Smart Models Miranda

8/20/2019 Smart Models Miranda

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SMARTER MODELSFORSMART SYSTEMS

Vladimiro Miranda

IEEE FellowDirector INESC TECPresident INESC P&D Brasil

Pioneer Portugal

In 2013 about 25.000 MW of wind generation capacity in Iberia(peak of 65.000 MW)

• About 5000 MW are in Portugal – today’s peak consumption isaround 9500 MW

Portugal: no.2 in the world in 2012 – 20% of electricity from wind –more capacity installed than Denmark (nº 1)

Only half of the windpower is monitored attransmission level

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10 million people5 million wind kW


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Wind generation, load and challenges (case of Portugal)

• Wind power integration in large scale requires reinforcedinterconnections

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2.March.2010

3.March.2010

Wind

Load

wind farms monitored attransmission level

Wind Power greater than load!!!

WHAT IS THE SMART GRID?

The smart grid is not just more of the same: it requires a new layer

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LAYER CHARACTERISTICS NATURE

Physical grid Generators, lines, transformers Physical

Local control Sensors, relays Physical

Communication Dedicated, optical fiber, gsm Physical

Data transmission Protocols Software

Information Data bases Software

Central control EMS, DMS Software

High levelintelligence

Local agents, parallel processing, distributedcomputing

Software


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Escalating the Concept Ladder

The Smart Grid should be built step by step when conditionsmake it feasible, necessary and economically justified

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Do you have a medium/high degree of distributed generation?

Do you have electrical vehicles?

Smart metering

Microgrids

Grid islanding

Intelligent Grid

TelemeteringDo you have large costs from meter reading with human agents?

Do you have flexile tariff systems or market based tariff system?

Do you

have

explicit

reliability

costs?

FROM THE CLEVER INTEGRATION TO THE SMART SYSTEM

The micro‐grids

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10 YEARS OF EXPERIENCE IN THE EU

MICROGRIDS (2003‐2006)14 partners, 7 EU countries

MoreMICROGRIDS (2006‐2008)22 partners, 11 EU countries

Work Packages

From: Steady State and Dynamic Simulation Tools

Development of Local Micro Source Controllers and Central Controller

Development of Emergency Functions

Telecommunication Infrastructures and Communication Protocols

To: Alternative Control

Strategies

(hierarchical

vs.

distributed)

Standardization of Technical and Commercial Protocols and Hardware

Evaluation of the system performance on power system operation

Impact on the Development of Electricity Infrastructures

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MULTI‐MICROGRIDS

MicrogridsLow Voltage distribution systems with small modular generation units providing power and heat to local loads.

Local communication

infrastructures.

A hierarchical management and control system.

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HV Network

VSI

Diesel

DFIM

MicroGri

d

MicroGri

d

MicroGri

d

Capacitor

Bank

Hydro

CHP

Sheddable

Loads

Operation Modes:

• Interconnected Mode• Emergency Mode


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WHAT DO WE GET WITH MULTI‐ GRIDS?

Added value in reliability:

a fault‐tolerant system

1. Islanding: the survival of network islands relying on local generationreduces load curtailment

2. Black start: the recomposition of the system, in case of major blackout, may be made bottom‐up: much faster than present day top‐down practice

Major disasters (or terrorist attacks) will no longer cause global electricpower supply disruption

The dynamic definition of operating conditions and control strategies for Grids, as well as new equipment specification, has been the object of

years of research in Europe.

EnergyCon 2012 ‐ Florence 9

ÉVORA, PORTUGAL: THE SMART CITY

Bringing reality to the concept

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THE SMART CITY IN PORTUGAL

The city of Évora connected as a Smart City

• 33,000 consumers equiped with the Energy Box

• Supply points for electric vehicles

• A communication network in parallel to the power network

• Distribution controllers concentrate messages

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LC Electric

appliances

MC

The PROSUMER: producer and consumer

The Energy Box controls load and micro‐generation

The Energy Box is the basic element of the InovCity initiative in Portugal

12FROM THE SMART GRID TO THE SMART CITY

Photo‐voltaic

Energy Box

Controling loads


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INTEGRATION WITH SMART METERING

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InovCity and InovGrid: the formula for an actual success

The success of the InovGrid project has its roots in:

1. A robust consortium led by a visionary PowerCompany

2. An alliance with the excellence of a Research

Institute3. A team work with highly professional Industrial

partners

AND…

4. The consistent support from the political power

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Smart Grid projects in Europe

Statistics 2012

204 European projects

111 projects with significative demo; 15 projects financed by the EC, 189 baystate members

SG projects: national budgets(industry + pub. financ.): 2500 M€through the JRC (excl. 2500 M€ forsmart‐meter roll‐out)

EU SG project budget (industry + EU financ.): 184 M€ to the JRC (withinFP6 and FP7)

National

93%

EC (FP6+7)

7%

• Cluster 1 Smart

customers:

92

pr.

• Cluster 2 Smart metering: 59 pr.

• Cluster 3 DER integration: 146 pr.

