138 results on '"Scott Backhaus"'
Search Results
2. Intermediate cooling from pulse tube refrigerator regenerators operating in the real-fluid regime
- Author
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Ryan Snodgrass, Gregory Swift, Joel Ullom, and Scott Backhaus
- Subjects
General Physics and Astronomy ,General Materials Science - Published
- 2023
3. Machine Learning Based Network Parameter Estimation Using AMI Data
- Author
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Chuan Qin, Bharat Vyakaranam, Pavel Etingov, Milton Venetos, and Scott Backhaus
- Published
- 2022
4. Operations- and Uncertainty-Aware Installation of FACTS Devices in a Large Transmission System
- Author
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Janusz Bialek, Priyanko Guha Thakurta, Vladimir Frolov, Scott Backhaus, and Michael Chertkov
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Physics - Physics and Society ,Control and Optimization ,Computer Networks and Communications ,Computer science ,020209 energy ,FOS: Physical sciences ,Systems and Control (eess.SY) ,Physics and Society (physics.soc-ph) ,02 engineering and technology ,Flexible AC transmission system ,FOS: Mathematics ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,Mathematics - Optimization and Control ,Flexibility (engineering) ,Transmission system ,AC power ,Grid ,Reliability engineering ,Electricity generation ,Transmission (telecommunications) ,Optimization and Control (math.OC) ,Control and Systems Engineering ,Signal Processing ,Scalability ,Computer Science - Systems and Control ,020201 artificial intelligence & image processing - Abstract
Decentralized electricity markets and greater integration of renewables demand expansion of the existing transmission infrastructure to accommodate inflected variabilities in power flows. However, such expansion is severely limited in many countries because of political and environmental issues. Furthermore, high renewables integration requires additional reactive power support, which forces the transmission system operators to utilize the existing grid creatively, for example, take advantage of new technologies, such as flexible alternating current transmission system (FACTS) devices. We formulate, analyze, and solve the challenging investment planning problem of installation in existing large-scale transmission grid multiple FACTS devices of two types (series capacitors and static var compensators). We account for details of the ac character of power flows, probabilistic modeling of multiple load scenarios, FACTS devices flexibility in terms of their adjustments within the capacity constraints, and long-term practical tradeoffs between capital versus operational expenditures. It is demonstrated that proper installation of the devices allows to do both—extend or improve the feasibility domain for the system and decrease long-term power generation cost (make cheaper generation available). Nonlinear, nonconvex, and multiple-scenario-aware optimization is resolved through an efficient heuristic algorithm consisting of a sequence of quadratic programmings solved by CPLEX combined with exact ac power flow resolution for each scenario for maintaining feasible operational states during iterations. Efficiency and scalability of the approach are illustrated on the IEEE 30-bus model and the 2736-bus Polish model from Matpower.
- Published
- 2019
5. Resilient design of large-scale distribution feeders with networked microgrids
- Author
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Arthur K. Barnes, Harsha Nagarajan, Scott Backhaus, Emre Yamangil, and Russell Bent
- Subjects
Distribution system ,Computer science ,020209 energy ,Distributed computing ,020208 electrical & electronic engineering ,0202 electrical engineering, electronic engineering, information engineering ,Extreme events ,Redundancy (engineering) ,Energy Engineering and Power Technology ,02 engineering and technology ,Electrical and Electronic Engineering ,Parametric statistics - Abstract
Electrical distribution systems are often vulnerable to severe weather. Upgrades, such as microgrids, system hardening, and line redundancy, can greatly reduce the number of electrical outages during such extreme events. More recently, the networking of microgrids has received attention as a solution to further improve the resilience of distribution feeders. Although these upgrades have the potential to improve resilience, a barrier to their execution is a lack of tools and approaches that support systematic exploration of the underlying parameters of these upgrades and their cost vs. resilience tradeoffs.To address this gap, we develop a method for designing resilient distribution grids, including networked microgrids, by posing the problem as a two-stage stochastic program. When resilience is defined as the ability of a network to supply load immediately following a storm event, we show that a decomposition-based heuristic algorithm scales to a 1200-node distribution system. We also vary the study parameters, i.e., the cost of microgrids relative to system hardening and target resilience metrics. In this study, we find regions in this parametric space that correspond to different resilient distribution system architectures, such as individual microgrids, hardened networks, and a transition region that suggests the benefits of microgrids networked via hardened circuit segments.
- Published
- 2019
6. Relaxations of AC Maximal Load Delivery for Severe Contingency Analysis
- Author
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Carleton Coffrin, Byron Tasseff, Russell Bent, Kaarthik Sundar, and Scott Backhaus
- Subjects
Mathematical optimization ,Operating point ,Computer science ,020209 energy ,Commodity computing ,Energy Engineering and Power Technology ,02 engineering and technology ,Transmission system ,AC power ,Power (physics) ,Electric power system ,Optimization and Control (math.OC) ,Scalability ,FOS: Mathematics ,0202 electrical engineering, electronic engineering, information engineering ,Relaxation (approximation) ,Electrical and Electronic Engineering ,Mathematics - Optimization and Control - Abstract
This work considers the task of finding an AC-feasible operating point of a severely damaged transmission network while ensuring that a maximal amount of active power loads can be delivered. This AC Maximal Load Delivery (AC-MLD) task is a nonconvex nonlinear optimization problem that is incredibly challenging to solve on large-scale transmission system datasets. This work demonstrates that convex relaxations of the AC-MLD problem provide a reliable and scalable method for finding high-quality bounds on the amount of active power that can be delivered in the AC-MLD problem. To demonstrate their effectiveness, the solution methods proposed in this work are rigorously evaluated on 1000 N-k scenarios on seven power networks ranging in size from 70 to 6000 buses. The most effective relaxation of the AC-MLD problem converges in less than 20 seconds on commodity computing hardware for all 7000 of the scenarios considered., The problem considered in this work is also known as AC Minimal Load-Shedding
- Published
- 2019
7. Optimal absorption of distributed and conductive heat loads with cryocooler regenerators
- Author
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Ryan Snodgrass, Joel Ullom, and Scott Backhaus
- Abstract
The second-stage regenerators of pulse tube refrigerators are routinely used to intercept heat in cryogenic systems; however, optimal methods for heat sinking to the regenerator have not been studied in detail. We investigated intermediate cooling methods by densely instrumenting a commercial, two-stage pulse tube refrigerator with thermometers and heaters. We then experimentally emulated heat loads from common sources such as arrays of electrical cables (a single-point conductive load) and 3He return gas for dilution refrigerators (a distributed load). Optimal methods to absorb these heat loads, whether applied independently or simultaneously, are presented. Our study reveals the importance of understanding the response of the regenerator temperature profle for optimal thermal integration of heat loads along the regenerator, i.e., temperatures and heat fows at all heat sink locations. With optimal utilization of regenerator intermediate cooling, 3He fow rates of up to 2 mmol/s can be cooled from 50 K to 3 K and fully condensed using this pulse tube refrigerator; alternatively, the heat leak from over 100 electrical cables can be cooled across that same temperature span while simultaneously condensing 1.4 mmol/s of 3He.
