Calculating PV Hosting Capacity in Low-Voltage Secondary Networks using Only Smart Meter Data
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Guam Power Authority (GPA) sought national lab assistance through the Energy Technology Innovations Partnership Project as part of the second cohort of applicants (i.e. the program’s second year). GPA is leading an effort to evolve the island’s energy generation portfolio to 50% renewable by 2030, and 100% renewable by 2040. These goals for renewable penetration are a function of public law and GPA is legally required to comply, though GPA’s timeline is more aggressive by five years in each case than the law requires.
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This paper introduces a novel approach for generating solar photovoltaic (PV) plant deployment scenarios for grid integration planning. The approach guarantees consistency among scenarios of the same deployment by ensuring that higher penetration scenarios contain PV units deployed in lower penetration scenarios. It also constrains the size and spatial distribution of the PV plants and considers three placement types. A case study on a real-world distribution system proves that the precepts of scenario consistency, deployment diversity, and placement are met. The study further investigates the impact of the resulting scenarios via a stochastic hosting capacity analysis. Results indicate that the ratio between PV and load sizes, referred to as the nodal PV penetration factor (NPPF), is a key driver of the grid integration impact. By reducing the NPPF from 5 to 2, the maximum hosting capacity increased by at least 112%. The study also reveals that scenarios under random placement can lead to higher hosting capacity values.
This paper introduces a novel approach for generating solar photovoltaic (PV) plant deployment scenarios for grid integration planning. The approach guarantees consistency among scenarios of the same deployment by ensuring that higher penetration scenarios contain PV units deployed in lower penetration scenarios. It also constrains the size and spatial distribution of the PV plants and considers three placement types. A case study on a real-world distribution system proves that the precepts of scenario consistency, deployment diversity, and placement are met. The study further investigates the impact of the resulting scenarios via a stochastic hosting capacity analysis. Results indicate that the ratio between PV and load sizes, referred to as the nodal PV penetration factor (NPPF), is a key driver of the grid integration impact. By reducing the NPPF from 5 to 2, the maximum hosting capacity increased by at least 112%. The study also reveals that scenarios under random placement can lead to higher hosting capacity values.
The benefits and risks associated with Volt-Var Curve (VVC) control for management of voltages in electric feeders with distributed, roof-top photovoltaic (PV) can be defined using a stochastic hosting capacity analysis methodology. Although past work showed that a PV inverter’s reactive power can improve grid voltages for large PV installations, this study adds to the past research by evaluating the control method’s impact (both good and bad) when deployed throughout the feeder within small, distributed PV systems. The stochastic hosting capacity simulation effort iterated through hundreds of load and PV generation scenarios and various control types. The simulations also tested the impact of VVCs with tampered settings to understand the potential risks associated with a cyber-attack on all of the PV inverters scattered throughout a feeder. The simulation effort found that the VVC can have an insignificant role in managing the voltage when deployed in distributed roof-top PV inverters. This type of integration strategy will result in little to no harm when subjected to a successful cyber-attack that alters the VVC settings.
Current distribution system planning (DSP) processes do not explicitly account for energy equity considerations, such as who is most affected by power system burdens, where those burdens are concentrated, and what investments can be made to improve baseline conditions. This paper proposes an iterative framework for advancing energy equity as an objective of the DSP process, showing how measurement strategies, or metrics (informed by conceptual foundations of energy justice), can be applied to benchmark equity performance at various stages. This methodology is applied for equity-aware distributed energy resource (DER) hosting capacity analysis and outage analysis to provide critical insights on infrastructure upgrade decisions compared to a business-as-usual (BAU) case. The analysis is performed on a taxonomy feeder representing the West Coast urban/semi-urban system with augmentation of electric vehicles (EVs) and rooftop solar photovoltaic (PV) generators. The study considers disadvantaged community (DAC) and non-disadvantaged community (NDAC) load regions to enable equity-aware simulations. The results demonstrate how equity-aware planning could reveal the limitations of the traditional DSP process as DAC regions are found to have lower DER hosting capacity and higher outage vulnerability. Overall, this work provides insights on the need to incorporate energy equity as an integral part of the DSP process.
