Birk Jones

Dr. Birk Jones

Sandia National Laboratories

Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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18 March 2021 in Energies
Verified
Journal Article
Open access
PDF file
Birk JonesBirk Jones
Birk Jones
MLMatthew Lave
Matthew Lave
WVWilliam Vining
William Vining
BGBrooke Garcia
Brooke Garcia
An increase in Electric Vehicles (EV) will result in higher demands on the distribution electric power systems (EPS) which may result in thermal line overloading and low voltage violations. To understand the impact, this work simulates two EV charging scenarios (home- and work-dominant) under potential 2030 EV adoption levels on 10 actual distribution feeders that support residential, commercial, and industrial loads. The simulations include actual driving patterns of existing (non-EV) vehicles taken from global positioning system (GPS) data. The GPS driving behaviors, which explain the spatial and temporal EV charging demands, provide information on each vehicles travel distance, dwell locations, and dwell durations. Then, the EPS simulations incorporate the EV charging demands to calculate the power flow across the feeder. Simulation results show that voltage impacts are modest (less than 0.01 p.u.), likely due to robust feeder designs and the models only represent the high-voltage (“primary”) system components. Line loading impacts are more noticeable, with a maximum increase of about 15%. Additionally, the feeder peak load times experience a slight shift for residential and mixed feeders (≈1 h), not at all for the industrial, and 8 h for the commercial feeder.

18 May 2020 in IEEE Journal of Photovoltaics
Verified
Journal Article
Open access
MTMarios Theristis
Marios Theristis
Andreas LiveraAndreas Livera
Andreas Livera
Birk JonesBirk Jones
Birk Jones
GMGeorge Makrides
George Makrides
George E. GeorghiouGeorge E. Georghiou
George E. Georghiou
Joshua S. SteinJoshua S. Stein
Joshua S. Stein
Although common practice for estimating photovoltaic (PV) degradation rate (R $_D$ ) assumes a linear behavior, field data have shown that degradation rates are frequently nonlinear. This article presents a new methodology to detect and calculate nonlinear R $_D$ based on PV performance time-series from nine different systems over an eight-year period. Prior to performing the analysis and in order to adjust model parameters to reflect actual PV operation, synthetic datasets were utilized for calibration purposes. A change-point analysis is then applied to detect changes in the slopes of PV trends, which are extracted from constructed performance ratio ( $PR$ ) time-series. Once the number and location of change points is found, the ordinary least squares method is applied to the different segments to compute the corresponding rates. The obtained results verified that the extracted trends from the PR time-series may not always be linear and therefore, “nonconventional” models need to be applied. All thin-film technologies demonstrated nonlinear behavior whereas nonlinearity detected in the crystalline silicon systems is thought to be due to a maintenance event. A comparative analysis between the new methodology and other conventional methods demonstrated levelized cost of energy differences of up to 6.14%, highlighting the importance of considering nonlinear degradation behavior.

30 June 2022 in Energy Reports
Verified
Journal Article
Open access
Birk JonesBirk Jones
Birk Jones
WVWilliam Vining
William Vining
MLMatthew Lave
Matthew Lave
THThad Haines
Thad Haines
CNChristopher Neuman
Christopher Neuman
DSDon R. Scoffield
Don R. Scoffield

2 February 2021 in Energies
Verified
Journal Article
Open access
PDF file
RDRachid Darbali-Zamora
Rachid Darbali-Zamora
Jay JohnsonJay Johnson
Jay Johnson
ASAdam Summers
Adam Summers
Birk JonesBirk Jones
Birk Jones
CHClifford W. Hansen
Clifford W. Hansen
CSChad Showalter
Chad Showalter
Real-time state estimation using a digital twin can overcome the lack of in-field measurements inside an electric feeder to optimize grid services provided by distributed energy resources (DERs). Optimal reactive power control of DERs can be used to mitigate distribution system voltage violations caused by increased penetrations of photovoltaic (PV) systems. In this work, a new technology called the Programmable Distribution Resource Open Management Optimization System (ProDROMOS) issued optimized DER reactive power setpoints based-on results from a particle swarm optimization (PSO) algorithm wrapped around OpenDSS time-series feeder simulations. This paper demonstrates the use of the ProDROMOS in a RT simulated environment using a power hardware-in-the-loop PV inverter and in a field demonstration, using a 678 kW PV system in Grafton (MA, USA). The primary contribution of the work is demonstrating a RT digital twin effectively provides state estimation pseudo-measurements that can be used to optimize DER operations for distribution voltage regulation.

14 June 2017 in IEEE Access
Verified
Journal Article
Open access
Birk JonesBirk Jones
Birk Jones
CCCedric Carter
Cedric Carter
Dynamic smart grid operations require that utilities that incorporate intermittent renewable energy resources provide creative and inclusive solutions to reduce and shift electrical demand. Commercial building HVAC systems have been used as a dispatchable load, but are limited by the lack of interoperability. Increased operability can be achieved using a secure and reliable interconnection device, which can provide direct bidirectional communications between the utility and the building automation system (BAS) controllers. This paper developed and tested a building automation intrusion detection system (BAIDS) that can provide a cyber-secure connection between public and private BAS networks. The BAIDS was used in a hardware-in-the-loop experiment that connected an actual photovoltaic array with a BAS control test bed and a building zone model. The BAIDS device allowed for critical control signals to pass from the public network directly to a fan controller in a BAS private network. At the same time, the BAIDS device provided intrusion detection monitoring to identify malicious activity. The network traffic was evaluated using an adaptive resonance theory (ART) artificial neural network. The ART algorithm was able to learn normal traffic activity on the private and public networks. The algorithm was then used to detect unauthorized attempts to access the interconnection device and a malicious cyber-physical attack on the BAS.

18 July 2022 in IEEE Open Access Journal of Power and Energy
Verified
Journal Article
Open access
Birk JonesBirk Jones
Birk Jones
CBCynthia J. Bresloff
Cynthia J. Bresloff
RDRachid Darbali-Zamora
Rachid Darbali-Zamora
MLMatthew Lave
Matthew Lave
EBErick E. Aponte Bezares
Erick E. Aponte Bezares
Limited access to transmission lines after a major contingency event can inhibit restoration efforts. After Hurricane Maria, for example, flooding and landslides damaged roads and thus limited travel. Transmission lines are also often situated far from maintained roadways, further limiting the ability to access and repair them. Therefore, this paper proposes a methodology for assessing Puerto Rico’s infrastructure (i.e., roads and transmission lines) to identify potentially hard to reach areas due to natural risks or distance to roads. The approach uses geographic information system (GIS) data to define vulnerable areas, that may experience excessive restoration times. The methodology also uses graph theory analysis to find transmission lines with high centrality (or importance). Comparison of these important transmission lines with the vulnerability results found that many reside near roads that are at risk for landslides or floods.