Journal ArticleDOI
Online scheduling of plug-in vehicles in dynamic pricing schemes
Anmar Arif,Muhammad Babar,Imthias Ahamed,Essam A. Al-Ammar,HP Phuong Nguyen,IG René Kamphuis,Nazar H. Malik +6 more
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In this article, the authors presented three algorithms for scheduling PEVs charging by an aggregator under different conditions, such as single-stage decision making problem, single stage decision-making problem in case prices are not known in advance and PEVs are independent atomic loads, and then the problem is formulated as a multistage decision making to determine the optimum schedule in more realistic scenario.About:
This article is published in Sustainable Energy, Grids and Networks.The article was published on 2016-09-01. It has received 37 citations till now. The article focuses on the topics: Job shop scheduling & Dynamic pricing.read more
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Reinforcement learning for demand response: A review of algorithms and modeling techniques
TL;DR: In this paper, a review of the use of reinforcement learning for demand response applications in the smart grid is presented, and the authors identify a need to further explore reinforcement learning to coordinate multi-agent systems that can participate in demand response programs under demand-dependent electricity prices.
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Reinforcement learning in sustainable energy and electric systems: a survey
TL;DR: The use of reinforcement learning in sustainable energy and electric systems will certainly change the traditional energy utilization mode and bring more intelligence into the system, and future challenges and opportunities will be explicitly addressed.
Journal ArticleDOI
A review of optimal charging strategy for electric vehicles under dynamic pricing schemes in the distribution charging network
Adil Amin,Wajahat Ullah Khan Tareen,Muhammad Usman,Haider Ali,Inam Bari,Ben Horan,Saad Mekhilef,Muhammad Asif,Saeed Ahmed,Anzar Mahmood +9 more
TL;DR: A critical review on EVs’ optimal charging and scheduling under dynamic pricing schemes, namely, Real Time Pricing (RTP), Time of Use (ToU), Critical Peak Pricing (CPP), and Peak Time Rebates (PTR), is presented.
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Modified deep learning and reinforcement learning for an incentive-based demand response model
TL;DR: The results showed that the proposed modified deep learning model can achieve more accurate forecasting results compared with some other methods and can support the development of incentive-based DR programs under uncertain environment.
Journal ArticleDOI
Reinforcement Learning Based EV Charging Management Systems–A Review
TL;DR: In this paper, a review of the existing literature related to the RL-based framework, objectives, and architecture for the charging coordination strategies of electric vehicles in the power systems is presented, and a detailed comparative analysis of the techniques used for achieving different charging coordination objectives while satisfying multiple constraints.
References
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Journal ArticleDOI
Assessment of the Impact of Plug-in Electric Vehicles on Distribution Networks
TL;DR: In this article, the impact of different levels of plug-in electric vehicle penetration on distribution network investment and incremental energy losses is evaluated based on the use of a large-scale distribution planning model which is used to analyze two real distribution areas.
Journal ArticleDOI
Real-Time Demand Response Model
TL;DR: An optimization model to adjust the hourly load level of a given consumer in response to hourly electricity prices is described, which materializes into a simple linear programming algorithm that can be easily integrated in the Energy Management System of a household or a small business.
Journal ArticleDOI
Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies
TL;DR: A comprehensive review of the literature of HEV, PHEV and EV penetration rate studies, their methods, and their recommendations can be found in this paper, where a suite of analytical and computational tools are applied to model the consumer acceptability of these technologies under a wide variety of policy and macroeconomic scenarios.
Journal ArticleDOI
Integrated scheduling of renewable generation and electric vehicles parking lot in a smart microgrid
TL;DR: In this paper, an energy resources management model for a microgrid (MG) is proposed, which considers practical constraints, renewable power forecasting errors, spinning reserve requirements and EVs owner satisfaction, and a case study with a typical MG including 200 EVs is used to illustrate the performance of the proposed method.
Proceedings Article
Distribution grid impact of Plug-In Electric Vehicles charging at fast charging stations using stochastic charging model
TL;DR: In this article, the authors investigated the impacts of fast charging stations on a distribution grid using a stochastic fast charging model and presented the charging model with some of its results.