- Published
- 2022
8. Arbitrage with Power Factor Correction using Energy Storage
- Author
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Umar Hashmi, Ana Bušić, Scott Backhaus, Deepjyoti Deka, Lucas Pereira, Dynamics of Geometric Networks (DYOGENE), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Los Alamos National Laboratory (LANL), Laboratory of Information, Network and Communication Sciences (LINCS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom [Paris] (IMT)-Sorbonne Université (SU), Madeira Interactive Technologies Institute [Funchal], University of Madeira [Funchal], ANR-16-CE05-0008,PARI,Approche probabiliste pour l'intégration des énergies renouvelables : le stockage virtuel en utilisant la flexibilité de la demande(2016), Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Département d'informatique - ENS Paris (DI-ENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)
- Subjects
Optimization problem ,Computer science ,020209 energy ,Energy Engineering and Power Technology ,Systems and Control (eess.SY) ,02 engineering and technology ,Power factor ,7. Clean energy ,Electrical Engineering and Systems Science - Systems and Control ,Energy storage ,Control theory ,FOS: Electrical engineering, electronic engineering, information engineering ,FOS: Mathematics ,0202 electrical engineering, electronic engineering, information engineering ,[INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY] ,Electrical and Electronic Engineering ,Mathematics - Optimization and Control ,business.industry ,[SPI.NRJ]Engineering Sciences [physics]/Electric power ,AC power ,Renewable energy ,Model predictive control ,Optimization and Control (math.OC) ,Distributed generation ,Arbitrage ,[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] ,business - Abstract
The importance of reactive power compensation for power factor (PF) correction will significantly increase with the large-scale integration of distributed generation interfaced via inverters producing only active power. In this work, we focus on co-optimizing energy storage for performing energy arbitrage as well as local power factor correction. The joint optimization problem is non-convex, but can be solved efficiently using a McCormick relaxation along with penalty-based schemes. Using numerical simulations on real data and realistic storage profiles, we show that energy storage can correct PF locally without reducing arbitrage profit. It is observed that active and reactive power control is largely decoupled in nature for performing arbitrage and PF correction (PFC). Furthermore, we consider a real-time implementation of the problem with uncertain load, renewable and pricing profiles. We develop a model predictive control based storage control policy using auto-regressive forecast for the uncertainty. We observe that PFC is primarily governed by the size of the converter and therefore, look-ahead in time in the online setting does not affect PFC noticeably. However, arbitrage profit are more sensitive to uncertainty for batteries with faster ramp rates compared to slow ramping batteries., 10 pages, 8 figures
- Published
- 2020
9. Integration of optimal operational dispatch and controller determined dynamics for microgrid survivability
- Author
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Scott Backhaus, Sreenath Chalil Madathil, and Alessandro Cattaneo
- Subjects
Electrical load ,Computer science ,020209 energy ,Mechanical Engineering ,Survivability ,02 engineering and technology ,Building and Construction ,010501 environmental sciences ,Management, Monitoring, Policy and Law ,01 natural sciences ,Electrical grid ,Cascading failure ,Reliability engineering ,Generator (circuit theory) ,General Energy ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,Microgrid ,Electric power ,0105 earth and related environmental sciences - Abstract
The reliability and resilience of the electrical power grids are essential to industry, economy and society. Microgrids that are able to island from the bulk electrical grid are one technology that may vastly improve electrical power service to customer loads. To achieve these improvements, an islanded microgrid should be able to operate through the loss of one of its generators without shedding electrical load. The loss of one generator will typically result in significant additional loads, including transient overloads, being placed on the remaining generators. There is also the possibility of additional generator tripping during these processes (i.e. cascading failures), which would likely result in the collapse of the microgrid. The novelty of our work consists in incorporating dynamic models of generator controllers into a microgrid optimal dispatch formulation with the ultimate goal to avoid operational failures and ensure the “survivability” of all-inverter microgrids to generator loss and transient overloads. The integration of generator and controller dynamics into the optimal dispatch formulation significantly increases the computational complexity. As we develop algorithms to restore speed of the optimization, our method can be readily implemented into a new operational strategy capable of an unprecedented level of reliability against generator contingencies. In addition, we quantitatively illustrate the effect of the survivability constraints on the microgrid operating costs and how the related trade-off between capital and operating costs should be taken into account at the design stage. The methods developed here also apply to the dispatch of off-grid microgrids.
- Published
- 2018
10. Resilient Off-Grid Microgrids: Capacity Planning and N-1 Security
- Author
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Sreenath Chalil Madathil, Salman Mashayekh, Arthur K. Barnes, Harsha Nagarajan, Scott Backhaus, Russell Bent, Scott J. Mason, Emre Yamangil, and Michael Stadler
- Subjects
Engineering ,General Computer Science ,business.industry ,020209 energy ,Node (networking) ,Reliability (computer networking) ,Distributed computing ,02 engineering and technology ,Transmission system ,AC power ,Grid ,Computer security ,computer.software_genre ,Capacity planning ,Transmission (telecommunications) ,0202 electrical engineering, electronic engineering, information engineering ,Electric power industry ,business ,computer - Abstract
Over the past century the electric power industry has evolved to support the delivery of power over long distances with highly interconnected transmission systems. Despite this evolution, some remote communities are not connected to these systems. These communities rely on small, disconnected distribution systems, i.e., microgrids to deliver power. However, as microgrids often are not held to the same reliability standards as transmission grids, remote communities can be at risk for extended blackouts. To address this issue, we develop an optimization model and an algorithm for capacity planning and operations of microgrids that include ${N}$ -1 security and other practical modeling features like ac power flow physics, component efficiencies, and thermal limits. We demonstrate the computational effectiveness of our approach on two test systems; a modified version of the IEEE 13 node test feeder and a model of a distribution system in a remote community in Alaska.
- Published
- 2018
11. Enabling Resiliency Operations Across Multiple Microgrids With Grid Friendly Appliance Controllers
- Author
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Kevin P. Schneider, Scott Backhaus, Francis K. Tuffner, Chen-Ching Liu, Dan Ton, Marcelo A. Elizondo, and Yin Xu
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Interconnection ,Engineering ,IEEE 1547 ,General Computer Science ,business.industry ,020209 energy ,Distributed computing ,Control engineering ,02 engineering and technology ,Transmission system ,Grid ,Electricity generation ,Smart grid ,Smart city ,Grid friendly ,0202 electrical engineering, electronic engineering, information engineering ,business - Abstract
Changes in economic, technological, and environmental policies are resulting in a re-evaluation of the dependence on large central generation facilities and their associated transmission networks. Emerging concepts of smart communities/cities are examining the potential to leverage cleaner sources of generation, as well as integrating electricity generation with other municipal functions. When grid connected, these generation assets can supplement the existing interconnections with the bulk transmission system, and in the event of an extreme event, they can provide power via a collection of microgrids. To achieve the highest level of resiliency, it may be necessary to conduct switching operations to interconnect individual microgrids. While the interconnection of multiple microgrids can increase the resiliency of the system, the associated switching operations can cause large transients in low inertia microgrids. The combination of low system inertia and IEEE 1547 and 1547a-compliant inverters can prevent multiple microgrids from being interconnected during extreme weather events. This paper will present a method of using end-use loads equipped with grid friendly appliance controllers to facilitate the switching operations between multiple microgrids; operations that are necessary for optimal operations when islanded for resiliency.
- Published
- 2018
12. Co-optimizing Energy Storage for Prosumers using Convex Relaxations
- Author
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Lucas Pereira, Ana Bušić, Umar Hashmi, Deepjyoti Deka, and Scott Backhaus
- Subjects
Model predictive control ,Optimization problem ,Peak demand ,Control theory ,Computer science ,Power factor ,Arbitrage ,AC power ,Energy storage ,Energy (signal processing) - Abstract
This paper presents a new co-optimization formulation for energy storage for performing energy arbitrage and power factor correction (PFC) in the time scale of minutes to hours, along with peak demand shaving in the time scale of a month. While the optimization problem is non-convex, we present an efficient penalty based convex relaxation to solve it. Furthermore, we provide a mechanism to increase the storage operational life by tuning the cycles of operation using a friction coefficient. To demonstrate the effectiveness of energy storage performing multiple tasks simultaneously, we present a case study with real data for a time scale of several months. We are able to show that energy storage can realistically correct power factor without significant change in either arbitrage gains or peak demand charges. We demonstrate a real-time Model Predictive Control (MPC) based implementation of the proposed formulation with AutoRegressive forecasting of net-load and electricity price. Numerical results indicate that arbitrage gains and peak demand shaving are more sensitive to parameter uncertainty for faster ramping battery compared to slower ramping batteries. However, PFC gains are insensitive to forecast inaccuracies.
- Published
- 2019
13. Experimental study of gravitational mixing of supercritical CO2
- Author
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Dennis L. Newell, Peter C. Lichtner, J. William Carey, and Scott Backhaus
- Subjects
Convection ,Materials science ,Mechanics ,Management, Monitoring, Policy and Law ,01 natural sciences ,Pollution ,Industrial and Manufacturing Engineering ,Supercritical fluid ,010305 fluids & plasmas ,Plume ,General Energy ,Mass transfer ,0103 physical sciences ,Convective mixing ,Caprock ,010306 general physics ,Porous medium ,Dissolution - Abstract
CO2 injection into saline aquifers for sequestration will initially result in buoyant supercritical (sc)CO2 trapped beneath the caprock seal. During this period, there is risk of CO2 migration out of the reservoir along wellbore defects or fracture zones. Dissolution of the scCO2 plume into brine results in solubility trapping and reduces this risk, but based on diffusion alone, this mechanism could take thousands of years. Gravitational (density-induced) mixing of CO2-saturated brine is shown to significantly accelerate this process in computational studies, but few experimental efforts have confirmed the phenomenon. Here, constant-pressure, 3-dimensional bench-scale experiments used the mass of added water to quantify the mass transfer of scCO2 into water-saturated porous media at 40–90 °C and 20 MPa, with Rayleigh numbers from 2093 to 16256. Experiments exhibit a period of 7–35X enhancement in mass transfer rates over diffusion, interpreted as gravitational mixing. Convective CO2 flux ranges from 1.6 × 10−2 to 4.8 × 10−3 mol s−1 m−2 in the experiments. Results are used to benchmark a computational model using PFLOTRAN. Experiments show an early diffusive onset period that is shorter with rates much higher than predicted by models and observed in analog experiments. Both experiments and models show convective mixing periods and similar overall rates of CO2 mass transfer.
