Composite power system reliability evaluation...Reliability Reliability can be addressed by considering two basic functional aspects of the power systems: • Adequacy—The ability

Composite power system reliability evaluation: Italian perspective

June 12, 2018

NERCComposite

Resource Adequacy and Transmission Reliability Planning

webinar

R. Calisti, M. V. Cazzol, G. Ceresa, E. Ciapessoni, D. Cirio, A. L’Abbate, G. Migliavacca

Outline

Reliability & adequacy New needs & challenges Approaches and tools in the Italian and European context Conclusions

2

Reliability

• Reliability of a power system refers to the probability of its satisfactory operation over the long run. It denotes the ability to supply adequate electric service on a nearly continuous basis, with few interruptions over an extended time period. (IEEE/CIGRE Joint Task Force on Stability Terms and Definitions, IEEE Tr. PS, VOL. 19, NO. 2, 2004)

• Reliability, in a bulk power electric system, is the degree to which the performance of the elements of that system results in power being delivered to consumers within accepted standards and in the amount desired. The degree of reliability may be measured by the frequency, duration, and magnitude of adverse effects on consumer service. (NERC)

• System adequacy of a power system is a measure of the ability of a power system to supply the load in all the steady states in which the power system may exist considering standard conditions. (ENTSO‐E)

3

Reliability

Reliability can be addressed by considering two basic functional aspects of the power systems:

• Adequacy—The ability of the electric system to supply the aggregate electrical demand and energyrequirements of the end‐use customers at all times, taking into account scheduled and reasonablyexpected unscheduled outages of system elements.

• Security—the ability of the power system to withstand sudden disturbances such as electric short circuits or non‐anticipated loss of system components.

4

Context: On‐line / real‐time When missing: Risk of widespread disturbances

Context: Planning, operational planning, quasi on line When missing: Controlled disconnections

Approaches

5

Generation only Generation + transmission(«composite»)

Deterministic

Probabilistic

Total load

Peak load

Max credible simultaneousoutages of…

G G+T

Monte Carlo simulation

Direct computation

Load scenarios

Availability status of… G G+T State enumeration becomes unfeasible

due to combinatorial explosion

Monte Carlo allows to deal with large numbers of stochastic variables and sequential events

Approaches

6

Generation + transmission(«composite»)

Non sequential

SequentialLoad scenarios

Availability status of… G G+T

Load scenarios

Availability status of… G G+T

Hydro status

Other quantities with sequential constraints: generator output (e.g. ramp rate), weather

correlations, …

Each simulated hour isindependent of the others

Monte Carlo simulation

Indices

• Loss Of Load Expectation (LOLE)– expected number of hours per year for which the available generation capacity is insufficient to cover the demand

• Loss Of Load Probability (LOLP)– likelihood of encountering loss of load = LOLE / 8760 hours

• Expected Energy Not Supplied (EENS)– amount of electricity demand (MWh) that is expected not to be met by generation in a given year

• Expected Energy Not Produced (EENP)– amount of electricity from Variable Energy Resources VERs (MWh) that is expected not to be fed into the grid due to system issues in a given year

7

Paradigm shift

8

https://www.sma‐italia.com/prodotti/referenze/montalto‐di‐castro.html

• VERs act as «base load» plants• DG currently «uncontrolled»• Conventional generation (including thermal) supplies the «residual», «net» load

https://www.nasa.gov/offices/oct/images/nasa‐derived‐northern‐power‐100‐wind‐turbines‐operating‐in‐italy

Paradigm shift

99

Net load demand in Italy on bank holidays , source TERNA

June 6 October 10

https://www.sma‐italia.com/prodotti/referenze/montalto‐di‐castro.htmlhttps://www.nasa.gov/offices/oct/images/nasa‐derived‐northern‐power‐100‐wind‐turbines‐operating‐in‐italy

Adequacy issues

10

Source: ENTSO‐E Mid‐Term Adequacy Forecast 2017

Summer Peak Load variation 2015/2014: +13% !!

Yearly demand variation 2015/2014: +2%

Heat wave on the electric energy demandItaly, July 2015

summer

winter

Variability

11

Extreme weather phenomenaEnergy & Power are different stories

Cold spell + low VER generation + outage of some nuclear plants

France, Jan/Feb 2017

Spatially correlated issues

Exceptional conditions, Adequacy alarm: unusual dispatch & import conditions(help by neighbors)

Persistence of low VER generation over wide areas

Are hydro (reservoir/pumped storage) resources enough to meet the demand? 13

2 wind falls, each lasting a week

Average wind power capacity factor is 7% in Finland and Sweden, and 9% in Norway Their respective annual are 20%, 30%, and 22%

Source: European Commission METISStudy S04, “Generation and System Adequacy Analysis”, Jan. 2016

Performances required to conventional generation

• Provide more reserve and cope with VER volatility

14

Ramping rates, startup time, minimum up/down time, minimum output power

Source ENTSO‐E MAF 2017

Risk of losing controllable capacity

15

Generation Capacity at risk of being mothballedImportance of good input information

Source ENTSO‐E MAF 2017 Absolute [MW] and relative [% of the thermal generation capacity]

Grid congestion

VER impact on the grid:new and diverse power flow patterns, is the grid ok?

16

Source: TERNA

Overgeneration

Caused by… Grid capacity constraints + reserve needs

Summary of weather-related impacts

• Load (temperature)• Wind• PV • Hydro

• Grid capacity (DLR)• Grid availability (extreme events)

17

Short term

Long term(seasonal)

Source ENTSO‐E Summer outlookwinter review 2017

More topics to be put into adequacy evaluations…

• Maintenance programs

• Resilience aspects– Withstand disturbances, especiallymultiple outages in an area due to extreme weather phenomena

– Recovery

18

Opportunities• Making transmission more flexible :

– PST– HVDC– FACTS

• Taking advantage of distributed resources:– Demand response– Distributed storage– EV and V2G

• Moreover, technologies such as…– Dynamic Line Rating (DLR) allows to operate lines at higher capacity when ambient conditions allow so

– High Temperature Low Sag (HTLS) OHL allow to increase line capacity with reducedrights‐of‐way

19

Resulting modeling needs for adequacy analyses

• Sequential simulation• Long‐term model (time series) of primary energy sources• Model of storages

• Unit commitment with minimum power output, minimum up/down time, ramp rates

• Grid components: power flow control devices

• Gridmodel: full AC

20

Modeling needs

21

Grid size

Time period (days, months, years)

Injection model LoadVERStorage Conventionalgeneration

Trade‐off needede.g. zonal vs. nodal analyses

Weather model

Grid modellinear DC vs. non linear AC Components (PST, FACTS…) Availability

Tools in use at the Italian TSO

22

• GRARE (Grid Reliability and Adequacy Risk Evaluator) owned by Terna, developed by CESI, • Reliability and economic operational capability using probabilistic Monte Carlo analysis

SAMPLING

Zonal representation Nodal representation

Deterministic (single year simulation)

Probabilistic (multiple year simulation)(non sequential Monte Carlo)(sequential Monte Carlo)

EXPANSION SELECTION

ADEQUACY(linear DC modeling)

ADEQUACY(non‐linear AC modeling)

TECH

NO‐ECO

NOMIC

ASSESSMEN

T

Research Tools @RSE

23

Zonal representation Nodal representation

Deterministic (single year simulation)

Probabilistic (multiple year simulation)(non sequential Monte Carlo)(sequential Monte Carlo)

PREESP ESPAUT

MTSIM

sMTSIM+

EXPANSION SELECTION

ADEQUACY(linear DC modeling)

