Experimental validation of multi-stage optimal energy management for a smart microgrid system under forecasting uncertainties

Gheouany, Saad; Ouadi, Hamid; Giri, F.; Bakali, Saida El

doi:10.1016/j.enconman.2023.117309

Cited by 41 publications

(5 citation statements)

References 41 publications

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“…Reference values are sent to LCs in real-time using communication links. Implementation and validation for EMSs are conducted using diverse solution methodologies, applied in different domains, using simulation validation [92][93][94][95] and experimental validation [96][97][98][99][100][101][102][103][104][105][106][107].…”

Section: Limitations and Challengesmentioning

confidence: 99%

On Hybrid Nanogrids Energy Management Systems—An Insight into Embedded Systems

Bitar,

El Tawil,

Benbouzid

et al. 2024

Applied Sciences

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In recent years, the growing demand for efficient and sustainable energy management has led to the development of innovative solutions for embedded systems. One such solution is the integration of hybrid nanogrid energy management systems into various applications. There are currently many energy management systems in different domains, such as buildings, electric vehicles, or even naval transport. However, an embedded nanogrid management system is subject to several constraints that are not sufficiently studied in the literature. Indeed, such a system often has a limited energy reserve and is isolated from any energy supply for a long time. This paper aims to provide a comprehensive overview of the current state of research, advancements, and challenges in the field of hybrid nanogrid energy management systems. Furthermore, it offers a comparative analysis between hybrid nanogrids and microgrids and the implications of their integration in embedded systems. This paper also discusses the key components, operation principles, optimization strategies, real-world implementations, challenges, and future prospects of hybrid nanogrid energy management systems. Moreover, it highlights the significance of such systems in enhancing energy efficiency, reducing carbon footprints, and ensuring reliable power supply.

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Section: Limitations and Challengesmentioning

confidence: 99%

On Hybrid Nanogrids Energy Management Systems—An Insight into Embedded Systems

Bitar,

El Tawil,

Benbouzid

et al. 2024

Applied Sciences

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“…Their work integrated uncertain seasonal hydroelectric supply and shortterm renewable supply variability into a two-stage stochastic programming framework, considering hydroelectric and battery storage solutions. Gheouany et al proposed a multi-stage energy management system for microgrids, incorporating a multi-objective particle swarm optimization algorithm for model predictive control and reactive layers using extremum seeking for real-time optimization [9]. The system effectively addressed forecast uncertainty and ensures significant reductions in daily energy costs and battery storage system degradation.…”

Section: Literature Review 21 Prior Workmentioning

confidence: 99%

Deep-Reinforcement-Learning-Based Vehicle-to-Grid Operation Strategies for Managing Solar Power Generation Forecast Errors

Jang,

2024

Sustainability

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This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G) operation strategy that focuses on the dynamic integration of charging station (CS) status to refine solar power generation (SPG) forecasts. To address the variability in solar energy and CS status, this study proposes a novel approach by formulating the V2G operation as a Markov decision process and leveraging DRL to adaptively manage SPG forecast errors. Utilizing real-world data from the Korea Southern Power Corporation, the effectiveness of this strategy in enhancing SPG forecasts is proven using the PyTorch framework. The results demonstrate a significant reduction in the mean squared error by 40% to 56% compared to scenarios without V2G. Our investigation into the effects of blocking probability thresholds and discount factors revealed insights into the optimal V2G system performance, suggesting a balance between immediate operational needs and long-term strategic objectives. The findings highlight the possibility of using DRL-based strategies to achieve more reliable and efficient renewable energy integration in power grids, marking a significant step forward in smart grid optimization.

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“…It studies the online scheduling of both load flexibility and optimal power flow management. In the study by authors [16], a Multi-stage Energy Management System is proposed. This system encompasses two main components: (i) a forecasting system that predicts load demand and renewable power generation for the next day using an Artificial Neural Network and (ii) an optimal power dispatching mechanism in a grid-connected Smart Microgrid (SMG), equipped with a PV system and an Energy Storage System (ESS).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the present paper, we address several key aspects that are not considered in the existing literature. Firstly, we tackle the issue of CO 2 emissions, which are not effectively curtailed in the studies referenced in [8,11,12,15,16]. Additionally, we take into account the variability of grid power fees based on energy market costs throughout the day, a factor that is often overlooked [11,14].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Bellman–Genetic Hybrid Algorithm Optimization in Rural Area Microgrids

Zahraoui,

Et-taoussi,

Chakir

et al. 2023

Energies

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Incorporating renewable Distributed Energy Resources (DER) into the main grid is crucial for achieving a sustainable transition from fossil fuels. However, this generation system is complicated by the fluctuating behavior of renewable resources and the variable load demand, making it less reliable without a suitable energy storage system (ESS). This study proposes an Optimal Power Flow Management (OPFM) strategy for a grid-connected hybrid Micro Grid (MG) comprising a wind turbine (WT), a photovoltaic (PV) field, a storage battery, and a Micro Gas turbine (MGT). This proposed strategy includes (i) minimizing the MG’s daily energy cost, (ii) decreasing CO2 emissions by considering the variable load, weather forecast, and main grid fees to optimize the battery charging/discharging strategy, and (iii) optimizing the decision-making process for power purchase/sell from/to the main grid. The suggested OPFM approach is implemented using a Genetic Algorithm and compared with the Bellman Algorithm and a restricted management system via several simulations under the Matlab environment. Furthermore, the hybridization of the Bellman Algorithm and the Genetic Algorithm is proposed to enhance the OPFMC strategy’s efficiency by leveraging both algorithms’ strengths. The simulation results demonstrate the effectiveness of the proposed strategy in lowering energy costs and CO2 emissions and enhancing reliability. Additionally, the comparison of the hybridized GA algorithm reveals a cost 16% higher than the Bellman Algorithm; however, the use of the hybridized GA algorithm leads to a reduction in GHG emissions by 31.4%. These findings underscore the trade-off between cost and environmental impact in the context of algorithmic optimization for microgrid energy management.

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Experimental validation of multi-stage optimal energy management for a smart microgrid system under forecasting uncertainties

Cited by 41 publications

References 41 publications

On Hybrid Nanogrids Energy Management Systems—An Insight into Embedded Systems

On Hybrid Nanogrids Energy Management Systems—An Insight into Embedded Systems

Deep-Reinforcement-Learning-Based Vehicle-to-Grid Operation Strategies for Managing Solar Power Generation Forecast Errors

Bellman–Genetic Hybrid Algorithm Optimization in Rural Area Microgrids

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