a novel hierarchical frequency based load control architecture

A Novel Hierarchical Frequency-Based Load Control Architecture

Takashi NishikawaNorthwestern University (lead)

Network Optimized Distributed Energy Systems (NODES)Annual Review Meeting

February 12-13, 2019

Project Summary

1

‣ Overall goal of the project: To develop and validate a new, hierarchically coordinated, frequency-based load control architecture that provides a reliable complement to generator inertia and governor response so as to enable a high penetration of renewable generation.

Project Summary

2

‣ Challenge: To control a large amount of appliances distributed in the whole system in a coordinated manner while simultaneously achieving the objectives of guaranteeing system stability, ensuring fast frequency regulation, and maintaining customers’ QoS.

‣ Deliverables:

1. Scalable algorithms for frequency-load control.2. Validation of the algorithms using real power-grid network

models and datasets via large-scale real-time simulations and HiL testing.

‣ Expected outcome: Demonstration that the developed control architecture can provide additional synthetic frequency response reserve to the existing grids, which would enable higher penetration of renewable generation.

Team

3

‣ Partners– Northwestern University (NU): Leader in research on complex network control

and frequency-synchronization phenomena.– Argonne National Laboratory (ANL) has extensive expertise/experience in

modeling, analysis, and control of power systems.– OPAL-RT is a leader in providing real-time simulation/HiL environment.– Schneider Electric (SE) has experience/expertise in hardware/software

development, providing microgrid solutions, and commercialization.– CPS Energy is a utility serving San Antonio area.– Lawrence Berkeley National Laboratory (LBNL) has facility & experience for

HiL testing.‣ NU designed control algorithms with help from ANL on power system modeling. ‣ OPAL is helping to perform real-time simulations and HiL validation.‣ CPS provided distribution network data.‣ SE is conducting a commercialization review.‣ LBNL is helping with HiL testing of the developed control algorithms.

Project Progress

4

1.2: network decomposition method

Year 1 Year 2 Year 3Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

2.1: ext. depth-L method

1.1: performance test on optimization algorithms

2.2: distribution network control

3.1: appliance model development

3.2: design decentralized control

3.3: design building EMC control

Task 1

Task 3

Task 2

Project Progress

5

4.2: power syst. topo. & control modeling

Year 1 Year 2 Year 3Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

5.1: Definition of HiL test plan

4.1: large-scale simulation scenarios and tools

5.2: HiL modeling & interfaces

Task 4

Task 5

4.3: validation

5.3: testing

Image removed to comply with NDAImage removed to comply with NDA

Hierarchical control architecture

6

Transmission gridcontroller

Distribution gridcontroller Building controller

Image removed to comply with NDA

Transmission Grid Optimization Controller

7

‣ Task 1 (completed): Load assignment algorithms for transmission system stability

ERCOT system

Reformulation of λmax optimization into an easier-to-solve OPF problem

Highly optimized commercial optimization software (Knitro)

+

Optimization of the ERCOT demo completes in 5–10 min and yields 10–15% improvement in λmax.

Sequential Nonlinear Programming (SNLP) Method:

Linearized power system DAE model:

Optimization problem: minimize the spectrum abscissa

where

Smooth NLP problemNon-smooth problem


8




where





where





where


(swing eq. + power flow)




where



9

Run power flow, initialize 𝑥𝑥 = 𝑥𝑥0;Set upper and lower limits of controllable load;

Set 𝜏𝜏 = 𝜏𝜏0, 𝑡𝑡𝑡𝑡𝑡𝑡 = 10−4, 𝑘𝑘 = 1.

Calculate eigenvalues and their derivatives of 𝐽𝐽(𝑥𝑥0)Set 𝑓𝑓0 = max

𝑖𝑖𝑅𝑅𝑅𝑅{𝜆𝜆𝑖𝑖(𝐽𝐽(𝑥𝑥0))}

𝜏𝜏 < 𝑡𝑡𝑡𝑡𝑡𝑡 ?

