Why information networks can pave the way

to green electricity Anna Scaglione

UC Davis

Motivation

• Projections on energy consumption 30% growth by 2035 in developed countries whopping 87% the rest of the world

• Resources (and their scarcity) are going to define our future geopolitical outlook

• Climate change?

The last significant change in the energy sector

• Electric Power Market: Energy Policy Act of 1992

implemented in ’96 Utility savings decreased

consumer prices

• A heavily regulated market Cost based, not market based the customer is not directly

involved

The Energy Pie • US Electric Power Industry Net Generation ‘09

Some facts to consider • Electricity is a fast

delivery medium • The physical

infrastructure for delivery, storage and reconversion in large scales are very expensive in comparison

• Generation is cheap: Steam Engine

1769- James Watt’s Steam engine

$$$$$$$

$

A market with a bottleneck: keeping the power in balance

Bus k

To bus i To bus j

Consequence • The distribution inability to store the goods

shortchanges the demand and supply mediator (public utility) inefficiency Facts (DOE)

• 10% of all generation assets and 25% of distribution infrastructure are used less than 400 hours per year (5% of the time)

• In the United States, the average generating station was built in the 1960s using even older technology. Today, the average age of a substation transformer is 42, two years more than their expected life span.

Energy too cheap to be smart about it

The power market structure • Day ahead Market The day ahead planning sets a price

• Real time Market Prices then fluctuate depending on consumption

during the day and congestion Short term price close to day ahead

Short term price very different from day ahead

What is happening now • The system is aging – we cannot “milk” the

transmission system maintaining safety and allowing for growing demand Infrastructure monitoring would allow to replace and

strategically use physical resources (storage) to repair the system progressively

• Increasing political pressure to include renewables introduces volatility For the Utilities the equation is:

Load’=Load –Renewables • The transportation system may be plugged on the grid

Opportunities in research in IT • Common sense: Improving situation awareness Networked state estimation and control

• Infrastructure monitoring problem with – Wide area deployment – Stringent accuracy and data integrity constraints

Metering consumption (no longer too cheap to meter)

• A growing cyber infrastructure: a growing problem… Cyber-physical security

• Game changing ideas Load scheduling of deferrable loads

• Towards real economic balance of demand and supply

NETWORKED MONITORING AND WIDE AREA CONTROL

Opportunities and challenges in

Legacy: SCADA Reference Model for Infrastructure Control

• Supervisory Control and Data Acquisition (SCADA) Supervisory Computer (SC)

system,

Human-Machine Interface (HMI)

Remote Terminal Units (RTUs)

Meters: Intelligent Electronic Devices (IED)

• Complementary Distributed Control System (DCS) Automatic Gain Control, Circuit

breakers, Voltage Control….

Central Control Station

INTERNET

Business Operators

LAN

WAN

RTU

IED

Conceived in the mid ‘70s

Shweppe & Handschin ‘74

Issues with the legacy systems

• Information is not fused to perform real time control because network delays are excessive and not controllable

• The centralized model is not scalable

Research status and prospects on new architectures

• Transmission grid thrust NASPInet interconnecting PMU measurements

• Will lower the cost of infrastructure – Higher congestion limits + maintain balance

• Advanced Metering Networks For now not profoundly new research Startups leveraging on past research on ad-hoc

networks and sensor networks • scalability issues

Machine 2 Machine communications

CYBER PHYSICAL SECURITY Short and long term needs for

Security of transmission and distribution monitoring

• Commercial SCADA systems use Ethernet and have doors to the Internet

• Safety engineering practices do not extend to the networks • No model for verification of digital inputs • Password protection and permissions are not used properly due to

lack of education about the threat • No provision to detect sensing issues properly – a sensor problem

or a communication problem?

• A number of researchers focusing on Data Injection Denial of service attacks and Jamming of sensors GPS spoofing arming Phase Measurment Units

New problem: Security of Advanced Metering Infrastructure

• Reliability, Data Integrity No different than other

telemetry problems if AMI is just used for monitoring

• Privacy: With in home metering

there are growing concerns about the intrusive nature of the sensing

Since [Hart ‘92] it is well known that one can build effective classifiers (Nonintrusive Appliance Load Monitoring)

DEMAND MANAGEMENT LOAD “CONTROL”

Exciting!

Where the true marriage of green Watts and Bits takes place

• We discussed uses of information technology that help the management of grid assets and market place

• Nothing said so far can impact the price of wind and solar energy These resources are not valued because the load

“cannot” wait to buy energy EV could wait!