Deep decarbonization of power system operations requires the maximal utilization of available renewable resources. At distribution-level operations, however, grid operators can face numerous challenges in integrating renewables at scale owing to the inherent intermittence of renewable energy resources. These include phenomena such as voltage fluctuations, which are typically mitigated through control actuators such as on-load tap changers (OLTC) as well as energy storage devices, such as battery energy storage systems (BESS). On the one hand, high intermittence of the available renewable portfolio may require increasingly aggressive control of actuators, thereby accelerating the probability of equipment failure. On the other hand, integrating BESS operations and having a diverse renewable generation portfolio can typically help stagger power/energy flow to mitigate the aforementioned adverse impacts. In this paper, we employ a Bayesian framework for equipment lifetime estimation to understand the impact of including tidal energy resources and BESS in distribution system operations for feeders having substantial distribution photovoltaic generation. Our results indicate that while tidal energy alone may slightly decrease equipment reliability, the adverse impact on reliability is significantly magnified by a generation portfolio consisting of tidal generation and photovoltaic generation. Here, we also study the tidal and photovoltaic hosting capacity problem with and without energy storage systems using equipment reliability as an added constraint. We conclude that energy storage increases the reliability-constrained hosting capacity of the distribution system.
This paper presents the results of an investigation into the value that medium voltage back-to-back (MVB2B) converters can be realized through two potential applications: voltage regulation via the provision of reactive power supply or absorption, and distributed energy resource (DER) hosting capacity improvement via the controlled transfer of real power within the distribution system. Two groups of distribution systems have been prepared for the value investigation: the IEEE 123-bus system for the voltage regulation study and two utility-provided, realistic distribution systems for the DER hosting capacity improvement study. The results demonstrate that the MVB2B converter can realize significant value for the grid through these two applications. In particular, with the MVB2B converter transferring power between two feeders, DER-based power can be routed to nearby loads instead of feed through the distribution substation. Additionally, the size of energy storage elements used to enable higher levels of DERs can be significantly reduced.
National Laboratory of the Rockies (NLR), in collaboration with the Dubai Electricity and Water Authority (DEWA) and Infra-X, has undertaken the Energy Visualization Analysis Project. The aim of this project is to enhance analytical and 3D visualization capabilities for distribution network planning and renewable energy integration. As modern grid continues to evolve with large-scale solar PV deployment and emerging distributed energy resources (DERs), the ability to effectively analyze, visualize, and communicate complex grid behaviors has become increasingly critical. The project focuses on developing empirical use cases based on real distribution feeder data and engineering workflows, ensuring the outcomes are directly aligned with operational environment. Through time-series power flow simulations and nodal hosting capacity analysis, the study quantifies the impacts of high PV penetration on voltage and thermal limits within representative 11 kV feeders. These analyses identify specific nodes and conditions where DER integration challenges arise. Furthermore, a Battery Energy Storage System (BESS) optimization algorithm was applied to determine the optimal size and placement of storage systems that can mitigate network constraints and enhance hosting capacity. The comparative results between base-case and BESS-augmented scenarios clearly demonstrate improvements in network stability and load management efficiency. In parallel, the NLR team developed an immersive 3D visualization framework, enabling interactive exploration of grid simulations using commodity head-mounted display (HMD) systems. This framework transforms conventional 2D simulation data into spatially intuitive visual environments - allowing engineers to analyze feeder conditions, PV hosting potential, and BESS effects in real time. This report represents the first foundational phase in establishing a visualization-driven analytical ecosystem. It provides a methodological foundation for data integration, visualization architecture, and simulation-based decision support, paving the way for large-scale adoption of immersive visualization across DEWA's Smart Grid Initiative, R&D activities, and future network resilience studies.