- Published
- 2018
14. Structure- and Physics-Preserving Reductions of Power Grid Models
- Author
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Colin Grudzien, Scott Backhaus, Deepjyoti Deka, and Michael Chertkov
- Subjects
Structure (mathematical logic) ,0209 industrial biotechnology ,020209 energy ,Ecological Modeling ,General Physics and Astronomy ,02 engineering and technology ,General Chemistry ,Computer Science Applications ,Computational science ,Visualization ,Power (physics) ,020901 industrial engineering & automation ,Transmission (telecommunications) ,Modeling and Simulation ,0202 electrical engineering, electronic engineering, information engineering ,Graph reduction ,Power grid ,Large size - Abstract
The large size of multiscale, distribution and transmission, power grids hinders fast systemwide estimation and real-time control and optimization of operations. This paper studies graph reduction ...
- Published
- 2018
15. Explaining inefficiencies in commercial buildings providing power system ancillary services
- Author
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Ian A. Hiskens, Johanna L. Mathieu, Scott Backhaus, Jeremiah X. Johnson, and Yashen Lin
- Subjects
Engineering ,business.industry ,020209 energy ,Mechanical Engineering ,02 engineering and technology ,Building and Construction ,Energy consumption ,010501 environmental sciences ,Environmental economics ,01 natural sciences ,Supply and demand ,Demand response ,Electric power system ,HVAC ,0202 electrical engineering, electronic engineering, information engineering ,Operations management ,Electrical and Electronic Engineering ,Duration (project management) ,Baseline (configuration management) ,Inefficiency ,business ,0105 earth and related environmental sciences ,Civil and Structural Engineering - Abstract
Ancillary services are required to balance supply and demand in electric power systems. Demand response may provide attractive options for these services, through means such as varying the power consumption of commercial building HVAC systems. However, experimental results from a 30,000 m 2 office building suggest that when a building provides ancillary services it consumes more energy than when it is operated normally. This translates to additional costs and environmental impacts. This paper investigates potential causes for building inefficiency associated with ancillary services provision. We develop a physics-based simulation model that captures heat exchange processes and fan and air duct dynamics. During an ancillary service event, we vary the fan power consumption, and then compute the difference between the baseline and actual energy consumption to determine the efficiency of the actions. We explore the impact of building parameters, control design, and baseline model accuracy on the efficiency. In simulation, we find that shorter duration power changes and less aggressive controllers result in less change in energy consumption. We also find that baseline error has outsized effects on the efficiency calculations. These results offer new understanding of the mechanisms underlying inefficiency and point to opportunities to reduce associated costs and environmental burdens.
- Published
- 2017
16. The microeconomics of residential photovoltaics: Tariffs, network operation and maintenance, and ancillary services in distribution-level electricity markets
- Author
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Riccardo Boero, Scott Backhaus, and Brian Keith Edwards
- Subjects
Renewable Energy, Sustainability and the Environment ,business.industry ,020209 energy ,Distribution (economics) ,Social Welfare ,02 engineering and technology ,Environmental economics ,Solar energy ,Net metering ,Photovoltaics ,0202 electrical engineering, electronic engineering, information engineering ,Electricity market ,General Materials Science ,Electricity ,Feed-in tariff ,business - Abstract
We develop a microeconomic model of a distribution-level electricity market that takes explicit account of residential photovoltaics (PV) adoption. The model allows us to study the consequences of most tariffs on PV adoption and the consequences of increased residential PV adoption under the assumption of economic sustainability for electric utilities. We validate the model using U.S. data and extend it to consider different pricing schemes for operation and maintenance costs of the distribution network and for ancillary services. Results show that net metering promotes more environmental benefits and social welfare than other tariffs. However, if costs to operate the distribution network increase, net metering will amplify the unequal distribution of surplus among households. In conclusion, maintaining the economic sustainability of electric utilities under net metering may become extremely difficult unless the uneven distribution of surplus is legitimated by environmental benefits.
- Published
- 2016
17. A Robust Approach to Chance Constrained Optimal Power Flow With Renewable Generation
- Author
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Scott Backhaus, Miles Lubin, and Yury Dvorkin
- Subjects
Mathematical optimization ,Engineering ,Computer science ,business.industry ,020209 energy ,Energy Engineering and Power Technology ,02 engineering and technology ,Power (physics) ,Set (abstract data type) ,Electric power system ,Transmission (telecommunications) ,Optimization and Control (math.OC) ,Computer Science::Systems and Control ,Robustness (computer science) ,Transmission line ,FOS: Mathematics ,0202 electrical engineering, electronic engineering, information engineering ,Probability distribution ,Limit (mathematics) ,Electrical and Electronic Engineering ,business ,Mathematics - Optimization and Control - Abstract
Optimal Power Flow (OPF) dispatches controllable generation at minimum cost subject to operational constraints on generation and transmission assets. The uncertainty and variability of intermittent renewable generation is challenging current deterministic OPF approaches. Recent formulations of OPF use chance constraints to limit the risk from renewable generation uncertainty, however, these new approaches typically assume the probability distributions which characterize the uncertainty and variability are known exactly. We formulate a Robust Chance Constrained (RCC) OPF that accounts for uncertainty in the parameters of these probability distributions by allowing them to be within an uncertainty set. The RCC OPF is solved using a cutting-plane algorithm that scales to large power systems. We demonstrate the RRC OPF on a modified model of the Bonneville Power Administration network, which includes 2209 buses and 176 controllable generators. Deterministic, chance constrained (CC), and RCC OPF formulations are compared using several metrics including cost of generation, area control error, ramping of controllable generators, and occurrence of transmission line overloads as well as the respective computational performance.
- Published
- 2016
18. Frequency Regulation From Commercial Building HVAC Demand Response
- Author
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Scott Backhaus, Ian Beil, and Ian A. Hiskens
- Subjects
Engineering ,business.industry ,Energy management ,Automatic frequency control ,Control engineering ,Thermostat ,law.invention ,Reliability engineering ,Demand response ,Electric power system ,Load management ,law ,Air conditioning ,HVAC ,Electrical and Electronic Engineering ,business - Abstract
The expanding penetration of nondispatchable renewable resources within power system generation portfolios is motivating the development of demand-side strategies for balancing generation and load. Commercial heating, ventilation, and air conditioning (HVAC) loads are potential candidates for providing such demand-response (DR) services as they consume significant energy and because of the temporal flexibility offered by their inherent thermal inertia. Several ancillary services markets have recently opened up to participation by DR resources, provided they can satisfy certain performance metrics. We discuss different control strategies for providing frequency regulation DR from commercial HVAC systems and components, and compare performance results from experiments and simulation. We also present experimental results from a single $\sim$ 30 000-m $^{2}$ office building and quantify the DR control performance using standardized performance criteria. Additionally, we evaluate the cost of delivering this service by comparing the energy consumed while providing DR against a counterfactual baseline.
