle the problem, we propose a novel chargeback fraud prediction method, PU GNN, which leverages graph attention networks with PU loss to capture both the players' in-game behavior with P2E token transaction patterns. With the adoption of modified GraphSMOTE, the proposed model handles the imbalanced distribution of labels in chargeback fraud datasets. The conducted experiments on three real-world P2E MMORPG datasets demonstrate that PU GNN achieves superior performances over previously suggested methods.Comment: Under Review, Industry Trac