scholarly journals DeLi2P - A User Centric, Scalable Demand Side Management Strategy for Smart Grids

2015 ◽  
Author(s):  
Syed Muhammad Ali ◽  
Mohammad Naveed ◽  
Fahad Javed ◽  
Naveed Arshad ◽  
Jahangir Ikram
Keyword(s):  
Smart Grids ◽  
Management Strategy ◽  
Demand Side Management ◽  
Demand Side ◽  
User Centric

AC Optimal Power Flow Incorporating Demand-Side Management Strategy

2019 ◽  
pp. 147-165
Author(s):  
Farkhondeh Jabari ◽  
Mousa Mohammadpourfard ◽  
Behnam Mohammadi-Ivatloo
Keyword(s):  
Management Strategy ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Demand Side Management ◽  
Demand Side ◽  
Optimal Power

scholarly journals Distributed Demand Side Management with Stochastic Wind Power Forecasting

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Sergio Grammatico ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Smart Grids ◽  
Demand Side Management ◽  
Expected Value ◽  
Noncooperative Game ◽  
Theory Approach ◽  
Demand Side ◽  
Wind Power Forecasting ◽  
Power Forecasting

In this paper, we propose a distributed demand side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach.<br>We assume that each user selfishly formulates its grid optimization problem as a noncooperative game.<br>The core challenge in this paper is defining an approach to cope with the uncertainty in wind power availability. <br>We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework.<br>In the latter case, we employ the sample average approximation technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability.<br><div>Numerical simulations on a real dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.</div><div><br></div><div>Preprint of paper submitted to IEEE Transactions on Control Systems Technology<br></div>

Application of Internet of Things (IoT) to Demand-Side Management in Smart Grids

2021 ◽  
pp. 169-183
Author(s):  
Armin Hosseini Rezaei Asl ◽  
Mir Mahdi Safari ◽  
Morteza Nazari-heris ◽  
Behnam Mohammadi-Ivatloo
Keyword(s):  
Internet Of Things ◽  
Smart Grids ◽  
Demand Side Management ◽  
Demand Side

A multi-objective demand side management considering ENS cost in smart grids

2017 ◽  
Author(s):  
Babak Yousefi Khanghah ◽  
Saeid Ghassemzadeh ◽  
Amjad Anvari-Moghaddam ◽  
Josep M. Guerrero ◽  
Juan C. Vasquez
Keyword(s):  
Smart Grids ◽  
Demand Side Management ◽  
Demand Side ◽  
Multi Objective

scholarly journals Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2047 ◽  
Author(s):  
Yung-Yao Chen ◽  
Yu-Hsiu Lin ◽  
Chia-Ching Kung ◽  
Ming-Han Chung ◽  
I-Hsuan Yen
Keyword(s):  
Artificial Intelligence ◽  
Data Science ◽  
Modern Society ◽  
Smart Homes ◽  
Demand Side Management ◽  
Demand Side ◽  
Smart Meters ◽  
Smart Power ◽  
Iot Applications ◽  
User Centric

In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today’s modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable.

Intermittent control for demand-side management of a class of networked smart grids

2019 ◽  
Vol 13 (8) ◽  
pp. 1166-1172 ◽  
Author(s):  
Jinhuan Wang ◽  
Xiaoye Gao ◽  
Yong Xu
Keyword(s):  
Smart Grids ◽  
Demand Side Management ◽  
Intermittent Control ◽  
Demand Side
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