Search NASA⌕ Search

Engineering topics

Pota, Hemanshu R.

Publications and source records attributed to Pota, Hemanshu R..

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Optimal Sizing and Placement of Battery Energy Storage in Distribution System Based on Solar Size for Voltage Regulation

This paper proposes a new strategy to achieve voltage regulation in distributed power systems in the presence of solar energy sources and battery storage systems. The goal is to find the minimum size of battery storage and its corresponding location in the network based on the size and place of the integrated solar generation. The proposed method formulates the problem by employing the network impedance matrix to obtain an analytical solution instead of using a recursive algorithm such as power flow. The required modifications for modeling the slack and PV buses (generator buses) are utilized to increase the accuracy of the approach. The use of reactive power control to regulate the voltage regulation is not always an optimal solution as in distribution systems R/X is large. In this paper the minimum size and the best place of battery storage is achieved by optimizing the amount of both active and reactive power exchanged by battery storage and its gridtie inverter (GTI) based on the network topology and R/X ratios in the distribution system. Simulation results for the IEEE 14-bus system verify the effectiveness of the proposed approach.

14 SOLAR ENERGY↗
DOE OSTI2014

Modified Pattern Sequence-based Forecasting for Electric Vehicle Charging Stations

Three algorithms for the forecasting of energy consumption at individual EV charging outlets have been applied to real world data from the UCLA campus. Out of these three algorithms, namely k-Nearest Neighbor (kNN), ARIMA, and Pattern Sequence Forecasting (PSF), kNN with k=1, was the best and PSF was the worst performing algorithm with respect to the SMAPE measure. The advantage of PSF is its increased robustness to noise by substituting the real valued time series with an integer valued one, and the advantage of NN is having the least SMAPE for our data. We propose a Modified PSF algorithm (MPSF) which is a combination of PSF and NN; it could be interpreted as NN on integer valued data or as PSF with considering only the most recent neighbor to produce the output. Some other shortcomings of PSF are also addressed in the MPSF. Results show that MPSF has improved the forecast performance.

Majidpour, Mostafa↗
DOE OSTI2014

Incomplete Data in Smart Grid: Treatment of Values in Electric Vehicle Charging Data

In this paper, five imputation methods namely Constant (zero), Mean, Median, Maximum Likelihood, and Multiple Imputation methods have been applied to compensate for missing values in Electric Vehicle (EV) charging data. The outcome of each of these methods have been used as the input to a prediction algorithm to forecast the EV load in the next 24 hours at each individual outlet. The data is real world data at the outlet level from the UCLA campus parking lots. Given the sparsity of the data, both Median and Constant (=zero) imputations improved the prediction results. Since in most missing value cases in our database, all values of that instance are missing, the multivariate imputation methods did not improve the results significantly compared to univariate approaches.

Majipour, Mostafa↗
DOE OSTI2014

Vehicle-to-Grid Automatic Load Sharing with Driver Preference in Micro-Grids

Integration of Electrical Vehicles (EVs) with power grid not only brings new challenges for load management, but also opportunities for distributed storage and generation. This paper comprehensively models and analyzes distributed Vehicle-to-Grid (V2G) for automatic load sharing with driver preference. In a micro-grid with limited communications, V2G EVs need to decide load sharing based on their own power and voltage profile. A droop based controller taking into account driver preference is proposed in this paper to address the distributed control of EVs. Simulations are designed for three fundamental V2G automatic load sharing scenarios that include all system dynamics of such applications. Simulation results demonstrate that active power sharing is achieved proportionally among V2G EVs with consideration of driver preference. In additional, the results also verify the system stability and reactive power sharing analysis in system modelling, which sheds light on large scale V2G automatic load sharing in more complicated cases.

Wang, Yubo↗
DOE OSTI2014

Fast Demand Forecast of Electric Vehicle Charging Stations for Cell Phone Application

This paper describes the core cellphone application algorithm which has been implemented for the prediction of energy consumption at Electric Vehicle (EV) Charging Stations at UCLA. For this interactive user application, the total time of accessing database, processing the data and making the prediction, needs to be within a few seconds. We analyze four relatively fast Machine Learning based time series prediction algorithms for our prediction engine: Historical Average, kNearest Neighbor, Weighted k-Nearest Neighbor, and Lazy Learning. The Nearest Neighbor algorithm (k Nearest Neighbor with k=1) shows better performance and is selected to be the prediction algorithm implemented for the cellphone application. Two applications have been designed on top of the prediction algorithm: one predicts the expected available energy at the station and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is about one second for both applications.

Majidpour, Mostafa↗