Transactive Systems for the Shared

Energy Economy

Mike Diedesch, AvistaAnjan Bose, WSU

Tom McDermott, PNNLAbstract: The WA Clean Energy Fund has funded this project that will advance and demonstrate the ability of batteries, photovoltaics and responsive loads to provide grid services, energy efficiency and resilience. Academic campus building models have been developed from standard energy audit information, enhanced with machine learning methods applied to 3-second Avista metering data. These building models are linked to a distribution feeder model in the Transactive Energy Simulation Platform for time-series simulation and evaluation of different use cases. A new multi-battery controller agent has been developed to optimize fleet operation in different grid conditions. All software and models will be open-source, which facilitates use by other researchers and adoption by industry.

Shared Energy Economy Microgrid – Project Overview

Goals: Economic Value

Optimization for Distributed Energy Resources

Customer Participation Model

Resilience Research Platform

Overview: Partnership with WA

Department of Commerce Clean Energy Fund

Sited on the WSU Spokane Campus

Will be energized in Summer 2021

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Asset Overview

Solar PV- 2 rooftops- 100 kW each- Smart Inverters

Building Load Controls- 2 buildings- Load Flexibility while Grid Connected- Load Management while Islanded

Battery Energy Storage- 500 KW / 1500 KWH- 167 KW / 337 KWH- Grid Forming Inverters

Microgrid Control System

Utility DMS DER Optimization3

Microgrid Demonstration Modes

1. Grid Service

2. DER Optimization

3. Building Fleet

Optimization

4. Critical Resiliency Example: Islanded Operation

Example: Tariff optimization scenarios, Transactive Energy

Example: Campus Demand Reduction, Coordination with DERs

Example: Volt/Var Management, Frequency Response, Capacity

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Microgrid Control System

Features:- Communications and Monitoring- Local Operation of Assets- Interface to Distribution Management System- Tested using Hardware-in-the-loop

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Data Integration at a Utility - Rationale

Traditional DMS are typically isolated from or loosely coupled with differententerprise applications like DERMS, AMS, OMS , SCADA, AMI, CCB systems.

To take advantage of the distribution automation and efficiently manage theDERs to be deployed, engineering analysis is required which prerequisiteshigh fidelity (up-to-date) models of the distribution network.

Requires Data integration available at the proprietary enterprise applications.

How to achieve systematic Data Integration ?

ADMS implementations - Extremely costly which limits their adoption atmost utilities .

Standards-based (CIM) system Integration - cost effective solution todevelop and implement advanced distribution applications by integratingand exchanging data from enterprise applications.

Fig. Modular application suites for ADMS1

1OSI, “Advanced Distribution Management Systems,” URL: https://www.osii.com/solutions/products/distribution-management.asp

Common Information Model

The Common Information Model (CIM) is an IECstandard that defines information access and exchangesemantics through a standard vocabulary.

The IEC 61970 series of standards define CIM packagesfor network modeling and EMS

IEC 61968 defines CIM packages for distributionsystems, enabling CIM to be used with enterpriseapplications.

CIM based integration facilitates inter-operabilitybetween existing applications and new advancedapplications.

Simple connection between two connectivity nodes.

TopologicalNode

ConnectivityNode ConnectivityNode

A

L

TopologicalNode

- ConnectivityNode- Disconnector

- Service/Meter

- LoadTransformer

- Connectivity ref

- Regulator- Capacitor

- Recloser

- LoadBreakSwitch

- Fuse

- Primary- SecondaryConnectivity Objects

Other Objects

Locational ObjectsPower System ObjectsPower System Sub - Objects

CIM Instances

ACLineSegment

AssetWireSpacingInfo

AssetWireInfoLocation

PositionPoint

Terminal Terminal

ACLineSegmentPhaseACLineSegmentPhase

[A,B,C,N, S1, S2]

1..*

1..*

BaseVoltage

1..*

PrimaryConductorCMP

Typical single phase load transformer configuration. Two ACLineSegments, each

representing one split phase leg (120V)

WirePosition [A,B,C,N,S1,S2]

