Deploying the more sustainable and resilient electric grid of the future requiresa sophisticatedusage of data. This begins with sensorsand measurement infrastructurecollecting a wide range of grid-relevant data, butalsoincludes various forms of analytics to usethedata tosolvea wide range ofgrid problems.Many advanced analytics methodsalreadyarebeing used,includingartificial intelligence and machine learning.Now,forward-looking electric utilities are exploringthe next step in enhancing these analytics,by understandinghow emerging computing technologies can be leveraged to provide higher levels of service. Among the mostcompellingexamples of this is the potential use of quantum computing for grid purposes.
This rapid evolution is happening in part toaccommodate additional distributed energy resources (DERs)on the grid, including the solarphotovoltaic (PV)and energy storage that helptoreduce emissions bylimitingthe need for fossil-fuel power plants. High levels of DER penetration not only necessitate reform in traditional grid planning and operation, but also facilitate unprecedented grid modernization to accommodate new types of loads (for example,electric vehicles)andbidirectional power transfer.
Electric utilities like Commonwealth Edison(ComEd)are in a unique position to develop and deploy grid-optimizing technologies to meet the demands of evolving systems and build a scalable model for the grid of the future.Serving over 4 million customers in northern Illinois and Chicago,Illinois, U.S.,ComEd ispartnering with leading academic institutionsincluding the University of Denver and the University of Chicago andleveraging its position as one of the largest electric utilities in theU.S.to explorequantumcomputing applications forgrid purposes.
What Is Quantum Computing?
The major difference between classical and quantum computers is in the way they process information.Whereas classical computing bits are either 0 or 1, quantum bits (qubits) can be both 0 and 1 at the same timethrougha unique quantum property called superposition. For example, an electron can be used as a qubit because it can simultaneously occupy its ground state (0) and its excited state (1).
Moreover, this superposition phenomenon scales exponentially. For example, two qubitscanoccupy four statessimultaneously: 00, 01, 10 and 11. More generally, N qubits can represent an exponential number of states (2N) at once, enabling a quantum computer to process all these states rapidly.This exponential advantageis the salient feature of quantum computers, enabling faster calculations in specific applications,such as factoringlargenumbers and searching datasets.
ComEd cohosted a workshop that brought together a dozen leaders in quantum computing and power systems to help determine the future applications of quantum computing for the grid.
A superconducting quantum computer from Professor David Schuster's laboratory at UChicago that can help drive the field forward. Credit: Yongshan Ding.
The data from these advanced sensors can be leveraged from quantum computing to provide higher levels of grid resiliency and support DER integration.
QuantumComputingApplications
To identify potential applications forquantumcomputing in the grid of the future,ComEdcohosted a workshop on Feb.27, 2020,with researchers from the University of Chicago,the University of Denverand Argonne NationalLaboratory. The purpose of theworkshop was to explore the potential benefitsquantumcomputingcouldbring to power systemsand collaborate on developing technologies that couldbe demonstrated to provide this value.
Recognizing these two fields historicallyhavenot been in close contact, the workshop began with two tutorial sessions, one forpowersystems and another forquantumcomputing, to provide backgroundonthe stateoftheart of the respective fields as well as the emerging challengesof each. Following the tutorial sessions, a technical discussionincludedbrainstormingpotential applications of existingquantumcomputing algorithms on large-scale power system problems requiring heavy computational resources.Followingare severalpotential power systemsapplicationsofquantum computingin deployingthe grid of the future.
Unit Commitment
Optimal system schedulingin particular,unit commitment(UC)is one of the most computationally intensive problems in power systems. UCis a nonlinear, nonconvexoptimizationproblem with a multitude of binary and continuous variables. There have been extensive and continuous efforts to improve the solutiontothis problem, from both optimality and execution time points of view. Recent advances in power systems, such astheintegration of variable renewable energy resources andagrowing number of customer-ownedgeneration units, add another level of difficulty to this problem and make it even harder to solve.
