Optimization Tools for Constrained Energy Markets

Author(s): Munsing, Eric | Advisor(s): Moura, Scott | Abstract: This dissertation develops an interdisciplinary approach to integrating renewable energy resources into energy markets, using tools from optimization theory, power systems, and economics. It advances prior work with the development of a set of tools for securing distributed and fully-decentralized optimization problems, including both algorithmic guards against attacks by malicious nodes, and system architectures which can enable decentralized security checks. Leveraging the emerging technologies of blockchains and smart contracts, it develops a new paradigm of blockchain-secured distributed optimization, demonstrated with simulation of a microgrid which is able to securely operate without oversight from a utility or central operator.As the goal of interdisciplinary research is to show mastery of each field and extend current knowledge by exploring their intersection, the chapters of this dissertation are designed to support that goal:• Chapter 1 provides background on the technical challenges of integrating high amounts of renewable energy into the electricity system, and discusses technologies and policies which can address those challenges. • Chapter 2 introduces the idea of applying optimization tools to energy systems by studying a small-scale energy harvesting system in which batteries and capacitors are used to meet a defined load, and constraints force the use of nonlinear optimization techniques. • Chapter 3 explores how large-scale energy storage systems can be designed and sited to maximize profits from participating in wholesale energy markets, using a linear program which demonstrates how convex optimization tools can be united with energy market data to create scalable tools for modeling large networks. • Chapter 4 expands the study of market-based models by examining the strategic operation of generation resources on a congested network. We use game theory to model participants’ behavior, power flow models to reflect the underlying constraints of the physical network, and robust convex optimization to explore how uncertainty is integrated into decision-making. • Chapter 5 discusses how decentralized optimization models can facilitate scalable optimization tools, and explores the security risks which these optimization models introduce. Tools for detection and mitigation of attacks are explored and tested on a simple problem. Potential architectures for securing decentralized optimization are explored, including blockchains and smart contracts. • Chapter 6 extends this approach by developing a blockchain-secured fully-decentralized optimization for a microgrid dispatch problem, coordinating distributed energy resources. By demonstrating the usefulness of this blockchain-secured optimization model, this culminating chapter shows how difficult optimization models can be scalably solved in a manner which respects privacy while guaranteeing security. • The Appendices present tutorial material on the tools used throughout the paper, and are supplemented by the code in the author’s github repository: https://github.com/emunsing/tutorials