scholarly journals Energy Management System Modeling of DC Data Center with Hybrid Energy Sources Using Neural Network

2017 ◽  
Author(s):  
Khalid Althomali
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Data Center ◽  
Management System ◽  
System Modeling ◽  
Energy Sources ◽  
Energy Management System ◽  
Hybrid Energy

scholarly journals Energy Management System Designed for Residential Grid connected Micro Grid

2021 ◽  
Vol 12 (3) ◽  
pp. 4627-4634
Author(s):  
N.Pooja Et.al
Keyword(s):  
Energy Management ◽  
Management System ◽  
System Modeling ◽  
Energy System ◽  
Pi Controller ◽  
Integrated System ◽  
Energy Sources ◽  
Energy Management System ◽  
Battery System ◽  
Micro Grid

This paper presents an energy management system supported by PI Controller for a residential grid connected micro grid with renewable hybrid generation (wind and photo voltaic) and battery system. Modeling hybrid system includes non conventional energy sources given at sporadic supply conditions and dynamic energy demand, and to make conceptual energy storage with the help of battery system . Designing  an  appropriate  scheme  that dynamically changes modes of renewable integrated system based on the availability of RES power and changes in load. Wind,PV are the primary power supply of the system; battery is going  to  be act  as  a  substitute.The  PI  controller  is developed and carried  out for the aimed hybrid(Wind and PV) energy system to integrate the non conventional energy sources to the serviceability either to grid or to Residential loads.main objective is improvement of transients during switching  periods  by  using an efficient PI controller.maximum power point tracking is also  other objective is energy management system designed for the residential grid connected Micro Grid. Simulations are carried out on the proposed Hybrid energy system using MATLAB/ SIMULINK.

Energy Management System for Hybrid Microgrids

2021 ◽  
Vol 69 (2) ◽  
pp. 21-30
Author(s):  
Nasreddine ATTOU ◽  
Sid-Ahmed ZIDI ◽  
Mohamed KHATIR ◽  
Samir HADJERI
Keyword(s):  
Energy Management ◽  
Electricity Market ◽  
Management System ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Supply And Demand ◽  
Energy Sources ◽  
Variable Load ◽  
Energy Management System ◽  
Price Variation

Energy management in grid-connected Micro-grids (MG) has undergone rapid evolution in recent times due to several factors such as environmental issues, increasing energy demand and the opening of the electricity market. The Energy Management System (EMS) allows the optimal scheduling of energy resources and energy storage systems in MG in order to maintain the balance between supply and demand at low cost. The aim is to minimize peaks and fluctuations in the load and production profile on the one hand, and, on the other hand, to make the most of renewable energy sources and energy exchanges with the utility grid. In this paper, our attention has been focused on a Rule-based energy management system (RB EMS) applied to a residential multi-source grid-connected MG. A Microgrid model has been implemented that combines distributed energy sources (PV, WT, BESS), a number of EVs equipped with the Vehicle to Grid technology (V2G) and variable load. Different operational scenarios were developed to see the behaviour of the implemented management system during the day, including the random demand profile of EV users, the variation in load and production, grid electricity price variation. The simulation results presented in this paper demonstrate the efficacy of the suggested EMS and confirm the strategy's feasibility as well as its ability to properly share power among different sources, loads and vehicles by obeying constraints on each element.

scholarly journals Enhanced Intelligent Energy Management System for a Renewable Energy-Based AC Microgrid

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3268
Author(s):  
Mehdi Dhifli ◽  
Abderezak Lashab ◽  
Josep M. Guerrero ◽  
Abdullah Abusorrah ◽  
Yusuf A. Al-Turki ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Optimal Operation ◽  
Green Energy ◽  
Energy Management System ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Ac Microgrid

This paper proposes an enhanced energy management system (EEMS) for a residential AC microgrid. The renewable energy-based AC microgrid with hybrid energy storage is broken down into three distinct parts: a photovoltaic (PV) array as a green energy source, a battery (BT) and a supercapacitor (SC) as a hybrid energy storage system (HESS), and apartments and electric vehicles, given that the system is for residential areas. The developed EEMS ensures the optimal use of the PV arrays’ production, aiming to decrease electricity bills while reducing fast power changes in the battery, which increases the reliability of the system, since the battery undergoes fewer charging/discharging cycles. The proposed EEMS is a hybrid control strategy, which is composed of two stages: a state machine (SM) control to ensure the optimal operation of the battery, and an operating mode (OM) for the best operation of the SC. The obtained results show that the EEMS successfully involves SC during fast load and PV generation changes by decreasing the number of BT charging/discharging cycles, which significantly increases the system’s life span. Moreover, power loss is decreased during passing clouds phases by decreasing the power error between the extracted power by the sources and the required equivalent; the improvement in efficiency reaches 9.5%.

Photovoltaics Energy Prediction Under Complex Conditions for a Predictive Energy Management System

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Martin Schmelas ◽  
Thomas Feldmann ◽  
Jesus da Costa Fernandes ◽  
Elmar Bollin
Keyword(s):  
Neural Network ◽  
Physical Model ◽  
Energy Management ◽  
Management System ◽  
Percentage Error ◽  
Positive Energy ◽  
Energy Management System ◽  
Energy Prediction ◽  
The Neural Network ◽  
Accurate Forecast

Solar energy converted and fed to the utility grid by photovoltaic modules has increased significantly over the last few years. This trend is expected to continue. Photovoltaics (PV) energy forecasts are thus becoming more and more important. In this paper, the PV energy forecasts are used for a predictive energy management system (PEMS) in a positive energy building. The publication focuses on the development and comparison of different models for daily PV energy prediction taking into account complex shading, caused for example by trees. Three different forecast methods are compared. These are a physical model with local shading measurements, a multilayer perceptron neural network (MLP), and a combination of the physical model and the neural network. The results show that the combination of the physical model and the neural network provides the most accurate forecast values and can improve adaptability. From April to December, the mean percentage error (MPE) of the MLP with physical information is 11.6%. From December to March, the accuracy of the PV predictions decreases to an MPE of 78.8%. This is caused by poorer irradiation forecasts, but mainly by snow coverage of the PV modules.

An Online Energy Management System for a Grid-Connected Hybrid Energy Source

2018 ◽  
Vol 6 (4) ◽  
pp. 2015-2030 ◽  
Author(s):  
Mohamed S. Taha ◽  
Hussein Hassan Abdeltawab ◽  
Yasser Abdel-Rady I. Mohamed
Keyword(s):  
Energy Source ◽  
Energy Management ◽  
Management System ◽  
Energy Management System ◽  
Hybrid Energy
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