Quantum optimization may solve the UC problem fasterthancurrent models used in classical computers. Thequantumapproximateoptimizationalgorithm(QAOA),analgorithm for quantum computers designed to solve complex combinatorial problems,may be wellsuited for the UC problem. While QAOA was designed for discrete combinatorial optimization, several interesting research directions could relaxthe algorithmto be compatible with mixed-integer programming tasksused inUC.
Contingency Analysis
Another potentialapplicationinvolvescontingency analysis. Traditional power system operators tend to assess system reliability byanalyzingN-1 contingency, to ensure thesystemcan maintainadequatepower flowduringone-at-a-time equipment outages. Systemoperators usually run this study after obtaining a state estimator solution todetermine whethersystem status is still within the acceptable operating condition.
Advanced computing capabilities like quantum computing can support the integration of clean energy generation like this deployment as part of the Bronzeville Community Microgrid.
The high-riskN-k contingencyhas beenintroduced toobtainbetter situational awareness. However, the combinatorial explosion in potential scenarios greatly challenges the existing computing power. Quantum computers could helptoaddress N-k scenarios by enabling access to an exponentially expanded state space.
State Estimation
Quantumcomputingalsohas the potential to enable large-scale distribution systemhybridstate estimation with phasor measurement units (PMUs)and advanced meteringinfrastructure (AMI).Utilitiesalreadyhave deployedthousandsofPMUsand millionsofsmart metersacross the grid that provide data toacentral management system. PMUsprovide time-synchronized three-phase voltage and current measurements at speeds up to 60 samples per second, which allow for linear state estimation at similar speeds.AMI provides voltage and energy measurementsat customer siteswith differenttimeresolutions.
As thesystem becomes more complex, the computationrequiredto usemany measurements estimating the states of apracticalnetwork increasesaccordingly. QAOA provides a promising path for state estimation withPMUsor hybrid state estimation with both PMUsand AMIata speed believed to be unachievable byclassicalcomputers. In addition, QAOA is within the computing capabilities of near-term quantum computers,called noisy intermediate-scale quantum(NISQ),now available.
AccurateForecasting
When it comes to system operation, forecasting is another issuequantumcomputing could address.The high volatility ofDERs, such assolar andwind, may disturb normal system operation and underminethesystems reliability. Accurate forecastingof variable generationwouldenablesystem operators to act proactively to avoid potential system frequency disturbances and stability concerns.
Quantumcomputing couldmake it possible to consider abroaderrange of data for forecasting (such as detailed weather projections and trends) and achieve a much more accurate forecast.The workshop identified Boltzmannas a potentially effective method to tackle this problem. In particular, thequantum Boltzmannmachine (QBM) is a model that has significantly greater representational power than traditional Boltzmannmachines. QBMsalreadyhavebeen experimentally realized on currently availablequantum computers.
AddressingUncertainties
An inherent part of modern power gridsistheuncertaintystemmingfrom various sources (such asvariable generation, component failures, customer behavior, extreme weatherandnatural disasters). Uncertainties cannot be controlled by grid operators, so the common practice is to define potential scenarios and plan for themaccordingly.However, these scenarioscanbe significantin some cases, making it extremely challenging to devise a viable plan for grid operation and asset management.
Quantum computers capabilityto solve numerous scenarios simultaneouslycould beuseful in addressing uncertainty in power systems. Quantum algorithms under development by financial firmsalsomaybe directly translatable to addressing uncertainties in power grids.
StudyingThese Applications
As part of thebroader collaboration,the University of Denver teamhas beenawarded a grant to study some of theapplicationsof quantum computing in power grids.Awarded by theColorado Office of Economic Development & International Trade,the grantaimstoexplorequantum computing-enhanced security and sustainability for next-generation smart grids. In particular, the team will investigate the quantum solution of the power flow problem as the most fundamentalcomputationalanalysis in power systems.
The workshop also identified that practical applications of quantum computing may soon be possible thanks to the development of quantum hardware.In 2019,Googleconducted aquantum supremacy experimentby running asimple program on a small quantum computer in secondsthatwould have taken days on the worlds largest supercomputer. IBM recently released a technology roadmapin whichmachineswilldoublein sizeoverthe next few years, with a target of over 1000 quantum bitsby2023whichlikelywould belarge enough for many of thepotentialpower gridapplications.
