Demand Side Integration in the Operation of LV Smart Grids

2019 ◽  
pp. 167-198
Author(s):  
Susanna Mocci ◽  
Simona Ruggeri
Keyword(s):  
Smart Grids ◽  
Demand Side

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>

scholarly journals Ontology-based demand-side flexibility management in smart grids using a multi-agent system

2016 ◽  
Author(s):  
J. L. Hippolyte ◽  
S. Howell ◽  
B. Yuce ◽  
M. Mourshed ◽  
H. A. Sleiman ◽  
...  
Keyword(s):  
Smart Grids ◽  
Multi Agent System ◽  
Demand Side ◽  
Agent System ◽  
Multi Agent

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

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

Genetic Based Reinforcement Learning Load Control for Smart Grids

2013 ◽  
Vol 860-863 ◽  
pp. 2423-2426
Author(s):  
Xin Li ◽  
Dan Yu ◽  
Chuan Zhi Zang
Keyword(s):  
Reinforcement Learning ◽  
Electric Charge ◽  
Smart Grids ◽  
Mathematic Model ◽  
Customer Participation ◽  
Load Control ◽  
Demand Side ◽  
Energy Usage ◽  
Take The Best ◽  
Simulation Results

As the improvement of smart grids, the customer participation has reinvigorated interest in demand-side features such as load control for domestic users. A genetic based reinforcement learning (RL) load controller is proposed. The genetic is used to adjust the parameters of the controller. The RL algorithm, which is independent of the mathematic model, shows the particular superiority in load control. By means of learning procedures, the proposed controller can learn to take the best actions to regulate the energy usage for equipments with the features of high comfortable for energy usage and low electric charge meanwhile. Simulation results show that the proposed load controller can promote the performance energy usage in smart grids.

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