A Data-Driven Customer Segmentation Strategy Based on Contribution to System Peak Demand
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Engineering↗
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A Review of Behind-the-Meter Solar Generation Modeling and Forecasting
Solar photovoltaic systems largely integrated within the distribution grid are operated 'behind-the-meter' and power generation cannot be directly monitored by most utilities. The increasing penetration of behind-the-meter solar photovoltaic systems can deter efficient network and market operations due to variability and uncertainty in net load, which is exacerbated by limited visibility and the difficulty in analyzing the hosting capacity. Risk introduced by behind-the-meter solar contributions may hinder reliable and secure grid operations due to biased system monitoring and forecasts. Accurate behind-the-meter estimations, together with capacity and specification forecasts, thus play a key role in balancing supply and demand and this article reviews the pertinent literature, identifying key characteristics and predictive methods for efficient behind-the-meter solar photovoltaic generation. Forecasting is central to methods herein. The fundamental characteristics of behind-the-meter solar forecasting, including which methods are applicable for scenario-driven use cases, are driven by the metrics most useful for system-wide performance evaluation. To this aim, the literature is reviewed with a focus on forecasting applications for aggregate, regional behind-the-meter generation useful to bulk system and utility operations. As distinguished from net load forecasting, subtleties in these coincident tasks are explored before concluding with recommendations for current practice and future implementations.
behind-the-meter↗
Machine Learning Based Network Parameter Estimation Using AMI Data
The expansion of distribution power system and the growing penetration of distributed energy resources present new challenges for situational awareness. Calibrating the extended system model with sensor measurements and maintaining the usability is critical for utilities. This paper presents a distribution network parameter estimation (DNPE) approach using machine learning (ML) and metering data that improve the quality of extended distribution power system modeling. The reliability model can improve the ability of endpoint data to be translated into network-level situational awareness in real time and help distribution system operators (DSOs) solve branch flow and voltage problems. In addition, a data analytic and automate processing scheme is proposed to improve the sensor data quality and prevent misleading information. The effectiveness of the proposed method is verified with actual advanced metering infrastructure (AMI) data on a real utility feeder model, while considering the higher penetration of photovoltaic power generation. The test of DNPE and study results are demonstrated in this paper.
Parameter estimation, machine learning, power dist↗
Online Detection of Inter-Turn Winding Faults in Single-Phase Distribution Transformers Using Smart Meter Data
Turn-to-turn faults between primary windings due to insulation degradation are a major cause of distribution transformer failure, and occur due to high levels of stress such as overloading and overheating. An additional consequence of these faults is an increased voltage on the transformer secondary due to effective change in turns ratio. This paper develops a novel method for early detection of insulation degradation and subsequent inter-turn winding failure by monitoring the transformer secondary voltage. The algorithm is based on a cumulative sum (CUSUM) statistic and compares voltages on neighbouring transformers to flag degrading assets. Results obtained from simulation as well as experimental data show that smart meter measurements can be utilized to achieve very high detection accuracy while keeping costs low. Here, the paper also demonstrates the validity of the algorithm in the presence of measurement noise, residential solar power injection etc.
24 POWER TRANSMISSION AND DISTRIBUTION↗
One Year In: Tracking the Impacts of NEM 3.0 on California’s Residential Solar Market
On December 15, 2022, the California Public Utilities Commission passed an overhaul of the net metering program for the state’s investor-owned utilities. The changes replaced the long-standing net energy metering (NEM) tariffs with a net billing tariff (NBT) structure—colloquially known as “NEM 3.0”—which significantly reduces the compensation for behind-the-meter solar photovoltaic (PV) systems. The NEM tariffs remained open for new interconnection applications until April 15, 2023, but after that date, all new interconnection applications were submitted under NBT. Now, one year later, we have an opportunity to evaluate how the California solar market has evolved under this new compensation regime. As a precursor to its annual Tracking the Sun report, Berkeley Lab has released a short technical brief describing key trends in the California residential solar market since the roll-out of the new NBT structure. The purpose of this analysis is to provide empirical insights into how the market has evolved over the past year, confirming some expectations while also revealing several striking surprises.
