Enabling the Internet of Energy through

Network Optimized Distributed Energy

Resources

Michael Kleinberg (DNV GL)

Mark Harral (GroupNIRE)

Ryan Wartena (Geli)

Network Optimized Distributed Energy Systems (NODES)

Annual Review Meeting

February 12th-13th, 2019

1

‣ Demonstrate an innovative Internet of Energy (IoEn) platform

– automated scheduling, aggregation, dispatch, and

performance validation of network optimized DER

‣ Develop a scalable approach for the fast registration and

automated dispatch of DER

‣ Simultaneously manage: 1) System level regulation (Category 2)

2) Local distribution support functions

3) Customer QoS

‣ Demonstrated and tested at Group NIRE’s utility-connected

microgrid test facility in Lubbock, Texas

Project Summary

Team

2

• Expertise in power systems

operations and control

• Industry accepted modeling

and simulation tools

• Independent testing and

performance evaluation

• Industry leading software to

integrate, network, and

economically operate

distributed energy systems

• Facilitating local and macro

operational decisions through

data method-driven

optimization

• Fully operational multi-MW,

dynamic renewables

integration and testing facility

• Distribution-connected

microgrid

• Home of ARPA-E CHARGES

energy storage testing

laboratory

Utility and Market Integration

3

GE, ABB

Topology

AMI/AMR

CIS

GIS

SCADA

EMS

DMS

OMS

DERMS

Utility Operations and Control Center

NODES DER

Aggregation

Platform

Market Operator

Bi-lateral Contracts

Other Aggregators

Smart Contracts

TCP/IP

Cloud

Distributed

Energy Assets

Utility and Market Integration

4

GE, ABB

Topology

AMI/AMR

CIS

GIS

SCADA

EMS

DMS

OMS

DERMS

Utility Operations and Control Center

NODES DER

Aggregation

Platform

Market Operator

Bi-lateral Contracts

Other Aggregators

Smart Contracts

TCP/IP

Cloud

Performance

Verification

Analytics

Utility Portal

Performance Reports

Distributed

Energy Assets

Project Summary

5

‣ Lightweight, portable micro-market server analytics for balancing

operational needs and DER availability

ISOs/RTOs

Utilities

Aggregators

Micro-Market Server

Customers

Communicate Spatial

& Temporal

Operational Needs

Communicate Spatial &

Temporal Availability

Dispatch

Validate

Customer QoS Performance Impact

6

Customer Type Peak Demand (kW)

Battery Energy Storage System

Power Rating (kW) Energy Capacity (kWh)

Stand-alone Retail 30 5 10

‣ Optimize customer net load for demand charge reduction (DCR)

– customer load profile

– tariff data

– installed DER/storage specification

‣ Co-optimize customer net load for DCR + GOS

‣ Compare obtained energy bill savings

– Difference provides the required incentive level for a customer to follow the GOS

Customer QoS Performance Impact

7

Base Case DCR Only DCR + GOS 1 DCR + GOS 2 DCR + GOS 3

Total Energy Consumption (kWh) 1,331.70 1,342.01 1,342.02 1,342.93 1,342.02

Peak Demand (kW) 29.94 27.54 28.31 27.54 29.37

Total Energy Charge $ 332.92 $ 335.50 $ 335.50 $ 335.73 $ 335.51

Peak Demand Charge $ 673.74 $ 619.57 $ 636.87 $ 619.57 $ 660.82

Non-coincident Demand Charge $ 658.76 $ 605.80 $ 622.72 $ 605.80 $ 646.14

Total Electric Charge $ 1,665.42 $ 1,560.87 $ 1,595.09 $ 1,561.10 $ 1,642.46

Savings compared to Base Case $ - $ 104.55 $ 70.33 $ 104.32 $ 22.96

Comparison of energy consumption, peak demand, cost, and savings for Customer 1

GOS used to characterize DR events Optimized net load for DCR/GOS

Distribution System Performance Impact

Assessment

8

Customer load profile creation

ES optimization in MGO

Distribution system

simulation in OpenDSS

Voltage and power

performance assessment

‣ % change in

peak load and

energy

consumption

‣ % changes in

minimum,

maximum, and

average voltages

‣ Residential and

commercial

customer load

profiles created

based on publicly

available building

data.

‣ Assumed that

5kW/10 kWh of

battery energy

storage (ES) is

installed at each

DER node.

‣ Optimize customer

net load with and

w/o GOS

‣ Distribution

system

simulations were

performed using

EPRI’s distribution

system solver,

OpenDSS.

Customer Net Load Profiles

9

‣ Customer net

load profiles

with optimized

energy storage

operation.

‣ Peak load day:

August 14th

0.40

0.60

0.80

1.00

1.20

08/13 00:00:00 08/14 00:00:00 08/15 00:00:00

Net

Lo

ad (

p.u

.)