ADEQUACY(non linear AC modeling)

TECH

NO‐ECO

NOMIC

ASSESSMEN

T

REMARK+

REMARK

ACRE

ACRE+

Research Tools @RSE

24

RES data generation

Transmission Expansion

ESPAUT“ESPansione AUTomatica”

“Automatic expansion” procedure to select the optimal reinforcement plan within a set of grid expansion alternatives to

fulfill a network development plan over a long term period

25

Transmission ExpansionESPAUT

Definition of the reinforcement plan

Candidates for the target year

Optimal reinforcement for the target year

Optimal reinforcement for intermediate years

0 1 … Target year

Commissioning year of reinforcements

26

COMPLEX AND LARGE DIMENSION PROBLEM (MIP)

Example: 6 scenarios generation/loadbase case + 27 contingencies

206 candidates750 nodes930 links

170 generators

477000 variables (206 binary)423000 constraints

1385000 non null elements

PREESPPRE‐ESPAUT

Generation and load curvespreparation through the resolutionof an optimisation problem of a

simplified grid with detailed model of storage, DSM, electric vehicles, before full grid implementation by

ESPAUT


27

LCHF111MSEF111

MORF111VLVF111

LONF111 MOUF111

CHEF111

MERE111 MASN111

VIGF111

HOUF111

GRAE111COUE111

TOUF111 ROUF111ARGF111

TERF111

DOMF111

VERF111CORF111DISF111

DAMF111

MEZF111

AVOF111

LQUF111LOUF111

PENF111

GATF111

LREF111

AVEF111HASO211 MANF111

LHVF111

DEEL111

WLAO211

WIAO211

WJ1O211 WJ2O211SU5O211

SU6O211

WYLL111PENL111

WF1O211

WF2O211

WF3O211CODO211

KISO211SU2O211

MULLINGAR

ENNIS

TURLOUGHHILL

THURLES

-ON- HILLGILRA ARVA

KINNEGAD

BALLYBEG

NENAGHARDNACRUSHA

CHARLESLAND

BALLYRAGGET

ORIO211

CKMC111

HUNC111BALC111WOOC111

DUNC111

MAYC111

ARKC111LAOC111

BA1C211

WEMB111

CAVA111-211CASTLEBAR

GALWAY

LANESBORO

CLOON

IKERRIN

DALTON

CAMUS

TYNAGH

FLAA111-411

CASA111-411 SHAA111-411

OLDB111-411

MONB111-211 KELC111

WHRW211

LAMF111

CHEF111

MEES111

HENS111

DODS111

ZWOS111

WARF111

GEES111MAVS111

SIZM111

PEWL111

BOLN111

LOVN111

DOGK112

DRXK111 YORK111

NORM112

NORM113

WALM111 NORM111

BRFM111

GRAN111

DIES111

DISS111

NORTHERNIRELAND

ISL0211

WS10211

WS20211

WS50211

ARG0211

WS40211

WS30211FAGH111

FWIH111

BRDH111

WIG0211 SOL0211

WNAO211

SU4O211

GRNH111HARJ111

HUTL111HUSL211

SU8O211

MANQ211

SU7O211

SHANKILLCUNGHILL

BUNBEG

TIEVEBRACK

SRANANAGH

*

KIN0211

OMAD111-211

STRD211

COOD211COLD211

MAGD211

TURD111-211

CACD211 KELD211BAFD211

TAND211

LOUA211BALA211

LEAD111-411

CATA111-411

SLIA111-411BELA111-211-411 SRAA111-411

HAND211GOLA111-411

WIRW211

WJNW211 WKNW211

MORH211-111

FORH113 FORH112FORH111

MOSH111-211

PETH111-211

STEJ111 HAWJ111

LACJ111

QUEL111

DOGK111

DOGK113

THRK111 HORK111 HORK112

MESS111

DIEZ111 WEDZ111

KASU111

HANU111

SELN111NURN111

TRAL111WF4O211

CASL211

PEMM111

BR1O211BR2O211

ALVN111

INDN111 LAGN111ABHN111

EXTN111

SWAM111WALM111

SCIN211

WIWO211

DOON

MALLOW

CHARLEVILLE

BANDONDUNMANWAYBRINNY

DUNGARVAN

KILB111-211

KILBARRY

OUGHTRAGH

TRALEE

CLASHAVOON

CULLENAGH

TIPPERARYKILLONAN

GLENLARA

CROSS

PROSPECT

COOMAGEARLAHY

CLAHANE

COOMACHEOBVKB111WF5O211

SU2O211

GLAO211

ARKO211

GREC111

LODC211KILC111

KNOB111

CAHC111

AGHB111

KN1B111-411TARB111-211WGRW211

THSK111

DRSK111

NOSM111

COCH111 TORH111

STSJ111

BSUE111

NSUS111

MESE111

WESZ111

KSSU111DSUU111

GSUZ111

DASH211-221

HUEH111-221

AUCH211-221

DOUH211-221

MYBH211-221

INVH111

BEAH111-211

MD1B111-211

TRIA111-411

ENND111-411

TASB111-211

CAML111

TREL111LEGL111

BOSN211

TKNK111

RCBK111DUDK111

SHHK111DOCK111

TKSK111

BICM111

LOAN111

GRGM111

GUFM111

COSN111

THAN111

WERK111

HUGK111

WALO211

WDUO211

BAIM111

BRBU111

MEPZ111AV1W211

AV3W211

AV2W211

OUGB211

CROB211

AF4W211

AF3W211

AF2W211

AF1W211

OBEZ111

GRNZ111KUSZ111

LIPZ111

HNEZ111

GIEZ111

BROZ111

NEHZ111

GROZ111

HAMZ111

LANZ111

WAHZ111WEHZ111

FRAF111

NETS111

GERZ111


1472 Candidates (ROW potential reinforcements)

Example of ESPAUT application: the case of Ireland

(Source: Eirgrid)

28

Medium‐Term Simulator of a day‐ahead zonal market (DAM)• system‐wide energy evaluations (i.e. fuel consumption)• emission evaluations (CO2 and other pollutants)• hourly clearing price throughout the year• Simplified linear DC Optimal Power Flow minimizing the energy price

• variable (fuel, O&M) costs• environmental costs• hourly bid‐up of each unit

• transmission modeled by an inter‐zonal equivalent system with HVAC (via PTDF) andHVDC

• planning modality allowing to consider additional interconnection capacity betweenthe market zones

Adequacy MTSIM

• MTSIM provides also the possibility of including innovative technologies: HVDC PST/FACTS Storage DSM/DR• MTSIM providesmain outputs for techno‐economic assessments: Hourly zonal generation dispatch Dispatch cost Inter‐zonal flow transits Load shedding (EENS) RES curtailment CO2 emissions Hourly zonal marginal costs/prices Fuel consumption

AdequacyMTSIM

• Represents the Monte Carlo evolution of MTSIM• Variable RES (solar, wind) generation is simulated at eachiteration

• «Stochastic Unit Commitment» algorithm to define UCaccounting for uncertainty in residual load forecast

• Reserve requirements included in the UC

31

AdequacysMTSIM+

Pan‐European zonal model (2030) Pan‐European zonal model (2050)GridTech EU30+ study

32

Hourly zonal generation dispatch Dispatch cost Inter‐zonal flow transits

Load shedding (EENS) RES curtailment (EENP)