Solve the NLP problem with 𝑥𝑥∗ = 𝑥𝑥𝑘𝑘−1 with KNITRO to obtain 𝑦𝑦∗, perform eigen decomposition on 𝐽𝐽(𝑦𝑦∗)

max𝑖𝑖𝑅𝑅𝑅𝑅{𝜆𝜆𝑖𝑖(𝐽𝐽(𝑦𝑦∗))} < 𝑓𝑓𝑘𝑘−1?


output 𝑥𝑥∗ = 𝑥𝑥𝑘𝑘−1 and stopYes

No

set 𝑥𝑥𝑘𝑘 = 𝑦𝑦∗, 𝑓𝑓𝑘𝑘 = max𝑖𝑖𝑅𝑅𝑅𝑅{𝜆𝜆𝑖𝑖(𝐽𝐽(𝑦𝑦∗))},

𝜏𝜏 ← 𝜏𝜏 × 2, 𝑘𝑘 ← 𝑘𝑘 + 1

Find the least integer 𝑛𝑛 such that max𝑖𝑖𝑅𝑅𝑅𝑅{𝜆𝜆𝑖𝑖(𝐽𝐽(𝑥𝑥∗ +

12𝑛𝑛 (𝑦𝑦

∗ − 𝑥𝑥∗)))} < 𝑓𝑓𝑘𝑘−1. Set 𝜏𝜏 ← 𝜏𝜏 1

2𝑛𝑛

No

Yes

• Maintain a trust region with radius 𝝉𝝉;• Use a gradient bundle;• Guarantee local optimality.

Distribution Grid Controller

10

‣ Task 2 (completed): Depth-L observability controlDistributed control of each node using signals from only the depth-L state information neighborhood (SIN) determined by the participation matrix.

Walzem distribution network used for testingData provided by CPS Energy

, ,

, ,1 1

trace( ).

trace( )c p o q

pq Nm Nnc p o qp q

W W

W Wj

= =

=å åParticipation matrix:


Distribution Grid Controller

11

Distribution Control

Transmission control gives rise to P ⇤0 , Q

⇤0 at the point of common

coupling

Distribution side active and reactive powers: P0, Q0

Error signals dp = P ⇤0 � P0 and dq = Q⇤

0 �Q0 sent to each buildingwith controllable loads

New active power target for the building:

P ⇤l

= Pl

+ wl

(P ⇤0 � P0) (9)

where wl

=

|Pl.dp|PLi=1|Pl.dp|

NW ARPAE Project Evanston, January 25, 2018

Current implementation

Control performance for Walzem network




coupling





P ⇤l

= Pl

+ wl

(P ⇤0 � P0) (9)

where wl

=

|Pl.dp|PLi=1|Pl.dp|





coupling





P ⇤l

= Pl

+ wl

(P ⇤0 � P0) (9)

where wl

=

|Pl.dp|PLi=1|Pl.dp|


(PCC), after PI controller loop for fast frequency regulation

Building Level Controller

12

Type I: On/off loads

Type II: Continuously adjustable loads

Development of Control Algorithms for Flexible

Appliances in Buildings

§ Building Level Energy Management Control (EMC) Design– Level II: Optimal Dispatch of Controllable Loads in Each Individual Building – Based on the desired demand generated in Level I, the following optimization

problem is formulated to dispatch Type I and Type II loads in each building

6

,, ,

,ˆmax

j i ji j i j i j

x p j j

q x P pt

t t t+ × +å å, ,ˆs.t. i j i j i j i

j j

q x P pt t t tq+ × + £å å,min ,max

, , , , ,i j i j i j i j i jp p pt t tx x× £ £ ×

{ }0,1jx Î

{ }, 0,1i jx Î

Maximize load power in each building

Total load power should not exceed the desired demand

Type II loads should not violate their power limits

On/off status of Type I loads

On/off status of Type II loads

‣ Task 3 (>90% complete): Building Level Energy Management Control (EMC) Design

– Based on the desired demand generated by distribution grid controller, the following optimization problem is solved:


13



§ Building Level Energy Management Control (EMC) Design

8

LLUP: local load update phase LTUP: load target update phaseIUP: intrusion update phaseACP: admissible control phase

LLUP LTUP ACP IUP

Summarize local fixed and flexible loads

Update local load target

Coordinate local controllable loads: formulation I

Coordinate local controllable loads: formulation II

‣ Task 3 (>90% complete): Building Level Energy Management Control (EMC) Design


14



§ Building Level Energy Management Control (EMC) Design– Test case: EMC covering 20�20 local buildings– Each building includes 36 Type I loads and 4 Type II loads– Use 24-hour data to validate the EMC design

9

Time (24-hour)

Desir

ed an

d Ac

tual

Load

Pow

er (k

W)

Time (24-hour)Re

lativ

e Er

ror (

%)

Limitation: 5%



§ Building Level Energy Management Control (EMC) Design– Test case: EMC covering 20�20 local buildings– Each building includes 36 Type I loads and 4 Type II loads– Use 24-hour data to validate the EMC design

9

Time (24-hour)

Desir

ed an

d Ac

tual

Load

Pow

er (k

W)

Time (24-hour)

