Smart Grid Challenges/Opportunities

und.nodak.edu

Smart Grid Challenges/Opportunities

Smart Grid

Source: http://antwrp.gsfc.nasa.gov/apod/ap001127.html

SMART GRID

An Overview, Challenges and how (research direction) to address them

Prakash Ranganathan


Talk Outline

� Introduction on SMART GRID

� Why SG?, Characteristics, Background, Transmission, Distribution,

Storage

Grid architecture: Grid design, stability and control

� Examples p overview:

� Network Optimization,

� Distribution Tree Problem,

� Agent g framework

� Renewable Sources adding to grid

� Security Issues

� Engineering work on SG

� Ckt Breaker, TFM Monitoring stations and Power Quality

� Good Resources on SG


Why the Smart Grid Revolution?

“Running today's digital society through yesterday's grid is

like running the Internet through an old telephone

SSwitchboard” hb d”

* Energy Future Coalition, Reid Detchon, AREVA T & D


Visualizing SMART GRID

Source: US Dept of Energy

http://www.oe.energy.gov/DocumentsandMedia/DOE_SG_Book_Single_P

ages.pdf


Context: Cities with 10 million people

Source: IEEE/Amin, UMN


Energy Consumption/Forecast [GAO]

� Today, fossil fuels (coal, oil, and natural

gas) provide about 86 percent of our

� total l energy consumption, with h the h rest

coming from nonfossil sources such as

nuclear (8 percent) and renewables, such

as hydroelectric y energy gy and

� wind power (6 percent). Overall, the

majority of the nation’s energy

consumption is met by domestic

production. However, imports of some

fuels have risen


Fuel Sources: Otter Tail Power Company

S

Source:

Otter Tail Power Company


In our area, using coal gen. electicity

S

Source:

Otter Tail Power Company


Industry Challenges

� Soaring energy demand

�� Power outages’ outages financial impact

� Green energy takes center stage

�� Electricity prices on the rise

� Aging infrastructure/workforce


Frequency and Impact of major disasters


Historical analysis of US outages (1991-2000)


Summary of August 14 Blackout Statistics

� Reported as affecting 50 million people

�� 60 60-65,000 65 000 MW of load initially interrupted

� Approximately 11% of Eastern Interconnection

400+ Generating units tripped

� Cascading lasted approximately 12 seconds

�� Thousands of discrete events to evaluate

Source: NERC and Joint U.S.-Canada Task Force


Electricity Price on rise

Source: EIA


Electricity cost by state – Jan 2009


Peak hours demand

� While supply and demand is a bedrock

concept in virtually all other industries, it is

one with which the current grid g struggles gg

mightily because, as noted, electricity must

be consumed the moment it’s generated.

Without being able to ascertain demand

precisely, p y, at a g given time, , having g the ‘right’ g

supply available to deal with every

contingency is problematic at best. This is

particularly true during episodes of peak

demand, , those times of ggreatest

need for

electricity during a particular period.

� Source: US DOE


Understanding Electric Power Grid

The electric power grid is a complex

adaptive system under semi-autonomous

distributed control.

It is spatially and temporally complex, nonconvex,

nonlinear and non-stationary with

a lot of uncertainties

uncertainties.

Source: IEEE PES 2009, M. Pipattanasomporn


Growing challenges…

� Increasing demand for energy and electricity

�� Moving towards an environmentally environmentally-friendlier friendlier energy mix

(lower carbon emissions, more renewable energy…)

� Volatile energy gy prices p and critical energy gy losses

� Emerging large regional transmission networks

RAISING THE NETWORK’S COMPLEXITY


Solar Potentials in USA

Source: GE data


Wind Potentials


Small Wind


Bio mass to power


Distributed generation: System Integration


Patterns in Power delivery


Source:DOE [Link]


Building Tx Line is Essential


Adding Renewable increases complexity

� The integration of renewable energy such as wind

farms farms, and plug-in vehicles further adds complexity

and challenges to the various operations, decision

making g and controls at all levels of the power p grid g –

generation, transmission and distribution.

Advanced computational methods are required for

planning, monitoring, control and optimization of

power system operations operations.


NREL’s WinDS (Wind Deployment System

Model)solution )

�� Good for Planning and Placement of renewables

� Does not account for power stability or grid resiliency

� Need Network optimization p approximations

pp

DOE “20% wind energy by 2030" report, DOE/GO-102008-2567.