CO2 emissions Hourly zonal marginal

costs/prices Fuel consumption

MTSIM Medium-Term Simulator of day-ahead zonal market

Pan‐European zonal model (2050)GridTech EU30+ study

33

Hourly zonal generation dispatch Dispatch cost Inter‐zonal flow transits

Load shedding (EENS) RES curtailment (EENP)

CO2 emissions Hourly zonal marginal

costs/prices Fuel consumption

MTSIM Medium-Term Simulator of day-ahead zonal market

• Simplified linear DC Optimal Power Flowminimizing operation costs

• Inter‐zonal equivalent system with HVAC (via PTDF)and HVDC

• UC and ramp limitations• Storage, DR• Planning modality• “Stochastic” version: UC performed considering

VER uncertainties

over 2030 S0 (base)

Planning modality application results (2030)

Planning modality application results (2050)

MTSIM results examplesGridTech EU30+ study

34

REMARK“ REliability & MARKet ”

Adequacy analysis on composite G&T systems based on zonal market structure

• Grid limitations and constraints that affect the economic system dispatch

• Simplified linear DC Optimal Power Flow minimizing operation costs

• Non sequential (REMARK+: sequential )

• Social Welfare (Consumers/Producers Surplus, congestion rent) • LMP, dispatch costs, CO2 emissions, Joule losses (ex‐post)

• Typical reliability parameters: EENS , LOLP, LOLE, as well as Expected Energy Not Produced (EENP)

35

• Power system model features:– Detailed model of the network: nodes, HVAC and HVDC lines, transformers, PSTs, generators, loads

– Nodal loads, characterised by yearly profile– Grid representation by simplified DC load flow equations– OPF objective: maximisation of Social Welfare – Geographic system/market zones subdivision– Generation: fixed (predefined profile), random variable, dispatchable

AdequacyREMARK & REMARK+

36

AdequacyREMARK

Statistical probabilistic analysis: “non‐sequential Monte Carlo” methodA sample, representative of the hourly system configurations at a target year, is randomlygenerated as a combination of:

– unavailability of lines, transformers, generators – maintenance schedules for generators– predefined profiles of load demand and non‐dispatchable generation (also for hydro reservoir

plants)– statistical profile of wind generation

37

4 OPFs are simulated with a different granularity of transmission system representation to determine the

additional costs due to market and grid constraints

N O R D

C E N T R O S U D

S U D

C E N T R O N O R D

S IC IL IA

G R E C IA

E S T E R O N O R D

C E N T R O S U D

S U D

C E N T R O N O R D

S IC IL IA G R E C IA

E S T E R O

N O R D

C E N T R O S U D

S U D

C E N T R O N O R D


E S T E R O E S T E R O

N O R D

C E N T R O N O R D

C E N T R O S U D

S U D


+

N O R D

C E N T R O S U D

S U D

C E N T R O N O R D


E S T E R O

+

1) Single area 2) Zonal market system

3) Zonal market system + interconnections

4) Zonal market system + whole grid

REMARK

38

AdequacyREMARK+

Statistical probabilistic analysis: “sequential Monte Carlo” method

The Monte Carlo method is tasked to feed the yearly system dispatch simulation via the datafeatured by random elements:• the hourly states of system components• the hourly expected production (wind, PV)

Each Monte Carlo run generates a deterministic problem that simulates the optimal yearly systemdispatch, to be calculated via optimisation.

MONTE CARLO SEQUENTIALITY

More precise storagemodeling

Higher complexity

39

ACRE

“ Alternating Current REliability”ASSESSMENT OF SPECIFIC SYSTEM DISPATCH CASES VIA

AC OPTIMAL POWER FLOW (OPF)

o Full AC Optimal Power Flowo Complete grid model (AC load flow equations)

o Objective functiono total cost minimisationo active losses minimisation

o Cost curveso quadratico linear

40

Sequential ACRE+: o Mixed AC/DC gridso HVDC LCC and VSC modelingo Storageo FACTSOngoing development

New indices:

o Levels of active power losses on transmission lines.o Levels of reactive power losses on transmission lines.o Levels of voltage amplitudes on load nodes and distribution interface nodes.o Levels of utilisation of capacitors banks and shunt reactors.o Limits of currents accounting for active and reactive power flows.o Levels of utilisation of subsystems (cables, lines and/or meshed grids) operated in

HVDC.

AdequacyACRE

41

AdequacyACRE+

o Mixed AC/DC gridso HVDC LCC and VSC modelingo Storageo FACTSo ...o Sequential Monte Carlo method

o Problem complexity: limit number of hours in a sequenceo Ongoing development

42

AMaChaStochastic Analysis with Markov Chains

AMaCha 1 ‐ analysis AMaCha 2 ‐ generation

43

Markov

•Model parameters

PCA‐1

• Production of n series of new states• Resizing of state series into data series

• Correlated data

STL‐1

• Data with seasonal behaviors

STL

• Input data: time series of power production from wind and solar generators

PCA

• Deseasonalized data, without trend and without annual, seasonal, daily seasonality

• Uncorrelated data • Transformation of data into system states

Markov• Model parameters

Security issues• Short circuit power• Voltage control • Inertia• Frequency regulation• Negative load seen from HV substations• Congestions• …

44

VER generators connected via power electronics do not sensibly contribute to short circuit currentNeed for frequency

regulation resources

Source TERNA

Some highlights from ENTSO-E Mid-term Adequacy Forecast 2017

• Extreme climate conditions impact• Common standards needed: data, models, metrics• Reliable generation plan from utility (mothballing, maintenance schedule)

• Coordinated studies needed • Probabilistic assessment of the residual load

45

https://www.entsoe.eu/outlooks/midterm/

Conclusions

• Adequacy evaluations more and more relevant due to VER penetration• Key modeling aspect: weather correlations; sequential studies• Key organisational aspect: study coordination• Different approaches for different objectives(model complexity vs. computation performances)

• Do not forget security

46 Source ENTSO‐E MAF 2017

Thank you for your attention

Questions?

47

diego.cirio@rse‐web.it

CONFIDENTIALRESTRICTEDPUBLIC INTERNAL

Pierre Henneaux – June 12, [email protected]

Probabilistic composite power system adequacy assessment in transmission planning

5

PUB

LIC

Chapter 1 Motivations

Chapter 2 Methodologies & tools

Chapter 3 Examples

Chapter 4 Conclusions

CONTENTS

Probabilistic composite power system adequacy assessment in transmission planning 6

Motivations

8Probabilistic composite power system adequacy assessment in transmission planning

PUB

LIC

Two-step approach1. Adequacy of the generation system

— Deterministic (e.g. capacity margin) or probabilistic (e.g. LOLE) criteria

— Single-area or multi-area analyses

2. Adequacy of the transmission system— Deterministic analysis

— Specific set of states analyzed, e.g. peak load and off-peak load (“extreme cases”)

— The transmission system must be able to supply the load while satisfying operational constraints (including security constraints – e.g. N-1 events)

13

MotivationsTraditional way to ensure power system adequacy


PUB

LIC

Limitations— Adequacy issues might be due to multiple contingencies, not covered in the deterministic analysis related to the

transmission system— If massive integration of renewable energy sources, difficulty to represent their variability

• Generation system might be adequate, but bottlenecks hampering the load supply can occur in the grid

13

MotivationsTraditional way to ensure power system adequacy


F. Montoya et al., "Renewable energy production in Spain: A review", Renewable and Sustainable Energy Reviews, 2014.

PUB

LIC

Ability to consider combinations of multiple failures in the generation and transmission systems

Ability to consider numerous load/generation patterns

13

MotivationsNeed for a probabilistic composite power system adequacy assessment


Lead not only to system-wide indices (e.g. EENS), but reveal also buses with an insufficient level of adequacy and main bottlenecks in the grid

Methodologies & tools

9Probabilistic composite power system adequacy assessment in transmission planning

PUB

LIC

Analytical methods not practicable for composite adequacy assessment— (Optimal) power flow analysis needed to estimate the potential load shedding in each case

— Major difference compared to pure generation adequacy assessment!• Load shedding = difference between the load and the available generation in case available generation < load

State enumeration: quickly lead to a combinatorial complexity when multiple contingencies are considered

Monte Carlo simulation— Even if several drawbacks (e.g. a large number of samples might be needed to reach a satisfying accuracy),

most relevant approach for composite adequacy assessment up-to-now

— “Easy to implement”

13

Methodologies & toolsWhy Monte Carlo simulation?


