Data Streams for Control · 2017-07-12 · Data Streams for Control Dr. Marc Mueller-Stoffels...

Data Streams for ControlDr. Marc Mueller-StoffelsProgram Director/R&D Lead, Power Systems Integration 2017 Alaska Wind-Diesel Workshop Fairbanks 2017

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“Ifwehavedata,let’slookatdata.Ifallwehaveareopinions,let’sgowithmine.”

–JimBarksdale,formerNetscapeCEO

Digi

tizat

ion

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hJp://www.mckinsey.com/industries/electric-power-and-natural-gas/our-insights/the-digital-uOlity-new-opportuniOes-and-challenges

Increased complexity

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Controls Automation is critical

• More objectives:•  Cheapest •  Most reliable •  Least diesel utilization •  Equitable demand management •  Maintenance schedules •  …

• More resources•  Renewable generation •  Energy storage •  Demand management •  …

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Controlled Elements20 40 60 80 100

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Test harness•  5-wire Distribution model

•  3p and 1p distributed resources (loads and generation)

•  Phase imbalance impacts

• Objectives•  Training environment for

optimization algorithms •  Flexible experimental setup to

extract information •  Driver for hardware-in-the-loop

emulation

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Ciric,etal.,2003

Phase balancing

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•  Phase imbalance causes energy loss •  Need to know

phase imbalance to fix•  Control

distributed resources (loads, PV, etc.) to balance grid

Information from sparse data• Nonlinear Time-Series Analysis Methods•  Powerful toolkits •  Determine system state from

limited data •  Proven in other areas to

provide state estimates/trending

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Mormann,etal.,ClinicalNeurophysiology,116(3),2005.

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0 200 400 600 800 1000 1200 14000

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Customer Example: Demand Charges• Large customer

•  14 (!) individual services •  4 with demand charges

• Data: monthly utility bills•  Energy use •  Peak use

• Without more data:•  Assess impact of combining all

demand meters •  Potential annual savings: $20k

• Needed: time-series energy use data•  Understand potential for load

leveling •  Max. potential annual savings:

$130k

• Direct cost of not having data•  ~ $100k/year

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Summary•  Digitalization

•  Convergence of data streams •  New data streams •  New use of existing data streams

•  Microgrid applications•  Better management of DER •  Increases in technical and economic

efficiency •  Novel approaches to sparse data problem?

Engineers like to solve problems. If there are no problems handily available, they will create their own problems.

- Scott Adams

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Thank you!Dr. Marc Mueller-StoffelsDirector, Power Systems Integration ProgramAlaska Center for Energy and PowerInstitute of Northern EngineeringUniversity of Alaska Fairbanksmmuellerstoffels@alaska.edu(907) 687 0259http://acep.uaf.edu

Partners:USDepartmentofEnergyUSDepartmentoftheInteriorUSDenaliCommissionUSEconomicDevelopmentAdministraOonUSOfficeofNavalResearchStateofAlaskaAlaskaEnergyAuthorityAlaskaPowerandTelephoneCordovaElectricCooperaOveCityofCordovaNomeJointUOlitySystemsKokhanokVillageCouncilCityofGalenaPowerandWaterCorporaOon,Darwin,AustraliaNaOonalRenewableEnergyLaboratorySandiaNaOonalLaboratoryLawrenceBerkleyNaOonalLaboratoryPacificNorthwestNaOonalLaboratoryTechnicalUniversityDarmstadt,GermanyABBShellHuntleyandAssociatesHatchAssociatesConsultantsOceanaEnergyLLC