Rela

tive

Erro

r (%

)

Limitation: 5%Desired load power (kW)Actual load power (kW)

Relative error (%)5% target

Time (24-hour) Time (24-hour)



Validation: Large-Scale Simulations

15

‣ Task 4 (~80% complete): Large-scale simulations– Using OPAL-RT ePHASORsim platform

– Transmission network modeling: Full Texas ERCOT model based on FERC 2017 data

– Distribution network modeling: Based on data from CPS Energy

– Building level modeling: Flexible appliance models developed in Task 3

Transmission Model Validation

16

• Power flow from ePHASORsim was compared with the PSS/E power flow results for ERCOT 2018 Summer Peak data

• Voltages at all the buses and currents on selected transmission lines were compared

• Deviations were within 0.5% for voltage and current magnitudes and 2 degrees for angles

Max. Voltage Magnitude Deviation 0.178% Max. Voltage Angle Deviation 0.039ºMax. Current Magnitude Deviation 0.1784%Max. Current Angle Deviation 0.208º

Distribution Model Validation

17

50% PV Penetration:• PV penetration level of 50% was accomplished by adding 776 ECGs in the

distribution feeder

• PV/ECG nodes were selected such that the substation power demand reduces by 50% during peak PV profile

• Results from ePHASORsim were compared with the CYMEdist load flow results• Bus voltages at all the nodes and currents at selected distribution lines

were compared

• Deviations were within 0.5% for voltage and current magnitudes and 2 degrees for angles

Max. Voltage Magnitude Deviation 0.0562% Max. Voltage Angle Deviation 0.048ºMax. Current Magnitude Deviation 0.1035%Max. Current Angle Deviation 0.735º

Building Load Model Validation

18

‣ Flexible appliance models (HVAC systems and battery-based loads) were validated in Task 3.1.

-+Pg*

Pg

PI-+

id*

id

PIvd'

PWM

ToDC/AC

Converter++

decoupling

vd*

-+Qg*

Qg

PI-+

id*

id

PIvd'

++

decoupling

vq*

dqtoabc

GridVoltageAngle

vd*vq*

Bi-directionalDC/DC

Converter

Li-ionBattery

DCLink

DC/ACConverter

GRID-

+

iL VdcPg,Qgid,iq

vd*,vq*

fg

fg,Vg

f

P

VgQ

V

Power-Frequencydroop

Volt-VarCurveForVoltageRegulation


19

Replace with Tasks_4_5_validation_procedure.pdf(2 pages)

TG DG BL

Step 1: Test performance criteria

Step 2: Identify the aggregate response model for BL and DG.

Step 3: Integrate the DG and BL model and run the simulation for the whole operation cycle with given loadand renewable generation profile. Assess the system-wide performance.

ercot cps flexlab1). Reserve Magnitude Target (RMT) (> 2%)2). Reserve Magnitude Variability Tolerance (RMVT) (< +/-5%)3). Duration (> 30s)4). Availability (> 95%)5). Initial Response Time (< 2s)6). Ramp time (< 8s)7). Contingency support (loss of renewables)8). Small-signal stability (improve 𝜆𝑚𝑎𝑥)9). Frequency deviation (< +/-0.5Hz) Device transients;

Controller dynamics;

Optimization dynamics in the loop.

BL: Flex LabDG model

Overall Procedure (to be used for HiL tests also)


20

BL Test: Building controller (BC) + LBNL devices (with or without PV)

DG Test: CPS + BC+LBNL

TX Test: ERCOT+ CPS + BC + LBNL

• Apply step change to target power of the whole building, and observe the overall response;• Compute the performance metrics;• Identify the overall response model of the building.

• Apply step change to target power of the distribution system at PCC, and observe the response;• Compute the performance metrics;• Identify the overall response model of the whole distribution system.

• Run the whole hierarchical model for the entire operation cycle;• Assess the performance the of three level controllers;• Apply contingency (sudden loss of renewables), and observe the system response.

Majority of the performance targets were met in 1st round of large-scale simulations.

