AI-Enabled Control for Distribution Systems with High Penetration of Renewable Energy and Electric Vehicles

This proposed project is designed to develop an IoT-based integrated framework for control of smart grids encountering high penetration levels of renewable energy and plug-in electric vehicles (PEV). For the KSA to meet its 2030 renewable energy goal, a more substantial fraction of the total energy production will be contributed from renewable resources. Moreover, with the rapidly evolving electric vehicle (EV) technologies and dropping battery cost, more EVs are expected on the KSAs roads within the coming few years. Both renewable energy and electric vehicles impose a set of looming challenges to power grid operators. Renewable energy sources are intermittent by nature which is adversely affecting the power grid, unless they are augmented with energy storage. EVs can collectively represent a significant demand increase when charging, ultimately mandating wide-scale infrastructure upgrade. Infrastructure upgrade can be deferred and the impact of EVs can be mitigated if they are intelligently managed and their charging/discharging process is coordinated and optimally scheduled. The solution to these challenges, in our view, requires the development of a set of optimal control strategies and energy management algorithms to reliably operate the power grid taking into account renewable energy sources, moving as well as stationary electric vehicles and controllable loads. Emerging Internet of Things (IoT) applications including e-healthcare, intelligent transportation systems, smart grid, and smart homes to smart cities, are poised to become part of every aspect of our daily lives. There is an emerging consensus that Fifth-Generation (5G) cellular technologies will enable and support these applications, as it will provide the global mobile connectivity to billions of things/devices attached to the IoT. We propose to use recent advances in IoT technologies to enable seamless communication among stationary renewable energy resources, microgrids, and movable EVs. Therefore, we will provide a roadmap for the creation of AI-Enabled Control for Distribution Systems with High Penetration of Renewable Energy and Electric Vehicles, a multidisciplinary research-education framework whose goals are: 1) To develop novel artificial intelligence-based control algorithms for renewable energy sources and microgrids; 2) To develop an integrated framework for operation of smart PEV charging parks as DC microgrids; 3) To investigate and design an Information and Communication Technology (ICT) system that's required to support the proposed PEVs-integrated DC microgrids concept, and 4) To develop a state-of-the-art hardware/software microgrid facility at King Fahd University of Petroleum & Minerals (KFUPM). This research will significantly contribute to the KSA's 2030 Vision, specifically the energy and pollution reduction goals. Also, it will set the stage for small/medium enterprises developing microgrids/PEV controllers and will require the education, training, and professional engagement of a new generation of domestic science, technology, engineering, and mathematics (STEM) workers.