14 SOLAR ENERGY↗
OT Operational Anomaly Detection (OAD) T&D + DER
The growth of utility-scale renewable energy resources, distributed energy resources (DER), and transportation electrification has increased uncertainty and cybersecurity risks in power grids. The Purdue Enterprise Reference Architecture model which is widely adopted by the utility industry is now insufficient to protect the power grid against cyber-attacks. There is a need to identify what cybersecurity model is effective on Energy Management System (EMS), Advanced Distribution Management System (ADMS), and DER Management System (DERMS) to address the fundamental cybersecurity challenges in the age of increasing renewable energy and DER share as well as consumer participation in the electric energy industry. The next generation of cybersecurity model for OT network should be able to detect inside attackers, mitigate the cybersecurity risks arising from the new grid participants including DER aggregators, electric vehicle owners, and behind-the-meter consumers outside the utility company, and develop the strategy to trust consumer measurement data. This panel will discuss the challenges and pathways for the development of an ensemble cybersecurity model based on predictive state estimation to detect cybersecurity anomalies in OT network including EMS, ADMS, and DERMS.
cybersecurity↗
A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements
Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops a machine learning based approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurement. The machine learning model is developed by using random forest algorithm. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.
advanced metering infrastructure↗
A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements
Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements: Preprint
Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Accuracy of hourly energy predictions for demand flexibility applications
Decarbonization goals in the United States electricity sector are increasing the levels of renewable energy generation in the electricity supply system, and are driving increased attention to building electrification, which will increase the magnitude and shift the timing of the electricity system peak. These changes are motivating new approaches to coordinate building electricity demand with low-carbon renewable generation, elevating the importance of demand flexibility (DF) in buildings and the need to quantify the temporal impacts of DF. In this paper, we first characterize the hourly predictive accuracy of six commonly used baseline models in an application context of quantifying building-level load shift. Our analysis revealed insights such as hours of the day (afternoons), periods of the week (weekends), and seasons (summer) that were predicted with more accuracy than other time periods. In addition, the analysis showed tendencies toward overprediction or underprediction of load. Secondly, we provide the first published investigation of baseline erosion from repeated dispatch of building load shifting. We observed that as the baseline period is pushed back further from the prediction day, the distribution of errors across baseline model predictions increases, with notable inflection points near the three-week erosion point for two of the three models.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Correcting impedance measurements for background parasitics to characterize circuit components in cryogenic environments
Predictable circuit response is a critical prerequisite for accurate electronic measurements. Here, we describe a powerful yet straightforward experimental method and analysis model that utilizes an affordable LCR meter in conjunction with an in situ parasitic-impedance background-correction procedure to measure the temperature-dependent impedance (magnitude and phase) of up to ten individual passive circuit elements in a single cryostat run. We show how the model unambiguously identified a ∼20× drop in capacitance for 22 μF 5XR multilayer ceramic capacitors cooled from 300 K to 360 mK in an environment with a parasitic capacitance of ∼300 pF. The same experimental procedure, based on a simple two-wire measurement, was also used to measure 10 and 22 pF thin-film capacitors and 100 MΩ thick-film resistors. The results showed that the resistor values increased by up to an order of magnitude when the devices were cooled from 300 K to 360 mK. Most importantly, we showed that the simple data-acquisition method, coupled with our analysis model, enabled the measurement of component parasitics, and effectively extended the accuracy of a commercial LCR meter beyond its manufacturer-guaranteed values for a wide range of measurement frequencies. We also showed that, in the current configuration, our simple approach is limited to a capacitance accuracy of ∼10 pF.
