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

由于人们对环境问题的认识日益提高，燃料成本的增加和电力需求的增加，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能资源，这应该使其成为利用这些可再生能源发电的国家中的佼佼者。在该提案中，将开发电力管理和控制方案，以运行基于可再生能源（风能和太阳能）发电机和混合储能系统（电池、超级电容器和燃料电池）的微电网，这些系统能够在并网和离网模式下运行。将研究用于集成可再生能源发电机的多电平变流器配置，以增加这些能源在发电系统中的渗透，并在故障情况下支持电网。能源储存系统将提高从风能和太阳能中提取最大电力的能力，并通过储存多余的电力并在低风速和太阳辐照度下使用来提高其效率，这将增加可再生能源在电力生产中的贡献，并减少偏远社区的燃料消耗。增加可再生风能和太阳能储能部署的好处包括在电网范围内节约能源，减少温室气体排放和空气污染物。这种系统将改善偏远社区和因电网故障而需要停电的地区的生活质量。高效运行的电力管理和控制系统将包括天气预报、电力预测和负荷概况，以有效运行可再生微电网。人工智能将用于开发智能电源管理和控制系统，这将有助于为下一代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寻找创新的、具有成本效益的解决方案。微电网和并网可再生能源系统的控制设计和实施将通过实时仿真和原型研究。建模和控制将被研究，以了解所有组件的行为和匹配系统与可靠的控制策略。将使用圣玛丽大学现有的研究设备进行模拟和实验，以验证所提出的控制和电源管理策略的性能。然后，将与工业伙伴合作，在基于微电网的实际混合可再生能源发电机上对研究成果进行工业规模的实验验证。拟议的研究将为可再生能源领域的HQP培训带来科学知识、工业解决方案和成功。

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.