- Published
- 2016
19. Cascading of fluctuations in interdependent energy infrastructures: Gas-grid coupling
- Author
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Scott Backhaus, Vladimir Lebedev, and Michael Chertkov
- Subjects
Physics - Physics and Society ,Wind power ,Petroleum engineering ,business.industry ,Mechanical Engineering ,Photovoltaic system ,FOS: Physical sciences ,Building and Construction ,Physics and Society (physics.soc-ph) ,Systems and Control (eess.SY) ,Management, Monitoring, Policy and Law ,Grid ,Renewable energy ,Pipeline transport ,General Energy ,Hydraulic fracturing ,Energy(all) ,Natural gas ,FOS: Electrical engineering, electronic engineering, information engineering ,Environmental science ,Capital cost ,Computer Science - Systems and Control ,business ,Civil and Structural Engineering - Abstract
The revolution of hydraulic fracturing has dramatically increased the supply and lowered the cost of natural gas in the United States driving an expansion of natural gas-fired generation capacity in many electrical grids. Unrelated to the natural gas expansion, lower capital costs and renewable portfolio standards are driving an expansion of intermittent renewable generation capacity such as wind and photovoltaic generation. These two changes may potentially combine to create new threats to the reliability of these interdependent energy infrastructures. Natural gas-fired generators are often used to balance the fluctuating output of wind generation. However, the time-varying output of these generators results in time-varying natural gas burn rates that impact the pressure in interstate transmission pipelines. Fluctuating pressure impacts the reliability of natural gas deliveries to those same generators and the safety of pipeline operations. We adopt a partial differential equation model of natural gas pipelines and use this model to explore the effect of intermittent wind generation on the fluctuations of pressure in natural gas pipelines. The mean square pressure fluctuations are found to grow linearly in time with points of maximum deviation occurring at the locations of flow reversals., Comment: 13 pages, 8 figures
- Published
- 2015
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20. Hierarchical Predictive Control Algorithms for Optimal Design and Operation of Microgrids
- Author
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Sai Krishna Kanth Hari, Kaarthik Sundar, Scott Backhaus, Harsha Nagarajan, and Russell Bent
- Subjects
020209 energy ,Time horizon ,Systems and Control (eess.SY) ,02 engineering and technology ,Transmission system ,Electrical Engineering and Systems Science - Systems and Control ,Electric power system ,Model predictive control ,Power system simulation ,Transmission (telecommunications) ,Optimization and Control (math.OC) ,Component (UML) ,FOS: Mathematics ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,Microgrid ,Mathematics - Optimization and Control ,Algorithm - Abstract
In recent years, microgrids, i.e., disconnected distribution systems, have received increasing interest from power system utilities to support the economic and resiliency posture of their systems. The economics of long distance transmission lines prevent many remote communities from connecting to bulk transmission systems and these communities rely on off-grid microgrid technology. Furthermore, communities that are connected to the bulk transmission system are investigating microgrid technologies that will support their ability to disconnect and operate independently during extreme events. In each of these cases, it is important to develop methodologies that support the capability to design and operate microgrids in the absence of transmission over long periods of time. Unfortunately, such planning problems tend to be computationally difficult to solve and those that are straightforward to solve often lack the modeling fidelity that inspires confidence in the results. To address these issues, we first develop a high fidelity model for design and operations of a microgrid that include component efficiencies, component operating limits, battery modeling, unit commitment, capacity expansion, and power flow physics; the resulting model is a mixed-integer quadratically-constrained quadratic program (MIQCQP). We then develop an iterative algorithm, referred to as the Model Predictive Control (MPC) algorithm, that allows us to solve the resulting MIQCQP. We show, through extensive computational experiments, that the MPC-based method can scale to problems that have a very long planning horizon and provide high quality solutions that lie within 5\% of optimal., Comment: To appear in "Power Systems Computation Conference", Dublin, Ireland
- Published
- 2018
21. Electric Power Outage Forecasting
- Author
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Kimberly Kaufeld, Donatella Pasqualini, Scott Vander Wiel, Mary Frances Dorn, and Scott Backhaus
- Subjects
business.industry ,Computer science ,Electrical engineering ,Electric power ,business - Published
- 2018
22. Approximating Flexibility in Distributed Energy Resources: A Geometric Approach
- Author
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Soumya Kundu, Scott Backhaus, and Karanjit Kalsi
- Subjects
Flexibility (engineering) ,business.industry ,Computer science ,020209 energy ,Distributed computing ,Control (management) ,Systems and Control (eess.SY) ,02 engineering and technology ,AC power ,Grid ,Energy storage ,Electric power system ,Optimization and Control (math.OC) ,Distributed generation ,Scalability ,ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION ,FOS: Electrical engineering, electronic engineering, information engineering ,FOS: Mathematics ,0202 electrical engineering, electronic engineering, information engineering ,Computer Science - Systems and Control ,business ,Mathematics - Optimization and Control - Abstract
With increasing availability of communication and control infrastructure at the distribution systems, it is expected that the distributed energy resources (DERs) will take an active part in future power systems operations. One of the main challenges associated with integration of DERs in grid planning and control is in estimating the available flexibility in a collection of (heterogeneous) DERs, each of which may have local constraints that vary over time. In this work, we present a geometric approach for approximating the flexibility of a DER in modulating its active and reactive power consumption. The proposed method is agnostic about the type and model of the DERs, thereby facilitating a plug-and-play approach, and allows scalable aggregation of the flexibility of a collection of (heterogeneous) DERs at the distributed system level. Simulation results are presented to demonstrate the performance of the proposed method., accepted for presentation at the Power Systems Computations Conference 2018
- Published
- 2018
23. Statistical Learning For DC Optimal Power Flow
- Author
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Scott Backhaus, Line Roald, Sidhant Misra, and Yeesian Ng
- Subjects
Operating point ,Mathematical optimization ,Basis (linear algebra) ,Computer science ,020209 energy ,Market clearing ,Control (management) ,02 engineering and technology ,Optimal control ,Electric power system ,Optimization and Control (math.OC) ,0202 electrical engineering, electronic engineering, information engineering ,FOS: Mathematics ,Affine transformation ,Mathematics - Optimization and Control ,Realization (probability) - Abstract
The optimal power flow problem plays an important role in the market clearing and operation of electric power systems. However, with increasing uncertainty from renewable energy operation, the optimal operating point of the system changes more significantly in real-time. In this paper, we aim at developing control policies that are able to track the optimal set-point with high probability. The approach is based on the observation that the OPF solution corresponding to a certain uncertainty realization is a basic feasible solution, which provides an affine control policy. The optimality of this basis policy is restricted to uncertainty realizations that share the same set of active constraints. We propose an ensemble control policy that combines several basis policies to improve performance. Although the number of possible bases is exponential in the size of the system, we show that only a few of them are relevant to system operation. We adopt a statistical learning approach to learn these important bases, and provide theoretical results that validate our observations. For most systems, we observe that efficient ensemble policies constructed using as few as ten bases, are able to obtain optimal solutions with high probability.
- Published
- 2018
24. Learning with End-Users in Distribution Grids: Topology and Parameter Estimation
- Author
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Sejun Park, Michael Chertkov, Deepjyoti Deka, and Scott Backhaus
- Subjects
Control and Optimization ,Correctness ,Computer Networks and Communications ,Computer science ,Estimation theory ,020209 energy ,020208 electrical & electronic engineering ,Topology (electrical circuits) ,02 engineering and technology ,Systems and Control (eess.SY) ,Topology ,Grid ,Network topology ,Electrical Engineering and Systems Science - Systems and Control ,Power (physics) ,Distribution (mathematics) ,Control and Systems Engineering ,Signal Processing ,Line (geometry) ,0202 electrical engineering, electronic engineering, information engineering ,FOS: Electrical engineering, electronic engineering, information engineering - Abstract
Efficient operation of distribution grids in the smart-grid era is hindered by the limited presence of real-time nodal and line meters. In particular, this prevents the easy estimation of grid topology and associated line parameters that are necessary for control and optimization efforts in the grid. This article studies the problems of topology and parameter estimation in radial balanced distribution grids where measurements are restricted to only the leaf nodes and all intermediate nodes are unobserved/hidden. To this end, we propose two exact learning algorithms that use balanced voltage and injection measured only at the end users. The first algorithm requires time-stamped voltage samples, statistics of nodal power injections, and permissible line impedances to recover the true topology. The second and improved algorithm requires only time-stamped voltage and complex power samples to recover both the true topology and impedances without any additional input (e.g., number of grid nodes, statistics of injections at hidden nodes, and permissible line impedances). We prove the correctness of both learning algorithms for grids where unobserved buses/nodes have a degree greater than three and discuss extensions to regimes where that assumption doesn't hold. Further, we present computational and, more important, the sample complexity of our proposed algorithm for joint topology and impedance estimation. We illustrate the performance of the designed algorithms through numerical experiments on the IEEE and custom power distribution models.
