UC Berkeley

Customers are adopting new technologies, fundamentally changing their relationship with the electrical grid. These technologies, known as distributed energy resources (DERs), allow customers to control their consumption. They give customers the flexibility to purchase energy when prices are lower and sometimes sell electricity back to the grid when prices are high. While DERs can benefit the individual customer and potentially contribute to decarbonization, they also require electrical utility companies to change how they operate the grid, especially in the distribution system. This dissertation examines the relationship between DER operations and the electrical distribution system. We first explore how “business as usual” approaches to DERs could negatively impact the grid. Specifically, how current electricity tariffs provide an incentive structure for customers to optimize their DERs and how their optimized consumption could lead to congestion issues on the distribution system. Then, we explore data-driven tools for addressing this congestion. We collect data from simulated smart meters and develop a voltage estimator to manage congestion through a centralized optimized DER dispatch approach. Finally, we propose an electricity tariff design that provides the benefits of centralized congestion management, but instead through price signals sent to customers.

In Chapter 1, we explore the relationship between current electricity tariffs and future DER operations. We model commercial customers optimizing their electric vehicle (EV) charging under different Pacific Gas and Electric (PG&E) tariffs, including a new tariff (BEV tariff) with a novel subscription-based power charge. We model customers optimizing their load across different seasons to capture the prices in each tariff and compare the total annual costs. Once we calculate the optimal charging strategy for each customer, we model them in an open-source grid simulation software using a feeder model based on a PG

amp;E distribution feeder, measuring voltage and wholesale energy costs. We find that undervoltage would occur in our distribution feeder under all four tariffs we examine. This result indicates that current tariff designs are insufficient for congestion management, and additional measures are required. We also find that customers could see nearly a 15% reduction in costs by switching from one tariff to another. This cost savings for the customer is also reflected in a potentially problematic revenue gap for the utility when customers change tariffs. Finally, we show that subscription-based tariffs are less efficient than traditional demand charge-based tariffs, but the lower prices in the BEV tariff mask its inefficiency.

In Chapter 2, we explore ways to add a voltage constraint to the same EV charging coordination problem as Chapter 1 for distribution system operators (DSOs) to perform direct, centralized control of DERs. This chapter presents a novel three-phase data-driven linear voltage magnitude estimator based on past smart meter and substation data. This estimator is trained offline solely on data readily available for the distribution system operator and reduces the size of the voltage constraint by only estimating voltage at customer service connections. We show that our voltage estimator can prevent undervoltage in the EV charging coordination problem and is faster and more accurate than three-phase linearized voltage approximations, requiring less computational memory and no knowledge of the distribution system network connectivity.

In Chapter 3, we examine price-signal control methods for congestion management of DERs. We describe a new pricing scheme for day-ahead congestion management of DERs, referred to as Load Responsive Prices (LRPs). Using the LRP method, we can determine the optimal load profile of customers from offline analyses and then use day-ahead prices (potentially taken from the wholesale market) to calculate prices that customers can independently optimize for without additional communication from the DSO beyond the prices themselves. We find that customers optimizing using LRPs consume the same way as customers following direct load control methods but without knowing the load levels the system operator requires. We end the chapter with a discussion of various implementation considerations. We discuss ways to reduce customer cost increases, methods for addressing incorrect forecasts, and other important issues for practitioners.

We conclude with a summary of findings, policy recommendations, and future research directions.