Date/Time

Residential Net Load Profile

Base Case Test 1 with DCR Test 2 with DCR and GOS

0.00

0.20

0.40

0.60

0.80

1.00

1.20

08/13 00:00:00 08/14 00:00:00 08/15 00:00:00

Net

Lo

ad (

p.u

.)

Date/Time

Commercial Net Load Profile

Base Case Test 1 with DCR Test 2 with DCR and GOS

Distribution Circuit Parameters: R4-12-1

10

Base Case Test 1 Test 2

Total residential customer nodes 476 476 476

Residential customer nodes with DER 0 80 120

Total commercial customer nodes 113 113 113

Commercial customer nodes with DER 0 20 30

System peak load (kW) 5009 5009 5009

System peak load day August 14th August 14th August 14th

Amount of installed DER (kW) 0 500 750

DER level as a percentage of peak load 0% 10% 15%

Case Description

Base Case

Distribution QoS performance assessed for the system with the

corresponding residential and commercial net load profiles with

no DER operation.

Test 1 (with DCR)

Distribution QoS performance assessed for the system with the

corresponding residential and commercial net load profiles

where the DER operation is optimized for demand charge

reduction only.

Test 2 (with DCR and GOS)

Distribution QoS performance assessed for the system with the

corresponding residential and commercial net load profiles

where the DER operation is optimized for demand charge

reduction and a grid operating signal from the utility.

Base w/ DCRw/ DCR

+ GOS

w/

DCR

w/

DCR +

GOS

Base w/ DCRw/ DCR

+ GOS

10.0% 4460.96 4474.39 4443.71 0.30 -0.39 4.53 4.52 4.53

15.0% 4460.96 4477.34 4440.43 0.37 -0.46 4.53 4.52 4.53

DER Level

as a % of

Peak Load

Total Losses (kWh)% Change in

Total Losses% Distribution Loss

Distribution System Power Performance Metrics

11

‣ Improved performance on peak reduction and total losses when DER

optimize for DCR + GOS.

Base w/ DCRw/ DCR

+ GOS

w/

DCR

w/

DCR +

GOS

Base w/ DCRw/ DCR

+ GOS

w/

DCR

w/

DCR +

GOS

10.0% 98433.28 98963.80 98161.46 0.54 -0.28 5009.35 4949.61 4946.85 -1.19 -1.25

15.0% 98433.28 99065.00 98109.69 0.64 -0.33 5009.35 4938.31 4934.53 -1.42 -1.49

DER Level

as a % of

Peak Load

% Change in

EnergyEnergy Consumed (kWh) Peak Demand (kW)

% Change in

Peak Demand

Base w/ DCRw/ DCR

+ GOS

w/

DCR

w/

DCR +

GOS

Base w/ DCRw/ DCR

+ GOS

w/

DCR

w/

DCR +

GOS

10.0% 98433.28 98963.80 98161.46 0.54 -0.28 5009.35 4949.61 4946.85 -1.19 -1.25

15.0% 98433.28 99065.00 98109.69 0.64 -0.33 5009.35 4938.31 4934.53 -1.42 -1.49

DER Level

as a % of

Peak Load

% Change in

EnergyEnergy Consumed (kWh) Peak Demand (kW)

% Change in

Peak Demand

Base w/ DCRw/ DCR

+ GOS

w/

DCR

w/

DCR +

GOS

10.0% 1.04 1.04 1.04 -0.08 -0.08

15.0% 1.04 1.04 1.04 -0.06 -0.06

Maximum Voltage (p.u.)% Vmax

ChangeDER Level

as a % of

Peak Load

Base w/ DCRw/ DCR

+ GOS

w/

DCR

w/

DCR +

GOS

Base w/ DCRw/ DCR

+ GOS

w/

DCR

w/

DCR +

GOS

10.0% 0.94 0.94 0.94 0.02 0.02 0.98 0.98 0.98 -0.02 0.01

15.0% 0.94 0.94 0.94 0.04 0.04 0.98 0.98 0.98 -0.02 0.01

Minimum Voltage (p.u.)% Vmin

ChangeAverage Voltage (p.u.)

% Vavg

ChangeDER Level

as a % of

Peak Load

Distribution System Voltage Performance Metrics

12

‣ Impact on voltage negligible.

Distribution System Voltage Performance Impact

(Sample)

13

‣ Voltages on all phases on peak load day for 10% DER

‣ The voltages for Test 1 and Test 2 are sufficiently close to those of the base case

throughout each 24-hour period.

‣ DER operations serve to reduce the net load and demand charges with negligible impact

on the baseline voltage characteristics.

