Development of an Energy Management System for a Renewable Energy Community and Performance Analysis via Global Sensitivity Analysis

Ahmadifar, Amir; Ginocchi, Mirko; Golla, Megha Shyam; Ponci, Ferdinanda; Monti, Antonello

doi:10.1109/access.2023.3235590

Cited by 21 publications

(5 citation statements)

References 51 publications

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“…This system ensures that the power flow aligns with specific applications and operational requirements. It plays a pivotal role in optimizing the utilization of stored energy based on real-time demands and conditions [98,99]. 5.…”

Section: Overview Of Battery Systemsmentioning

confidence: 99%

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

Dini,

Colicelli,

Saponara

2024

Batteries

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Lithium-ion batteries have revolutionized the portable and stationary energy industry and are finding widespread application in sectors such as automotive, consumer electronics, renewable energy, and many others. However, their efficiency and longevity are closely tied to accurately measuring their SOC and state of health (SOH). The need for precise algorithms to estimate SOC and SOH has become increasingly critical in light of the widespread adoption of lithium-ion batteries in industrial and automotive applications. While the benefits of lithium-ion batteries are undeniable, the challenges related to their efficient and safe management cannot be overlooked. Accurate estimation of SOC and SOH is crucial for ensuring optimal battery management, maximizing battery lifespan, optimizing performance, and preventing sudden failures. Consequently, research and development of reliable algorithms for estimating SOC and SOH have become an area of growing interest for the scientific and industrial community. This review article aims to provide an in-depth analysis of the state-of-the-art in SOC and SOH estimation algorithms for lithium-ion batteries. The most recent and promising theoretical and practical techniques used to address the challenges of accurate SOC and SOH estimation will be examined and evaluated. Additionally, critical evaluation of different approaches will be highlighted: emphasizing the advantages, limitations, and potential areas for improvement. The goal is to provide a clear view of the current landscape and to identify possible future directions for research and development in this crucial field for technological innovation.

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Section: Overview Of Battery Systemsmentioning

confidence: 99%

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

Dini,

Colicelli,

Saponara

2024

Batteries

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show abstract

“…LCOE can be defined as the effective cost of energy generated by the REC, in $/kWh [57]. It is calculated by the ratio of the sum of the total annualized cost (C annual ) and the annual electrical energy served by the system (E served ), as expressed in Equation (8).…”

Section: Economic Criteriamentioning

confidence: 99%

“…C annual E served (8) where C annual is the product of the net present cost (NPC) and the capital recovery factor (CRF), as expressed by Equation ( 9):…”

Section: Lcoe =mentioning

confidence: 99%

“…On one hand, these communities promote the decentralization of energy production, increase energy efficiency, enhance energy security, and ensure greater independence of participants from conventional energy sources. On the other hand, these communities face significant variability and unpredictability due to renewable energy production [8]. Nonetheless, this variability and unpredictability can be mitigated by the complementarity of resources (such as wind and solar) to balance the variability in energy production, or by the introduction of energy storage systems [9].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Sizing of Renewable Energy Communities: A Multiple Swarms Multi-Objective Particle Swarm Optimization Approach

Faria,

Marques,

Pombo

et al. 2023

Energies

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Renewable energy communities have gained popularity as a means of reducing carbon emissions and enhancing energy independence. However, determining the optimal sizing for each production and storage unit within these communities poses challenges due to conflicting objectives, such as minimizing costs while maximizing energy production. To address this issue, this paper employs a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with multiple swarms. This approach aims to foster a broader diversity of solutions while concurrently ensuring a good plurality of nondominant solutions that define a Pareto frontier. To evaluate the effectiveness and reliability of this approach, four case studies with different energy management strategies focused on real-world operations were evaluated, aiming to replicate the practical challenges encountered in actual renewable energy communities. The results demonstrate the effectiveness of the proposed approach in determining the optimal size of production and storage units within renewable energy communities, while simultaneously addressing multiple conflicting objectives, including economic viability and flexibility, specifically Levelized Cost of Energy (LCOE), Self-Consumption Ratio (SCR) and Self-Sufficiency Ratio (SSR). The findings also provide valuable insights that clarify which energy management strategies are most suitable for this type of community.

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“…The community microgrid can be used at the end of the branches of the feeder to improve the consumer power supply, resiliency, flexibility, and stability of the electrical distribution network [12,13]. This energy management system was developed with the approach of balancing generation and consumption within the renewable energy community [14].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Renewable Energy Planning in the Indonesian Context: A Lombok Island Study Case

Rajabnia,

Ali,

Vasquez

et al. 2023

Applied Sciences

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In recent years, community microgrids have expanded their power systems with many aims. One of the most important goals of microgrids is to increase resiliency. The main objective of this paper is to develop effective planning tools for community microgrids within electrical distribution networks, with a specific focus on ensuring the provision of critical loads during natural disasters. Additionally, this paper emphasized emphasises long-term planning considerations by using DIgSILENT 15.2 tools. The primary goal of this issue is to create the best planning tools for community microgrids in order to increase the network’s resilience to natural disasters, with a focus on important loads like hospitals and hotels. Also, the second goal is an optimization that seeks to reduce overall expenses. Finally, we’ll talk about how to get two results: one is to choose a few microgrid-based loads to link to the electrical distribution network, and the other is to show that each microgrid has the power needed to support both its local loads and the system’s vital loads in the event of a natural disaster. In this paper, the use of community microgrids for energy access against natural disasters in Indonesia is investigated by considering the case study of microgrids on Lombok Island. The study results, using the proposed framework, show that the presence of a microgrid structure in the distribution network expansion planning helps to improve operating conditions and supply critical loads in natural disasters.

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Development of an Energy Management System for a Renewable Energy Community and Performance Analysis via Global Sensitivity Analysis

Cited by 21 publications

References 51 publications

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

Optimal Sizing of Renewable Energy Communities: A Multiple Swarms Multi-Objective Particle Swarm Optimization Approach

Long-Term Renewable Energy Planning in the Indonesian Context: A Lombok Island Study Case

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