PUB

LIC

Non-sequential Monte Carlo simulation— Relevant when no (or weak) temporal interdependencies between system states

— Independent analysis of each state

Sequential Monte Carlo simulation— Necessary when strong temporal interdependencies between system states (e.g. presence of storage), or

when frequency and duration of load shedding events are needed

— Positive correlation between successive system states → more samples needed to reach the same accuracy, compared to a non-sequential Monte Carlo simulation

— Computationally more challenging → nested optimization loops (e.g. annual and weekly optimization)

Methodologies & toolsNon-sequential and sequential Monte Carlo simulation


System state sampling Optimal Power Flow Load shedding

Possible multi-step approach to avoid the formal resolution of a OPF when not necessary

Sampling of load, of RES, of generators’ availability, and of

transmission elements’ availability

PUB

LIC

Generation adequacy— LOLE/LOLH

• Reference values, independent of the size of the system → benchmark possible

Transmission adequacy— PLC (Probability of Load Curtailment)

• Not meaningful index: tends towards 1 for very large systems (or when a large number of weakly dependent systems are considered jointly) → benchmark not possible

— EENS (Expected Energy Not Supplied)?• Meaningful value, but dependent on the size of the system → benchmark difficult

— AIT (Average Interruption Time)• Defined as the ratio between the EENS and the average load in the system (interpreted as the equivalent duration of the loss of all

load during average load conditions)

• Normalization by the load → benchmark possible

• No standard, but statistical data available for some countries

Methodologies & toolsIndicators


PUB

LIC

Methodologies & toolsAIT for some European Countries


Variability, but typical order of magnitude: a few minutes

PUB

LIC

SCANNER

— Software tool developed by Tractebel

— Implement both non-sequential and sequential Monte Carlo simulation methods

— Double aim: adequacy assessment & production cost simulation

— DC power flow

— Non-sequential version: 1986

— Sequential version: 2012

— Used in numerous studies for clients all around the world

Other tools with similar approaches: Antares (RTE), GRARE (CESI), REMARK (RSE), etc.

Methodologies & toolsTools


Examples

Probabilistic composite power system adequacy assessment in transmission planning10

PUB

LIC

13

ExamplesCôte d’Ivoire (Ivory Coast)


322,462 km2

24,842,117 hab.9.5 TWh/year

PUB

LIC

Transmission master plan 2030

13



PUB

LIC

Optimal selection and phasing of reinforcements— Deterministic quasi-steady-state security analysis

— Deterministic dynamic security analysis (EUROSTAG)

— Probabilistic composite power system adequacy assessment (SCANNER)

13



Year EENS (GWh) AIT (min)2015 74 4077

2020 0.7 28

2025 0.4 11

2030 0.5 11

PUB

LIC

13

ExamplesDjibouti


23,200 km2

810,179 hab.377 GWh/year

PUB

LIC

Need to reinforce the transfer capacity between two substations (Lac Assal and Tadjourah)

Two main possibilities meeting the deterministic reliability criteria— Single-circuit 230kV line

— Double-circuit 63kV line

Best choice?

13

ExamplesDjibouti


?

?

PUB

LIC

Comparison of— Investment costs

— Transmission network losses

— Future potential development

— Contribution to adequacy (probabilistic evaluation)

Chosen solution: double-circuit 63kV line

13

ExamplesDjibouti


PUB

LIC

13

ExamplesRomania


238,397 km2

19,638,000 hab.48.3 TWh/year

PUB

LIC

Computation of standard key adequacy indicators for the generation and the transmission system— Analysis of the impact of increasing

frequency reserves to deal with renewable variability

Identification of situations leading to load shedding

Identification of weak points in the grid?— Beyond the N-1 analysis (critical N-k

contingencies)

13

ExamplesRomania


Conclusions

Probabilistic composite power system adequacy assessment in transmission planning22

PUB

LIC

Massive integration of renewable energies lead to variability of power flows in the grid Fundamental to consider in a couple way the generation system and the transmission system in

adequacy analyses— Generation might be available, but at the wrong place

Tools for composite power system adequacy assessment exist and have been used for several decades in various countries— Based mainly on non-sequential Monte Carlo simulations

Redesign of the tools to consider temporal interdependencies— Methodological questions about the way to consider storage and uncertainty

Conclusions


12-June-2018

An Integrated Solution for Reliability

Assessment Applied to the Transmission

Planning of Electric Power Systems

Tito Inga-Rojas

BC Hydro, Canada

Outline

2

BC Hydro Quick Facts

Transmission Planning Tasks

Integrated Solution for Reliability Assessment

MECORE1 - Tool for Composite System Reliability

Evaluation

Wrap Up

1 Reliability Assessment of Electric Power Systems Using Monte Carlo Methods

Roy Billinton, Wenyuan Li, Plenum Press, New York 1994

3

BC Hydro Quick Facts

North American Bulk Power System

WECC

BC Hydro

BC

Providing customers

with reliable,

affordable and clean

electricity throughout

BC, safely

4

BC Hydro Quick Facts (2017)

A commercial crown corporation owned

by the province of BC

Provides 4,000,000 customers with

reliable power. Residential customers

pay the third lowest rates in North

America

98.4% clean electricity generated in BC

30 Hydro Plants, 79,000+ kms of T&D

lines and 300+ substations (500kV -

12.5kV)

11,869MW generating capacity

10,194 MW peak demand

EPA with 114 IPPs with total capacity of

4800MW (non firm), 717MW wind gen.

Planned, built & operated to NERC

MRS standards

Transmission Planning Tasks

5

Adding reliability assessment to the traditional deterministic criteria

in each of these tasks provides an essential framework for optimized

asset investment justifications


6

BCH has several in-house software tools for reliability assessment. These

tools are integrated into RSIP (Reliability Software Integrated Package).

Assists planners in performing reliability evaluation of generation,

transmission, substation and distribution systems and comparing alternatives

of capital investments.

Speeds up the preparation of reliability data, load curves and other

information and create tables and charts of results for comparisons

Contains a database of all data including outage data, equipment attributes,

load curves, substation one-line diagrams, and power flow cases

Has an intuitive and user friendly interface for data input, control and output

of results


7

• MECORE: composite generation and

transmission system reliability

• MCGSR: power source system reliability

• SDREP: substation and distribution system

reliability

• NETREL: general engineering system reliability

(fault tree or series/parallel network reliability)

• MEANLIFE: mean life and standard deviation of

station equipment

• SPARE: station equipment reliability

assessment for aging failure and spare analysis

• PLOSS: power loss (MW and MWh) evaluation


8

MECORE – Tool for Composite Reliability

Evaluation

Monte Carlo simulation and Enumeration approach for COmposite system

Reliability Evaluation. It was initially developed at the University of

Saskatchewan and later enhanced at BC Hydro2

Used to assess:

composite generation and transmission reliability,

generation reliability in a composite system, or

transmission reliability in a composite system

It calculates several indices that can be utilized to compare different planning

alternatives from a reliability point of view

It has an intuitive user interface that allows easy data entry, application control

and results presentation for comparison of alternatives and sensitivity analysis.

