scholarly journals Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 323
Author(s):  
Simone Polimeni ◽  
Alfredo Nespoli ◽  
Sonia Leva ◽  
Gianluca Valenti ◽  
Giampaolo Manzolini
Keyword(s):  
Energy Management ◽  
Management System ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Operating Cost ◽  
Power Sharing ◽  
Forecast Errors ◽  
Energy Management System ◽  
Generation Scheduling ◽  
Forecast Models

Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (i) day-ahead model, (ii) intraday corrections and (iii) nowcasting technique. The experimental study demonstrates the capability of the proposed management system to operate an islanded microgrid in safe conditions, even with inaccurate day-ahead photovoltaic forecasts.

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 Energy Management System Connected for Microgrid using Fuzzy Logic Algorithm

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2522-2527
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Management System ◽  
Power Sharing ◽  
Control Procedure ◽  
Energy Management System ◽  
Energy Control ◽  
Fuzzy Logic Algorithm ◽  
Points Of Interest ◽  
Suggested Technique

In this paper, energy control procedure is designed for the lattice associated combined energy stockpiling with the cell and the supercapacitor under various working ways. The primary points of interest in the recommended EMS are efficacious power sharing among the different energy stockpiling system. The viability of the suggested technique are supported by both simulation and trail studies.

scholarly journals Time-Constrained Nature-Inspired Optimization Algorithms for an Efficient Energy Management System in Smart Homes and Buildings

2019 ◽  
Vol 9 (4) ◽  
pp. 792 ◽  
Author(s):  
Ibrar Ullah ◽  
Sajjad Hussain
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Energy Cost ◽  
Management System ◽  
Heuristic Algorithms ◽  
Renewable Energy Sources ◽  
Smart Homes ◽  
Energy Management System ◽  
End User ◽  
Residential Sector

This paper proposes two bio-inspired heuristic algorithms, the Moth-Flame Optimization (MFO) algorithm and Genetic Algorithm (GA), for an Energy Management System (EMS) in smart homes and buildings. Their performance in terms of energy cost reduction, minimization of the Peak to Average power Ratio (PAR) and end-user discomfort minimization are analysed and discussed. Then, a hybrid version of GA and MFO, named TG-MFO (Time-constrained Genetic-Moth Flame Optimization), is proposed for achieving the aforementioned objectives. TG-MFO not only hybridizes GA and MFO, but also incorporates time constraints for each appliance to achieve maximum end-user comfort. Different algorithms have been proposed in the literature for energy optimization. However, they have increased end-user frustration in terms of increased waiting time for home appliances to be switched ON. The proposed TG-MFO algorithm is specially designed for nearly-zero end-user discomfort due to scheduling of appliances, keeping in view the timespan of individual appliances. Renewable energy sources and battery storage units are also integrated for achieving maximum end-user benefits. For comparison, five bio-inspired heuristic algorithms, i.e., Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search Algorithm (CSA), Firefly Algorithm (FA) and Moth-Flame Optimization (MFO), are used to achieve the aforementioned objectives in the residential sector in comparison with TG-MFO. The simulations through MATLAB show that our proposed algorithm has reduced the energy cost up to 32.25% for a single user and 49.96% for thirty users in a residential sector compared to unscheduled load.

scholarly journals Energy Management Electronic Device for Islanded Microgrids Based on Renewable Energy Sources and Battery-based Energy Storage

2021 ◽  
Vol 41 (1) ◽  
pp. e83905
Author(s):  
Elkin Dario Granados Hernández ◽  
Nelson Leonardo Diaz Aldana ◽  
Adriana Carolina Luna Hernández
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Electronic Device ◽  
Renewable Energy Sources ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Energy Management System ◽  
Distributed Energy ◽  
Energy Management Strategy

Energy management systems are one of the most important components in the operation of an electric microgrid. They are responsible for ensuring the supervision of the electrical system, as well as the coordination and reliability of all loads and distributed energy resources in order for the microgrid to be operated as a unified entity. Because of that, an energy management system should be fast enough at processing data and defining control action to guarantee the correct performance of the microgrid. This paper explores the design and implementation of an energy management system deployed over a dedicated electronic device. The proposed energy management device coordinates the distributed energy resources and loads in a residential-scale islanded microgrid, in accordance with a rule-based energy management strategy that ensures reliable and safe operation of the battery-based energy storage system. A hardware-int-he-loop test was performed with a real-time simulation platform to show the operation of the electronic device

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