W. Short, N. Blair, and D. Heimiller, Solar Today, pages 28–29, 2003.


Grid Design

� placement of generations and storage

� intermittent wind as an integrated

capacity i

� accounting for outages and intermittency

in planning p g


Network Optimization problem: Optimal Sparsity

[Chertkov, Smartgrids]

Grid is represented as graph

�� Nodes in grid are generators, generators storage, storage interconnects or

demand units

� Edges g of the graph, g p , namely y the connections between

nodes, are the transmission lines.

� Seek Optimal Placement of Tx for a given set of nodes.


Net.Opt contd.,

� Load flow approximation with resistive loads

� Ci – effective cost per line

{ cost of building the transmission line over the life time)

� K(y) is the sparse conductivity matrix

� J- current (injection/consumption), y is conductivity of a node

Knowledge of KCL/KVL laws

gamma < 1? For optimal Sparsity


Analysis of Control parameter γ

The cost function models the tradeoff between the cost of the dissipated

power and the cost of the transmission lines.

�� The parameter γ varies from one to zero zero, and models economies of scale scale.

� When γ= 1, the cost is linear: doubling the capacity doubles the price.

� A more realistic model is given by a small γ, where the cost rises abruptly with

the addition of a new line, even if ˜y is very small. Intuitively, we expect that

smaller values of γ lead to sparser networks networks.

� The value of this concrete model is that it enables us to determine the optimal

sparsity quantitatively. The algorithmic approaches are quite different in the two

cases.

�� Wh When γ = 1 1; th the problem bl i is convex and d has h a single, i l easily il computed t d optimum. ti

� When γ < 1, the problem is non-convex and there are possibly many local

minima, which requires the use of simulated annealing type algorithms.

The complexity of the latter model faithfully captures several facets of the

complexity l i of f the h real l grid.

id


Power flow Basics can be applied on a network level [F [Fundamental d t l Electrical El t i l Circuit Ci it concept] t]

CComponent t llevel l


Chertkov Proposal deliverables


T, D, C Domains

� The Transmission Domain carries bulk electricity over power transmission lines over long distances,

connecting the bulk generation to the energy consumption centers of the smart grid. It also contains the

power system substations; the transmission and the distribution substations. It may also connects to energy

storage facilities and alternative distributed energy resources at the transmission level level.

� The Distribution Domain distributes the electricity to and from the end customers. The distribution

network connects the smart meters and all intelligent field devices; manages and controls them through a

two-way y wireless or wireline communications network. It may y also connects to energy gy storage g facilities and

alternative distributed energy resources at the distribution level.

� The Customer Domain is where the end users (home, commercial/building, and industrial) of electricity

are connected to the electric distribution network through the smart meters. The smart meters control

and d manage the h flow fl of f electricity l i i to and d from f the h customers and d provides id energy information i f i about b

energy usage and patterns. Each customer has its own domain comprised of electricity premise and twoway

communications networks. It may also generate, store, and manage the use of energy and the

connectivity with plug-in-vehicles.

� Source: IEEE SMART GRID


According to DOE/Smart Grid

Characteristics

� self-healing,

�� consumer friendly friendly,

� attack resistant,

�� provides power quality for 21st century needs needs,

� able to accommodate all generation and storage options,

�� enables markets and

� optimizes assets and operates efficiently.


Agent Modeling Smart Grid [ieee-PSCE09]


Distribution (Heuristic, Tabu)

� Distributed Tree Problem (DTP)

� DTP takes into account characteristics of the substations and

consumer demand. It also determines the optimal topology of

the network to distribute electrical energy at minimum cost.

� Heuristic Determination of Distribution Trees, 2010 IEEE Transactions on Power Delivery, Victor Parada,

Jacques et.al. IEEE paper


Simulated Annealing Vs Tabu Search


Assumptions made in Dist. Planning

The DTP considered in this paper determines the

primary distribution network using radial topology

for a specific period, assuming that:

� the installation costs and the physical locations of

substations and consumer points are known;

� additional transformers may have to be added to

some power substations;

�� each consumer point has a fixed power demand;

� each consumer point is connected to a substation

along a path that may include other consumer

points;

� there is a set of conductor types available with

known parameters (ampacity (ampacity, resistance resistance, reactance, reactance

and cost per unit length);

� the selected conductors for each path are capable

of transmitting the necessary power flow;

� the sum of the capacities of all substations is

sufficient ffi i t ttosupply l power t to all ll customers; t

� several consumer points may belong to the same

client.