2 Reliability Assessment of Electric Power Systems Using Monte Carlo Methods

Roy Billinton, Wenyuan Li, Plenum Press, New York 1994

9

Basic concepts and methods of MECORE

10

Load

Time

(hr)

100%

70%

8760

Multi-step Model of the

Annual Load Curve

Load Priority Levels Load Pattern Types

Load follows shape

of the load curve

Load is kept flat

Flowchart of MECORE

11

Monte Carlo

simulation technique

OPF model for corrective actions

Yes

Yes

Yes

No

No

No

Linearized load flow method

• Multi-step load model

• Network reduction

• Reliability data

• Study scenarios

• Control options

Compile reliability indices

Unavoidable load curtailments

Number of simulation samples

Reliability Indices Calculated by MECORE

12

Most Used

EENS

EDC

PLC

Expected Energy Not

Supplied (MWh/yr)

Expected Damage

Cost (K$/yr)

Probability of Load

Curtailments

Other Indices IEEE Suggested

ELC

ENLC

EDLC

ADLC

EDNS

Expected Load

Curtailments (MW/yr)

Expected Number of Load

Curtailments Index (#/yr)

Expected Duration of Load

Curtailments (hr/yr)

Average Duration of Load

Curtailments (hr/outage)

Expected Demand Not

Supplied (MW)

BPII

BPECI

BPACI

MBECI

SI

Bulk Power Interruption

Index (MW/MW – yr)

Bulk Power Energy

Curtailment Index (MWh/MW-yr)

Bulk Power Supply Average

MW Curtailment Index

(MW/disturbance)

Modified Bulk Energy

Curtailment Index

Severity Index

(System Minutes/yr)

13

Composite Reliability Evaluation – Example

Option 2 Option 1

Option 3

Option 6 Option 5

Option 4

Control Options

14

Average Reliability Data (BCH and CEA)

15

Load Curve Retrieval

16

Load Curve

17

Output Summary Selection

18

Summary Results

19

Wrap Up

20

The purpose of reliability assessment is to add one more dimension to

enhance the transmission planning process rather than to replace the

traditional deterministic criteria.

Utilities need to develop processes, indices and have comprehensive

data collection schemes and tools for reliability assessment. This

includes training for planners and budgets for implementation.

BC Hydro has implemented an integrated software solution for

reliability assessment that is applied when needed to each of its

transmission planning tasks.

References

21

• Roy Billinton and Wenyuan Li, Reliability Assessment of Electric

Power Systems Using Monte Carlo Methods, Plenum Press, 1994

• Wenyuan Li, Probabilistic Transmission Planning, IEEE and Wiley, 2011

• Wenyuan Li, Risk Assessment of Power Systems – Models, Methods, and Applications, IEEE and Wiley, 2005

• Roy Billinton and Ronald N Allan, Reliability Evaluation of Power Systems, Pitman Publishing, 1984

• Roy Billinton and Ronald N Allan, Reliability Evaluation of Engineering Systems: Concepts and Techniques, Pitman Publishing, 1983

• Applied Reliability Assessment in Electric Power Systems. Edited by Roy Billinton, Ronald N Allan, Luigi Salvaderi, IEEE Press, 1991

BC Hydro: technical reports and published papers

http://www.google.ca/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwie9amRsrHSAhUQ92MKHRNUAY8QjRwIBw&url=http://www.clipartpanda.com/categories/question-mark-icon&psig=AFQjCNFa3Hk05Ip0QWOHFJ-IEvKM-CgsmQ&ust=1488323069847097

Probabilistic Models for Manitoba Hydro’s Transmission Asset Planning, Investment and Management

B. BagenSystem Planning Department, Manitoba Hydro, Canada

June 12, 2018

A presentation to the NERC Composite Resource Adequacy and Transmission Reliability Planning Webinar

OutlineIntroductionSystem Reliability and Risk Model (SRRM) SRRM‐AC SRRM‐DC SRRM‐ST SRRM‐RD SRRM‐SC (Under Development) SRRM‐TS (Research Stage)

Closing Comments

Introduction (Manitoba)

Introduction (Manitoba Hydro)

A Crown Corporation and a vertically integrated utility providingservices for over 500,000 electricity and 250,000 gas customers.A total generating capacity about 5700 MW produced mainly by15 hydroelectric stations, and 2 gas stations.The backbone of Manitoba Hydro transmission is the twoNelson River Bipolar HVDC Systems referred to as BP I and BP II.A new Bipolar HVDC System referred to as BP III is underconstruction and will be in‐service in October 2018

Introduction(Manitoba Hydro’s Probabilistic Planning Philosophy)Contingency Selection: use a subsystem based approachto limit the contingencies/disturbances to be evaluated ina manageable way

Contingency Evaluation: Take advantage of thecalculation accuracy of widely used commercial programfor power flow, transition stability and short circuitanalysis if possible

Reliability Indices Calculation: Use efficient computingtechniques such as the breadth‐first search algorithm, therecursive invocation method and parallel computing tofacilitate the evaluation process.

5

Introduction(Manitoba Hydro’s Probabilistic Planning Strategy) Short‐Term (1‐5 Years)Problem solving/model development oriented for exampledevelop system model in MARS (Achieved)

Mid‐Term (5‐10 Years)Application/result oriented for example using commercialor in‐house programs to help decision‐making process (InProgress)

Long‐Term (>10 Years)A new theory and knowledge oriented for exampledeveloping new models and techniques using traditionalreliability assessment methods and/or other new approaches(In progress)

Introduction(Manitoba Hydro’s Probabilistic Planning Practices)MARSAnnual Planning Reserve Margin AssessmentNERC Biannual Probabilistic AssessmentAssessment of Major Projects and Their Alternatives

System Reliability Risk Model (SRRM‐AC, SRRM‐DC, SRRM‐ST,SRRM‐RD and SRRM‐SC)Transmission Capital PrioritizationAC Network PlanningHVDC PlanningRemote Generation Planning

7

Introduction(Transmission APIM: Current Practice)

Project Justification

Project Approval

Project Prioritization

Introduction(Transmission APIM: Past/Current Practice)

Project Portfolio Spreadsheets (data from SAP)

Capital Budget Ranking Tool

Capital Performance Working Group

Prioritized Project Plan(≈10 years)

Targets Determined by Executive Committee

System Reliability Risk Model (ΔEUE)

Introduction(Transmission AIPM: Evolving Capabilities )

Capital Performance Working Group

Prioritized Project Plan(20 years)

Targets Determined by Executive Committee

System Reliability Risk Model (ΔEUE)

AIPM Analytics

Asset Condition Assessment

Scores

Other Models

SRRM‐AC(General)

A composite system reliability evaluation model

State selection uses the analytical contingency enumerationapproach

State evaluation uses AC power flow (PSS/E)

11

SRRM‐AC (Sub‐system)

12

SRRM‐AC (Inputs/Outputs)

13

Major InputsSystem topology (PSS/E Cases)Study Area (Boundary)Load Data (PL and LDC)Equipment Reliability DataSystem Specific Data (tapped lines, SPS, common‐mode,corridor)

Major OutputsEUE/ΔEUE and risk costContributions to ΔEUE

SRRM‐AC (Examples)