- Published
- 2018
- Full Text
- View/download PDF
25. Market Based Intraday Coordination of Electric and Natural Gas System Operation
- Author
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C. Russ Philbrick, Scott Backhaus, Aleksandr M. Rudkevich, Pablo A. Ruiz, Michael C. Caramanis, Anatoly Zlotnik, Aleksandr Beylin, Richard Hornby, Richard D. Tabors, and Evgeniy Goldis
- Subjects
Market based ,Market mechanism ,Natural gas ,business.industry ,020209 energy ,0202 electrical engineering, electronic engineering, information engineering ,02 engineering and technology ,Business ,Industrial organization - Published
- 2018
26. Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids
- Author
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Deepjyoti Deka, Michael Chertkov, and Scott Backhaus
- Subjects
FOS: Computer and information sciences ,Computer science ,020209 energy ,Gaussian ,Energy Engineering and Power Technology ,Topology (electrical circuits) ,Machine Learning (stat.ML) ,02 engineering and technology ,Systems and Control (eess.SY) ,Network topology ,Topology ,symbols.namesake ,Statistics - Machine Learning ,0202 electrical engineering, electronic engineering, information engineering ,FOS: Electrical engineering, electronic engineering, information engineering ,FOS: Mathematics ,Graphical model ,Electrical and Electronic Engineering ,Mathematics - Optimization and Control ,Tree (data structure) ,Conditional independence ,Optimization and Control (math.OC) ,symbols ,Computer Science - Systems and Control ,Probability distribution ,Random variable - Abstract
Distribution grid is the medium and low voltage part of a large power system. Structurally, the majority of distribution networks operate radially, such that energized lines form a collection of trees, i.e. forest, with a substation being at the root of any tree. The operational topology/forest may change from time to time, however tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This paper develops a learning framework to reconstruct radial operational structure of the distribution grid from synchronized voltage measurements in the grid subject to the exogenous fluctuations in nodal power consumption. To detect operational lines our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions of the nodal consumption and Gaussian injections in particular. Moreover, our algorithm applies to the practical case of unbalanced three-phase power flow. Algorithm performance is validated on AC power flow simulations over IEEE distribution grid test cases., Comment: 12 pages 9 figures
- Published
- 2018
- Full Text
- View/download PDF
27. Joint Expansion Planning for Natural Gas and Electric Transmission with Endogenous Market Feedbacks
- Author
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Seth Blumsack, Scott Backhaus, Conrado Borraz Sanchez, Russell Bent, and Pascal Van Hentenryck
- Subjects
Mathematical optimization ,Electric power transmission ,Natural gas ,business.industry ,020209 energy ,Convex optimization ,0202 electrical engineering, electronic engineering, information engineering ,Environmental science ,02 engineering and technology ,business ,Joint (geology) - Published
- 2018
28. Round-trip efficiency of fast demand response in a large commercial air conditioner
- Author
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Ian Beil, Scott Backhaus, and Ian A. Hiskens
- Subjects
Consumption (economics) ,Engineering ,business.industry ,Mechanical Engineering ,Building and Construction ,Energy consumption ,Reliability engineering ,Demand response ,Load management ,Air conditioning ,HVAC ,Electrical and Electronic Engineering ,business ,Operating cost ,Simulation ,Civil and Structural Engineering ,Efficient energy use - Abstract
Air conditioning (AC) of large commercial buildings represents an attractive target for many different forms of demand response (DR) including DR for ancillary services (AS) such as frequency regulation. The operating cost of such DR is typically discussed in terms of occupant discomfort. However at fast timescales, perturbations to well-functioning building controls may increase the total energy consumption relative to a baseline that does not provide DR ancillary services (DR-AS). The extra energy is a cost of control to the asset owner and should be factored into the cost of providing DR-AS. We performed DR experiments on a ∼30,000 m 2 office building, and at the 15-min time scale of these experiments, we find the extra energy consumption to be significant. Similar to battery energy storage, we express the energy cost in terms of a round-trip efficiency and use this metric in a simple economic analysis of the cost of frequency regulation from these resources and discuss potential impacts on advanced load management methods.
- Published
- 2015
29. Optimal Distributed Control of Reactive Power Via the Alternating Direction Method of Multipliers
- Author
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Scott Backhaus, Michael Chertkov, and Petr Šulc
- Subjects
Optimization problem ,Computer science ,020209 energy ,020208 electrical & electronic engineering ,Photovoltaic system ,Constrained optimization ,Energy Engineering and Power Technology ,Systems and Control (eess.SY) ,02 engineering and technology ,AC power ,Topology ,Power (physics) ,Optimization and Control (math.OC) ,Distributed algorithm ,FOS: Mathematics ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,Computer Science - Systems and Control ,Inverter ,Electrical and Electronic Engineering ,Mathematics - Optimization and Control ,Voltage - Abstract
We formulate the control of reactive power generation by photovoltaic inverters in a power distribution circuit as a constrained optimization that aims to minimize reactive power losses subject to finite inverter capacity and upper and lower voltage limits at all nodes in the circuit. When voltage variations along the circuit are small and losses of both real and reactive powers are small compared to the respective flows, the resulting optimization problem is convex. Moreover, the cost function is separable enabling a distributed, on-line implementation with node-local computations using only local measurements augmented with limited information from the neighboring nodes communicated over cyber channels. Such an approach lies between the fully centralized and local policy approaches previously considered. We explore protocols based on the dual ascent method and on the Alternating Direction Method of Multipliers (ADMM) and find that the ADMM protocol performs significantly better.
- Published
- 2014
30. Integrated Multi-Scale Data Analytics and Machine Learning for the Distribution Grid
- Author
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Anthony R. Florita, Deepjyoti Deka, Matthew J. Reno, Scott Backhaus, Ciaran Roberts, Sean Peisert, Michael Chertkov, Emma Stewart, Andrey Y. Lokhov, Philip Top, Valerie Hendrix, and Thomas J. King
- Subjects
0209 industrial biotechnology ,Analytics ,Computer science ,Interface (computing) ,02 engineering and technology ,010501 environmental sciences ,Machine learning ,computer.software_genre ,01 natural sciences ,Field (computer science) ,Machine Learning ,020901 industrial engineering & automation ,Resilience (network) ,0105 earth and related environmental sciences ,incipient failure ,validation ,business.industry ,Data stream mining ,Distribution Grid ,prediction ,Grid ,DER ,Smart grid ,Data analysis ,Artificial intelligence ,business ,verification ,computer - Abstract
We consider the field of machine learning and where it is both useful, and not useful, for the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper we consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors - such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals - such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis are becoming significant, with more data and multi-objective concerns. Efficient applications of analysis and the machine learning field are being considered in the loop. This paper describes benefits and limits of present machine-learning applications for use on the grid and presents a series of case studies that illustrate the potential benefits of developing advanced local multi-variate analytics machine-learning-based applications.
- Published
- 2017
31. Estimating topology and injection statistics in distribution grids with hidden nodes
- Author
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Deepjyoti Deka, Scott Backhaus, and Michael Chertkov
- Subjects
Smart grid ,Distribution (mathematics) ,Computer science ,Feature (computer vision) ,020209 energy ,Line (geometry) ,0202 electrical engineering, electronic engineering, information engineering ,Adjacency list ,Topology (electrical circuits) ,02 engineering and technology ,Graphical model ,Grid ,Topology - Abstract
Topology estimation is a critical problem in distribution grids that affect real-time control and optimization of grid operations. It is however hindered by the limited presence of real-time meters at the grid nodes and lines. This paper discusses a framework to learn the radial topology in distribution grids using limited nodal meters and no line meters. The most significant feature of the learning framework is that it is able to provably learn the topology as long as two hidden nodes (without meters) are not adjacency to one another in the operational grid. Our learning problem allows for greater number of hidden nodes than known prior work in this area. Further the algorithm does not require historical data for the hidden nodes and estimates their injection covariances. The learning algorithm uses ordered trends as well as equality constraints in nodal voltage fluctuations that arise from the radial topology. The efficiency of the designed algorithm is discussed by presenting simulation results for topology recovery in test radial grids.
