A model-based parametric and optimal sizing of a battery/hydrogen storage of a real hybrid microgrid supplying a residential load: Towards island operation

Ferrario, Andrea Monforti; Bartolini, Andrea; Segura, F.; Vivas, F.J.; Comodi, Gabriele; McPhail, Stephen J.; Andújar, José Manuel

doi:10.1016/j.adapen.2021.100048

Cited by 41 publications

(14 citation statements)

References 58 publications

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“…There are two possible operating conditions; i) grid connected condition-where the loads are compensated by solar, wind plants and grid if needed. In this condition during excess power generation by the renewable sources it is injected to grid after compensating the load and ii) standalone condition-where the grid is not available for the support of the load, therefore the renewable micro grid need to compensate the load [10], [11]. Therefore a battery backup module is activated in the micro grid which supports the load during deficit renewable power conditions.…”

Section: System Configurationmentioning

confidence: 99%

Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller

Swathi,

Kumar,

Upendar

2024

Bulletin EEI

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A micro grid system with renewable source operation control is a complex part as each source operates at different parameters. This renewable micro grid with multiple sources like solar plants, wind farm, fuel cell, battery backup has to be operated in both grid connected and standalone condition. During grid connection the micro grid, inverter has to inject power to the grid and compensate load in synchronization to the grid voltages. And during standalone condition the inverter is controlled with droop control module which stabilizes the voltage and frequency of the system even during grid disconnection. The droop control module is further updated with new advanced controllers like fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) replacing the traditional proportional integral derivative (PID) and proportional integral (PI) controllers improving the response rate and for achieving better stabilization. This paper has comparative analysis of the micro grid system with different droop controllers under various operating conditions. Parameters like voltage magnitude (Vmag), frequency (F), load and inverter powers (Pload and Pinv) of the test system are compared with different controllers. A numeric comparison table is given to determine the optimal controller for the inverter operation. The analysis is carried out in MATLAB/Simulink software with graphical and parametric validations.

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Section: System Configurationmentioning

confidence: 99%

Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller

Swathi,

Kumar,

Upendar

2024

Bulletin EEI

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“…The study demonstrated that VRFB systems could achieve a high round-trip efficiency of 80-90% and provide long-duration storage capabilities. Monforti et al [114] evaluated the performance of a hydrogen storage system for solar energy integration. The study demonstrated that the hydrogen storage system achieved a round-trip efficiency of 35-40% and provided long-duration storage of up to several weeks.…”

Section: Emerging Storage Technologiesmentioning

confidence: 99%

Harnessing Solar Power: A Review of Photovoltaic Innovations, Solar Thermal Systems, and the Dawn of Energy Storage Solutions

Hasan,

Hossain,

Mofijur

et al. 2023

Energies

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The goal of this review is to offer an all-encompassing evaluation of an integrated solar energy system within the framework of solar energy utilization. This holistic assessment encompasses photovoltaic technologies, solar thermal systems, and energy storage solutions, providing a comprehensive understanding of their interplay and significance. It emphasizes the importance of solar energy as a renewable resource and its role in addressing global energy demand and mitigating climate change. The review highlights the significance of advancements in various solar energy technologies, focusing on their environmental benefits, including greenhouse gas emissions reduction and air and water pollution mitigation. It explores the evolution of photovoltaic technologies, categorizing them into first-, second-, and third-generation photovoltaic cells, and discusses the applications of solar thermal systems such as water heaters, air heaters, and concentrators. The paper examines key advancements in energy storage solutions for solar energy, including battery-based systems, pumped hydro storage, thermal storage, and emerging technologies. It references recent published literature to present findings on energy payback time, carbon footprint, and performance metrics. Challenges to widespread adoption are discussed, including cost and economic viability, intermittency, environmental impacts, and grid integration. Strategies to overcome these challenges, such as cost reduction, policy support, energy storage integration, and sustainable practices, are presented based on published literature. By bridging gaps in existing literature, this comprehensive resource aims to equip researchers, policymakers, and industry professionals with insights into forging a sustainable and renewable energy future.

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“…It is hard to meet the control performance demand of a high proportion of largecapacity new energy grid-connected mode with the traditional centralised AGC as a vital means of grid scheduling. Moreover, the control performance of LFC largely depends on the control strategy 4, while the traditional LFC control strategy 5 is no longer adequate to cope with the regulation and control tasks under the trend of large-scale new energy grid-connectedness and the stochastic fluctuation of uncertain loads on the customer side (Ferrario et al, 2021;Li et al, 2022). Therefore, from the perspective of distributed LFC, it is of great significance to seek a class of optimal LFC control strategies for large-scale grid integration of new energy sources based on modern control theory and intelligent optimization methods.…”

Section: Introductionmentioning

confidence: 99%

Quantum-inspired deep reinforcement learning for adaptive frequency control of low carbon park island microgrid considering renewable energy sources

Shen,

Tang,

Pan

et al. 2024

Front. Energy Res.

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The low carbon park islanded microgrid faces operational challenges due to the high variability and uncertainty of distributed renewable energy sources. These sources cause severe random disturbances that impair the frequency control performance and increase the regulation cost of the islanded microgrid, jeopardizing its safety and stability. This paper presents a data-driven intelligent load frequency control (DDI-LFC) method to address this problem. The method replaces the conventional LFC controller with an intelligent agent based on a deep reinforcement learning algorithm. To adapt to the complex islanded microgrid environment and achieve adaptive multi-objective optimal frequency control, this paper proposes the quantum-inspired maximum entropy actor-critic (QIS-MEAC) algorithm, which incorporates the quantum-inspired principle and the maximum entropy exploration strategy into the actor-critic algorithm. The algorithm transforms the experience into a quantum state and leverages the quantum features to improve the deep reinforcement learning’s experience replay mechanism, enhancing the data efficiency and robustness of the algorithm and thus the quality of DDI-LFC. The validation on the Yongxing Island isolated microgrid model of China Southern Grid (CSG) demonstrates that the proposed method utilizes the frequency regulation potential of distributed generation, and reduces the frequency deviation and generation cost.

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A model-based parametric and optimal sizing of a battery/hydrogen storage of a real hybrid microgrid supplying a residential load: Towards island operation

Cited by 41 publications

References 58 publications

Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller

Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller

Harnessing Solar Power: A Review of Photovoltaic Innovations, Solar Thermal Systems, and the Dawn of Energy Storage Solutions

Quantum-inspired deep reinforcement learning for adaptive frequency control of low carbon park island microgrid considering renewable energy sources

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