Data-driven Solutions for Power Systems During Energy Transition

With the accelerating global energy transition, more and more emerging motivations appear in power systems with complex operational environments and unprecedented uncertainties. To accommodate these evolving demands, novel operational approaches are required to address the limitations inherent in both conventional paradigms and newly arising challenges. This dissertation involves four representative topics in power systems, i.e., artificial intelligence (AI) online application, energy sustainability enhancement, energy affordability promotion, and heat pump integrated with thermal energy storage (ASHP-TES) deployment promotion. For each topic, a data-driven solution is proposed to more effectively achieve the intended objectives. In the first topic, three practical data-driven correction methods are proposed for the defective online-measured input data to protect smooth AI online applications in power systems. A deep neural network (DNN)-based power system stability enhancement case on a reduced 179-bus model is then used to demonstrate the effective correction performance of the proposed methods. In the second topic, an incentive-based demand response (I-DR) capacity assessment tool is developed to effectively evaluate the potential of residential energy within a given service territory to respond to load reduction or carbon reduction goals to enhance energy flexibility and sustainability in power systems. In the third topic, an innovative energy trading fusion market is proposed with the fully decentralized P2P-based energy trading process and energy-affordability-based multistage dispatch structure to promote affordable and autonomous energy trading market in power systems. Both the IEEE 33-bus and 123-bus distribution systems validate the feasibility of the proposed energy trading fusion market. Finally, a grid-level benefit assessment tool is developed to effectively evaluate the potential grid-level benefits of ASHP-TES deployment based on the Texas 2k-bus synthetic system. All the proposed data-driven solutions are validated and demonstrate with promising performance. Although numerous emerging topics lie beyond the scope of this dissertation, this dissertation offers valuable contributions through the proposed effective data-driven approaches across the four key topics—particularly relevant in the context of the ongoing energy transition in power systems.