On-grid/off-grid DC microgrid optimization and demand response management

Bai, Wenshuai; Sechilariu, Manuela; Locment, Fabrice

doi:10.23919/epe20ecceeurope43536.2020.9215679

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…The PV day-ahead predicted data are hourly forecast data obtained from a day-ahead prediction model that comprises a simplified PV maximum power point tracking (MPPT) estimation [27] and metadata. The metadata referring to the accumulated ground solar radiation from Météo France, is freely accessible for day-ahead predicted data and is applied in day-ahead optimization [28]. However, data of poor accuracy are sometimes missing or cannot be updated in time.…”

Section: A Data Descriptionmentioning

confidence: 99%

An Online Multi-Level Energy Management System for Commercial Building Microgrids With Multiple Generation and Storage Systems

Bai

Wang

Sun

et al. 2023

IEEE Open J. Power Energy

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This paper presents an online multi-level energy management system for local microgrids of commercial buildings that integrate roof-top photovoltaic sources, battery storage systems, utility grids, diesel generators, supercapacitors, and commercial buildings consisting of active orchestrated loads, to solve the uncertainty problem of sources and loads, while also optimizing the local microgrid operation cost of commercial buildings and the utilization rate of local renewable energy. The energy management system includes a long-term rolling optimization level, rule-based optimization level, and load demand optimization level. At the long-term rolling optimization level, an online rolling method of data restructuring is proposed, where measurement data, short-term prediction data, and day-ahead prediction data are reconstructed to reduce the uncertainty in photovoltaic source prediction and load demand prediction. Four methods are proposed for the energy management system and simulated in MATLAB/Simulink under three typical weather conditions, cloudy, sunny, and rainy. Simulation results show that the performance of Method 3 is closest to that of Method 4, whose data conditions are ideal; Method 3 reduces the operational cost of the commercial building microgrid and improves the utilization rate of photovoltaic sources, at the slight cost of non-critical load shedding.

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Section: A Data Descriptionmentioning

confidence: 99%

An Online Multi-Level Energy Management System for Commercial Building Microgrids With Multiple Generation and Storage Systems

Bai

Wang

Sun

et al. 2023

IEEE Open J. Power Energy

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show abstract

“…A microgrid is a small power generation and distribution system that comprises distributed power sources, energy storage systems, energy conversion devices, loads, supervisory systems, protection devices, and other components that can promote integration and consumption of local renewable energy, ensure the reliability of the power supply, and optimize grid operation [10][11][12][13]. Microgrids can be viewed from a triple perspective in terms of energy, economy, and society.…”

Section: Introduction 1contextmentioning

confidence: 99%

The Design and Application of Microgrid Supervisory System for Commercial Buildings Considering Dynamic Converter Efficiency

et al. 2023

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This paper presents a supervisory system that considers converter efficiency for local microgrids of commercial buildings to solve the uncertainty problem of the sources and loads while also optimizing local microgrid operating costs and maintaining power supply quality for commercial buildings. The supervisory system includes an energy management layer and a power management layer. In the energy management layer, a long-term optimization approach is used to reduce the operating costs by considering the dynamic converter efficiency. In the power management layer, a real-time power optimization method is structured to deal with the uncertainty problem of the sources and loads, and to ensure that the direct current bus power is balanced while also guaranteeing the power quality by considering the dynamic converter efficiency. Four cases are proposed for the supervisory system, and these cases are simulated in MATLAB/Simulink under three typical weather conditions: cloud, sunshine, and rain. The comparison of simulation results for cases 1 and 2 illustrates the impact of converter efficiency on energy coordination in microgrids. The simulation results of cases 3 and 4 verify that the performance—in terms of the power supply quality and the operating costs—of the proposed microgrid supervisory system considering dynamic converter efficiency outperforms that of the microgrid supervisory system considering fixed converter efficiency.

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On-grid/off-grid DC microgrid optimization and demand response management

Cited by 2 publications

References 13 publications

An Online Multi-Level Energy Management System for Commercial Building Microgrids With Multiple Generation and Storage Systems

An Online Multi-Level Energy Management System for Commercial Building Microgrids With Multiple Generation and Storage Systems

The Design and Application of Microgrid Supervisory System for Commercial Buildings Considering Dynamic Converter Efficiency

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