Optimization tools for emerging challenges in power systems
Creators: name: Zhang, Nan
name: 0000-0002-4325-0718
Publisher: name: The University of Texas at Austin
Year: 2022
Subjects: Power system
Optimization

description: The expansion and operation of electric power systems have always been viewed as a critical economic and social topic. With the rapid evolution of the electricity industry, a combination of factors, including the increasing renewable penetration, deployment of storage, customer preferences, and environmental regulations, bring challenges to traditional decision-making paradigms in power systems. Therefore, well-designed operations research applications in power system are becoming increasingly important to maintain and improve their performance. This dissertation aims to deploy novel optimization models and methods to address these emerging challenges and support the decision-making in modern power systems. The dissertation starts at the customer side in power system operations. In Chapter 2, we consider the problem of operating a battery storage unit in a home with a rooftop solar photovoltaic (PV) system so as to minimize expected long-run electricity costs under uncertain electricity usage, PV generation, and electricity prices. We implement a data-driven dynamic programming (DDP) algorithm that uses historical data observations to generate empirical conditional distributions and approximate the cost-to-go function, and formulate two robust data-driven dynamic programming (RDDP) algorithms to address the overfitting. Numerical results reveal that DDP and RDDP outperform common existing methods with acceptable computational effort. We also show that implementation of these superior operational algorithms significantly raises the break-even battery cost compared with existing feed-in tariff (FIT) or net energy metering (NEM) program. Following from Chapter 2, we move forward to the system-wide resource planning decision-making framework regarding distributed generation. In Chapter 3, we develop a sequential optimization framework that allows a utility to anticipate distributed energy resource (DER) adoption and choose its own generation, transmission, and distribution investments accordingly. Specifically, we adapt a capacity expansion model to represent both centralized and decentralized decision-making paradigms between the utility and customers under various electricity rate structures. By constructing a real-world case study of a utility in the Western U.S., we show that a centralized planning approach could save 7% to 37% of total system costs over a 15-year time horizon. We demonstrate that centralized decision-making deploys substantially more utility-scale solar and distributed storage compared to a decentralized decision-making paradigm. We also analyze the technological difference between the two paradigms and demonstrate how a utility could largely overcome the complications of decentralized distributed resource decision-making by incentivizing regulators to develop electricity rates that more closely reflect time- and location-specific, long-run marginal costs. Motivated from Chapter 3, we continue developing another complex decision-making framework in power system planning that includes more decision-makers on demand-side resources. In Chapter 4, we construct a novel bilevel programming model that co-optimizes a utility's capacity expansion and rate design decisions in the presence of multiple prosumers, including load-shifting customers, distributed PV adopters, and electric vehicle (EV) owners. We further develop a case study on a test power system and present several key findings. We show that co-optimized rate designs can more successfully incentivize behind-the-meter decisions and lead to substantial cost reduction in the whole power system compared to traditional, uncoordinated capacity planning. We also show that time-varying rates determined by the net load profile can both increase the utility's profit and decrease the total cost, and they enable the utility to design more effective locationally differentiated feed-in tariff policies. Finally, we find that providing smart EV charging outlets with their own electricity rates allows the utility to induce beneficial EV load shifting that reduces total system costs. In Chapter 5, we study another critical issue, resource adequacy, which refers to the ability of a power system to reliably satisfy electricity demands using its available resources. Monitoring and maintaining RA is becoming increasingly complex and challenging due to the drastic, ongoing changes in the electricity technologies, policies, and markets. In this chapter, We first diagnosis the with traditional RA assessments. We analyze the current RA assessment practices in integrated resource planning (IRP) by examining 11 plans from load serving entities over U.S., and show that LSEs could improve RA assessments in their IRPs by developing net load forecasts that capture load uncertainty and impacts of demand-side resources, or considering risks in market imports with its effect on reliability performance. In the second part, we develop a technical RA framework that applies a dispatch model to study the performances of RA assessments under different modeling assumptions. By constructing a case study, we distinguish operational details that are critical to include in any accurate RA assessment from details that are computationally burdensome but do not significantly affect the evaluation of RA. We show that the representations of detailed chronological operations of thermal generators and storage units are essential for accurate RA assessments. We also present the need of developing new metrics that beyond traditional expectation-based metrics in identifying the impact of modeling choices on RA assessments. Lastly, we show that non-economic dispatches without cost considerations can lead to fairly accurate RA assessments when coordinated with detailed operational strategies. The four core chapters of this dissertation improve the economic and reliability performance of power systems by leveraging novel optimization tools, and they yield high-level implications that can benefit to electricity decision-makers including regulators, utilities, and end-use customers. While each chapter looks at different components in power systems, they serve the common goal of applying operations research models and methods to address the emerging challenges and support the decision-making in modern power systems. The findings in this dissertation can be valuable in the ways to develop more economic, reliable, and sustainable electric power systems.