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Flexibility in power demand, diverse usage patterns and storage capability of plug-in electric vehicles (PEVs) grow the elasticity of residential electricity demand remarkably. This elasticity can be utilized to form the daily aggregated…

最优化与控制 · 数学 2015-12-22 Farshad Rassaei , Wee-Seng Soh , Kee-Chaing Chua

Plug-in Electric Vehicles (PEVs) are a rapidly developing technology that can reduce greenhouse gas emissions and change the way vehicles obtain power. PEV charging stations will most likely be available at home and at work, and…

最优化与控制 · 数学 2011-12-06 Nicole Taheri , Robert Entriken , Yinyu Ye

Plug-in electric vehicles (PEVs) can significantly increase the elasticity of residential electricity demand. This elasticity can be employed to shape the daily aggregated electricity demand profile of a system comprised of a large number…

最优化与控制 · 数学 2015-05-26 Farshad Rassaei , Wee-Seng Soh , Kee-Chaing Chua

In the Smart Grid environment, the advent of intelligent measuring devices facilitates monitoring appliance electricity consumption. This data can be used in applying Demand Response (DR) in residential houses through data analytics, and…

信号处理 · 电气工程与系统科学 2020-08-10 Abdelkareem Jaradat , Hanan Lutfiyya , Anwar Haque

Electricity usage is a major portion of utility bills and the best place to start lowering them. An effective home energy management approach is introduced to decrease customers' electricity bills by determining the optimal appliance…

系统与控制 · 电气工程与系统科学 2019-11-28 Mohammad Rasoul Narimani

Under Smart Grid environment, the consumers may respond to incentive--based smart energy tariffs for a particular consumption pattern. Demand Response (DR) is a portfolio of signaling schemes from the utility to the consumers for load…

信号处理 · 电气工程与系统科学 2019-05-28 Shashank Singh , Aryesh Namboodiri , M. P. Selvan

One of the major issues with the integration of renewable energy sources into the power grid is the increased uncertainty and variability that they bring. If this uncertainty is not sufficiently addressed, it will limit the further…

最优化与控制 · 数学 2017-05-15 Joshua Comden , Zhenhua Liu , Yue Zhao

Power sector decarbonization plays a vital role in the upcoming energy transition towards a more sustainable future. Decentralized energy resources, such as Electric Vehicles (EV) and solar photovoltaic systems (PV), are continuously…

Demand-side response programs which also called Demand Response (DR) are interesting ways to attract consumers' participation in order to improve electric consumption patterns. DR programs motivate customers to change consumption patterns…

物理与社会 · 物理学 2021-11-29 Mohammadreza Shekari , Hamidreza Arasteh , Alireza Sheikhi Fini , Vahid Vahidinasab

With the ongoing integration of Renewable Energy Sources (RES), the complexity of power grids is increasing. Due to the fluctuating nature of RES, ensuring the reliability of power grids can be challenging. One possible approach for…

分布式、并行与集群计算 · 计算机科学 2022-11-01 Pezhman Nasirifard , Hans-Arno Jacobsen

This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand…

Nowadays the emerging smart grid technology opens up the possibility of two-way communication between customers and energy utilities. Demand Response Management (DRM) offers the promise of saving money for commercial customers and…

系统与控制 · 电气工程与系统科学 2022-03-07 Hossein Mohammadi Rouzbahani , Abolfazl Rahimnezhad , Hadis Karimipour

Demand response (DR) is a cost-effective and environmentally friendly approach for mitigating the uncertainties in renewable energy integration by taking advantage of the flexibility of customers' demands. However, existing DR programs…

最优化与控制 · 数学 2017-05-11 Joshua Comden , Zhenhua Liu , Yue Zhao

Demand Response (DR) schemes are effective tools to maintain a dynamic balance in energy markets with higher integration of fluctuating renewable energy sources. DR schemes can be used to harness residential devices' flexibility and to…

计算机与社会 · 计算机科学 2018-05-16 Davide Frazzetto , Bijay Neupane , Torben Bach Pedersen , Thomas Dyhre Nielsen

Demand response (DR) for smart grids, which intends to balance the required power demand with the available supply resources, has been gaining widespread attention. The growing demand for electricity has presented new opportunities for…

系统与控制 · 电气工程与系统科学 2020-07-24 Haider Tarish Haider , Ong Hang See , W. Elmenreich

Electric vehicles (EVs) add significant load on the power grid as they become widespread. The characteristics of this extra load follow the patterns of people's driving behaviours. In particular, random parameters such as arrival time and…

计算工程、金融与科学 · 计算机科学 2014-08-12 Farshad Rassaei , Wee-Seng Soh , Kee-Chaing Chua

Demand response (DR) programs play a crucial role in improving system reliability and mitigating price volatility by altering the core profile of electricity consumption. This paper proposes a game-theoretical model that captures the…

综合经济学 · 经济学 2024-11-26 Arega Getaneh Abate , Rosana Riccardi , Carlos Ruiz

This paper presents a capacity-constrained incentive-based demand response approach for residential smart grids. It aims to maintain electricity grid capacity limits and prevent congestion by financially incentivising end users to reduce or…

机器学习 · 计算机科学 2026-02-19 Shafagh Abband Pashaki , Sepehr Maleki , Amir Badiee

The evolution of the power grid towards the so-called Smart Grid, where information technologies help improve the efficiency of electricity production, distribution and consumption, allows to use the fine-grained control brought by the…

最优化与控制 · 数学 2017-05-03 Nguyen Hoang Son Duong , Patrick Maillé , Ashish Kumar , Laurent Toutain

The Internet of Things (IoT) paradigm brings an opportunity for advanced Demand Response (DR) solutions. It enables visibility and control on the various appliances that may consume, store or generate energy within a home. It has been shown…

网络与互联网体系结构 · 计算机科学 2017-12-01 Rim Kaddah , Daniel Kofman , Fabien Mathieu , Michal Pioro
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