Control Systems for Renewable Wind and Solar Photovoltaic Energy Integration and Smart Microgrid

可再生风能、太阳能光伏能源并网及智能微电网控制系统

• 批准号: RGPIN-2018-05381
• 负责人: Merabet, Adel
• 金额: $ 2.04万
• 依托单位: Saint Mary's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753010
• 关键词: Control; Systems; Renewable; Wind; Solar

Renewable energy is a growing source of electricity in the world due to the increasing awareness of the environmental problems, the increase in the fuel cost and the high power demand. Canada is a large country and has various areas rich in wind and solar resources, which should make it to the top of countries capitalizing on these renewable sources for electricity production.In this proposal, power management and control schemes will be developed to operate microgrids based on renewable (wind and solar) generators and hybrid storage energy systems (battery, supercapacitor and fuel cell) with capability to operate in grid-tied and off-grid modes. Multilevel converter configurations for integrating renewable generators will be investigated to increase the penetration of these sources in the power generation systems and support the grid in case of failure. Energy storage systems will improve the capacity to extract maximum power from wind and solar sources and improve their efficiency by storing the excess power and using it at low wind speed and sun irradiance, which will increase the contribution of renewable energy in electricity production and reduce the fuel consumption in remote communities.The benefits of increased deployment of renewable wind-solar power with storage include grid wide energy savings and reductions in greenhouse gas emissions and air contaminants. Such systems will improve the quality of life in remote communities and locations required to operate under power outage due to grid failure.Power management and control systems for efficient operation will include weather forecasting, power prediction and load profile to efficiently operate the renewable microgrid. Artificial intelligence will be used to develop smart power management and control systems, which will contribute to providing leading edge technology for the future generation of hybrid renewable energy systems. This research will be committed to finding innovative, cost effective solutions in renewable energy and storage systems.Control design and implementation for microgrids and grid-tied renewable power systems will be investigated through real time simulation and prototyping. Modeling and control will be investigated in order to understand the behavior of all components and match the system with reliable control strategies. Simulation and experimentation will be conducted using the research equipment available at Saint Mary's University to verify the performance of the proposed control and power management strategies. Then, industrial scale experimental validation of the research findings will be carried out in collaboration with industrial partners on real hybrid renewable generators based microgrids.The proposed research will bring contributions to scientific knowledge, solutions for industry and success for HQP training in the field renewable energy.

由于对环境问题的认识，燃料成本的增加和高电力需求的意识越来越多，可再生能源是世界上日益增长的电力来源。加拿大是一个大国，拥有丰富的风能和太阳资源的各个领域，这应该使其成为利用这些可再生能源来利用这些用于电力生产的国家。在此建议中，将开发出该建议，电力管理和控制方案，以基于可再生（风能和太阳能）的生成器和混合储能系统（电池，超级校准和燃料）的能力，以可再生（风能和太阳能）的可再生能源（风能和太阳能）来操作，以磨损能力。将研究用于集成可再生发电机的多级转换器配置，以增加这些源在发电系统中的渗透，并在失败时支持网格。储能系统将提高从风能和太阳能源中提取最大动力的能力，并通过存储过量的功率并在低风速和太阳辐照度下使用它来提高其效率，这将增加可再生能源在电力生产中的贡献，并降低偏远社区的燃油消耗。在储存中增加了可再生能源的储存量，包括储存量的储存量增加了越来越多的储存量，包括储存量和加油量。此类系统将改善由于电网故障而在停电下运行所需的偏远社区的生活质量。有效操作的功率管理和控制系统将包括天气预报，电力预测和负载概况，以有效地操作可再生的微电网。人工智能将用于开发智能电源管理和控制系统，这将有助于为未来的混合可再生能源系统提供前沿技术。 这项研究将致力于在可再生能源和存储系统中找到创新的，具有成本效益的解决方案。将通过实时仿真和原型设计来研究微电网的控制和实施微电网和网格可再生能源系统。将研究建模和控制，以了解所有组件的行为，并与可靠的控制策略相匹配。模拟和实验将使用圣玛丽大学可用的研究设备进行，以验证拟议的控制和电源管理策略的性能。然后，研究结果的工业规模实验验证将与基于实际混合可再生生成器的工业伙伴合作进行基于实际混合可再生生成器的微电网。拟议的研究将为科学知识，工业解决方案和成功进行HQP培训提供贡献。