WirePosition [A,B,C,N,S1,S2]

WirePosition [A,B,C,N,S1,S2]

Fig. Modelling AC Line Segments and referencing catalog data in CIM

Integration of Enterprise Systems to CIM model

Source Systems - Data required from primary enterprise systems Automated Mapping and Facility Management (AM/FM) system (A) Asset Management System (AMS) (B) Customer & Billing (C&B) Managements System (C) SCADA & AMI Systems (D)

Internal Model Abstract representation of the detailed model information structured in

a way to enable derivation and correction of data inconsistencies. (E)

CIM Platform Specific Model The diverse models are categorized into a series of profiles which are

populated with relevant data to define how each power system modelelement is composed with CIM. (F&G)

Serialization Customized using reflection allowing for an object’s properties, fields,

methods to be queried and invoked without casting them to a specifictype. (H)

Integration of Enterprise ApplicationsAFM to CIM Conversion Architecture

AFM [ESRI] IBM Maximo

ArcObjects SDKOracle

ManagedDataAccess

Oracle CC&B

Oracle ManagedDataAccess

Independent Internal Connectivity Model

OSISoft PI

PI SDK

Internal CIM Model

CIM RDF XMLCIM Platform Specific Model

CIM Serializer

A B C D

E

F

G H

I

Phase A: Export CIM from Enterprise Systems

2M. Mukherjee, E. Lee, A. Bose, J. Gibson, and T. McDermott, “A CIM Based Data Integration Framework for Distribution Utilities”, IEEE PES General Meeting, Montreal Canada, 2020.

Fig. Integration of enterprise systems using CIM2

CIM Based Data Integration and Application Development Framework

Fig. Conceptual architecture of the CIM based framework

CIM-based Framework: Dashboard

AMI measurement of the node (selected in map) for the day

SCADA measurement of the (selected) Feeder for the day

Fig. Dashboard for the CIM-based Framework

MM: We can insert a one-minute video that give a demo of some of the tool functions

Model Validation using CIM Tool

November2020

Simulation: The tool populates all the customers (~500) in theGridLAB-D model with appropriate AMI data from PI server.

Comparison: Simulation results are compared with actualSCADA measurements for the corresponding day.

Deviation: Mainly due to inconsistent AMI data

Functionality : Facilitates emulating real-network scenarios

December 2020

Fig. Dashboard for the CIM-based Framework

Fig. Model Validation: AMI populated GridLAB-D vs SCADA

Simulation: Incorporating Solar PVs 2 rooftops - 100 kW each Along with Inverters Appropriate Weather data from TMY3 of Spokane

Micro-Transactive Grid (Incorporating DERs)

pv_scamp

Emulating November 24-25th, 2020

Fig. Net feeder demand with and without Solar PVs Fig. Street view of the U-district with DERs

bat_teach

pv_scamp

Micro-Transactive Grid (Incorporating DERs)

Feeder Config Optimization Cost ($)Base Feeder None 38181.64Base Feeder+Solar PVs None 38106.08Base Feeder+Solar PVs +Batteries Cost Minimization 37808.83

Simulation: Incorporating Battery Energy Storage BATT_EWU: 500 KW / 1506 KWH, SOC ɛ 0.05,0.95 BATT_TEACH: 167 KW / 334.8 KWH, SOC ɛ 0.05,0.95 Optimization: Minimizing net operational cost

Fig. Net feeder demand with and without Batteries

Fig. Street view of the U-district with DERs

Fig. Operational states of the batteries

Fig: Nearby CAL-ISO node

Simulation: Incorporating Flexible building Models Energy Plus Building Model HELICS base Co-simulation Implemented on TESP3

Micro-Transactive Grid (Incorporating DERs)

bat_teach

Bookie

pv_scamp

Fig. Net feeder demand with Energy+ building models

Fig. Performance of E+ building model for HSB

Fig. TESP Architecture

3Q. Huang et al., "Simulation-Based Valuation of Transactive Energy Systems," in IEEE Transactions on Power Systems, vol. 34, no. 5, pp. 4138-4147, Sept. 2019, doi: 10.1109/TPWRS.2018.2838111.