14

A variable 5‐year window evaluationScenarios with and without projects (ΔEUE)SUPK, SUOP and WIPK operating conditionsMost up‐to‐date PL and LDC informationLast 8 years’ statistics for equipment reliability dataTapped lines, SPS and common‐mode outagesThe evaluation results of 6 projects will be presented as examplesP1: Addition of a new 230 kV line (125 km)P2: Sectionalizing new line of P1 to create a new 230/66 kV station and movesome of the load in the area to the new stationP3: Addition of a new 115 kV line (75 km), adding a new 115/66 kV station (2transformers) and moving some of the load in the area to the new stationP4: Addition of a new 230 kV line (70 km) and capacitor banks to provide voltagesupport in the area P5: Addition of a new 230/66 kV station (2 transformers) and moving some ofthe load in the area to the new stationP6: Addition of a new 230/66 kV bank to increase firm capacity of the station

15

Line 7

Bus 4

Line 1Bus 2

Bus 6

Bus 1

Line 12

Line 2

115 kV System

Bus 5

Bus 3

Line 9

Line 3

Line 6

Line 11

Line 4

Line 5

Line 10

Line 8Bus 7

SRRM‐AC (Examples: P1 and P2)

16

SRRM‐AC (Examples: P1 ΔEUE, Line 12)

0

10

20

30

40

50

60

70

80

90

100

Year 1 Year 2 Year 3 Year 4 Year 5

∆EUE (MWh/year)

17

SRRM‐AC(Examples: P1 and P2 ΔEUE, Line 12 and Station 7)

0

500

1000

1500

2000

2500

3000

3500

4000

4500


∆EUE (MWh/year)

Line 12

Line 12+Station 7

3500

3600

3700

3800

3900

4000

4100

4200

4300

4400


∆EUE (MWh/year)

18

SRRM‐AC ( Examples: Project 1 EUE Contributions)

0

500

1000

1500

2000

2500

3000

3500

Line 8 Bank 1 Bank 2 Bank 3 Bank 4 Bank 5 Bank 6 Bank 7 Line 6

EUE Co

ntrib

ution (M

Wh/year)

Contingency

Year 1

Without Project

With Project

0

5

10

15

20

25

30

35

40

Bank 2 Bank 3 Bank 4 Bank 5 Bank 6 Bank 7 Line 6

EUE Contribution (MWh/year)

Contingency

Year 1

Without Project

With Project

19

SRRM‐AC ( Examples: Project 1 EUE Contributions)

0

500

1000

1500

2000

2500

3000

3500

EUE C

ontribution (M

Wh/year)

Contingency

Year 5

Without Project

With Project

0

10

20

30

40

50

60

70

80

EUE Co

ntribu

tion

(MWh/year)

Contingency

Year 5

Without Project

With Project

20

SRRM‐AC (Examples: Average ΔEUE)

44

4078

885

60

797

70

500

1000

1500

2000

2500

3000

3500

4000

4500

P1 P2 P3 P4 P5 P6

Average An

nual Red

uctio

n in EUE

(MWh/Ye

ar)

P1 alone offers minimal reliability benefit to the system (1).The reliability impact of P2 (including both Line 12 and Station 7)are significant (10).The reliability benefit of P3 highly depends on the reliability levelof the new line‐Line 7 (6).The reliability benefit of P5 is significant (10).Minimal reliability benefit can be achieved from Projects 4 and 6and these projects may be deferred (1).

21

SRRM‐AC (Examples: Summary)

A probabilistic evaluation model for HVdc systemState selection uses the analytical contingency enumerationapproachState evaluation use capacity sufficiency

22

SRRM‐DC (General)

23

Converter Transformer

Valve Group

Smoothing Reactor

DC Line

Sending End

Receiving End

Bipole I

Bipole II

SRRM‐DC (HVdc System Components)

Sending End

Receiving End

Converter Transformer Smoothing

ReactorDC Line

Valve Group

Filter

Filter

Filter

Synchronous Condensers

SRRM‐DC (Reliability Data for HVdc Schemes)HVdc component and system reliability data are available from various sourcesfor example:Equipment Manufactures (Design phase): ABB, Siemens and AlstomPublished Industry Standard: IEEE Standard 500‐Reliability DataIndustry Experience (Operational Phase): CEA and CIGRE Reports.Manitoba Hydro has supported the activities of both CEA and CIGRE for datacollection and submitted data on the Nelson River HVdc system since early1970’s.

Statistics on HVdc system reliability can be broadly classified into twocategories as:Line related outages: DC line, Smoothing ReactorsTerminal related outages: Valve Group, Pole and Bipole

24

SRRM‐DC (Basic Modeling Technique)It is not realistic to analyze a complex engineering systems like HVdc schemes ina single step instead the system is modeled as a network of smaller sub‐systems.A reliability model for a particular HVdc system can be developed based onfailure mode and effect analysis:Outages of valve group controls, converter transformers, valves and switchingequipment that cause a loss of transmission capability equal to that of a valve groupcan be combined into a single reliability block representing a valve group by suitablecombination of their reliability models in series or parallel using the networkreduction technique.Similarly, outages of pole control equipment, d.c. filters, smoothing reactors andtransmission lines that result in a loss of capability equal to a pole capacity can becombined into a single pole element reliability model.Failures of main station control, a.c. filters, reactive power supply equipment (suchas synchronous condensers), transmission lines or ground electrode cause a loss ofcapability equal to that of a bipole can be lumped into a bipole reliability model.

Note: The HVdc system model developed using the above technique can beeasily incorporated into composite system reliability assessment.

25

SRRM‐DC (Basic Modeling Technique‐Reliability Diagram)

26

Load

Valve Group

Pole Station

Pole line

Pole Station

Valve Group

Bipole Station

Bipole Line

Generation

SRRM‐DC (Basic Modeling Technique‐Equivalent Reliability Diagram)

27

Load

Valve Group

Pole

Bipole

Generation

SRRM‐DC (Example‐Assumptions)

28

The generating system feeding into the HVdc transmissionsystem is 100% reliable with a capacity equal to the maximumcapacity of the HVdc system.All DC components are represented by two‐state modelsNo transmission lossesLoad is 100% factor equal to the maximum transmission capacityThe capacities associated with HVdc transmission system are asfollows:

Bipole I Bipole II

Valve Group 278 Valve Group 500

Pole 834 Pole 1000

Bipole 1668 Bipole 2000

SRRM‐DC (Example‐Component Outage Statistics)

29

Bipole I Bipole II

Component FOR r Component FOR r

Valves and controls 0.021 1.82 h Valves and

controls 0.0156 24 h

ConverterTransformers 0.01 6 m w/o spare

6 d w spareConverter

Transformers 0.01 6 m w/o spare6 d w spare

Pole‐Station 0.000325 0.52 h Pole‐Station 0.00096 1.2 h

Pole Transmission

line0.000085 0.20 h

Pole Transmission

line0.00017 0.5 h

Smoothing Reactor 0.00007 24 h Smoothing

Reactor 0.0066 6 m w/o spare6 d w spare

Pole DC line 0.00048 7 days Pole DC line 0.00048 7 days

Bipole Station 0.000025 0.19 h Bipole Station 0.000185 0.6 h

Bipole DC line 0.00038 7 days Bipole DC line 0.00038 7 days

SRRM‐DC (Example‐Scenarios Evaluated)

30

Case 1: Base CaseCase 2: Spare smoothing reactor on Bipole IICase 3: Spare smoothing reactor and converter transformer onBipole IICase 4: Spare smoothing reactor and converter transformer onBipole II and spare converter transformer on Bipole ICase 5: 100% Reliable HVdc