Carpenter, R. J. [Santa Clara Univ., Santa Clara, ↗
Energy Management Information Systems Technical Resources Report
Guide supports federal facility staff in understanding, designing, procuring, and implementing Energy Management Information Systems (EMIS) as a valuable component of their portfolio-level energy and water planning and management strategies. As a broad and rapidly evolving family of tools that monitor, analyze, and control building energy use and system performance, EMIS tools present significant opportunities for federal sector energy savings and improved operational performance. EMIS are at the forefront of transforming energy management best practices by providing building owners and operators with well-organized building performance and energy consumption data, enabling a host of analytic capabilities. These capabilities include portfolio-wide energy benchmarking, data visualization, and key performance indicator tracking; automated fault detection and diagnostics (AFDD); artificial intelligence for predictive analytics and control; automated measurement and verification of energy conservation measures; and supervisory control enabling automated system optimization and demand management.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Developing New Q-meters for NMR Measurements of Polarized Solids
Enhanced polarization of target nuclei can be measured via the complex impedance of a resonant circuit coupled to the target while sweeping the circuit through the nuclear Larmor frequency, observing the absorption or emission of energy by the spins in the material. For decades, solid polarized targets at Jefferson Lab have relied upon continuous-wave Nuclear Magnetic Resonance polarization measurements using Liverpool Q-meter systems. These Liverpool Q-meters are no longer produced, and replacing the aging components is becoming increasingly difficult. The polarized target group at Bochum has produced replacement Q-meters in the Liverpool style using modern components, and we are following their example while researching improvemnts to the technique. We discuss our efforts to produce improved Q-meters for upcoming experiments utilizing solid polarized targets in Jefferson Lab's Hall B.
Maxwell, James↗
Performance of synthetic DAS as a function of array geometry
Distributed Acoustic Sensing (DAS) can record acoustic wavefields at high sampling rates and with dense spatial resolution difficult to achieve with seismometers. Using optical scattering induced by cable deformation, DAS can record strain fields with spatial resolution of a few meters. However, many experiments utilizing DAS have relied on unused, dark telecommunication fibers. As a result, the geophysical community has not fully explored DAS survey parameters to characterize the ideal array design. This limits our understanding of guiding principles in array design to deploy DAS effectively and efficiently in the field. A better quantitative understanding of DAS array behavior can improve the quality of the data recorded by guiding the DAS array design. Here we use steered response functions, which account for DAS fiber’s directional sensitivity, as well as beamforming and back-projection results from forward modelling calculations to assess the performance of varying DAS array geometries to record regional and local sources. A regular heptagon DAS array demonstrated improved capabilities for recording regional sources over other polygonal arrays, with potential improvements in recording and locating local sources. These results help reveal DAS array performance as a function of geometry and can guide future DAS deployments.
58 GEOSCIENCES↗
Emerging Technologies for Improved Plug Load Management Systems: Learning Behavior Algorithms and Automatic and Dynamic Load Detection
Plug loads are responsible for a significant portion of the energy consumed in commercial buildings, yet their distributed and ever-changing nature makes them one of the most challenging building end uses to manage. Plug load management systems exist today that utilize smart plugs to meter and control devices at the outlet level, however, their uptake has been relatively slow in part due to the significant labor required for installation and maintenance. Learning behavior algorithms and automatic and dynamic load detection have been identified as two technology areas that could accelerate the adoption of plug load management systems by reducing these labor demands and providing additional energy efficiency and non-energy benefits. Learning behavior algorithms learn occupant behavior and adjust plug load management systems accordingly, allowing for the automatic creation of optimized control schedules. Automatic and dynamic load detection allows a plug load management system to identify devices as they are plugged in to a building and keeps the system up to date as devices are moved throughout a building. In this paper, we present our findings with respect to the current state of these two technologies based on a review of existing research and patents, as well as a series of interviews with companies working in the plug load space. We have found that, as of now, no commercialized solutions exist for these plug load technologies and that more work is needed to bring them to market. In addition, we summarize our findings related to the technology challenges, market barriers, drivers, and opportunities for these technologies moving forward.