Group NIRE - Testbed

14

‣ Current Generation Assets– $30M installed research equipment

– partners include Alstom, GE, Gamesa, Younicos, State of Texas, SPEC

‣ Distribution System– $1.5M in distribution system

upgrades;

– 30MW Peak Distribution Load

– 10MW Off-Peak Distribution Load

‣ NODES Expansion– Energy Vehicle Chargers;

– Electric Vehicle;

– Roof Top Solar;

– Residential Wind Turbines;

– Web Relay-Controlled Breakers

– Electric Pumps;

– Residential Battery;

Solar and Solar+Storage

15

Rooftop PV

SunSpark Panels – 9 kW

Canadian Solar Panels – 9 kW

Outback Solar+Storage – Lead-acid AGM Batteries

Lithium-ion Battery Systems

16

SolarEdge StorEdge Inverter +

LG RESU10H Lithium Ion

Battery

Nissan Leaf EV

SMA Sunny Island Inverter +

LG RESU6.5 Li-Ion Battery

Controllable Loads

17

Hydroponic Grow Stations – 7 kW

Belkin WeMo Insight

Smart Plug

Electro Industries Shark Meters

Third-Party Performance Validation with Veracity

18

Other data

providers (e.g. MET, sensor

data)

OEMs

Owner/

operators

Assurance

providers and

class societies (e.g. DNV GL)

Analytics and

software

providers

Provider or consumer

development team(OEMs, asset owners, regulators)

Assurance

providers and

class societies

OEMs

Owner/

operators

Regulators

Insurance

companies

Data providers: End consumers:

Analytics service

providers:

Other end consumers

Providers’ toolbox

Marketplace

VERACITY – MULTI-SIDED OPEN PLATFORM

Asset owners’ data container

Qu

ality

Assessm

en

t

Cloud Storage and Analytics Platform

‣ Veracity by DNV GL

– The Veracity industry data platform is designed to help companies improve data quality, manage data ownership and access control, maintain data security, and access analytical tools or other standalone digital services

‣ The NODES team has been exploring the use of this platform to automate our performance evaluation methodology and provide additional pathways to commercialization and adoption of DER aggregation platforms.

‣ https://www.veracity.com/

19

Validation Platform

20

Remote data acquisition

Host data on cloud storage

platform

Automated analytics

Visualization of data and

performance metrics

Validation Platform

‣ Data input – Remote acquisition of data from Geli GENI API and storage in Veracity Data Containers

‣ Analytics – Analyses of time series data hosted on Veracity, implemented through web-based solutions such as Data Science Virtual Machines

– Category 2 performance validation analytics

– Distribution analytics

– Market analytics

‣ Visualization and reporting

– Visualization through Veracity Power BI framework, that enables sharing of reports with external parties

– Utilities and stakeholders will be ale to login securely to access these results

21

‣ DERs: Wemo devices and Battery

DER Response Visualization

22

DER Response Metrics

23

DER Response Metrics (subset)

24

Geli Internet of Energy Platform

Analyze & Design Connect & Automate Manage & Aggregate

Geli ESyst Geli EOS Geli GENI

Geli Global Energy Network Interface

26

1

2

3

4 5

10

11

12

13

14, 15

16

17, 18

19

20, 21

22

23, 24

25

26

27

MicroMarket

Broker

Server is

Activated

1-3

15-19

4-10

20-2711-13

Power

Profile is

Requested

Energy

Accounts

Forward

Availability

Profiles are

Bid

Utility,

Market, or

Aggregator

Energy

Transactions

are Computed

Energy

Asset

Owners &

Operators

MicroContracts

are Created &

Authorized

Each Energy Asset

Performs Contracted

Operation

Total Power Profile is

Delivered from Contracted

Aggregated Assets

MicroContracts are

Validated &

Executed

Request & Offers from

Nodes Sent to

MicroMarket Server

MicroMarket Server

Calculates Economic

Dispatch & Notifies Nodes

Energy Assets Operated &

Validated according to

MicroContracts

Energy

Assets

Step 1 Step 2 Step 3

6 7

8 9

Automating Energy Transactions with Network Optimized Distributed Energy Resources

Geli Energy Operating System Scalability

Multi-site Optimization

Site 1

Site 2

Site N

Optimized by

Geli Engine

@Geli GENI

Geli GENI API or other protocol SEP2.0, Open ADR, AGC, ICCP, DNP3

Geli Energy Server for

Utility, Retailers,

Aggregators, & Brokers

- Demand Response

- Capacity

- Frequency regulation

Geli

Energy

Server

Geli Energy Server Architecture

29

Geli

GENI

API

Ge

li G

EN

I

AP

IGeli

Energy

Server

Coordinating (Optimizers)

Frequently (Enough)

with (Lots of) Forecasting

What are we doing?

