centralized vs. de-centralized ict approaches and ... · and challenges* for smart metering and...

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Trial lecture, 12.05.2011Máté J. Csorba

Máté J. Csorba

[email protected]

Centralized vs. de-centralized ICT approaches

and challenges* for smart metering and smart

grid

* (including performance aspects)

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o The Power Grid today

o The Smart Grid of tomorrow

o The Smart Grid worldwide

o Smart Metering and the AMI

o Concluding thoughts

o Further reading / references

Contents

… …

Today Tomorrow ?

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o 3100 (345) electric utilities (generation,

transmission, distribution)

o 10000 (850+) power plants

o 131 (2.75) million customers

o 29000 (120) TWh consumed every

annualy

• consumer electronics >50% of

consumption in a typical US home

o Over ~157000 miles (300000 km) of high

voltage transmission lines

Power Grid numbers – e.g. US (NO) Power Grid

o Optimization problem without analytical solution

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o Broadcast energy

o Central generators, large number of users

o Based on Nikola Tesla’s design from 1888

(what was thought possible in the 19th century)

o Supervisory Control And Data Acquisition (SCADA)

o AMR – 1-way communication only

o Load-following mode

• Static load

• Dynamically operated generation

o Has not kept pace with modern challenges:

• Security threats, cyber attacks

• Intermittent supply of alternative power generation sources

• Conservation goals, lessen peak demand surges during the day

The Power Grid today

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o Centralized Energy Management System (EMS) as control center

• All control entities directly connected to the EMS

• Entities have no mechanism to communicate with each other

• The communication graph is a star centered at the EMS

• The EMS queries (round-robin) the current status of the devices

o The Remote Terminal Unit (RTU) in each substation collects status

information and reports it to the EMS

o EMS estimates the entire grid state

using all the information acquired

from the power grid


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o Applications in the EMS:

• Contingency analysis, depends on the estimated state of the grid

• State estimation is an off-line computation

• Precision relies on predefined models

• Incapable of rapid response in emergency situations

• Notifications require end-to-end latency of 8-12 ms

o Special Protection Scheme (SPS)

• Local

• Complimentary to the EMS

• Pre-defined critical events

• Response within time constraints

• Required actions pre-configured

=> unable to react to all situations


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Timing and reaction times in the Power Grid

0.001 0.01 0.1 1 10 100 1000 t [sec]

Short-circuits

Blackouts,

Voltage collapse,

Oscillation,

Cascade failures

SCADA / EMS

- Steady-state view

- Slow reaction time

Local protection

- Direct local action

- Fast reaction time

Wide area measurement &

protection

- Dynamic wide are view

- Fast coordinated action

- Human/machine intelligence

Un

co

ord

ina

ted

Co

ord

ina

ted

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o Different time scales

• Bid in the market every hour vs. resolve a generator instability within

300 ms ?

o Locally installed new components require modification at the central

controller

o Limited communication bandwidth

• Local measurements, cannot be sent nor stored

o Limited central storage

• Data summaries computed by each local entity sent to the EMS

• Wide-area monitoring and control relies on summary data => flexibility

and adaptation is limited

The Power Grid today (cont’d)

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o New generation energy delivery network

• Save energy, reduce costs, increase reliability

• Two-way communications

• Control appliances (at consumers’ homes)

• Communicate towards users (level the load through “invisible hand”)

• Address energy independence (centralized control infeasible with

multiple providers)

• Enable supplier selection

The Smart Grid

o Upgrading/overlaying the ordinary electrical

grid with a new metering system

• Specific solutions by region

o Estimated investments (IEA) by 2030

• 500bn EUR - transmission and distribution

networks

• additional 180bn EUR in ICT

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The Smart Grid

Sensor

SystemActuators

Communication

Infrastructure

Control

System

o Enhancement of the eletricity grid, with

• sensor systems, communication infrastructure,

control systems and actuators

o Enable Distributed Energy Resources (DERs)

• Actively manage solar, wind and other

renewable resources

• Enable businesses and homes

to sell the surplus back

o Latency is major concern

• For faster response delegate more

control to local level

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o A cluster of distributed

generation installations

• Collectively run by a

central control entity

o Small number of peak-load

generators

• Balance demand and supply locally

• Reduce carbon emission and operating costs

o e.g. combined power plant test-project, solar, wind, biogas and hydro-

storage to provide load-following power (University of Kassel)

The Virtual Power Plant (a DER)

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o (Data) Storage

• Many entities in the grid can have their own storage

• For historical data, high-volume disks within a secured perimeter

• For time critical data, distributed network storage system, with a hash

function shared by all entities

o Communication

• Using IP, reduce cost, configuration complexity and maintenance

• Provision Mbits, control power with kbits, sell the rest (IPTV, Internet,

Broadband over Power Lines, etc..)

