Fast demand forecast of Electric Vehicle Charging Stations for cell phone application

Majidpour, Mostafa; Qiu, Charlie; Chung, Ching-Yen; Chu, Pao-Hsien; Gadh, Rajit; Pota, H. R.

doi:10.1109/pesgm.2014.6938864

Cited by 17 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the Fair Charging Algorithm counts heavily on the accuracy of the prediction of user's stay time. A forecast of users to the PEV charging station in [ 18 ] can be used for more accuracy on user's stay time. If the prediction of the user's stay time is accurate, fairness maximization can be obtained while only switching charging once.…”

Section: Proposed Control Scheme and Results For Level 1 Evsementioning

confidence: 99%

Master-Slave Control Scheme in Electric Vehicle Smart Charging Infrastructure

Chung

Chynoweth

Chu

et al. 2014

The Scientific World Journal

Self Cite

View full text Add to dashboard Cite

WINSmartEV is a software based plug-in electric vehicle (PEV) monitoring, control, and management system. It not only incorporates intelligence at every level so that charge scheduling can avoid grid bottlenecks, but it also multiplies the number of PEVs that can be plugged into a single circuit. This paper proposes, designs, and executes many upgrades to WINSmartEV. These upgrades include new hardware that makes the level 1 and level 2 chargers faster, more robust, and more scalable. It includes algorithms that provide a more optimal charge scheduling for the level 2 (EVSE) and an enhanced vehicle monitoring/identification module (VMM) system that can automatically identify PEVs and authorize charging.

show abstract

Section: Proposed Control Scheme and Results For Level 1 Evsementioning

confidence: 99%

Master-Slave Control Scheme in Electric Vehicle Smart Charging Infrastructure

Chung

Chynoweth

Chu

et al. 2014

The Scientific World Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is important to note that the areas under both curves are equal to each other, meaning that they both report the same amount of energy being consumed. If the ultimate goal is predicting the available energy at each outlet as in [36], the predictions based on either one are expected to behave competitively.…”

Section: Comparing Two Datasetsmentioning

confidence: 99%

Forecasting the EV charging load based on customer profile or station measurement?

et al. 2016

Self Cite

View full text Add to dashboard Cite

In this paper, forecasting of the Electric Vehicle (EV) charging load has been based on two different datasets: data from the customer profile (referred to as charging record) and data from outlet measurements (referred to as station record). Four different prediction algorithms namely Time Weighted Dot Product based Nearest Neighbor (TWDP-NN), Modified Pattern Sequence Forecasting (MPSF), Support Vector Regression (SVR), and Random Forest (RF) are applied to both datasets. The corresponding speed, accuracy, and privacy concerns are compared between the use of the charging records and station records. Real world data compiled at the outlet level from the UCLA campus parking lots are used. The results show that charging records provide relatively faster prediction while putting customer privacy in jeopardy. Station records provide relatively slower prediction while respecting the customer privacy. In general, we found that both datasets generate comparable prediction error.

show abstract

“…Previous studies focused on developing various forecast models of high accuracy given this randomness. They use various types of data sources, from historical data to mathematical models, weather data, and other societal data [11,12]. Zhu et al run demand forecast and solar generation forecast from history data, and then develop a battery (dis)charging scheduling algorithm [13].…”

Section: Forecasting Energy Activitiesmentioning

confidence: 99%

“…The MP also implements forecast services for optimization. In particular, it adopts three different models for forecasting: A persistence model, an auto regressive moving average (ARMA) model, and a machine learning model for load forecasting, PV forecasting [11], and EV forecasting [12], respectively. The MP uses the CAISO's forecast data to provide market forecast services.…”

Section: Microgrid Controlmentioning

confidence: 99%

“…We can also observe load shedding and load shifting from the results: the LEDs are shedded, the EVs are shifted to the time when the price is low, and the total charging energy of the EVs is also reduced. The total operational cost of the testbed using the optimized schedule is 12, 424 KRW, compared with 7899 KRW without scheduling, saving the cost by 12,424−7899 12,424 = 36.43%. Next, we use the schedule produced by the algorithm to control the hardware-in-the-loop simulators in the testbed in order to validate the simulation in real-hardware settings.…”

Section: Energy Schedulingmentioning

confidence: 99%

See 1 more Smart Citation

Design and Implementation of a Microgrid Energy Management System

et al. 2016

Self Cite

View full text Add to dashboard Cite

A microgrid is characterized by the integration of distributed energy resources and controllable loads in a power distribution network. Such integration introduces new, unique challenges to microgrid management that have never been exposed to traditional power systems. To accommodate these challenges, it is necessary to redesign a conventional Energy Management System (EMS) so that it can cope with intrinsic characteristics of microgrids. While many projects have shown excellent research outcomes, they have either tackled portions of the characteristics or validated their EMSs only via simulations. This paper proposes a Microgrid Platform (MP), an advanced EMS for efficient microgrid operations. We design the MP by taking into consideration (i) all the functional requirements of a microgrid EMS (i.e., optimization, forecast, human-machine interface, and data analysis) and (ii) engineering challenges (i.e., interoperability, extensibility, and flexibility). Moreover, a prototype system is developed and deployed in two smart grid testbeds: UCLA Smart Grid Energy Research Center and Korea Institute of Energy Research. We then conduct experiments to verify the feasibility of the MP design in real-world settings. Our testbeds and experiments demonstrate that the MP is able to communicate with various energy devices and to perform an energy management task efficiently.

show abstract

Fast demand forecast of Electric Vehicle Charging Stations for cell phone application

Cited by 17 publications

References 17 publications

Master-Slave Control Scheme in Electric Vehicle Smart Charging Infrastructure

Master-Slave Control Scheme in Electric Vehicle Smart Charging Infrastructure

Forecasting the EV charging load based on customer profile or station measurement?

Design and Implementation of a Microgrid Energy Management System

Contact Info

Product

Resources

About