- Published
- 2017
32. Coordinated scheduling for interdependent electric power and natural gas infrastructures
- Author
-
Michael Chertkov, Line Roald, Göran Andersson, Anatoly Zlotnik, and Scott Backhaus
- Subjects
Economic efficiency ,Engineering ,Computer science ,business.industry ,020209 energy ,Market clearing ,Energy Engineering and Power Technology ,Electric generator ,Control engineering ,02 engineering and technology ,7. Clean energy ,Reliability engineering ,law.invention ,Electric power system ,law ,Natural gas ,0202 electrical engineering, electronic engineering, information engineering ,Electricity ,Electric power ,Electrical and Electronic Engineering ,business ,Gas compressor - Abstract
The extensive installation of gas-fired power plants in many parts of the world has led electric systems to depend heavily on reliable gas supplies. The use of gas-fired generators for peak load and reserve provision causes high intraday variability in withdrawals from high-pressure gas transmission systems. Such variability can lead to gas price fluctuations and supply disruptions that affect electric generator dispatch, electricity prices, and threaten the security of power systems and gas pipelines. These infrastructures function on vastly different spatio-temporal scales, which prevents current practices for separate operations and market clearing from being coordinated. In this paper, we apply new techniques for control of dynamic gas flows on pipeline networks to examine day-ahead scheduling of electric generator dispatch and gas compressor operation for different levels of integration, spanning from separate forecasting, and simulation to combined optimal control. We formulate multiple coordination scenarios and develop tractable physically accurate computational implementations. These scenarios are compared using an integrated model of test networks for power and gas systems with 24 nodes and 24 pipes, respectively, which are coupled through gas-fired generators. The analysis quantifies the economic efficiency and security benefits of gas-electric coordination and dynamic gas system operation.
- Published
- 2017
33. Optimal topology design for disturbance minimization in power grids
- Author
-
Harsha Nagarajan, Deepjyoti Deka, and Scott Backhaus
- Subjects
0209 industrial biotechnology ,Spanning tree ,Computer science ,020209 energy ,Graph theory ,Systems and Control (eess.SY) ,02 engineering and technology ,Combinatorial topology ,Network topology ,Topology ,020901 industrial engineering & automation ,Optimization and Control (math.OC) ,Robustness (computer science) ,FOS: Electrical engineering, electronic engineering, information engineering ,FOS: Mathematics ,0202 electrical engineering, electronic engineering, information engineering ,Computer Science - Systems and Control ,Algorithm design ,Minification ,Robust control ,Mathematics - Optimization and Control - Abstract
The transient response of power grids to external disturbances influences their stable operation. This paper studies the effect of topology in linear time-invariant dynamics of different power grids. For a variety of objective functions, a unified framework based on $H_2$ norm is presented to analyze the robustness to ambient fluctuations. Such objectives include loss reduction, weighted consensus of phase angle deviations, oscillations in nodal frequency, and other graphical metrics. The framework is then used to study the problem of optimal topology design for robust control goals of different grids. For radial grids, the problem is shown as equivalent to the hard "optimum communication spanning tree" problem in graph theory and a combinatorial topology construction is presented with bounded approximation gap. Extended to loopy (meshed) grids, a greedy topology design algorithm is discussed. The performance of the topology design algorithms under multiple control objectives are presented on both loopy and radial test grids. Overall, this paper analyzes topology design algorithms on a broad class of control problems in power grid by exploring their combinatorial and graphical properties., 6 pages, 3 figures, a version of this work will appear in ACC 2017
- Published
- 2017
34. An experimental study of energy consumption in buildings providing ancillary services
- Author
-
Johanna L. Mathieu, Ian A. Hiskens, Salman Nazir, Scott Backhaus, Yashen Lin, John P. Wolfe, Drew Geller, Arthur K. Barnes, Jeremiah X. Johnson, and Sina Afshari
- Subjects
Consumption (economics) ,Engineering ,business.industry ,Settling time ,020209 energy ,Airflow ,02 engineering and technology ,Energy consumption ,Automotive engineering ,law.invention ,law ,Air conditioning ,Ventilation (architecture) ,HVAC ,0202 electrical engineering, electronic engineering, information engineering ,Waveform ,business ,Simulation - Abstract
Heating, ventilation, and air conditioning (HVAC) systems in commercial buildings can provide ancillary services (AS) to the power grid, but by providing AS their energy consumption may increase. This inefficiency is evaluated using round-trip efficiency (RTE), which is defined as the ratio between the decrease and the increase in the HVAC system's energy consumption compared to the baseline consumption as a result of providing AS. This paper evaluates the RTE of a 30,000 m2 commercial building providing AS. We propose two methods to estimate the HVAC system's settling time after an AS event based on temperature and the air flow measurements from the building. Experimental data gathered over a 4-month period are used to calculate the RTE for AS signals of various waveforms, magnitudes, durations, and polarities. The results indicate that the settling time estimation algorithm based on the air flow measurements obtains more accurate results compared to the temperature-based algorithm. Further, we study the impact of the AS signal shape parameters on the RTE and discuss the practical implications of our findings.
- Published
- 2017
35. Optimal Transmission Line Switching under Geomagnetic Disturbances
- Author
-
Mowen Lu, Russell Bent, Scott Backhaus, Arthur K. Barnes, Harsha Nagarajan, and Emre Yamangil
- Subjects
Engineering ,Optimization problem ,business.industry ,020209 energy ,Energy Engineering and Power Technology ,02 engineering and technology ,Systems and Control (eess.SY) ,AC power ,law.invention ,Geomagnetically induced current ,Electric power system ,Electric power transmission ,law ,Transmission line ,0202 electrical engineering, electronic engineering, information engineering ,Electronic engineering ,FOS: Electrical engineering, electronic engineering, information engineering ,Computer Science - Systems and Control ,Electrical and Electronic Engineering ,Alternating current ,business ,Transformer - Abstract
In recent years, there have been increasing concerns about how geomagnetic disturbances (GMDs) impact electrical power systems. Geomagnetically induced currents (GICs) can saturate transformers, induce hot spot heating, and increase reactive power losses. These effects can potentially cause catastrophic damage to transformers and severely impact the ability of a power system to deliver power. To address this problem, we develop a model of GIC impacts to power systems that includes 1) GIC thermal capacity of transformers as a function of normal alternating current (AC), and 2) reactive power losses as a function of GIC. We use this model to derive an optimization problem that protects power systems from GIC impacts through line switching, generator redispatch, and load shedding. We employ state-of-the-art convex relaxations of AC power flow equations to lower bound the objective. We demonstrate the approach on a modified RTS96 system and the UIUC 150-bus system and show that line switching is an effective means to mitigate GIC impacts. We also provide a sensitivity analysis of optimal switching decisions with respect to GMD direction.
- Published
- 2017
36. Model Development and Identification for Fast Demand Response in Commercial HVAC Systems
- Author
-
Scott Backhaus, Joseph Klose, and Gary Goddard
- Subjects
Engineering ,State variable ,General Computer Science ,business.industry ,System identification ,ComputerApplications_COMPUTERSINOTHERSYSTEMS ,Control engineering ,System model ,Demand response ,Identification (information) ,Resource (project management) ,HVAC ,business ,Building automation - Abstract
Large commercial HVAC systems are attractive targets for fast demand response (DR) applications, e.g., integrating time-intermittent renewable generation. By leveraging the communications in the building automation system (BAS) already present in most buildings, large commercial HVAC systems provide easier access to a large controllable resource than aggregating a large number of small residential loads. However, large commercial HVAC systems are complex with many variables, many end point controllers, and several internal control loops that interact with each other. In addition, the existing fleet of large commercial buildings is diverse with many different HVAC configurations and BAS architectures. Capturing these buildings as DR resources requires a method to greatly reduce the complexity of the HVAC DR control and is general and flexible enough that it can be easily deployed across the diverse fleet of existing buildings. We create such a DR control by developing a system model that uses a single state variable instead of the several hundred variables in a commercial HVAC system. The model includes a small number of system parameters, and we demonstrate how their values can be determined via system identification measurements. Finally, we test our model on a large commercial HVAC system to investigate its control performance.