Transactive Energy Simulation Platform includes a “consensus mechanism” example with E+ reference buildings and the PNNL taxonomy feeder GC1.

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Message Bus (FNCS or HELICS)

GridLAB-DTSO• PYPOWER• MOST• AMES

OpenDSS EnergyPlus ns-3

Study CaseConfiguration

Intermediate Metrics and Dictionaries (JSON or HDF5)

Load Shed

Thermostat

Double Auction

Buildings

Post Processing (Python)

Final Valuations

Precooler

Rationer

Weather

TESP Developer AgentsPython, C++, Java

https://tesp.readthedocs.io/en/latest/ https://github.com/pnnl/tesp/releases

GC_12_47_1_meter_12.1x Load

GC_12_47_1_meter_250.0x Load

GC_12_47_1_meter_331.25x Load

2.5 MVA, 5.75%12.47/0.48 kV

2.5 MVA, 5.75%12.47/0.48 kV

2.5 MVA, 5.75%12.47/0.48 kV

12 MVA, 8%115/12.47 kV

1.8 MVAR

13.2 kVLLATS2

CB_12F1

CB_12F7B6_ATS2

13.8/13.2 kV 13.2 kVLLREG-1_SEC

13.8/13.2 kV13.2 kVLL

REG-1_SEC

13.2 kVLLF1B0

13.2 kVLLF7B0

13.2 kVLLF1B1

13.2 kVLLF7B1

13.2 kVLLF1B2

13.2 kVLLF7B2

13.2 kVLLF1B3

13.2 kVLLF7B3

13.2 kVLLF1B4

13.2 kVLLF1B5

13.2 kVLLF7B5

13.2 kVLLF1B6

13.2 kVLLF7B6

13.2 kVLLF7B7

CB_12F1B6_ATS2

13.2 kVLLF7B8

13.2 kVLLPCC

CB_F1B4_F7B7

CB_12F7

CB_F1B1_F7B4

13.2 kVLLF7B4 13.2 kVLL

JE-JS1683

13.2 kVLLHSB-2

225 kVA1.30% R3.52% X

0.208 kVLLBKE-3

Load 3170 kW

105 kVAR

500 kVA1.10% R4.88% X

Load 450 kW

31 kVAR

SCAMP_PV100 kW

500 kVA1.10% R4.88% X

0.48 kVLLTEACH_BAT

CB3

13.2 kVLLBKE-1

0.48 kVLLBKE-4

13.2 kVLLBKE-2 13.2 kVLL

TEACH_BESS

13.2 kVLLJE-NEW-A

13.2 kVLLJE-NEW-B

13.2 kVLLJE-NEW-C

13.2 kVLLJE-NEW-D

13.2 kVLLHSB-1

300 kVA1.30% R4.83% X

0.48 kVLLEWU_BAT

13.2 kVLLEWU_BESS0.48 kVLL

HSB-4

750 kVA0.97% R5.11% X

HSB_PV100 kW

Load 2515 kW

319.2 kVARLoad 580 kW

49.6 kVAR

0.208 kVLLHSB-3

Load 176 kW

47.1 kVAR

13.2 kVLLBUS-P

13.2 kVLL

BUS_CB3

Power Factor0.85

0.48 kVLLHSB-5

HSB-GEN100 kW

CB_G5

Service Transformer13.2/0.208 kVLL

Y-Y

Service Transformer13.2/0.480 kVLL

Y-Y

Cable Z10.02 + j0.8 Ω/mile

Cable Z00.20 + j0.6 Ω/mile

300 kVA1.30% R4.83% X

CB_EWU_BESS

115 kVLLGrid

115.5/13.8 kVΔ-Yg

30 MVA, 10.1%

CB_TF-1

CB_TF-3

115/13.8 kVΔ-Yg

30 MVA, 10.1%

115 kVLLSource

Source equivalent impedance(entire transmission system)

115 kVLLTF-1_PRI

115 kVLLTF-3_PRI

13.2 kVLLTF-1_SEC

13.2 kVLLTF-3_SEC

ReducedCircuitModel for CEF2

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Component Count

Substation Transformers 2

Regulators 2

DG 1

PV 2

Batteries 2

Spot Loads 5

House Groups 14

Houses per Group 42

Tie-Switches 3

External Feeders

Microgrid

GridAPPS-D: https://doi.org/10.1109/ACCESS.2018.2851186CIMHub: https://github.com/GRIDAPPSD/CIMHub/tree/develop

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Consensus mechanism setup tabulates the EnergyPlus response to thermostat setpoint changes over two days. HSB much less responsive.