• Light-weight protocol (connection setup) needed

• Reliability is a basic requirement

• Non-low Latency & Reliable (TCP, RDS/UDP, RTP/TCP) vs.

Low Latency & Non-reliable (RTP/UDP)

• Authentication & encryption

• End-to-end (problems with multicast) vs. hop-to-hop, or a combination

• Grid overlay ~ structured P2P network

The Smart Grid (cont’d)

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o Power Grid Models

• Optimal control, Ecology, Human cognition, Glassy dynamics,

Information theory, Microphysics of clouds, Kuramoto oscillators,

Dolphin social network, Neural networks

o Computational Intelligence

• Incorporated in sensing, measuring and metering circuits to reduce the

burden of communications

• Dynamically reconfigure and recover (self-healing)

• e.g. reinforcement learning controllers

Smart Grid research

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o Intelligent agents

• Monitor & forecast power consumption of each individual load

• Take anticipatory actions to prevent cascade of faults, keep the

problem local

• Prototype - Transmission Entities with Learning capabilities and On-line

Self-healing (TELOS) at Argonne National Laboratory

• Types of agents

• A physical entity, e.g. a controller

• A virtual entity, e.g. software making energy market bids

• All the agents collaborate to achieve global objectives

• e.g. allow continental/national backbones to fail without causing local

smart grids to fail

• Isolated islands

• Islands can reorganize using local resources

• Islands operate independently and adhere to the global goals

• Local controllers coordinate the joining of islands

Smart Grid research

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Siemens vision

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o Australia

• WiMAX-based smart grid, over five sites in New South Wales

• Increasing summer demand peaks (due to AC)

• Rollout from 2006, projected 1 million meters in 2013

o China

• Part of the current 5-year plan, building a Wide Area monitoring

system, all communication via broadband using a private network

The Smart Grid by countries

o Austria

• Targeted research since 2003

• Feldkirch – 17000 smart meters over 12 years

o Canada

• Smart meters in all Ontario businesses

and households (4.3 million customers) by

2010

• Each company free to develop own

metering frameworks

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o EU

• The SmartGrids European Technology Platform for Electricity

Networks of the Future began its work in 2005

• The SuperSmart Grid, a hypothetical wide area electricity network

connecting Europe with northern Africa (e.g. DESERTEC), the Middle

East, Turkey and the CIS countries


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o Germany

• Mannheim, BPL communications in a Model City

• Kassel, Combined power plant

o Italy

• Earliest, and still largest deployment

• Enel S.p.A. – Telegestore project – 33 million smart meters installed,

aim 36 million in 2011

• Communication over PLC

• Cost: 2.1 billion EUR, estimated savings: 0.5 billion EUR / year

o Malta

• Implementing smart meters in all commercial and private households

o Japan

• >80% energy imported

• Public utilities have started to test metering

• GE Fuji Meter Co. established 02.02.11.


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o The Netherlands

• Starting in 2008, all residential

customers will get a smart

meter within 6 years

• Register both electricity and gas

• Oxxio – Customers also have

entry to a personal website

showing the actual energy use and energy costs

• Privacy concerns / protests

o Northern Ireland

• Prepayment meters became obsolete by 2000

• 155k meters installed by 2005 (22% of costumers)

• Trials in e.g. offering different rates


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o Nordic countries

o Sweden

• First studies in 2001

• In 2003 bill passed, monthly readings required of all electricity

meters by 2009

o Finland

• Vattenfall, Fortum and E.ON decided to deploy AMI in Finland,

legal requirement to deploy 80% of the smart meters by 2014

o Denmark

• Developments took off in 2004, installed 300k smart meters for

industrial consumers

o Norway

• In 2007 NVE recommends new legislation requiring smart meters

to take effect in 2013

• Full deployment expected to end in 2014


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o South Korea

• Operation of micro-grids with distributed generation, energy storage

and electric vehicle charging facilities

• Reduce imports, cut greenhouse gases, build wind farms

• Jeju – Smart Grid pilot

project, fully integrated

in 6000 households

• Various electricity rates

for consumers

• Full deployment by 2030

o UK

• Roll out of the system by 2020

o New Zealand

• Christchurch plans to install over 112k smart meters by 2009


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o US

• Unified Smart Grid

• Austin Energy

260k residential smart meters

in 2008

• Boulder, Colorado, first phase

completed in 2008

• Centerpoint Energy, Houston

2M electricity customers

• Oncor Electric Delivery, Dallas

3M customers

• California

increase reliability by reducing

peak demand, one of the

largest AMI deployments by

2012


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o What?

• Sensing, measurement and control in

• Electricity production, transmission, distribution and consumption parts of

the network

o How?

• Two-way communication via

• GSM, GPRS, satellite, radio, ADSL, PLC, fixed wireless, mesh network,

Wi-Fi, ASCII data on serial port, trend towards TCP/IP

o Why?

