On Energy Management Control of a PV-Diesel-ESS Based Microgrid in a Stand-Alone Context

Belila, Ahmed; Benbouzid, Mohamed; Berkouk, El Madjid; Amirat, Yassine

doi:10.3390/en11082164

Cited by 29 publications

(6 citation statements)

References 39 publications

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“…A stand-alone micro grid (MG) can be integrated with RES for the stable operation in a power distribution system. It includes, diesel generator sets (DGs) and battery storage systems (BSS) [3].The MG must balance the power generation and demand and also fix uncertainties in RES [4].Therefore implementation of EMS is important in regulating HS efficiently [2] These HS can be used to overcome peak demand in high-rise buildings and can deliver power at a point of common coupling (PCC) in AC bus system. In commercial and industrial buildings DGs and BSS acts as a backup supply during the outages of main supply [5] [6].…”

Section: Introductionmentioning

confidence: 99%

RECLAIM: Renewable Energy Based Demand-Side Management Using Machine Learning Models

et al. 2023

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The diesel generators sets (DGs) and battery storage systems (BSS) are the essential energy sources in a modern high-rise buildings. In this paper DG, BSS and Photovoltaic system (PV) has been considered to minimize the grid power injection using a centralized Energy Management System (EMS). Machine Learning (ML) techniques are used to predict the performance of various regression models by comparing grid power and load curves. It includes Artificial Neural Network (ANN), Wide Neural Network (WNN), Linear Regression (LR), Linear Regression Interaction (LR-I), Linear Regression Stepwise (LR-S), Regression Fine Tree (RF-T), Regression Coarse Tree (RC-T) and Gaussian Process Regression (GPR) based techniques. The Demand Side Management (DSM) techniques such as peak shaving and valley filling is integrated with ML technique in a Hybrid energy source (HS) system.The comparative analysis of results depicts the effective reshaping of the grid profile without scheduling or disconnecting the loads. Matlab simulation software is used to validate the results.

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Section: Introductionmentioning

confidence: 99%

RECLAIM: Renewable Energy Based Demand-Side Management Using Machine Learning Models

et al. 2023

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“…One of the most severe cases of high-RES penetration in a distribution system is a stand-alone microgrid (MG) including an RES, an ESS, a diesel generator (DG), and several loads [2,3]. Therefore, the given MG must balance the generation and demand by its own capacities.…”

Section: Introductionmentioning

confidence: 99%

MPC Based Energy Management System for Hosting Capacity of PVs and Customer Load with EV in Stand-Alone Microgrids

Ryu

Kim

et al. 2021

Energies

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This paper presents the improvements of the hosting capacity of photovoltaics (PVs) and electric vehicles (EVs) in a stand-alone microgrid (MG) with an energy storage system (ESS) by consider-ing a model predictive control (MPC) based energy management system. The system is configured as an MG, including PVs, an ESS, a diesel generator (DG), and several loads with EVs. The DG is controlled to operate at rated power and the MPC algorithm is used in a stand-alone MG, which supplies the energy demanded for several loads with EVs. The hosting capacity of the load in-cluding the EV and PVs can be expanded through the ESS to the terminal node of the microgrid. In this case, the PVs and the load can be connected in excess of the capacity of the diesel genera-tor, and each bus in the feeder complies with the voltage range required by the grid. The effec-tiveness of the proposed algorithm to resolve the hosting capacity is demonstrated by numerical simulations.

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“…Various RESs have been used for DC MGs in the literature. In [4]- [6], PV and battery potentials have been explored for applicability to DC MGs. In [7], a DC MG is investigated as a promising solution for solar and wind power integration.…”

Section: Introductionmentioning

confidence: 99%

Energy Management System for an Islanded Renewables-based DC Microgrid

Zia

Nasir

Elbouchikhi

et al. 2020

2020 2nd International Conference on Smart Power &Amp; Internet Energy Systems (SPIES)

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DC microgrids are gaining attention of researchers and engineers due to the increasing deployment of renewable energy sources with energy storage systems, enhanced utilization of DC power electronics devices, and added advantages of no harmonics and synchronization issues. They are viable solutions for providing electricity to off-grid remote communities, like islands and remote areas. However, they need energy management systems for optimally scheduling the distributed energy generation and storage systems. Hence, this paper proposes a supervisory energy management system for optimal operation of islanded DC microgrid. Energy management system is responsible for determining optimal scheduling of each energy source and ensuring maximum utilization of renewable energy sources and supply demand balance. The proposed energy management model has been validated experimentally and practical results prove the effectiveness of the proposed method.

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On Energy Management Control of a PV-Diesel-ESS Based Microgrid in a Stand-Alone Context

Cited by 29 publications

References 39 publications

RECLAIM: Renewable Energy Based Demand-Side Management Using Machine Learning Models

RECLAIM: Renewable Energy Based Demand-Side Management Using Machine Learning Models

MPC Based Energy Management System for Hosting Capacity of PVs and Customer Load with EV in Stand-Alone Microgrids

Energy Management System for an Islanded Renewables-based DC Microgrid

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