- Published
- 2014
37. Detection of Cyber-Physical Faults and Intrusions from Physical Correlations
- Author
-
Nathan Lemons, Scott Backhaus, Thomas C. McAndrew, Andrey Y. Lokhov, and Aric Hagberg
- Subjects
FOS: Computer and information sciences ,Physics - Physics and Society ,Computer science ,Distributed computing ,FOS: Physical sciences ,Systems and Control (eess.SY) ,Physics and Society (physics.soc-ph) ,02 engineering and technology ,Statistics - Applications ,01 natural sciences ,Set (abstract data type) ,010104 statistics & probability ,020204 information systems ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,Applications (stat.AP) ,0101 mathematics ,Building automation ,Social and Information Networks (cs.SI) ,Mathematical model ,business.industry ,Cyber-physical system ,Computer Science - Social and Information Networks ,Identification (information) ,Physics - Data Analysis, Statistics and Probability ,Key (cryptography) ,Computer Science - Systems and Control ,business ,Data Analysis, Statistics and Probability (physics.data-an) ,Energy (signal processing) - Abstract
Cyber-physical systems are critical infrastructures that are crucial both to the reliable delivery of resources such as energy, and to the stable functioning of automatic and control architectures. These systems are composed of interdependent physical, control and communications networks described by disparate mathematical models creating scientific challenges that go well beyond the modeling and analysis of the individual networks. A key challenge in cyber-physical defense is a fast online detection and localization of faults and intrusions without prior knowledge of the failure type. We describe a set of techniques for the efficient identification of faults from correlations in physical signals, assuming only a minimal amount of available system information. The performance of our detection method is illustrated on data collected from a large building automation system., 10 pages, 9 figures
- Published
- 2016
38. Cyber-Physical Security: A Game Theory Model of Humans Interacting Over Control Systems
- Author
-
James W. Bono, Dongping Xie, Ritchie Lee, Scott Backhaus, David H. Wolpert, Yildiray Yildiz, Brendan D. Tracey, and Russell Bent
- Subjects
FOS: Computer and information sciences ,Computer Science - Cryptography and Security ,General Computer Science ,Computer science ,Cyber-physical system ,Systems and Control (eess.SY) ,Computer security ,computer.software_genre ,Grid ,Outcome (game theory) ,Computer Science - Computers and Society ,Operator (computer programming) ,Smart grid ,Computer Science - Computer Science and Game Theory ,Software deployment ,Control system ,Computers and Society (cs.CY) ,FOS: Electrical engineering, electronic engineering, information engineering ,Computer Science - Systems and Control ,Cryptography and Security (cs.CR) ,computer ,Game theory ,Computer Science and Game Theory (cs.GT) - Abstract
Recent years have seen increased interest in the design and deployment of smart grid devices and control algorithms. Each of these smart communicating devices represents a potential access point for an intruder spurring research into intruder prevention and detection. However, no security measures are complete, and intruding attackers will compromise smart grid devices leading to the attacker and the system operator interacting via the grid and its control systems. The outcome of these machine-mediated human-human interactions will depend on the design of the physical and control systems mediating the interactions. If these outcomes can be predicted via simulation, they can be used as a tool for designing attack-resilient grids and control systems. However, accurate predictions require good models of not just the physical and control systems, but also of the human decision making. In this manuscript, we present an approach to develop such tools, i.e. models of the decisions of the cyber-physical intruder who is attacking the systems and the system operator who is defending it, and demonstrate its usefulness for design., Comment: 8 pages, 7 figures, IEEE Transactions on Smart Grids pending
- Published
- 2013
39. Getting a grip on the electrical grid
- Author
-
Scott Backhaus and Michael Chertkov
- Subjects
business.industry ,Computer science ,Electrical engineering ,General Physics and Astronomy ,business ,Electrical grid ,GeneralLiterature_MISCELLANEOUS - Abstract
As our electrical grid systems become smarter and more autonomous, they require greater control technologies to protect them from failing.
- Published
- 2013
40. Learning topology of distribution grids using only terminal node measurements
- Author
-
Deepjyoti Deka, Scott Backhaus, and Michael Chertkov
- Subjects
Computational complexity theory ,020209 energy ,Topology (electrical circuits) ,Systems and Control (eess.SY) ,02 engineering and technology ,Topology ,Grid ,Smart grid ,Optimization and Control (math.OC) ,FOS: Mathematics ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,Computer Science - Systems and Control ,Graph (abstract data type) ,Node (circuits) ,Algorithm design ,Observability ,Mathematics - Optimization and Control ,Mathematics - Abstract
Distribution grids include medium and low voltage lines that are involved in the delivery of electricity from substation to end-users/loads. A distribution grid is operated in a radial/tree-like structure, determined by switching on or off lines from an underling loopy graph. Due to the presence of limited real-time measurements, the critical problem of fast estimation of the radial grid structure is not straightforward. This paper presents a new learning algorithm that uses measurements only at the terminal or leaf nodes in the distribution grid to estimate its radial structure. The algorithm is based on results involving voltages of node triplets that arise due to the radial structure. The polynomial computational complexity of the algorithm is presented along with a detailed analysis of its working. The most significant contribution of the approach is that it is able to learn the structure in certain cases where available measurements are confined to only half of the nodes. This represents learning under minimum permissible observability. Performance of the proposed approach in learning structure is demonstrated by experiments on test radial distribution grids., A version of this paper will appear in IEEE Smartgridcomm 2016 (7 pages, 7 figures)
- Published
- 2016
41. Plume dynamics in Hele-Shaw porous media convection
- Author
-
Robert E. Ecke and Scott Backhaus
- Subjects
Convection ,Mass transport ,Molecular diffusion ,Materials science ,Meteorology ,General Mathematics ,Dynamics (mechanics) ,General Engineering ,General Physics and Astronomy ,Mechanics ,Articles ,01 natural sciences ,humanities ,010305 fluids & plasmas ,Plume ,Physics::Geophysics ,Physics::Fluid Dynamics ,0103 physical sciences ,010306 general physics ,Porous medium ,Physics::Atmospheric and Oceanic Physics - Abstract
Mass transport in multi-species porous media is through molecular diffusion and plume dynamics. Predicting the rate of mass transport has application in determining the efficiency of the storage and sequestration of carbon dioxide. We study a water and propylene–glycol system enclosed in a Hele-Shaw cell with variable permeability that represents a laboratory analogue of the general properties of porous media convection. The interface between the fluids, tracked using an optical shadowgraph technique, is used to determine the mass transport rate, the spatial separation of solutal plumes, and the velocity and width characteristics of those plumes. One finds that the plume dynamics are closely related to the mass transport rate. This article is part of the themed issue ‘Energy and the subsurface’.
- Published
- 2016
42. Networked Microgrids Scoping Study
- Author
-
Salman Mashayekh, Larisa Dobriansky, Scott Backhaus, Michael Stadler, Meng Yue, Chen-Ching Liu, Steve Glover, Annabelle Pratt, Michael Starke, Jianhui Wang, Kevin P. Schneider, and Patrick Looney
- Subjects
Engineering ,Subject-matter expert ,Process management ,Management science ,business.industry ,Research needs ,Microgrid ,Energy planning ,Scoping study ,business - Abstract
Much like individual microgrids, the range of opportunities and potential architectures of networked microgrids is very diverse. The goals of this scoping study are to provide an early assessment of research and development needs by examining the benefits of, risks created by, and risks to networked microgrids. At this time there are very few, if any, examples of deployed microgrid networks. In addition, there are very few tools to simulate or otherwise analyze the behavior of networked microgrids. In this setting, it is very difficult to evaluate networked microgrids systematically or quantitatively. At this early stage, this study is relying on inputs, estimations, and literature reviews by subject matter experts who are engaged in individual microgrid research and development projects, i.e., the authors of this study The initial step of the study gathered input about the potential opportunities provided by networked microgrids from these subject matter experts. These opportunities were divided between the subject matter experts for further review. Part 2 of this study is comprised of these reviews. Part 1 of this study is a summary of the benefits and risks identified in the reviews in Part 2 and synthesis of the research needs required to enable networked microgrids.
- Published
- 2016
43. Experimental Determination of Demand Response Control Models and Cost of Control for Ensembles of Window-Mount Air Conditioners
- Author
-
Drew Geller and Scott Backhaus
- Subjects
Demand response ,Energy conservation ,Computer science ,Air conditioning ,business.industry ,business ,Electrical grid ,Automotive engineering ,Simulation ,Control models - Abstract
Control of consumer electrical devices for providing electrical grid services is expanding in both the scope and the diversity of loads that are engaged in control, but there are few experimentally-based models of these devices suitable for control designs and for assessing the cost of control. A laboratory-scale test system is developed to experimentally evaluate the use of a simple window-mount air conditioner for electrical grid regulation services. The experimental test bed is a single, isolated air conditioner embedded in a test system that both emulates the thermodynamics of an air conditioned room and also isolates the air conditioner from the real-world external environmental and human variables that perturb the careful measurements required to capture a model that fully characterizes both the control response functions and the cost of control. The control response functions and cost of control are measured using harmonic perturbation of the temperature set point and a test protocol that further isolates the air conditioner from low frequency environmental variability.