-25

-20

-15

-10

-5

0

0 1 2 3 4 5

Build

ing

Dem

and

Chan

ge [k

W]

Cooling Setpoint Change [deg F]

EnergyPlus Model Response to Thermostat

CCRS

HSB

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Consensus participants exchange bids with their neighbors and determine the market clearing point independently.

Consensus Mechanism: https://doi.org/10.1109/TESC.2019.8843369

commshed.pyLoad > Cap?

eplus_agent_helics.cpp

consensus.cpp

Metrics

Metrics

eplus_agent_helics.cpp

consensus.cpp

Metrics

LoadFeederLoad

∆T

∆T

5-minuteMarket

Bids (All)

Legend• HELICS Bus• File I/O

p

q

k[$/°F]∆P

∆T

p

q

k[$/°F]∆P

∆T

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The consensus mechanism applied to the buildings responded indirectly, via thermostat setpoint changes.

Three kinds of resources acting as virtual batteries

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P + jQ

Q

P

SoC

BSET: https://doi.org/10.1109/PESGM.2015.7285820DSOT: https://doi.org/10.1109/TD39804.2020.9299896Virtual Battery: https://doi.org/10.1109/PESGM40551.2019.8974107Nantucket: https://doi.org/10.1109/PESGM41954.2020.9281982

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None of the building simulators meet all needs for large-scale grid integration with large-commercial loads; the same is probably true of other DER domains.

Air Mass

• For many houses

• One HVAC zone

• 1-60s steps

• For energy efficiency, not load modeling or voltage response

• 3D modeling expertise• Quasistatic, 5-60m steps

• Dynamics expertise• Solution speed• Flexible time steps• Needs commercial solver

Power-flow connection

• Controllable BTM DER models

• 1 minute time resolution

• Multiple zones• Residential only

Source: EERE Distribution System Research Roadmap, draft report

SCADA feeder F1, F7 data on 10-second intervals; supplements day-of-week, week-of-year and hour-of-day scheduling data.

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Building meter data available on 3-second intervals

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Weather Data available from NOAA on 5-minute intervals for Spokane airport.

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https://www.ncei.noaa.gov/pub/data/uscrn/products/subhourly01/https://www.ncei.noaa.gov/access/search/data-search/global-hourly

Data-driven model of buildings incorporates load survey, usage scheduling and time-series data streams.

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Load Survey CCRS [kW] HSB [kW]

Category Min Max Min Max

HVAC 9 73 113 296

Plug 18 18 116 117

Lighting 80 143 185 188

Total 107 234 414 601

Schedule: Day, Hour

Meter data 208V, V, P, QSCADA data Amps

208 V Load states

Weather data Output P, Q

208V, P, Q

5 min datasets for 1 data length of 1 year

Neural Network

Meter data 480V, V, P, QSCADA data Amps

480V, P, Q480 V Load states

Interpretationblock

Neural Network

Neural Network

5 min datasets for 1 data length of 1 year

Schedule: Day, Hour

Discussions CIM based data integration framework has been developed in this effort.

The platform standardizes interfaces of vendor-specific distribution applications (at Avista), including spatio-temporal data and DMS systems.

The platform facilitates network simulation scenarios With high-fidelity up-to-date models With actual telemetry measurements

Future Work Microgrid Demonstration Modes

Design Coordination Mechanism for the DER Assets in the Microgrid Develop Use-cases for Microgrid Demonstration Modes Simulation based evaluation of the Use-cases through CIM-based Platform Design Experiments to be conducted on the U-District Microgrids.

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