• Enable Demand Response mode

• Combining load-following with load shaping

• Evening daily variations

• Fluctuating power supply from wind, solar, tidal, etc.

• Remotely regulate throughput (e.g. turn power on/off), read usage

information, detect service outage, detect unauthorized use, change

the maximum demand

Smart Metering*

*(from a technical point of view)

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o Wide-Area Measurement Protection

and Control Systems (WAMPACS)

replacing SCADA

o Unique and addressable identifier

o (near) real-time sensors, power outage

and quality notification

o Standardization of communication

ongoing

o Electricity, natural gas or water

consumption

Smart Metering

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Display,

Control,

etc.

o Fast reaction times

• 60-720 samples/sec => 16 - 1.4 msec

• From thousands of sensors !

o Increased storage & processing

• Austin Energy – 400MB/meter/year (15min sampling)

• 2.75M meters => 1.1PB/year

• (Google ~ 20 PB daily)

Smart Metering

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o Architecture openness

• Allows manufacturers (e.g. of DER units) to embed programmable agents

• Provides “plug and play” capability of future units

o Pub-Sub instead of Master-Slave communication

• Distributed, peer-to-peer, enables multicast

• More secure and scalable

• No single point of failure or bottleneck

Smart Metering

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Advanced Metering Infrastructure (AMI)

o Collect raw data using smart meters

o Store and prepare measurements for

further processing

o Metering today

• Resolution: 15 mins, killer app:

billing

o AMI: near real-time

o Meter Data Management (MDM)

• Data gathering & processing (e.g.

validation, estimation, etc.)

• Long-term data storage, analysis,

billing, forecasting, etc

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o Concentrator layer

• Meters connect via various

(proprietary) protocols to report

• Interface between many low-speed,

heterogeneous, asynchronous and

one or more high-speed, synchronous

channels

• Aggregating meter reading data and

submitting it to the metering server

o Meter layer

• The last mile, passively measuring

consumption / production

(End-points – routers – servers)

Advanced Metering Infrastructure (AMI)

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o The Internet

• “Best-Effort” service

• QoS is secondary

• Allocate bandwidth so that

data is delivered efficiently

o The Energy “Internet”

• Satisfy demand anytime

• Peak demand forecasted

(anticipatory control)

• Traffic control important,

routing options are limited

• Bottlenecks are more likely

Energy ”Internet”(The electricity distribution network perspective)

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o Electricity cannot be stored

(at a large scale)

• Energy might be

o Create a virtual energy buffer

• Demand side management

o Load-forecasting agents

o Priority provisioning of power

• hospitals, shelters, etc.

• “priority routing”

• “access-control”

Energy ”Internet”(The electricity distribution network perspective)

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o Today: centralized control

• Star-like communication network, local enhancements

• One-way communication

o Tomorrow: de-centralized structures (production too)

• Two-way and peer-to-peer communication

• Integrated with the power network or dedicated?

• Better scaling

• Scalability issue with respect to meter data?

• Eliminate bottlenecks and single points of failure

• AMI graph is a two-level tree => Single point of failure e.g. in concentrators

Conclusions

… …

Today Tomorrow ?

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1) L.H. Tsoukalas, and R. Gao, “From smart grids to an energy internet: Assumptions,

architectures and requirements”, 3rd Int’l Conf. on Electric Utility Deregulation and

Restructuring and Power Technologies, 2008.

2) S. Karnouskos, P. Goncalves da Silva, and D. Ilic, Dejan, “Assessment of high-

performance smart metering for the web service enabled smart grid era”, 2nd Joint

WOSP/SIPEW Int’l Conf. on Performance Engineering, 2011.

3) S. Vukmirovic, S. Lukovic, A. Erdeljan, and F. Kulic, “Software architecture for Smart

Metering systems with Virtual Power Plant”,15th IEEE Mediterranean Electrotechnical

Conference, 2010.

4) R. Van Gerwen, S. Jaarsma and R. Wilhite, “Smart Metering - Briefing Paper”,

www.leonardo-energy.org, 2006.

5) Y.-J. Kim, M. Thottan, V. Kolesnikov, and W. Lee, “A secure decentralized data-centric

information infrastructure for smart grid”, IEEE Communications Magazine 48(11),

2010.

6) Z. Jiang, “Computational Intelligence Techniques for a Smart Electric Grid of the

Future”, 6th Int’l Symp. on Neural Networks, 2009.

7) L.O. AlAbdulkarim, Z. Lukszo, “Smart Metering for the Future Energy Systems in the

Netherlands”, 4th Int’l CRIS Conf. on Critical Infrastructures, 2009.

8) K. E. Nygard, P. Ranganathan, S. Bou Ghosn, Md. M. Chowdhury, D. Loegering, R.

McCulloch, “Optimization Models for Energy Reallocation in a Smart Grid”, IEEE

INFOCOM M2MCN Workshop, 2011.

Further reading

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Thank You!

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