IEEE Metropolitan Los Angeles Section Power & Energy Society Chapter

Blockchain technology is gaining momentum in revolutionizing power systems amidst the global transition towards renewable energy sources and climate change mitigation. As power systems evolve from large-scale, centralized systems to networks of small-sized, distributed electricity systems, blockchain’s decentralized ledger capabilities offer efficient transaction management for small-scale power systems. With features like tamper-resistant data, privacy protection, smart contracts, and real-time settlement, blockchain facilitates secure and transparent energy transactions. Integrated with advancements in smart grids and microgrids, blockchain enables peer-to-peer energy transactions, better grid management, and real-time payments. Hence, the application of BCT in energy industry is being investigated rigorously. This tutorial offers a comprehensive introduction to blockchain technology, covering its fundamental principles, components, operational mechanisms, and potential applications within power systems. Additionally, the tutorial will include a live demonstration on how to use blockchain platforms such as Ethereum and Cosmos. Presenter(s): Disha L Dinesha, PhD Research Scholar, Indian Institute of Science

This tutorial covers the principles of load forecasting for distribution planning with a focus on the change in growth and characteristics of load introduced by electrification. The course begins with the planning process and load forecast as its first building block. It will cover the key requirements of a robust and defendable forecast via bottom-up and top-down approaches, will review several proven methods, and will discuss modern techniques for end-use growth to forecast electrification trends. Instructors will discuss the concepts related to magnitude, temporal (e.g., 8,760-hr forecasts), spatial, and weather normalization aspects of forecast. The course will study legacy methods the industry used for forecasting in past when it faced major load. The course drills down into different components of transportation and stationary electrification forecast including light, medium and heavy vehicles, transit, charging networks, and building electrification. Finally, approaches to address uncertainty in forecast and its implications will be discussed. Presenter(s): • Lee Willis (Author of Spatial Electric Load Forecasting, and Power Distribution Planning Reference Books (CRC)). Quanta Technology • Farnaz Farzan, Quanta Technology • Gerardo Sanchez: Quanta Technology

Grid-forming inverters and microgrids are helping to transform the electrical grid by enabling renewable energy integration and improving grid stability. Digital twins, or virtual models of these systems’ electrical and software components, are the key to optimize performance, monitor operations, and mitigate anomalies. Presenter(s): Jose Montoya Bedoya, Jose Montoya Bedoya

With the increasing adoption of renewables, decentralization of energy infrastructure, and the electrification of transportation, new challenges arise, including the need for enhanced reliability and efficiency. Presenter(s): Dr. Mohsen Aleenejad, MathWorks, [email protected]

The DER functionalities specified in some grid codes (e.g. IEEE 1547 & Rule 21), and the associated communication interfaces are not suitable for direct integration with monitoring and control systems (e.g., DERMS). These functionalities were designed only to expose the raw, inherent capabilities of the DER, but (intentionally) omit additional logic, management features, and security requirements because these were believed to vary by utility and region. Added business logic, centralized management, and cybersecurity capabilities are needed by utilities, and DER network gateways can provide these capabilities. Presenter(s): • Ben Ealey, EPRI • Ajit Renjit, EPRI

The increasing demand for advanced grid support functionality from a large number of DERs has sparked significant interest in that focuses on optimization methods for large-scale unbalanced power distribution systems, aimed at enhancing operational efficiency and resilience. Both the traditional mathematical optimization methods and machine-learning (ML) -based approaches are gaining traction to attain optimal solutions for the scaled power distribution systems. However, when developing any optimal power flow (OPF) algorithm for distribution system, it is crucial to integrate unbalanced distribution power flow models as constraints. This poses the most challenging aspects of formulating any OPF problem. Besides, for ML-based approaches, creating extensive training data is time-consuming task that significantly delays the process. This tutorial aims at presenting a python-based distribution OPF tool (DistOPF) that develops power flow models as a constraint from the standard network inputs, allowing users to focus on other aspects. Specifically, this python-based package can quickly create the underlying mathematical equations of the power flow model for any distribution system and allows researchers to avoid the need to repeatedly model power flow models and instead leverage existing models that can be readily selected. It also formulates standard OPF related constraints. Thus, this package alleviates challenges to formulate OPF problems to develop and provide grid-support functionality and enhance grid resilience for the power distribution system. In short, the DistOPF package/tool will provide researchers/users a unique platform (i) to create new OPF algorithms by leveraging formulated unbalanced power flow models as the optimization constraints, (ii) to benchmark developed OPF algorithms (for both planning stage solutions and operation stage controls) for the distribution networks using provided models/solutions, and (iii) to create extensive training data to train the advanced machine learning based OPF algorithms.” Presenter(s) 1. Dr. Rabayet Sadnan, Research Scientist, Pacific Northwest National Laboratory (PNNL) 2. Dr. Anamika Dubey Associated Professor, School of Electrical Engineering and Computer Science, Washington State University 3. Nathan Gray Research Assistant, School of Electrical Engineering and Computer Science, Washington State University

The Automated Distributed Energy Resource Management System (DERMS) represents a groundbreaking advancement in grid management. By integrating demand response (DR) optimization into the DERMS engine’s objective function, the Automated DERMS enables efficient management of registered behind-the-meter (BTM) assets. These assets are pivotal in enhancing grid reliability, resiliency, and affordability. This tutorial will explore its integration with the CalFUSE pilot project, focusing on managing registered behind-the-meter (BTM) assets for optimal DR programs. This tutorial aims to equip participants with the knowledge and practical skills to leverage Automated DERMS effectively. Presenter(s): • Dr. Ashkan R. Kian (Quanta Technology) • Mark Martinez (SCE)