DTP [PARADA et.al]


Security [Cisco] [ Silverline]

Smart grid technologies better identify and respond to man-made or natural disruptions. Real-time

information enables grid operators to isolate affected areas and redirect power flows around damaged

facilities.

�� O One of f th the most t iimportant t t i issues of f resist i t attack tt k i is th the smart t monitoring it i of f power grids, id which hi h i is th the bbasis i

of control and management of smart grids to avoid or mitigate the system-wide disruptions like blackouts.

The traditional monitoring is based on weighted least square (WLS) which is very weak and prone to fail

when gross errors (including topology errors, measurement errors or parameter errors) are present. New

technology of state monitor is needed to achieve the goals of the smart grids.

Video SVP,/GM SG Solution CISCO


Smart Grid Overview


Engineering work on SMART Grid


Control Strategies


Grid Control

� load balancing

� queuing and scheduling

� optimal power flows

� feeder lines control (In power

engineering, a feeder line is part

of an electric distribution

network, usually a radial circuit of

intermediate voltage) g)

� distribution and switching with

redundancy


Self healing grid


Source: EPRI


Situation Awareness tool (SAT)


Smart Meters

Beginning next month, Houston-based

CenterPoint Energy Inc. is preparing to install

more o e than t a two million o ssmart a t meters ete s ove over five ve

years. During a two-year test of the technology,

consumers were able to call up a Web portal

showing the energy consumption of the

hhome's ' major j appliances. li

Consumers also could calculate energy bills in

different situations: What would be the effect

of keeping the house at 75 degrees in the

summer instead of 65?

What adjustments would be necessary to keep

summer electric bills under $200?

Next, a new generation of smarter appliances

could help consumers curtail energy use and

help utilities reduce pressure on the grid.


Smart Grid Challenges/Opportunities

– Infrastructure for Generation/Transmission/Distribution Systems

– Infrastructure for Smart Customer Interface

– Distribution Automation

Smart metering improves load models and profiles

– DDevice i monitoring i i and d self‐healing lf h li di diagnostics i

– Communication infrastructure provides opportunities for monitoring

and diagnostics

– Distributed Sensing g and Control

– Alternative Smart Grid Architectures

– Infrastructure Security: Controls, Communications and Cyber Security

– Markets and Policy

– Distributed generation and storage adds complexity


TRANSFORMER

MONITORING STATIONS

� Continuous supervision Exact statements about

operating condition

� Optimized operating and lifetime management

� Early detection of incipient faults

� Avoid failures, outages & collateral damage

� Knowledge of transformer life & lifetime

consumption

� Extended transformer lifetime

� Reduction of life-cycle cost

SMART NETWORK

SUPPORTING DEVICE

IMPROVING POWER QUALITY

� All-in-one Real-time

� Compensation

CIRCUIT BREAKER MONITORING

SYSTEMS (24/7)

� MINIMIZING COSTS AND ENSURING RELIABILITY

Main features

� Monitors, via sensors major components of the circuit breakers

� Analyses information and triggers alarms locally and/or remotely

� Current Harmonics

� Voltage Stabilisation

� Flicker Reduction

� Power Factor Correction

� Load Currents Balancing

� Reaction Time is less than 2ms

� Universal Communication Connectivity

C b li d ll Hi h V l Ci i b k Li T k D d T k G

� Can be applied to all High-Voltage Circuit breakers : Live Tank, Dead Tank, Generator

Circuit Breaker


FUTURE SMART GRID VISION


Today’s Grid Vs SMART GRID

Source: DOE


SMART GRID

Publications/Standards/policies

Top 25 papers

�� http://smartgrid.ieee.org/publications/top-ieee-xplore-

http://smartgrid.ieee.org/publications/top ieee xplore

publications/smartgrid

Standards

� http://smartgrid.ieee.org/standards/approved-ieee-

smartgrid-standards

Policies

� http://smartgrid.ieee.org/public-policy/key-smart-gridlegislation


References

� EPRI, GE, DOE,IEEE

� The Electric Power Grid, Today and Tomorrow, Stringer, Amin

�� http://cdtlnet http://cdtlnet.cdtl.umn.edu/presentations.html

cdtl umn edu/presentations html

� http://www.americanprogress.org/issues/2009/02/grid_101.html

� PHEV, Source of Distributed regulation, Sara

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