• Cluster 4 Smart Distribution: 113 pr.

INESC TEC LAB ON SMART GRIDS AND ELECTRIC VEHICLES

Sailing ahead

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INESC TEC Lab on Smart Grids and Electric Vehicles

Electric vehicles and battery charging

Research in the EU on driving habits

The business model will have a decisive impact on the powersystem, by conditioning the citizens behavior

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UTOPIA Dumb charg./Double tariff


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Impact on reliability: LOLE and LOLF

Chronologic Monte Carlo including conventional generation and wind, storage, conditioned demand and EV batteries.

Simulation in 6 European countries:

→ unless a smart business model is put in place, the integration of EVs will have a negative impact on reliability

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h/ano ‐/ano

LOLE LOLF

Charging EVs and network stress

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No EV Dumb charging– 52% EV

Double tariff – 52% EV Smart charging – 52% EV


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Intelligent V2G

INESC TEC Smart charger

Bi‐directional communications with the DTC or energyaggregator


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INESC TEC Lab on Smart Grids and Electric Vehicles

The lab architecture is based on a conceptual model for actual and future networks

Control and communication requirements•

Scenario validation through simulationMulti‐technologyAllows combining emulation and physical implementationIntegration of distinct entities and information flows – technical and marketIntegration of a diversity of equipament e and their APIs

Wind Integration in Smart Environments W I S E

Solar Integration in Smart Environments S I S E

For weak connection points:

• includes an inverter allowing the connection of small wind generators up to 3 kW to Low Voltage networks

• avoids an excessive raise in voltage at the connection point

• can track frequency changes and adjust power output

• droop control principle

WISE SISE technology

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UNVEILING HIDDEN INFORMATION

To know better

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DATA: THE CHALLENGE

Added technologies:Distributed generationDistributed vehicle connectionDistributed control

Massive data flow

Added uncertainty elements: RenewablesDistributed decision factors (V2G, tariffs/prices…)

Deterministic models replaced by probabilistic

26EnergyCon 2012 ‐ Florence


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State Estimation in the Smart Grid context

Change of paradigm

• Decentralized

• Clusterized

• Based on probabilistic models

REPLACEMENT OF THE CONVENTIONAL CONCEPT OF TOPOLOGICAL

OBSERVABILITY

BY

STATISTICAL OBSERVABILITY

NETWORK OBSERVABILITY

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UNVEILING THE HIDDEN INFORMATION

How to estimate the current system (or multi‐microgrid) topology, when no breaker status signal arrives at the Control Centre?

HYPOTHESIS:

the

network

topology

information is embedded in the manifold supporting the electrical data.

We need Information Theory to unveil

the meaning of this “background

micro‐wave radiation”

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Breakers with status unknown