• Change: Commodity Model Service Model Mapping use of power in digital transactions Holding the transaction until green cheap energy flows

Example: cloud computing

[A. Qreshi et al. ‘09]

• Elasticity of web servers (industrial customer) Time (postponing load) common Space (displacing the task) specific

Routing the bulk of data/ computations where energy costs less

State of the art • Demand Side Management (DSM) and

Demand Response (DR) are not new • Two prominent ideas – opposite sides of the

control spectrum: Load Control Through Curtailment

• Events cannot happen frequently - emergency situations • Rebound peaks

Priced Based Load Control / Real Time Pricing • Uncertainty in demand response to price • difficulties in price computation

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Direct Load Control • Interruptible loads (typically Air Conditioners)

• Interruption signal from the control center • Drawbacks customer inconvenience not well defined Rebound peaks

Real Time Pricing • A pricing signal is sent to the consumer

• HEMS schedule consumption to minimize cost • Challenges: Uncertainty and difficulties in price computation Distaste of price uncertainty from the consumer side

Demand side management Demand response

• Home Energy Management Systems (HEMS) Perry Stoneman [Capgemini’s Smart Energy Services]: “Home energy management is expected to grow rapidly with ON

World predicting global revenues for HEM equipment and services will reach $3.3 billion by 2014.”

GTM Research: home communications technologies that will be associated with the smart grid will become a $750 million market by 2015.

• Many attempts to shoehorn this in a feedback control framework…

• It is intrinsically a large scale scheduling problem with an economic objective

• What is the right management model and strategy? Energy cannot be “switched” between “clients”

Representing power injections as digital codes

• AMI data are not Gaussian Broad Classification Load Type 1: Deferrable known duration

• PHEV and EVs, washer, dryer

Load Type 2: Delay sensitive duration unknown Lighting, entertainment

systems Load Type 3:

Thermostatically Controlled duration varies

depending on state evolution

• Heating and cooling

LOAD CONTINUOUS MODEL

AD conversion: Watts Jobs • Each load corresponds to • Digital signature Quantized into codes Quantize time, record the # of loads arriving with

a certain code

Load Synthesis

Arrivals in a specific load class

Smart deferrable loads: submetering and scheduling

• Smart deferrable loads can communicate the job they “want” Uplink information

• arrival time and quantized load • Aggregate information: per queue arrivals

Downlink information • Activation time – basically a “turn on now” signal

Control? Schedule the transactions

• Deferrable appliances communicate the injection they want and allow to shift it

• Extensive literature emerging on aggregators scheduling the charge of electric vehicle fleets See e.g, [He,Venkatesh,Guan, ‘12], [Ghijsen, D’huslt, ‘11], [Wu,

Mohsenian-Rad, Huang, ‘12], [Wu, Aliprantis, Ying, ‘12], [Richardson, Flynn, Keane, ‘12]

Capacity Unscheduled load

Scheduled load

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Queuing model for scheduler The load corresponding to activating the

scheduled appliances is approximately

Sch

edul

er

Load Synthesis

Downlink Feedback

Costs

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• Inconvenience cost (experienced by the community, not individuals) queues weighted differently (different QoS)

• Cost of deviation from available power (power purchased on the day ahead + renewables)

Real-time Scheduling Optimization

• A sequential decision maker that determines the schedules for the appliances over a sliding finite horizon (N∆)

• Objective: minimize the expected increment in the accumulated cost of operation over the time horizon

• Uncertainty: arrival of smart loads, traditional load, renewables, price

• The DDLS has: Predictions of local marginal prices (LMP) for deviating from the

day ahead bid at the particular load injection bus The statistics of both smart and traditional loads Predictions of available local (green) generation

• Output: a decision matrix • N-1 dummy decision vectors to account for the future

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Numerical Results 18k Electric Vehicles

0 ≤ Charge time ≤ 8 hours

Optimization is run every 15 minutes

Charge code quantization step = 15 minutes

Arrival process is Poisson with constant rate λ =3 arrivals/each 15 minutes for each queue (32 queues)

Solver: Certainty equivalent controller that uses LP to schedule the Electric Vehicles Look-ahead horizon = 8 hours For fairness, the number of scheduled appliances is equal in the two profiles and no arriving appliances is delayed beyond t = 50 h

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Summary on load control • Load Scheduling Store energy job requests

and dispatch them to follow generation Scheduling and not control! Define the application rather than try to sell

sensor networking standards in the vacuum Extension to other loads is the next step

• How to make this viable? A lot of activity on defining “ancillary services”

• Ex: business models for Vehicle 2 the Grid Again not so concerned about the “plumbing” –

information flow, scalability – IT can play a big role

Conclusion • High risk (but high impact) coupling of demand response

and renewable sources Best leverage on information

technology

• Lower risk Cyber-Physical Security:

immediate need that will grow in the future Wide Area Measurement

Systems: there is a real market

Load Control

CPS security

WAMS

Time

Rese

arch

exc

item

ent