- Published
- 2016
44. Tractable Structure Learning in Radial Physical Flow Networks
- Author
-
Deepjyoti Deka, Michael Chertkov, and Scott Backhaus
- Subjects
Computational complexity theory ,Computer science ,020209 energy ,Node (networking) ,Monotonic function ,02 engineering and technology ,Function (mathematics) ,Systems and Control (eess.SY) ,Minimum spanning tree ,Topology ,Network topology ,Graph ,Flow (mathematics) ,Optimization and Control (math.OC) ,0202 electrical engineering, electronic engineering, information engineering ,FOS: Mathematics ,FOS: Electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,Computer Science - Systems and Control ,Mathematics - Optimization and Control - Abstract
Physical Flow Networks are different infrastructure networks that allow the flow of physical commodities through edges between its constituent nodes. These include power grid, natural gas transmission network, water pipelines etc. In such networks, the flow on each edge is characterized by a function of the nodal potentials on either side of the edge. Further the net flow in and out of each node is conserved. Learning the structure and state of physical networks is necessary for optimal control as well as to quantify its privacy needs. We consider radial flow networks and study the problem of learning the operational network from a loopy graph of candidate edges using statistics of nodal potentials. Based on the monotonic properties of the flow functions, the key result in this paper shows that if variance of the difference of nodal potentials is used to weight candidate edges, the operational edges form the minimum spanning tree in the loopy graph. Under realistic conditions on the statistics of nodal injection (consumption or production), we provide a greedy structure learning algorithm with quasilinear computational complexity in the number of candidate edges in the network. Our learning framework is very general due to two significant attributes. First it is independent of the specific marginal distributions of nodal potentials and only uses order properties in their second moments. Second, the learning algorithm is agnostic to exact flow functions that relate edge flows to corresponding potential differences and is applicable for a broad class of networks with monotonic flow functions. We demonstrate the efficacy of our work through realistic simulations on diverse physical flow networks and discuss possible extensions of our work to other regimes., A version of this paper will appear in IEEE CDC 2016 (8 pages, 5 figures)
- Published
- 2016
45. Uncertainty sets for wind power generation
- Author
-
Yury Dvorkin, Scott Backhaus, Miles Lubin, and Michael Chertkov
- Subjects
Mathematical optimization ,Engineering ,Wind power generation ,Wind power ,business.industry ,020209 energy ,Energy Engineering and Power Technology ,02 engineering and technology ,Wind speed ,Reliability engineering ,Wind profile power law ,Conflicting objectives ,Optimization and Control (math.OC) ,FOS: Mathematics ,0202 electrical engineering, electronic engineering, information engineering ,Probability distribution ,Transmission system operator ,Electrical and Electronic Engineering ,business ,Mathematics - Optimization and Control - Abstract
As penetration of wind power generation increases, system operators must account for its stochastic nature in a reliable and cost-efficient manner. These conflicting objectives can be traded-off by accounting for the variability and uncertainty of wind power generation. This letter presents a new methodology to estimate uncertainty sets for parameters of probability distributions that capture wind generation uncertainty and variability.
- Published
- 2016
46. Control policies for operational coordination of electric power and natural gas transmission systems
- Author
-
Anatoly Zlotnik, Michael Chertkov, Line Roald, Göran Andersson, and Scott Backhaus
- Subjects
Engineering ,business.industry ,020209 energy ,Electric generator ,Control engineering ,02 engineering and technology ,Transmission system ,Automotive engineering ,law.invention ,Electric power system ,Natural gas ,law ,Distributed generation ,0202 electrical engineering, electronic engineering, information engineering ,Grid energy storage ,Electric power ,business ,Gas compressor ,Astrophysics::Galaxy Astrophysics - Abstract
The abundance of natural gas in the United States and the need for cleaner electric power have prompted widespread installation of gas-fired power plants and caused electric power systems to depend heavily on reliable gas supplies. The use of gas generators for peak load and reserve generation causes high intra-day variability in withdrawals from high pressure gas transmission systems, which leads to gas price fluctuations and supply disruptions that affect electric generator dispatch and threaten the security of both power and gas systems. In this manuscript, we investigate different gas compressor operation policies and their influence on the affected power system. Specifically, we consider constant pressure boost ratios and dynamic adjustment of these ratios to track pressure set-points. We also implement a joint optimization of generator dispatch schedules and gas compressor protocols using a dynamic gas flow model. We develop tractable, physically accurate implementations that are compared using an integrated model of test networks for power and gas systems with 24 and 25 nodes, which are coupled through gas-fired generators. This demonstrates the benefits that can be achieved with globally optimized gas system operations and increased gas-electric coordination.
- Published
- 2016
47. Optimal Resilient transmission Grid Design
- Author
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Harsha Nagarajan, Pascal Van Hentenryck, Russell Bent, Scott Backhaus, and Emre Yamangil
- Subjects
Engineering ,business.industry ,020209 energy ,media_common.quotation_subject ,02 engineering and technology ,Transmission system ,law.invention ,Reliability engineering ,Electric power system ,Extreme weather ,Upgrade ,Electric power transmission ,law ,0202 electrical engineering, electronic engineering, information engineering ,Electronic engineering ,Psychological resilience ,Electric power ,Transformer ,business ,media_common - Abstract
As illustrated in recent years (Superstorm Sandy, Northeast Ice Storm of 1998, etc.), extreme weather events pose an enormous threat to the electric power transmission systems and the associated socio-economic systems that depend on reliable delivery of electric power. These threats motivate the need for approaches and methods that improve the response (resilience) of power systems. In this paper, we develop a model and tractable methods for optimizing the upgrade of transmission systems through a combination of hardening existing components, adding redundant lines, switches, generators, and transformers. While many of these components are included in traditional design (expansion planning) problems, we uniquely assess their benefits from a resiliency point of view.
- Published
- 2016
48. Optimal power flow with wind power control and limited expected risk of overloads
- Author
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Line Roald, Göran Andersson, Scott Backhaus, Sidhant Misra, and Michael Chertkov
- Subjects
Engineering ,Wind power ,business.industry ,020209 energy ,Control engineering ,02 engineering and technology ,Grid parity ,Reliability engineering ,Power optimizer ,Stand-alone power system ,Electric power system ,Electricity generation ,Base load power plant ,0202 electrical engineering, electronic engineering, information engineering ,Power-flow study ,business - Abstract
Over the past years, the share of electricity production from wind power plants has increased to significant levels in several power systems across Europe and the United States. In order to cope with the fluctuating and partially unpredictable nature of renewable energy sources, transmission system operators (TSOs) have responded by requiring wind power plants to be capable of providing reserves or following active power set-point signals. This paper addresses the issue of efficiently incorporating these new types of wind power control in the day-ahead operational planning. We review the technical requirements the wind power plants must fulfill, and propose a mathematical framework for optimizing wind power control. The framework is based on an optimal power flow formulation with weighted chance constraints, which accounts for the uncertainty of wind power forecasts and allows us to limit the expected risk of constraint violations. In a case study based on the IEEE 118 bus system, we use the developed method to assess the effectiveness of different types of wind power control in terms of operational cost, system security and wind power curtailment.
- Published
- 2016
49. Estimating distribution grid topologies: A graphical learning based approach
- Author
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Scott Backhaus, Deepjyoti Deka, and Michael Chertkov
- Subjects
Computer science ,020209 energy ,Topology (electrical circuits) ,Systems and Control (eess.SY) ,02 engineering and technology ,Topology ,Network topology ,Grid ,Conditional independence ,Optimization and Control (math.OC) ,FOS: Mathematics ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,Computer Science - Systems and Control ,Probability distribution ,Graphical model ,Observability ,Mathematics - Optimization and Control ,Low voltage - Abstract
Distribution grids represent the final tier in electric networks consisting of medium and low voltage lines that connect the distribution substations to the end-users. Traditionally, distribution networks have been operated in a radial topology that may be changed from time to time. Due to absence of a significant number of real-time line monitoring devices in the distribution grid, estimation of the topology is a problem critical for its observability and control. This paper develops a novel graphical learning based approach to estimate the radial operational grid structure using voltage measurements collected from the grid loads. The learning algorithm is based on conditional independence tests for continuous variables over chordal graphs and has wide applicability. It is proven that the scheme can be used for several power flow laws (DC or AC approximations) and more importantly is independent of the specific probability distribution controlling individual bus power usage. The complexity of the algorithm is discussed and its performance is demonstrated by simulations on distribution test cases., 7 pages, 4 figures, A version of this paper will appear in PSCC 2016
- Published
- 2016
50. A Lyapunov Function Based Remedial Action Screening Tool Using Real-Time Data
- Author
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Joydeep Mitra, Omar Faruque, Scott Backhaus, Mohammed Benidris, and Sidart Deb
- Subjects
Remedial action ,Lyapunov function ,symbols.namesake ,Control theory ,Computer science ,symbols ,Screening tool ,Real-time data - Published
- 2016
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