Study on Aggregator’s Strategy of Controlling Electric Vehicles to Compensate Imbalances in Power Systems Using Reinforcement Learning

2019 ◽  
Vol 7 (2) ◽  
pp. 67-71
Author(s):  
Ryuhei Furuta ◽  
◽  
Ryuji Matsuhashi
Keyword(s):  
Reinforcement Learning ◽  
Power Systems ◽  
Electric Vehicles

scholarly journals Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1090
Author(s):  
Charilaos Latinopoulos ◽  
Aruna Sivakumar ◽  
John W. Polak
Keyword(s):  
Power Systems ◽  
Electric Vehicles ◽  
Power Grids ◽  
Local Distribution ◽  
Technical Problems ◽  
Vehicle To Grid ◽  
Distribution Transformers ◽  
Demand Models ◽  
Multiple Vehicles ◽  
Infrastructure Investments

The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments.

scholarly journals Load area aggregation considering integration of electric vehicles to the system

2015 ◽  
Vol 35 (1Sup) ◽  
pp. 42-49 ◽  
Author(s):  
Luis Fernando Rodríguez-García ◽  
Sandra Milena Pérez-Londoño ◽  
Juan José Mora-Flórez
Keyword(s):  
Power Systems ◽  
Power System ◽  
Electric Vehicles ◽  
System Analysis ◽  
Original System ◽  
Computational Effort ◽  
Computational Technique ◽  
Equivalent Network ◽  
Optimal Dispatch ◽  
Equivalent Load

<span>Current electric power systems have an increasing penetration of electric vehicles, and its effect has to be considered in different <span>studies, such as optimal dispatch or voltage stability, among others. Additionally, considering that power system analysis becomes <span>complex when the number of buses increase, this paper presents a methodology for aggregation of load areas that use a measurement-based load modeling approach based on an evolutionary computational technique and a classical reduction method. This aggregate <span>load area model is proposed to reduce areas that consider electric vehicle (EV) load models. The proposed method provides a static <span>equivalent load model and an equivalent network that can be used to reduce the computational effort required by power system<br /><span>studies. In order to validate the application of the proposed methodology, a 30-bus power system considering several disturbances <span>and levels of penetration of the electric vehicles was used. The results show that the equivalent network model allows the reproduction <span>of different events with an acceptable accuracy when it is compared to the original system behavior.</span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>

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