19

17

18 21 22

16 20

23

15 14

24 11 12

13

3 9 10

4

12

5

7

8

6

138 kV

230 kV

~~~

~

~

~ ~

~

~~


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INFORMATION THEORY NEEDED

By simulation: a data set of {electric values M, topology T}

MUTUAL INFORMATION I(M,T) – how much information aboutT is contained in M?

I(M,T) = H(M) + H(T) – H(M,T)

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Entropy of the joint pdf

Entropy of the

topology pdf

Entropy of theelectrical data

pdf

ENTROPY

ENTROPY is a measure of information content

Shannon's Entropy

Renyi's Entropy

Renyi’s Quadratic Entropy

Extension to continuous pdf

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N

1k k R

plog1

1H

1,0

N

1k k k S

p

1log pH

dz)z(f logH 2Y2R

N

1k

2k 2R

plogH


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MUTUAL INFORMATION MAP

Impact of breaker 6‐10 in system power flow, measured bymutual information between each line power flows andbreaker status

High MI

Low MI

The flow in some

branches

is much more informative

than in other branches

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Br. 6‐10

IDENTIFICATION IN TOPOLOGY CELLS

Information serving diagnoses

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CLUSTER DISTINCTION WITH AUTOENCODERS ‐ CAM

Admit 2 autoencoders trained for 2 distinct data clusters A and B, corresponding to 2 distinct topologies. For a new input vector, one of the autoencoders will be “in tune” while

the other will display a large error.

Manifold for

Topology A

Manifold

for

Topology B

New point

New point

A

B

A

B

B breaker closed

2 autoencoders to diagnose the

inner state of the topology cell

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11

12

13

3

9 10

4

12

5

7

8

6 ~ ~

~

~~

breaker1

breaker3

breaker4 breaker5

breaker6

23

23

16

3

used measurements

measurements not

available

No information!

AB

Electric pattern

min


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A COMPETITIVE APPROACH: CAM

Input to autoencoders selected with Mutual Information!

TOPOLOGY ESTIMATOR a MOSAIC of local autoencoders

results in 10,000 scenariosincluding noise

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Simult. miss.

signals

Signal reconstructions Topology reconstruction

Wrong Corr. Efficien. Wrong Corr. Efficien.1 5 995 99.50% 5 995 99.50%2 10 1990 99.50% 10 990 99.00%3 11 2989 99.63% 11 989 98.90%4 18 3982 99.55% 17 983 98.30%5 23 4977 99.54% 23 977 97.70%6 27 5973 99.55% 27 973 97.30%7 33 6967 99.53% 32 968 96.80%8 41 7959 99.49% 39 961 96.10%9 47 8953 99.48% 44 956 95.60%

10 51 9949 99.49% 48 952 95.20%Total 266 54734 99.52% 256 9744 97.44%

AB

New point

min

TOPOLOGY IN BLACK BOXES

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Even with remote informationonly, breaker status may berecovered: but…

less statistical observability

less precision

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11

12

13

3

9 10

4

12

5

7

8

6 ~ ~

~

~~

breaker1

breaker3

breaker4 breaker5

breaker6

23

23

16

3

used measurements

measurements not

available

B R E A K E R LOCAL INFORMATION

I n p u t ,

O u t p u t

H i d d e n

l a y e r Wrong Corr. Efficien.

1 15 10 0 10000 100.00%2 19 14 131 9869 98.69%3 17 12 0 10000 100.00%4 17 12 0 10000 100.00%5 17 12 0 10000 100.00%6 15 10 0 10000 100.00%7 17 12 11 9989 99.89%8 13 8 67 9933 99.33%9 13 8 3 9997 99.97%

10 17 12 4 9996 99.96%Total 2 16 9 97 84 9 9. 78 %

B R E A K E R REMOTE INFORMATION

I n p u t ,

O u t p u t

H i d d e n

l a y e r

Wrong Corr. Efficien.

1 13 10 210 9790 97.90%2 19 15 1278 8722 87.22%3 21 17 0 10000 100.00%4 19 15 194 9806 98.06%5 13 10 2555 7445 74.45%6 15 12 19 9981 99.81%7 11 9 3863 6137 61.37%8 11 9 1203 8797 87.97%9 13 10 4461 5539 55.39%

10 11 9 1523 8477 84.77%Total 15306 84694 84.69%

No information!


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TOPOLOGY IN BLACK BOXES

External signals may also unveil the hidden topology in substations… or multi‐microgrids…

Subsystem topology

reconstructionWrong Effic.

Split bus y/n 0 100.00%

No split bus

L1 0 100.00%L2 29 99.42%L3 0 100.00%L4 0 100.00%

Tot.: no Sp. bus 29 99.42%

Split bus

C1 86 98.28%C2 198 96.04%C3 6 99.88%

Total: split bus 2 79 94.42%Global results 308 96.92%

TRAINING WITH INFORMATION THEORETICCRITERIA

How to improve autoencoder accuracy

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Maximizing information flow

A perfect autoencoder must preserve all information thatcrosses through ‐ from input to output.

Backpropagation under MSE does not guarantee that.The first half , trained in unsupervised mode, should yield at the output thesame amount of information as presented at the input.

Possible criteria:

• Max Mutual Information between input and middle layer

• Min Mutual Information among middle layer nodes

• Max Entropy at the middle layer

supervised

unsupervised

Maximizing information flow

INESC TEC experience: maximizing Entropy at the middle layeris the best criterion overall.

Renyi’s Entropy

• For continuous variables…

Entropy is a measure of information quantity.

• Initializing weigths with values derived from PCA is alsoa fine strategy [as if the neural network had only linear activation functions]

N

1k k R

plog1

1H

1,0

dz)z(f logH 2Y2R


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Learning macro features

Experiments with deep sparse networks have shown thatmacro features could be learned from data

From 10 million image frames

collected randomly from youtube

videos, a half ‐autoencoder trained in

unsupervised mode under an ICA

criterion learned to identify faces

and

cats!

[Quoc et al, 2012]

Macro features exist!

Faces… bodies… cats…

Is there a neuron that captures the

essential concept?

YES!

cat body


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EXAMPLE

Autoencoder Model MSE

Breaker 2 open

Single step 0.0046

2‐step ME 0.00192‐step MQMI 0.0014

Breaker 2 closed

Single step 0.0053

2‐step ME 0.00152‐step MQMI 0.0007

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TP FP FN TNSingle step 4928 104 5 49632‐step MQMI 4915 0 14 5067 Remarkable! 14 in 10,000!

True Positive: br. closed, prediction: closedFalse Positive: br. open, prediction: closed

False Negative: br.

closed,

prediction:

open

True Negative: br. open, prediction: open

(Only power, no voltage information)

EXAMPLE

There is a reason why 14 answers were wrong: confusion!

The active and reactive power flows are close to zero in all 14 FN cases (prediction open when breaker closed).

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Active Reactive

B. open B. closed B. open B. closed


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EXAMPLE – HEURISTIC RULE (HR)

What if a heuristic rule is applied, defining

that “if |P,Q|


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CONCLUSIONS

No Smart Grid without smart control

No Smart Grid without smart distributed intelligence

No Smart Grid without smart information theory

No Smart Grid without smart business models

No Smart Grid without smart people…

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VISIT Porto: the Formosa city!

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Smart Models Miranda