Validation: HiL Tests

21

‣ Task 5 (~60% complete): HiL testingTesting sites & schedule:

– LBNL FLEXLAB(Phase 1: Jan ’19, Phase 2: Mar/Apr ’19)

• 100 controllable hardware load devices: HVAC fans/pumps (6), blowers (60), heaters (31), PV/inverter/battery systems (3)

– Stone Edge Farm (SEF) Microgrid (May ’19)• ~20 controllable hardware devices: gas turbine (1),

electrolyzer (1), fuel cells (1), batteries (9), PV (2), motors (3), HVAC (1)


22

Building 90

PG & E (utility)

Rotational testbedTB90XR-ATB90XR-B

Testbed #1TB90X1-ATB90X1-B



PV system3 inverters3 batteries

Cell A

Cell B

uPMU

uPMU

uPMU

HVAC(2 fans, 4 pumps)

SSR/Heaters(30)

VFD/Motors(10 x 6 racks)

Powermeters

SM_ERCOT

TX_SOLVER(transmission)

TX_CONTROL

Hostcomputer

DG_SOLVER(distribution)

DG_CONTROL

BL_CONTROL(ctrl. BLs/inv.)

BL_OPT

TX_OPT solver

NU server

MILP solver

SS_WALZEM

SC_CONSOLE

Opal-RT system

SSHTCP/IP

Power measurements (NI)

Phasor measurement(C37.118)

PQ targets (API, HTTP)

Power targets (NI)

On/off status targets(DO)

On/off status targets(AO)

LBNL/FLEXLAB/FLEXGRID

2 progmmableload banks

OP5600

LBNL_BL_Model

LBNL_BL_Battery

HIL Test at LBNL FLEXLAB


23

Phase 1 testing at LBNL FLEXLAB

(Photos courtesy of LBNL)


24

Phase 1 testing at LBNL FLEXLAB

Project Challenges

25

‣ Challenges overcome:– Proposed network decomposition method was not

adequate when scaled up à reformulation of optimization problem + commercial optimization software

– Completely re-planned HiL tests with new sites– Validated communication links to ~100 devices via

multiple protocols at LBNL FLEXLAB‣ Remaining challenges:

– Complete 2nd round large-scale simulations– Minimize communication delays and complete LBNL

FLEXLAB HiL test– Set up & complete SEF Microgrid test

Publications

26

‣ Y. Yang, T. Nishikawa, and A.E. Motter, Vulnerability and Co-susceptibility Determine the Size of Network Cascades, Phys. Rev. Lett. 118, 048301 (2017).

‣ Software for simulation of physical cascade model and identification of cosusceptibile groups, https://github.com/yangyangangela/determine_cascade_sizes

‣ Y. Yang, T. Nishikawa, and A.E. Motter, Small vulnerable sets determine large network cascades in power grids, Science 358, eaan3184 (2017).

‣ Y. Yang and A.E. Motter, Cascading failures as continuous phase-spacetransitions, Phys. Rev. Lett. 119, 248302 (2017).

‣ A. Haber, F. Molnar, and A.E. Motter, Global network control from localinformation, to be published.

‣ Publications being prepared on:– State information neighborhood and nonlinear localized control– LBNL FLEXLAB HiL test configuration and results

https://github.com/yangyangangela/determine_cascade_sizes

Tech-to-Market Path

27

‣ Commercial objectives: License/patent developed control

algorithms

‣ Target markets/segments: Building managers, Microgrid

operators, ISOs

‣ IAB members:– Sunny Elebua, Vice President, Corporate Strategy, Exelon Corporation

– Troy Nergaard, Director of Technical Product Management, Doosan GridTech

– Dave Kaplan, Chief Technology Officer, Doosan GridTech

– Holly Benz, Senior Vice President, Commercial & Industrial, CLEAResult

– Jean Bélanger, Chief Executive Officer & Chief Technology Officer, OPAL-RT

Technologies

– Mark Feasel, Vice President, Electric Utility Segment & Smart Grid, Schneider

Electric

– Shaneshia McNair, Manager, Customer Engineering, CPS Energy

Tech-to-Market Path Update

28

‣ Tasks completed:– Defined process for selecting target market segment (“beachhead”),

and value proposition.– Reviewed technology reports, and identified potential value

propositions, target market segments, and channel access to target market segment.

– Completed team discussions on value propositions for residential, and medium to large commercial building segments, and reviewed competing offers addressing similar value propositions to identify solution differentiations.

– Concluded that demand response type of solution is best positioned to deliver a strongly differentiated solution to load aggregators and large commercial customers.

– Completed team discussion on product definition.

Remaining Tasks

29

‣ Task 3: Customize building level controller for SEF testing‣ Task 4: Complete the 2nd round large-scale simulation‣ Task 5: Complete HiL tests at LBNL FLEXLAB & SEF

Microgrid‣ Task 6 (Tech-to-Market tasks):

– Review demand response programs to confirm value proposition and level of competitive differentiation.

– Define whole product solution– Complete two more rounds of feedback from IAB– Identify and interview commercial building customers participating in

demand response programs to confirm value proposition.

a novel hierarchical frequency based load control architecture

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