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.

scholarly journals A Case Study on Increasing Microgrid Resiliency Using Reserve Power Procurement Method

2018 ◽  
Author(s):  
Halim LEE ◽  
Gil-Seong BYEON ◽  
Jin-Hong JEON ◽  
Akhtar HUSSAIN ◽  
Hak-Man KIM ◽  
...  
Keyword(s):  
Power System ◽  
Energy Management ◽  
Management System ◽  
Renewable Energy Sources ◽  
Operating Conditions ◽  
Small Scale ◽  
Energy Management System ◽  
Time Duration ◽  
Special Events ◽  
Islanded Mode

Power system decentralization has been an emerging topic for the past decade in an effort to improve power quality and environment protection via increased integration of renewable energy sources. Towards these objectives, decentralized microgrids have been proposed and thoroughly investigated in terms of technical capabilities and economic performance. In fact, the planning and actual operation of small-scale, decentralized microgrids has started in countries such as Canada, Japan, USA, UK and other countries. It is expected that the research in this area will progress and eventually take over the existing paradigm of large-scale power generation in the future. These small-size decentralized microgrids could be connected with nearby microgrids under normal operating conditions, but under special events, such as natural or man-made disasters, they would be disconnected and run in islanded mode. Under such high impact – low probability events, the microgrid must have resiliency to successfully re-connect with other microgrids and the main grid. In this paper, an Energy Management System (EMS) for a microgrid having a resiliency function, allowing to operate under islanded mode after an accident, is proposed. The proposed tool, called Resilient Energy Management System (ResEMS), aims at procuring reserve power into the microgrid’s Battery Energy Storage System (BESS) effectively, by importing it from the nearby connected power system. The accident is assumed to be a predictable natural disaster, which means that the accident occurrence time, duration and level of damage can be estimated. The proposed ResEMS has been applied to a microgrid comprising of a BESS, a diesel generator and several photovoltaic devices. The simulation results verify its beneficial operation.

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 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 Secure Communication Modeling for Microgrid Energy Management System: Development and Application

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 68 ◽  
Author(s):  
Taha Selim Ustun ◽  
S. M. Suhail Hussain
Keyword(s):  
Energy Management ◽  
Management System ◽  
Secure Communication ◽  
Storage System ◽  
Distribution Networks ◽  
Cyber Attacks ◽  
Small Scale ◽  
Energy Management System ◽  
Sensitive Information ◽  
Active Components

As the number of active components increase, distribution networks become harder to control. Microgrids are proposed to divide large networks into smaller, more manageable portions. The benefits of using microgrids are multiple; the cost of installation is significantly smaller and renewable energy-based generators can be utilized at a small scale. Due to the intermittent and time dependent nature of renewables, to ensure reliable and continuous supply of energy, it is imperative to create a system that has several generators and storage systems. The way to achieve this is through an energy management system (EMS) that can coordinate all these generators with a storage system. Prior to on-site installation, validation studies should be performed on such controllers. This work presents a standardized communication modeling based on IEC 61850 that is developed for a commercial microgrid controller. Using commercial software, different terminals are set up as intelligent electronic devices (IEDs) and the operation of the EMS is emulated with proper message exchanges. Considering that these messages transmit sensitive information, such as financial transactions or dispatch instructions, securing them against cyber-attacks is very important. Therefore; message integrity, node authentication, and confidentiality features are also implemented according to IEC 62351 guidelines. Real-message exchanges are captured with and without these security features to validate secure operation of standard communication solution.

scholarly journals Microgrid Energy Management System for Residential Microgrid Using an Ensemble Forecasting Strategy and Grey Wolf Optimization

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8489
Author(s):  
Usman Bashir Tayab ◽  
Junwei Lu ◽  
Seyedfoad Taghizadeh ◽  
Ahmed Sayed M. Metwally ◽  
Muhammad Kashif
Keyword(s):  
Energy Management ◽  
Management System ◽  
Ensemble Forecasting ◽  
Small Scale ◽  
Energy Management System ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Scheduling Strategy ◽  
Load Demand ◽  
Microgrid Energy Management

Microgrid (MG) is a small-scale grid that consists of multiple distributed energy resources and load demand. The microgrid energy management system (M-EMS) is the decision-making centre of the MG. An M-EMS is composed of four modules which are known as forecasting, scheduling, data acquisition, and human-machine interface. However, the forecasting and scheduling modules are considered the major modules from among the four of them. Therefore, this paper proposed an advanced microgrid energy management system (M-EMS) for grid-connected residential microgrid (MG) based on an ensemble forecasting strategy and grey wolf optimization (GWO) based scheduling strategy. In the forecasting module of M-EMS, the ensemble forecasting strategy is proposed to perform the short-term forecasting of PV power and load demand. The GWO based scheduling strategy has been proposed in scheduling module of M-EMS to minimize the operating cost of grid-connected residential MG. A small-scale experiment is conducted using Raspberry Pi 3 B+ via the python programming language to validate the effectiveness of the proposed M-EMS and real-time historical data of PV power, load demand, and weather is adopted as inputs. The performance of the proposed forecasting strategy is compared with ensemble forecasting strategy-1, particle swarm optimization based artificial neural network, and back-propagation neural network. The experimental results highlight that the proposed forecasting strategy outperforms the other strategies and achieved the lowest average value of normalized root mean square error of day-ahead prediction of PV power and load demand for the chosen day. Similarly, the performance of GWO based scheduling strategy of M-EMS is analyzed and compared for three different scenarios. Finally, the experimental results prove the outstanding performance of the proposed scheduling strategy.

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