Gridable Electric Vehicle (GEV) Aggregation in Distribution Network to Support Grid Requirements: A Communication Approach

2017 ◽  
Vol 18 (3) ◽  
Author(s):  
Santoshkumar Hampannavar ◽  
Suresh Chavhan ◽  
Udaykumar Yaragatti ◽  
Anant Naik
Keyword(s):  
Energy Storage ◽  
Power Distribution ◽  
Signal To Noise Ratio ◽  
Past History ◽  
Storage System ◽  
Distribution Network ◽  
Energy Storage System ◽  
Load Flow ◽  
Optimal Placement ◽  
Power Distribution Network

Abstract Electric Vehicles (EV) can be connected to the grid for power transaction and also serve as distributed resource (DR) or distributed energy storage system (DESS). The concept of connecting group of EVs or gridable EVs (GEV) to the grid is called Vehicle-to-Grid (V2G). V2G is a prominent energy storage system as it is flexible and can be used to support the grid requirements in order to meet the time varying load demand. Optimal placement of GEV aggregation in power distribution network is very challenging and helps in maintaining stability of the power system for a shorter duration of time. In this paper, algorithm is developed for estimating parameters like Ploss, Qloss, Vpu based on past history and wireless access support for Control and Monitoring Unit (CMU) to aggregator agent communication is proposed using Long Term Evolution (LTE) protocol. The load flow studies are carried using MiPOWER software in order to obtain the optimal location for the placement of GEV aggregation in power distribution network. LTE physical layer is modeled using MATLAB/SIMULINK and the performance is analyzed using bit error rate (BER) v/s signal to noise ratio (SNR) curves.

Review on the optimal placement, sizing and control of an energy storage system in the distribution network

2019 ◽  
Vol 21 ◽  
pp. 489-504 ◽  
Author(s):  
Ling Ai Wong ◽  
Vigna K. Ramachandaramurthy ◽  
Phil Taylor ◽  
J.B. Ekanayake ◽  
Sara L. Walker ◽  
...  
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Distribution Network ◽  
Energy Storage System ◽  
Optimal Placement ◽  
And Control

scholarly journals Optimal Placement and Sizing of an Energy Storage System Using a Power Sensitivity Analysis in a Practical Stand-Alone Microgrid

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1598
Author(s):  
Dongmin Kim ◽  
Kipo Yoon ◽  
Soo Hyoung Lee ◽  
Jung-Wook Park
Keyword(s):  
Sensitivity Analysis ◽  
Energy Storage ◽  
Power System ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Energy Storage System ◽  
Optimization Techniques ◽  
Optimal Placement ◽  
Stable Operation ◽  
Analytical Approaches

The energy storage system (ESS) is developing into a very important element for the stable operation of power systems. An ESS is characterized by rapid control, free charging, and discharging. Because of these characteristics, it can efficiently respond to sudden events that affect the power system and can help to resolve congested lines caused by the excessive output of distributed generators (DGs) using renewable energy sources (RESs). In order to efficiently and economically install new ESSs in the power system, the following two factors must be considered: the optimal installation placements and the optimal sizes of ESSs. Many studies have explored the optimal installation placement and the sizing of ESSs by using analytical approaches, mathematical optimization techniques, and artificial intelligence. This paper presents an algorithm to determine the optimal installation placement and sizing of ESSs for a virtual multi-slack (VMS) operation based on a power sensitivity analysis in a stand-alone microgrid. Through the proposed algorithm, the optimal installation placement can be determined by a simple calculation based on a power sensitivity matrix, and the optimal sizing of the ESS for the determined placement can be obtained at the same time. The algorithm is verified through several case studies in a stand-alone microgrid based on practical power system data. The results of the proposed algorithm show that installing ESSs in the optimal placement could improve the voltage stability of the microgrid. The sizing of the newly installed ESS was also properly determined.

Optimal allocation of distributed energy storage in active distribution network via hybrid teaching learning and multi-objective particle swarm optimization algorithm

2021 ◽  
pp. 002072092098369
Author(s):  
Jijun Liu ◽  
Yuxin Bai ◽  
Yingfeng He
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Optimal Allocation ◽  
Storage System ◽  
Distribution Network ◽  
Particle Swarm ◽  
Economic Cost ◽  
Energy Storage System ◽  
Swarm Optimization ◽  
Active Distribution Network

This work aims at solving complex problems of the optimal scheduling model of active distribution network, teaching strategies are proposed to improve the global search ability of particle swarm optimization. Moreover, based on the improved Euclidean distance cyclic crowding sorting strategy, the convergence ability of Li Zhiquan algorithm is improved. With the cost and voltage indexes of the energy storage system of the distribution network as the goal, different optimized configuration schemes are constructed, and the improved HTL-MOPSO algorithm is adopted to find the solution. The results show that compared with the traditional TV-MOPSO algorithm, the proposed algorithm has better convergence performance and optimization ability, and has a lower economic cost. In short, the algorithm proposed can provide a basis for improving the optimization of active distribution network scheduling strategies.

Statistical Energy Storage Sizing for Point Absorber Wave Energy Converters (WECs): A Device for Operation off the U.S. East Coast

2018 ◽  
Author(s):  
Xiang Zhou ◽  
Mehdi Jafari ◽  
Ossama Abdelkhalik ◽  
Umesh A. Korde ◽  
Lucia Gauchia
Keyword(s):  
Time Series ◽  
Energy Storage ◽  
Wave Energy ◽  
Power Distribution ◽  
Body Wave ◽  
Storage System ◽  
Ground Truth ◽  
Energy Storage System ◽  
Sensing Applications ◽  
Extracted Power

This paper addresses the sizing problem of an energy storage system (ESS) while considering statistical tolerance for a two-body wave energy converter (WEC), which is designed to support ocean sensing applications with sustained power for long-term functioning. The power is extracted by assuming ideal power take-off (PTO) based upon historical ocean data record (significant wave height and period of wave swell) from Martha’s Vineyard Coastal Observatory. A gamma distribution is applied to generate the extracted power distribution of each sample in the time-series using Bayesian methodology. The means and standard deviation of the extracted power distributions compose the statistical annual power time-series. Finally, the required capacities for the ESS sizing are estimated and discussed while considering both ground truth values and statistical values.

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