Implementation of an active demand management procedure through of automatic load control and management of prosumers developed under the concept of smart grids

2013 ◽  
Author(s):  
M. Cassin
Keyword(s):  
Smart Grids ◽  
Demand Management ◽  
Load Control ◽  
Management Procedure ◽  
Control And Management

scholarly journals Proposal and Implementation of a User-Centered Design Approach for Smart Grid Technologies: Example of the VERTPOM® Project

2021 ◽  
Vol 14 (4) ◽  
pp. 57
Author(s):  
Helios Raharison ◽  
Emilie Loup-Escande
Keyword(s):  
Distribution System ◽  
Smart Grids ◽  
Energy Demand ◽  
Demand Management ◽  
Real Life ◽  
Positive Energy ◽  
User Centered Design ◽  
Electrical Networks ◽  
Evaluation Approach ◽  
User Centered

Acting to preserve our planet as much as possible is no longer optional in today's world. To do so, Smart Grids within the framework of electrical networks - involving not only Distribution System Operators (DSOs), but also consumers in their Energy Demand Management (EDM) activity - represent an innovative and sustainable solution. However, the integration of Smart Grids into network management or into consumers' homes implies changes at several levels: organizational, social, psychological, etc. This is why it is essential to consider the human factor in the design of the technologies used in these Smart Grids. This paper proposes the integration of DSO operators and consumers within a user-centered evaluation approach in order to design Smart Grids that are sufficiently acceptable to users to enable Positive Energy Territories that produce more energy than they consume. This demonstration will be illustrated by the VERTPOM® project aiming at facilitating the use of renewable energies specific to each territory in order to contribute to the reduction of greenhouse gases and make the territories less dependent on traditional energies, and thus make Picardy (in France) a Positive Energy Territory. This paper presents the user-centered evaluation approach applied to three technologies (i.e., the VERTPOM-BANK® supervision tool intended for DSO operators, the private web portal and the IBox smart meter intended for households) from the upstream design phase to the implementation of the technologies in real-life situations.

Implementation of Load Control for Smart Metering in Smart Grids

2022 ◽  
pp. 127-164
Author(s):  
Abdelmadjid Recioui ◽  
Fatma Zohra Dekhandji
Keyword(s):  
Smart Grids ◽  
Control Process ◽  
Electrical Energy ◽  
Load Control ◽  
Smart Meters ◽  
Cumulative Energy ◽  
Photovoltaic Panel ◽  
Labview Software ◽  
Data Acquisition Card ◽  
Cost Calculations

The conventional energy meters are not suitable for long operating purposes as they spend much human and material resources. Smart meters, on the other hand, are devices that perform advanced functions including electrical energy consumption recording of residential/industrial users, billing, real-time monitoring, and load balancing. In this chapter, a smart home prototype is designed and implemented. Appliances are powered by the grid during daytime, and a photovoltaic panel stored power during the night or in case of an electricity outage. Second, consumed power from both sources is sensed and further processed for cumulative energy, cost calculations and bill establishment for different proposed scenarios using LABVIEW software. Data are communicated using a USB data acquisition card (DAQ-USB 6008). Finally, a simulation framework using LABVIEW software models four houses each equipped with various appliances. The simulator predicts different power consumption profiles to seek of peak-demand reduction through a load control process.

Distributed Algorithms for Demand Management and Grid Stability in Smart Grids

2012 ◽  
pp. 19-55 ◽  
Author(s):  
Monica Navarro ◽  
Lorenza Giupponi ◽  
Christian Ibars ◽  
David Gregoratti ◽  
Javier Matamoros
Keyword(s):  
Distributed Algorithms ◽  
Smart Grids ◽  
Demand Management

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.

Integrated Management of Energy Storage for Sustainable Operation of Energy Microgrids

2011 ◽  
Author(s):  
Ratnesh K. Sharma ◽  
Koji Kudo
Keyword(s):  
Energy Storage ◽  
Smart Grids ◽  
Integrated Management ◽  
Operating Cost ◽  
Stable Operation ◽  
Distributed Energy ◽  
Government Grants ◽  
Sustainable Operation ◽  
Control And Management ◽  
The Cost

Energy microgrids are a key building block of smart grids. Energy microgrids can not only provide voltage and VAR support to the power grid but also reduce the emission footprint of the overall power generation infrastructure. While it provides added advantages like grid decongestion and reduced operating cost for system operators, it creates significant challenges in stable operation and meeting economic goals of the microgrid owners. Currently, energy microgrids are heavily subsidized through government grants/rebates and require high maintenance in terms of skilled operating staff and advance control systems. In this paper, we propose a microgrid energy storage architecture that could reduce the cost of ownership and simplify control and management of energy microgrids while retaining the advantages of reduced emissions and resource consumption. The controls existing in normal energy storage also offers unique opportunities in simplifying the control system of such distributed generation infrastructure and improving the reliability of microgrid in meeting local demand constraints. From a utility operator’s perspective, energy storage provides a reliable and dispatchable source as opposed to intermittent distributed energy resources.

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