SRRM‐DC (Example‐Base Case COPT)

31

State Number State Capacity (MW) Probability1 3668 0.733379143

2 3390 0.139788552

3 3168 0.076589129

4 3112 0.011102056

5 2890 0.014598566

6 2834 0.002017872

7 2668 0.015765682

8 2612 0.001159423

9 2556 0.000158699

10 2390 0.003005078

11 2334 0.000210733

12 2278 4.83E‐06

13 2168 0.000718817

14 2112 0.000238664

15 2056 1.66E‐05

16 2000 0.000360017

17 1890 0.000137013

SRRM‐DC (Example‐Base Case COPT)

32

State Number State Capacity (MW) Probability18 1834 4.34E‐05

19 1778 5.04E‐07

20 1668 0.000531788

21 1612 1.09E‐05

22 1556 3.41E‐06

23 1500 3.76E‐05

24 1390 0.000101364

25 1334 1.98E‐06

26 1278 1.04E‐07

27 1112 8.05E‐06

28 1056 1.56E‐07

29 1000 7.74E‐06

30 834 1.46E‐06

31 778 4.73E‐09

32 556 1.15E‐07

33 500 3.53E‐07

34 278 3.50E‐09

35 0 2.61E‐07

SRRM‐DC (Example‐Reliability Indices Calculations)

33

Annual Expected Energy Generated (EEG)

Annual Expected Energy Transmitted (EET)

Annual Expected Energy Curtailed (EEC)

8760max CEEG

n

iiiPCEET

18760

EETEEGEEC

SRRM‐DC (Example‐Results)

34

Case EEG (GWh/yr) EET (GWh/yr) EEC (GWh/yr)

1 32123 31067 10562 32123 31175 9483 32123 31342 7814 32123 31480 6435 32123 32123 0

Closing Comments

35

Relative values (∆EUE) versus absolute values. Energy related index such as ∆EUE can easily be translated into a monetaryvalues. For now MH is focusing on the physical aspect of power system in reliabilityassessment, MH will consider the impact of cyber aspect as well in the future. The number of influencing factors on power system risk assessment increasesconsiderably into the future, resulting from expected changes in the powerindustry such as smart‐grid initiatives, increased utilization of variable energysources, penetration of electric vehicles, distributed generation, storagetechnologies and demand response programs. Electric power utilities need to make relevant and necessary changes to thecurrent risk assessment approaches and practices, and new models andtechniques are needed to be developed in order to deal with those structuraland technological changes in the power industry. Big data and machine learning could play a crucial rule in future powersystem reliability assessment.

36

© 2018 Electric Power Research Institute, Inc. All rights reserved.

Anish Gaikwad, EPRIPr. Project Manager

Nick Wintermantel, Astrape ConsultingPrincipal

NERC Webinar on Composite Resource Adequacy & Trans. Reliability Planning

2018 June 12

Composite Reliability Analysis using Probabilistic Tools - Linking Probabilistic Resource Adequacy & Transmission Reliability Tools

2© 2018 Electric Power Research Institute, Inc. All rights reserved.

Outline

Conceptual background

Overview of the tools used

Case studies

Proposed future work

Reference: EISPC Case Studies Report (https://pubs.naruc.org/pub.cfm?id=536DCE1C-2354-D714-5175-E568355752DD)


Resource Adequacy & Production Costing Tools. Most use probabilistic techniques.

Transmission Planning Tools. Most are deterministic in nature, very few use probabilistic methods (only for power flow analysis).

The premise of the work is to link probabilistic resource adequacy and probabilistic transmission planning tools for comprehensive reliability assessment.


Resource Adequacy & Production Costing Tools Aim to simulate how generators on a given system are likely to operate

over a specified length of time, usually at least one year.

Most of them use probabilistic approaches

Various levels of detail on– Generator characteristics– Load forecasts– Variable generation forecasts– Ancillary services– Transmission– Fuel prices– Energy limits on hydro– Outage rates & maintenance schedules– Area interchange


Resource Adequacy & Production Costing ToolsStudies are performed for hourly or sub-hourly analysis

Models could be “zonal” or “nodal”– Zonal models do not capture detailed transmission representation– Nodal models capture the impact of transmission congestion &

associated rescheduling of generationHowever, transmission deliverability is not robustly assessed

– No full AC power flow is availableCommercially available tools

– SERVM, PROMOD IV, PLEXOS, ProMaxLT, UPLAN, PSO, AURORA, GE MAPS

– SERVM was used for the case studies described later


Probabilistic Transmission Reliability Tools Full representation of the transmission system along with generation

systemAll the modern tools have full AC power flow solutionCan’t typically analyze hourly scenarios due to computational burdenVery few tools use probabilistic approaches TransCARE was used for the studies described later

Name Power Flow Approach

Contingency Selection Approach

Availability

TransCARE from EPRI

AC and DC State enumeration Commercially available

SIEMENS PTI PSSE’s Reliability Assessment Module

AC and DC State enumeration Commercially available

NH2 from CEPEL (Brazil)

AC and DC Monte Carlo Not known

MECORE DC Hybrid analytical and Monte Carlo

No, used in-house at BC Hydro


Thousands of views of future to account for uncertainties

Simulate hourly production costs for the study year (8760 hours) using SERVM

Select a few hours using various criteria (for e.g. high load hours, MW on outage)

for which no EUE is reported

Build network cases for the selected hours

Perform probabilistic reliability analysis in TransCARE or PSS/E to analyze impact

of transmission constraints

Approach to Linking the Two Analyses Research questions to address:

– Which scenarios to pick from SERVM?

– What is the incremental Expected Unserved Energy (EUE) due to transmission unreliability?

– Can we extrapolate results for a few hours to the entire (8760) load duration curve?

– What other risk-based indices, in addition to EUE can be used for analysis?

– How scalable the approach is for practical interconnections (ISO-wide or for a vertically integrated utility)?


An Overview of SERVM


SERVMSERVM has over 30 years of use and development

Probabilistic hourly and intra-hour chronological production cost model designed specifically for resource adequacy and system flexibility studies Takes into account all unit constraints: ramp rates, startup times, minup/mindown times, etc. Commitment decisions on the following time intervals allowing for recourse Week Ahead Day Ahead 4 Hour Ahead, 3 Hour Ahead, 2 Hour Ahead, 1 Hour Ahead, and Intra-Hour Load, Wind, and Solar uncertainties at each time interval (decreasing as the prompt hour

approaches)

SERVM calculates both resource adequacy metrics and costs


SERVM Applications Resource Adequacy Loss of Load Expectation Studies Optimal Reserve Margin Operational Intermittent Integration Studies Penetration Studies System Flexibility Studies

Effective Load Carrying Capability of Energy Limited Resources Wind/Solar/Demand Response/Storage

Fuel Reliability Studies Gas/Electric Interdependency Analysis Fuel Backup/Fixed Gas Transportation Analysis

Transmission Interface Studies

Resource Planning Studies Market Price Forecasts Energy Margins for Any Resource System Production Cost Studies Evaluate Expansion Plans/Environmental Decisions/Retirement Decision


SERVM Framework Capture Uncertainty in the Following Variables Weather (35 years of weather history) Impact on Load and Resources (hydro, wind, PV, temp derates on thermal resources) Economic Load Forecast Error (distribution of 5 points) Unit Outage Modeling (1000s of iterations) Fuel Price and Availability Regulatory Uncertainty