A Quantum Leap
The 2020 workshopthat ComEd,theUniversity of Chicago andtheUniversity of Denver engaged inhas only scratched the surface ofquantumcomputingas a new paradigm to solve complex energy system issues. However, this first step presents a path toward understanding the capabilities ofquantumcomputing and the role it can play in optimizing energy systems.That path toward understanding is best taken together, as academics and engineers,government and institutions,andutilitiescollaborate to share knowledge to build theelectricgrid of the future.
ComEdand the two universities have sustained a bimonthlycollaboration since the workshopto explorepower systems applications of quantum computing.Some preliminary results on quantum computing approaches to theUCproblem were presentedbytheUniversity of Chicago in the IEEE 2020 Quantum Week.As this collaboration develops, it becomes increasingly likely the next generation of grid technologies will engage the quantum possibilities of ones, zeros and everything in between.
Honghao Zheng(honghao.zheng@comed.com)isaprincipalquantitativeengineer insmart grid emerging technology atCommonwealthEdison(ComEd),where he supportsnew technology ideation, industrialresearch and development,and complex project execution. Prior to ComEd,heworkedasatechnical leadof Spectrum PowerOperator Training Simulator and TransmissionNetwork Applicationsmodulesfor Siemens DG SWS.ZhengreceivedhisPh.D. inelectricalengineering fromtheUniversity ofWisconsin-Madison in 2015.
Ryan Burg(ryan.s.burg@comed.com)is aprincipalbusinessanalyst insmartgridprograms at ComEd,where he supports academic partnerships. He previously taught sustainable management and business ethics at Bucknell, HSE and Georgetown Universities.Burgholds a joint Ph.D.in sociology and business ethics from the Wharton School of Businessof the University of Pennsylvania.
AleksiPaaso(esa.paaso@comed.com)is director ofdistributionplanning,smartgridandinnovation at ComEd, where he is responsible for distribution planning activities, distributed energy resource (DER) interconnection, andsmart grid strategy and project execution. He is a senior member ofthe IEEE and technical co-chair for the 2020 IEEE PES Transmission & Distribution Conference and Exposition. He holds a Ph.D.in electrical engineering from the University of Kentucky.
RozhinEskandarpour(Rozhin.Eskandarpour@du.edu)is aseniorresearchassociateintheelectrical andcomputerengineeringdepartment at the University of Denver. Her expertise spans the areas ofquantumcomputing andartificialintelligenceapplications in enhancingpowersystemresilience.Shealsois the CEO and founder of Resilient Entanglement LLC, a Colorado-based R&D company focusing on quantumgrid.She is a senior member of the IEEE society. Rozhin holds a Ph.D. degree inelectrical and computer engineering from the University of Denver.
AminKhodaei(Amin.Khodaei@du.edu)isa professor ofelectrical andcomputerengineering at the University of Denver andthe founder of PLUG LLC, an energy consulting firm. He holds a Ph.D.degree inelectricalengineering from the Illinois Institute of Technology. Dr.Khodaeihas authored more than 170 technical articles on various topics in power systems, including the design of the grid of the future in the era of distributed resources.
Pranav Gokhale(pranavgokhale@uchicago.edu)iscofounder and CEO ofSuper.tech, a quantum software start-up. He recently defended his Ph.D.in computer science fromtheUniversity ofChicago(UChicago), where he focused on bridging the gap from near-term quantum hardware to practical applications.Gokhales Ph.D.research led to over a dozen publications, three best paper awards and two patent applications. Prior toUChicago,hestudied computer science and physics at Princeton University.
Frederic T.Chong(chong@cs.uchicago.edu)is the Seymour Goodman Professor in thedepartment ofcomputerscience at the University of Chicago. Healsoisleadprincipalinvestigator for the Enabling Practical-scale Quantum Computing(EPiQC) project, a National Science Foundation (NSF)Expedition in Computing. Chong received his Ph.D. from MIT in 1996. He is a recipient of the NSF CAREER award, the Intel Outstanding Researcher Award andninebest paper awards.
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