Exploring Simulation-Based Decision Making Frameworks For Energy Management In Electrical Networks Of The Future
As the single largest source of greenhouse gas emissions to the atmosphere, electricity generation is at the front and center of debates on climate change. Efforts to address these impacts have resulted in a critical need for cleaner energy technologies. To this end, there has been a significant shift towards the development and integration of renewable resources; in the United States, 17\% of total electricity generation in 2019 was from solar and wind, which is only going to increase with improving technologies. However, significant integration of these resources in the electrical grid is no trivial task due as they are intermittent, and difficult to forecast, making them effectively non-dispatchable. As a result, renewable resources add significant variability to the supply side of power grid operations, which has to be managed with reserves from traditional, fossil-based, generating units. The need to enhance environmental protection and sustainable development are also leading to an evolution on the demand side with increasing presence of new technologies and small scale distributed energy resources near load centers. These technologies, combined with responsive demand, is leading towards a transformation of the distribution systems to include more community-owned independent networks or microgrids. These changes in supply and demand-side technologies is adding a significant amount of stress to the daily operation of the system. The real-time uncertainty induced by intermittent resources such as wind or price-responsive demand has created new and complex challenges for the operational security of power systems. As optimization and simulation approaches are a key part of the operation and control, the evolution of existing frameworks is essential for survival in the advent of smartening power systems. These frameworks need to provide robust operational decisions while being adaptable to the diverse expectations of both operators and stakeholders. Motivated by this need, this dissertation seeks to explore different frameworks that can efficiently support decision making under uncertainty, which is practical under the changing dynamics of the power system. This question is explored in three contexts: 1) Robust decision making for energy management in large scale networks with high renewable penetration while providing much-needed adaptability to the system operator, 2) Exploring the value of daily energy management in a microgrid with a multiobjective perspective, and 3) Implementing a decision analysis tool for microgrid energy management to enhance stakeholder participation in grid operation and planning. To this end, this body of work explores hybrid optimization frameworks that prioritize adaptability, scalability, and robust decision making, while staying true to the physics of power system networks. The overarching conclusion of this work points to using hybrid frameworks for a more holistic approach to analyze system operation, which can then be followed by eliciting stakeholder preferences before selecting management actions. The different frameworks explored in this work introduce novel and interesting lines of questioning in power system literature. They add a dimension of explainability to optimization framework that can promote stakeholder participation in grid operation and planning in a meaningful manner. The various case studies demonstrate these frameworks' ability to critically analyze system performance under unplanned conditions as well as deconstruct the decision-making process which can help long term system planning and operation. The work also opens interesting possibilities for future research in grid operation under extreme operating scenarios and exploring the potential value of long term strategic planning in place of day-to-day approach. These contributions are critical in providing enhanced support in ever-evolving power systems.
Topaloglu, Huseyin; Mount, Timothy
Biological and Environmental Engineering
Ph. D., Biological and Environmental Engineering
Doctor of Philosophy
dissertation or thesis