Multi-Area Modeling – Pipe and Bubble Representation

Total Base Case Scenario Breakdown

x =

175Load Scenarios

x 100Unit Outage Draws

= 17,5008760 Hour Simulations

35Weather Years

(Equal Probability)

5LFE Points

(Associated Probabilities)

175Load Scenarios

(Associated Probabilities)


An Overview of TransCARE


TransCARE - Tool for Assessing Transmission Reliability

Up to 10 Power Flow

Cases

Outage Data

StudySettings

Frequency, Duration,

Expected Values of:

Overloads

Voltage Violations

Load Curtailment

System islanding

Reliability indices can be calculated at system level, bulk load points, or for individual components

Load Shapes & Duration or Probabilities

TransCARE is a research grade tool. Other than PSS/E Reliability Module and TransCAREno viable tools are available


TransCARE at a Glance• PSS/E .sav or PSLF .epc• Up to 75K buses, 112,500 circuits• Up to 10 different power flow cases

Network Information

• Enumeration, as well as user defined can be analyzed• Outage of up to 5 lines and 4 units per contingency• Protection & Control Group

Contingencies

• Unit margin• Participation factor• Full economic (not fully tested)

Generation Dispatch

• Forced outage rate/year and duration per outage• Common mode outage stats (if used)• % split between interruptible, firm, & critical load

Outage Stats

• For breaker-to-breaker contingencies• Automatically places breakers for evaluation (beta version)Protection & Control Groups

• Up to 11 elements out (5 gen, 6 lines)• Reports system prob., load loss, gen. tripping, ckt. Tripping, islands, redispatch info.Cascading Analysis

• System/bus/asset reliability indices, load loss indices, bus load loss summary, remedial actions summary

• Database feature for easy data processingReporting


TransCARE: Input/Output FilesLoad Flow

Data

Generator

Data

Generator

Outage

Common Mode

Outage

Must Run

Contingencies

Circuit/PCG

Outage

Load Curve

DataBus

Characteristic

Network

Adjustments

TransCARE

INPUT FILES

Group I - Base case resultsGroup II - Input data filesGroup III - Contingency ranking reportsGroup IV - Contingency solution reportsGroup V - System failure reportsGroup VI - Reliability indices reportsGroup VII - PCG reports

REPORT FILES:

7 GROUPS


Reliability Assessment using Siemens PTI PSS/E – Process


Case Study 1 – Composite Reliability Analysis Using Roy-Billinton Test System


RBTS – Case Setup 6 buses, 9 transmission lines, 230 kV transmission system185 MW peak load, 240 MW

installed capacity, 11 unitsLoad duration curve is shown:

0

20

40

60

80

100

120

140

160

180

200

125

250

375

410

0512

5615

0717

5820

0922

6025

1127

6230

1332

6435

1537

6640

1742

6845

1947

7050

2152

7255

2357

7460

2562

7665

2767

7870

2972

8075

3177

8280

3382

8485

35

MW

Hour of Year (Sorted by Load)


RBTS – Study Summary SERVM was used to create multiple load-generation scenarios– Out of this, 50 scenarios were selected– For each scenario, a power flow case was

developed (a total of 50 power flow cases)

For each scenario, a corresponding list of contingencies involving generation and transmission components was generated

For each case, the contingencies were run in TransCARE along with remedial actions

For each scenario, EUE was computed

A non-linear curve was fit to the data. The formula was extrapolated to the entire load duration curve

Capacity deficiency in SERVM – 9MWh/yr

Total network related EUE based on TransCARE analysis – 123MWh/yr


RBTS – Key Takeaways

EUE was found to be significantly correlated to load levelThe approach computes two separate EUEs

– Generation deficiency in SERVM (9MWh)– Network related EUE (123 MWh)

The case study illustrated that resource adequacy modeling that assumes perfect deliverability within a balancing authority may be ignoring a significant portion of possible reliability events.


Case Study 2 – Composite Reliability Analysis Using TVA System


Probabilistic Economic Analysis –Probabilistic Production Costing Analysis Using SERVM Study 2015, 2020, 2025, and 2030

Capture uncertainty in the following variables by stochastically simulating many scenarios with 1000s of iterations Weather (33 years of weather history) Impact on Load Impact on Intermittent Resources Economic Load Forecast Error (distribution of 7 points) Unit Outage Modeling (1000s of iterations) Multi-state Monte Carlo Frequency and Duration Multi-Area Modeling Neighbor Load and Resources Fuel/CO2 Forecasts

Total Scenario Breakdown: 33 weather years x 3 LFE x 3 Fuel CO2 Scenarios = 297 scenarios for each year Total Iteration Breakdown: 297 scenarios * 10 unit outage iterations = 2,970 iterations for each year


TVA Case Study Setup 20 scenarios were picked for the case study (6 are shown in the figure)

For each scenario, a power flow case was developed

For each scenario, 3000 contingencies were developed using Monte Carlo simulation– Contingency depth was limited to

9 components (both generator and transmission elements)

27,000

28,000

29,000

30,000

31,000

32,000

33,000

34,000

35,000

36,000

0 2 4 6 8 10 12 14 17 19 21 23 25 27 29

TVA

Load

Lev

el

Hour of Year


TVA Case Study Analysis SERVM runs by themselves did not indicate any generation adequacy problemsSome correlation exists

between load level and system problemsOverall, about 20,000

contingencies resulted in problems– About 2200 resulted in load

loss

0

500

1,000

1,500

2,000

2,500

25,000 27,000 29,000 31,000 33,000 35,000

Cou

nt o

f Con

tinge

ncie

s W

ith

Syst

em P

robl

ems

TVA Load Level (MW)

The assumption of perfect delivery of power is likely not reasonable.


TVA Case Study Analysis

Positive correlation between forced MW outage and system problemsOnly 30% - 50% of contingencies with < 500 MWs forced offline resulted

in system problems. A much larger 60% - 90% of contingencies with 1,500 MW to 2,000 MW

forced offline resulted in system problems

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 500 1,000 1,500 2,000 2,500

Perc

enta

ge o

f Con

tinge

ncie

s w

ith

Syst

em P

robl

ems

Generation Forced Offline (MW)


TVA Case Study – EUE by Load LevelNot a strong correlation

between load level and EUE– Many contingencies could not

be solved in TransCARE and their contribution to EUE could not be assessed

The linear curve was extrapolated for the TVA load duration curve– 1128 MWh for the entire year

0

2,000

4,000

6,000

8,000

10,000

12,000

25,000 27,000 29,000 31,000 33,000 35,000

EUE

(MW

h)

TVA Load Level (MW)

Even with this approximate analysis, it is evident that potential reliability problems due to transmission unreliability need to be considered


TVA Case Study – EUE by Load LevelEUE/contingency is shown on the 2nd Y-axisHigher load level corresponds to higher EUE/contingency

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

15,000

17,000

19,000

21,000

23,000

25,000

27,000

29,000

31,000

33,000

35,000

0 500 1,000 1,500 2,000 2,500 3,000 3,500

EUE

(MW

h)

TVA

Load

Lev

el

Hour of Year

Load

EUE


Future Work

Expand on the concepts developed in the two case studies through more thorough case studies– Plan to work with NERC on new case studies

– How many scenarios should be considered? Is the answer system dependent?

– How to make the process of power flow case creation more efficient?Manual, time consuming process

– Provide guidance on when to perform composite reliability assessments


Together…Shaping the Future of Electricity

Documents

Composite power system reliability evaluation...Reliability Reliability can be addressed by considering two basic functional aspects of the power systems: • Adequacy—The ability