UC Berkeley

To decarbonize the electric power grid, there have been increased efforts to utilize clean renewable energy sources, as well as demand-side resources such as electric loads. This utilization is challenging because of uncertain renewable generation and inelastic demand. Furthermore, the interdependencies between system states of power networks or interconnected loads complicate several decision-making problems. Growing interactions between power and energy systems and human agents with advances in sensing, computing and communication technologies also increase the need for personalized operations.

In this dissertation, we present three control and optimization tools to help to overcome these challenges and improve the sustainability of electric power systems. The first tool is a new dynamic contract approach for direct load control that can manage the financial risks of utilities and customers, where the risks are generated by uncertain renewable generation. The key feature of the proposed contract method is its risk-limiting capability, which is achieved by formulating the contract design problem as mean-variance constrained risk-sensitive control. To design a globally optimal contract, we develop a dynamic programming solution method based on a novel dynamical system approach to track and limit risks. The performance of the proposed contract framework is demonstrated using data from the Electricity Reliability Council of Texas. The second tool is developed for combinatorial decision-making under system interdependencies, which are inherent in interconnected loads and power networks. For such decision-making problems, which can be formulated as optimization of combinatorial dynamical systems, we develop a linear approximation method that is scalable and has a provable suboptimality bound. The performance of the approximation algorithm is illustrated in ON/OFF control of interconnected supermarket refrigeration systems. The last tool seeks to provide a personalized control mechanism for electric loads, which can play an important role in demand-side management. We integrate Gaussian progress regression into a model predictive control framework to learn the customer's preference online and automatically customize the controller of electric loads that directly affect the customer's comfort. Finally, we discuss several future research directions in the operation of sustainable cyber-physical systems, including a unified risk management framework for electricity markets, a selective optimal control mechanism for resilient power grids, and contract-based modular management of cyber-physical infrastructure networks.