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 Smart Microgrid Management: A Hybrid Optimisation Approach

2020 ◽  
Author(s):  
amoura yahia ◽  
Ana Isabel Pereira ◽  
José Lima ◽  
Angela Ferreira ◽  
Fouad Boukli-Hacene ◽  
...  
Keyword(s):  
Energy Management ◽  
Smart City ◽  
Management System ◽  
Renewable Energy Sources ◽  
Market Price ◽  
Inverse Correlation ◽  
Optimization Method ◽  
Electrical Network ◽  
Small Scale ◽  
Energy Management System

Abstract Background: The association of distributed generators, energy storage systems and controllable loads close to the energy consumers gave place to a small-scale electrical network called microgrid. The stochastic behavior of renewable energy sources, as well as the demand variation, can lead in some cases to problems related to the reliability of the microgrid system. On the other hand, the market price of electricity from mainly non-renewable sources becomes a concern for a simple consumer due to its high costs.Method: In this work, an energy management system was developed based on an innovative optimization method, combining linear programming, based on the simplex method, with particle swarm optimisation algorithm. Two scenarios have been proposed to characterise the relation price versus gas emissions for optimal energy management. The objective of this study is to nd the optimal setpoints of generators in a smart city supplied by a microgrid in order to ensure consumer comfort, minimising the emission of greenhouse gases and ensure an appropriate operating price for all smart city consumers. Results: The simulation results have demonstrated the reliability of the optimisation approach on the energy management system in the optimal scheduling of the microgrid generators power ows, having achieved a better energy price compared to a previous study with the same data. Conclusion: The energy management system based on the proposedoptimisation approach gave an inverse correlation between economic and environmental aspects, in fact, a multi-objective optimisation approach is performed as a continuation of the work proposed in this paper.

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