Current Multi-Node, Multi-Site

Forecasting Energy Storage

‣ #1, A site load is forecasted with Nearest Neighbor

forecasting methods

‣ 5-6x Power Variations lead to large (>100%+/-) local error of

‘knn forecasting’, but maintains good daily trending

Forecasting Loads

‣ #3, A switch load is forecasted with Nearest Neighbor forecasting methods

‣ Low Power Variations lead to small local error of Back-casting with Pattern

Matching and maintains good daily trending

‣ Error rates are <+/-1% for 12 hours and then increase to +3%

Geli Aggregation Groups, Lists, & Scheduling

Automated & Dynamic Allocations

10 DERs for 7 Days with 13 Aggregation Requests

10 DERs for 7 Days with 13 Aggregation Requests

Geli VPP Performance

Towards 50 DERs Aggregated in Lists & Sites

37 DERs Aggregated in Lists & Sites

Geli VPP for BTM & Market Energy Apps

40

• Residential

– Solar Self Consumption

– Time of Use Bill Optimization

– Day Ahead Energy

– Frequency Response

• Commercial

– Peak Shave Demand

– Day Ahead Energy

– Frequency Response

List of Achievements

41

‣ Magazine Articles:

– Greentech Media - Geli’s ‘Internet of Energy’ Software Gets Real-World Test in Texas Microgrid, January 28, 2016.

– Microgrid Knowledge - How to Juggle 100 Distributed Energy Resources on a Microgrid, April 8th, 2016.

– RTO Insider, New York Sees Storage in Retail and Wholesale Markets, Aug 7, 2017

‣ Conference Panels:

– Infocast Microgrid Convergence - San Mateo, CA, October 25-26, The Role for Blockchain Technologies in Microgrids & Smart Cities.

– GridNEXT 2016 (TREIA Annual Conference) - November 9-11, Georgetown, TX. - Enabling the Internet of Energy through Network Optimized Distributed Energy.

– Transactive Energy: Bringing The Maximal Vision Of A Customer-Centric Grid 2.0 Into Focus, Mountain View, CA, Feb 21, 2017.

– NY Energy REVolution Summit, Integrating Energy Storage, August 1-3, 2017, New York, NY

– CAISO Stakeholder Symposium, Invited Speaker, How Can Power Technologies Help Unlock the Renewable Energy Dividend, Sacramento CA, October 18, 2018

– Outside Energy, DNVGL Podcast on Internet of Energy New Business Models, Oct. 2017

– US-China New Energy Economic Forum, Energy Storage-as-a-Service & the Digital Energy Retailer, Santa Clara CA, Nov 18, 2018

– Denki Shimbun (The Electric Daily News) & Japanese Utility Delegation presentation on Internet of Energy Technology for New Energy Markets, Feb. 2018

– Storage Week Conference, Software For Energy Storage, San Francisco, CA Feb. 13, 2018

– MISO Market Symposium, Indianapolis, IN, Aug. 2018

– The Battery Show, Novi. MI, Sept. 2018.

‣ Webinars:

– TREIA Webinar Series - Enabling the Internet of Energy through Network Optimized Distributed Energy, June 28th, 2016.

– Stanton Report, Unpacked the DER Tech Stack, Aug 28, 2017

Technology to Market

42

VPP Growth Plan

43

Plan Implement Integrate Operate Asses

‣ DER potential studies

‣ DER hosting capacity of current distribution grid in targeted areas.

‣ Estimate customer, distribution, and full system benefits of reduced demand in

targeted areas and time periods.

‣ Financial models and investment pro forma to support rate-based DER

deployments.

VPP Growth Plan

44

Plan Implement Integrate Operate Asses

‣ Customer outreach and marketing programs

‣ Recruitment, training, and oversight of local installation

contractors

‣ Provision of measure design and installation quality

control.

‣ Management of incentive processing and disbursements.

VPP Growth Plan

45

Plan Implement Integrate Operate Asses

‣ Utility/ISO backend integration and operator

training

‣ Asset networking & integration

‣ Integration for VPP Growth Program Pilot lays

groundwork for large-scale DER deployments

Distribution System Performance Impact

Assessment

46

Plan Implement Integrate Operate Asses

‣ Pilot Geli Internet of Energy platform

for VPP Networks of 50-1000-

10,000+ DERs where Utility & retail

partners provide or arrange

distributed assets at residential,

commercial, & utility-scale sites

‣ Bring-Your-Own energy assets have

a significantly lower cost of assets

‣ Can be used to diversify an

aggregation portfolio, groupings, and

sub-groupings

VPP Growth Plan

47

Plan Implement Integrate Operate Asses

‣ Performance assessment of DER fleet to system

requirements

‣ Automated DER and VPP performance

assessments via DNV GL’s Veracity platform