30 DIRECT ENERGY CONVERSION↗
Institutional and Policy Landscape for Solar-Plus-Storage Deployment by Electric Cooperatives
As co-location with battery storage has become a significant solar project-design trend across the United States, policies at all levels have started to adjust. Generally, these policies address battery storage development in major, urban markets, served by large investor-owned utilities (IOUs). Yet all kinds of utilities nationwide are seeking ways to tap into the fast-growing battery storage market, and they are all stakeholders in the federal, state, and local policies that impact the storage market's development. The electric cooperative sector, which includes some 830 distribution utilities and 63 generating and transmission (G&T) utilities across a mostly rural landscape, is no exception. It is impacted by federal, state, and local government policy requirements and incentives, as well as by policies that are controlled by the sector's own institutions. The objective of this paper is to identify and discuss the various policy and institutional factors that are relevant to the electric cooperative sector, and especially to local, distribution-level electric cooperatives (co-ops) that may be interested in procuring energy storage. Given this, the paper is largely written from the local co-op perspective. It also may be useful to wholesale G&T cooperatives. Other parties that may find the paper useful include storage developers, who are often unfamiliar with co-ops; local and state policymakers addressing policy options for this sector, and local renewable or storage stakeholders, who wish to understand the policy landscape affecting their local utility. Although co-op energy storage programs may tap customer-side batteries, this examination is focused on utility-side "front of the meter" projects and the policy issues they raise.
14 SOLAR ENERGY↗
Meter-Based Assessment of the Time and Locational Benefits of a Large Utility’s DSM Portfolio
As decarbonization goals drive increasing levels of renewable generation, there is a need to understand the time- and location-based savings benefits of demand-side management (DSM) programs. The challenges of the 'duck curve' are driving the utility industry to consider how programs can be optimized to match demand profiles with low carbon generation resources. From an infrastructure standpoint, time- and location-targeted DSM could serve as a ‘non-wires alternative’ (NWA) to defer equipment upgrades. Additional DSM value streams are motivating innovation in savings evaluation, providing more resolved insights beyond the total annual program impact. Methods grounded in the principles of billing analysis, leveraging hourly metering at the distribution grid, can provide new visibility into the spatial and temporal savings achieved through DSM. A large body of work has investigated related topics including interval meter-based savings analysis, the time- varying nature of efficiency measures, and NWA. A less studied topic concerns the impact of DSM on the grid, based on metered consumption. This paper presents an analysis of interval data across more than 25,000 customers and twelve substations, from the Sacramento Municipal Utility District. The results show for different locations on the grid: achieved savings and the impact on grid consumption; hourly savings shapes for DSM program participants and non-participants, and how those shapes vary with season; and the impact of the programs on peak demand. These findings show the current impact of DSM, with implications for future, more intentional targeting as the utility continues to pursue aggressive electrification, efficiency, load flexibility, and reliable NWA.
Granderson, Jessica↗
Consumer Benefits of Clean Energy: Renewable Energy
Meeting national and state decarbonization goals requires a transition to clean energy technologies. Energy efficiency, demand flexibility, renewable energy and storage can reduce consumers’ electricity bills, lower total electricity system costs, and provide health and resilience benefits. Berkeley Lab developed a series of briefs that explore these consumer benefits of a clean energy transition. This brief discusses some of the possible consumer benefits of utility-scale and behind the meter renewable energy, with a focus on how these resources can contribute to a low-cost electricity system. It begins with a literature review of modeled impacts, primarily considering consumer benefits, of the Inflation Reduction Act and Bipartisan Infrastructure Law. Next, it discusses how utility-scale renewable energy can contribute to a low-cost electricity system (e.g., in some cases, low resource costs relative to other alternatives). It concludes with a discussion of behind-the-meter renewable energy consumer benefits (e.g., reduced host electricity bill, increased property value, resilience).
29 ENERGY PLANNING, POLICY, AND ECONOMY↗