Homer-Based Multi-Scenario Collaborative Planning for Grid-Connected Pv-Storage Microgrids With Electric Vehicles

Zhang, Yifan; Yan, Shiye; Yin, Wang; Wu, Chao; Ye, Jilei; Wu, Yuping; LIU, Lili

doi:10.20944/preprints202306.2181.v1

2023

DOI: 10.20944/preprints202306.2181.v1

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Homer-Based Multi-Scenario Collaborative Planning for Grid-Connected Pv-Storage Microgrids With Electric Vehicles

Yifan Zhang¹,

Shiye Yan²,

Wang Yin³

et al.

Abstract: Microgrid is one of the important ways to accommodate distributed PV generation. However, the optimal planning scheme of microgrids is closely related to solar resources, load characteristics, and the key components of microgrid systems. This paper uses HOMER software to conduct collaborative planning for grid-connected PV-storage microgrids with electric vehicles in multiple scenarios. Specifically, we construct a multi-scenario capacity optimization model for PV generation, energy storage, and converters, co… Show more

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Cited by 4 publications

References 34 publications

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Optimizing Microgrid Planning for Renewable Integration in Power Systems: A Comprehensive Review

Quizhpe,

Arévalo,

Ochoa-Correa

et al. 2024

Electronics

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The increasing demand for reliable and sustainable electricity has driven the development of microgrids (MGs) as a solution for decentralized energy distribution. This study reviews advancements in MG planning and optimization for renewable energy integration, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to analyze peer-reviewed articles from 2013 to 2024. The key findings highlight the integration of emerging technologies, like artificial intelligence, the Internet of Things, and advanced energy storage systems, which enhance MG efficiency, reliability, and resilience. Advanced modeling and simulation techniques, such as stochastic optimization and genetic algorithms, are crucial for managing renewable energy variability. Lithium-ion and redox flow battery innovations improve energy density, safety, and recyclability. Real-time simulations, hardware-in-the-loop testing, and dynamic power electronic converters boost operational efficiency and stability. AI and machine learning optimize real-time MG operations, enhancing predictive analysis and fault tolerance. Despite these advancements, challenges remain, including integrating new technologies, improving simulation accuracy, enhancing energy storage sustainability, ensuring system resilience, and conducting comprehensive economic assessments. Further research and innovation are needed to realize MGs’ potential in global energy sustainability fully.

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Optimizing Microgrid Planning for Renewable Integration in Power Systems: A Comprehensive Review

Quizhpe,

Arévalo,

Ochoa-Correa

et al. 2024

Electronics

View full text Add to dashboard Cite

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Driving Urban Energy Sustainability: A Techno-Economic Perspective on Nanogrid Solutions

Turan,

Durusu,

Yumurtaci

2023

Energies

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In response to technological advances, environmental concerns, and the depletion of conventional energy sources, the world is increasingly focusing on renewable energy sources (RES) as a means of generating electricity in a more sustainable and environmentally friendly manner. Türkiye, with its advantageous geographical location, long hours of sunshine, and favourable climatic conditions, has a high potential for the use of solar energy. The objective of this study was to identify an energy system that minimizes investment costs while optimizing the levelized cost of energy (LCOE) and minimizing greenhouse-gas (GHG) and carbon dioxide emissions. To achieve this, the study used the concept of nanogrids (NGs) and carried out different evaluations for electric vehicle charging stations (EVCS) at different energy levels connected to the grid. The research focused on classic apartment buildings and multistory condominium-style buildings in Istanbul, Türkiye. Using HOMER Grid 1.11.1 version software, the study identified two optimal configurations: a PV–GRID system with 7 kW photovoltaic capacity and a PV–WT–GRID system with 90 kW PV capacity and 6 kW wind-turbine capacity. These configurations had a significantly lower LCOE compared to the cost of electricity from the conventional grid. When examining the sensitivity to economic factors, it was observed that the net present cost (NPC) and LCOE values fluctuated with electricity prices, inflation rates, and equipment costs. In particular, the two optimal configurations did not include a battery energy-storage system (BESS) due to the low energy demand in the PV–GRID system and the efficiency of the wind turbines in the PV–WT–GRID system. This highlights the need to tailor energy solutions to specific consumption patterns and resource types. In conclusion, the adoption of PV–GRID and PV–WT–GRID systems in Istanbul’s urban buildings demonstrates economic viability and environmental benefits, highlighting the importance of renewable energy sources, particularly solar PV, in mitigating energy-related environmental challenges, such as reducing CO2 emissions and reducing dependence on conventional grid electricity.

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Applying a Novel Image Recognition Curve-Fitting Control Strategy Combined with a Cloud Monitoring Technique into an Electric Self-Driving Vehicle (ESDV) to Improve Its Operation Efficiency

Liu,

Lin,

Lai

et al. 2023

Processes

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This study aims to develop an image recognition curve-fitting (IRCF) control strategy integrated with a cloud monitoring technique for application in electric self-driving vehicles (ESDVs) to improve their operation efficiency. The study focuses on an electric vehicle designed to reduce the carbon emissions and promote sustainability. The main camera, combined with the IRCF control strategy, was used to control the ESDV to enhance its operational efficiency. The proposed ESDV employs a pair of cameras to capture images and transmit them to the cloud-based web monitoring platform in real time. This allows the researchers to adjust the control parameters and promptly remove the road obstacles. The ESDV is equipped with a horn, two ultrasonic sensors, and an LED display, which can instantly detect the obstacles ahead of and behind the vehicle. When there are obstacles on the road, the vehicle will automatically stop, and the LED display will provide a visual representation of the obstacles, accompanied by the sounding of the horn as a warning signal. Meanwhile, the secondary camera detects the signal mark and feeds it back to the LED display, thereby informing passengers and other road users about the prevailing driving conditions. The proposed IRCF control strategy was compared with the traditional Hough line detection method on a 110 m ring road. The results revealed that the proposed control strategy outperformed the traditional Hough line detection method in terms of speed, efficiency, and running dexterity. Therefore, integrating the proposed control strategy into the automatic assistance driving system can improve the ESDV’s operation efficiency. Furthermore, the combination of the obstacle detection and signal sign detection functions for the ESDV used in this study can better fulfill the actual ESDV operation requirements on the road.

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Homer-Based Multi-Scenario Collaborative Planning for Grid-Connected Pv-Storage Microgrids With Electric Vehicles

Cited by 4 publications

References 34 publications

Optimizing Microgrid Planning for Renewable Integration in Power Systems: A Comprehensive Review

Optimizing Microgrid Planning for Renewable Integration in Power Systems: A Comprehensive Review

Driving Urban Energy Sustainability: A Techno-Economic Perspective on Nanogrid Solutions

Applying a Novel Image Recognition Curve-Fitting Control Strategy Combined with a Cloud Monitoring Technique into an Electric Self-Driving Vehicle (ESDV) to Improve Its Operation Efficiency

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