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Petten, 04 December 2014
CHALLENGES OF
REPRESENTING
ELECTRICITY SYSTEM
FLEXIBILITY IN
ENERGY SYSTEMS
MODELS
Vera Silva, EDF R&D
Co-authors: Gregoire Prime, Timothee Hinchliffe,
Dominique Lafond, François Rehulka, Miguel
Lopez-Botet
| 2
A system that has sufficient capacity to meet peak load is adequate but
if this capacity is composed mostly of low flexible plants it can
experience problems for handling demand and generation variability.
Flexibility has always been an essential ingredient to handle the
variability and uncertainty in the demand-generation balance. It is
required at the operational time scales but needs to be considered at
planning stage.
Assessing the flexibility adequacy will probably emerge as a new task
in power system planning and metrics and models are being developed
to help with this task.
The representation of flexibility at the energy and power systems
planning will help to deliver a system that can handle variability in a
cost effective way.
THE PROBLEM OF FLEXIBILITY ADEQUACY
| 3
INTEGRATION OF ELECTRICITY SYSTEM FLEXIBILITY TO ENERGY
SYSTEMS MODELS
Goal: to obtain an a long term electricity system expansion solution that ensures a
flexible system by solving a problem that includes : 1) the interaction between the
energy and the electricity systems 2) the long term uncertainties and 3) the relevant
short term operation constraints.
Long term forecasts of demand,
commodity prices, etc Reliability and flexibility requirements
Multi-energy system model
Transmission expansion options and candidate generation technologies
Generation and Transmission planning
Production cost simulations and operation flexibility assessment
Adequate and flexible transmission and
generation expansion solution
Electricity system model
FlexAssessment Continental Model with
Investment loop
MADONE
| 4
DIFFERENT APPROACHES TO ADDRESS THE INTEGRATION OF ENERGY
AND ELECTRICITY SYSTEMS PLANNING
Option 1) Representation of electricity system flexibility in the
Times model by increasing the simulation granularity and
including additional constraints=> MADONE
Option 2) Coupling energy system models with electricity
system models using a chain of simulation tools with the
possibility of back feeding relevant information
• Energy system optimization : Madone
• Electricity system planning : Continental Model with Investment
loop
• Detailed near term flexibility assessment : Continental with
FlexAssessment
| 5
Perimeter EU27+NO+CH (Europe 29)
with different levels of detail depending on the country
Trans-national networks represented
electricity and gaz, CO2
Storage capacities
hydro (one lake per country + hydro-pumping), gaz, CO2
Pipelines/electricity injections at the frontier of EU29
NordStream, Southstream, Nabucco, DESERTEC…
National resource potentials & limits
wind off-shore: km2(depth, wind speed, distance to coast) X Capacity density; Wind on-shore km2 ( area potentially available) X Capacity density; solar PV: area available, roofs surfaces; CO2 storage; biomass resources
Period and simulation time step
yearly from 2005 to 2010, every 10 years from 2010 to 2050
representation of each year with load curves eg: 24, 288 points
Outputs
technology mix & detailed energy balances , energy dependency, and environmental indicators, balance for electricity (including exchanges), association of energy uses and activities….
275 Mtep / 549 TWh
1339 Mtep / 2114 TWh
258 Mtep / 580 TWh
12/11/2005 EFESE/OSIRIS lot MADONE / Enerbat / EPI / MFEE/ EIFER4
2/1.
80.8/0.7
0.08/0.020.65/0.65
1.2/0.47
1.9/0.
9
2.05/1.5
1.9/2.75
3/3.85
0.6/0.6
3.2/1.5
2.5/0.8
1.1/1.2
3.2/2.2
2.4/2.4
0.5/1.4 0.995/2.65
2.3/3.2
2/2
0.5/0.6
0.75/0.75
0.95/0.95
1.98/2.44
1.2/1.3
0.3/0
0.35/0.35
0.75/0.75
2.05/1.65
0.5/0.5
1.5/0.8
0.9/0.6
0.43/0.16
4.24/1.81
1.3/1.5
2.3/2.2
1.75/0.8
0.5/0.5
0.6/0.1
2/1
Capacités d’interconnexion en 2005 (enGW)
TO /FROMLégende:
MADONE PERFORMS A MULTI-ANNUAL AND MULTI-ENERGY SIMULATION
OF EACH OF THE 29 INTERCONNECTED EUROPEAN COUNTRIES
detailed interm. aggregated
| 6
The model simulates a “realistic” European electricity system,
including:
description of different countries generation mix and key transmission corridors
interconnection capacities between countries
management of water reservoirs and pump storage
a large number of scenarios of climate years represented by demand and variable
generation across the European system => time-synchronise data with hourly (or lower)
resolution
several scenarios of generation availability
Some key challenges of this problem:
hydro and storage flexibility optimization => stochastic problem
generation scheduling needs to be performed across the whole Europe including
interconnection and key transmission constraints => high performance computing
analysis of system static and dynamic security => hierarchical approach
CONTINENTAL MODEL WITH INVESTMENT LOOP FOR MODELING THE
EUROPEAN INTERCONNECTED ELECTRICITY SYSTEM
| 7
The objective is to obtain the thermal generation
mix that ensures that for every new unit the
revenues equals its annuitized fixed costs :
Fixed costs include investment and O&M
Variable costs include start-up and fuel costs
The conventional generation mix is optimized in
two iterative steps:
Load duration curve based heuristic to propose a
candidate solution
Validation of the heuristic solution solving the
hourly load-generation dispatch => creates a price
signal that feeds the investment loop
The generation mix needs to respect an adequacy
criterion
Maximum of 3h/year with marginal price = VOLL
CONTINENTAL
Investment loop
Demand
Variable generation
profiles
Interconnection
constraints
Storage
Investment costs
Commodity prices
CO2 price
INPUT DATA
Optimal thermal
generation mix
Production dispatch
Production costs
Market clearing prices
CO2 emissions
Hydro stock level paths
Interconnection uses
OUTPUT
CONTINENTAL MODEL WITH INVESTMENT LOOP: ELECTRICITY
GENERATION INVESTMENT MODEL FOR INTERCONNECTED SYSTEMS
| 8
Reference: Langrene, N., van Ackooij, W., Breant, F., « Dynamic Constraints for Aggregated Units: Formulation and Application », Power
Systems, IEEE Transactions on , vol.26, no.3, Aug. 2011
Minimize global production cost for each
zone Unit commitment and economic dispatch minimizes thermal and
hydro generation cost over all the scenarios
Constraints include primary, secondary and tertiary reserve and
generation dynamic ratings
Multi area optimization with interconnection constraints
represented by NTC
Stochastic hydro-generation
scheduling Maximize the reduction in terms of generation costs
using dynamic optimization to obtain the « water
value » for each time step
Define a set of strategies of the optimal use of
hydro reservoirs in order to minimize the global
generation cost
Scenario based representation of stochastic parameters : Large number of annual scenarios of demand, wind and PV generation, water
inflows, fuel costs, thermal unit availabilities
Scenarios
data
Water
values
For each dispatch period and for each zone the dispatch
solution and the market clearing prices are obtained to access
the revenues of generation units
CONTINENTAL MODEL HYDRO-THERMAL GENERATION SCHEDULING
| 9
MADONE
Bottom-up TIMES model : all 29 interconnected
European countries
Renewables national resources potentials & limits detailed: Wind off-shore, wind on-shore, roof for PV etc…
Horizon = 2005 to 2050 with a perfect foresight of each year
Time-slices: 24 or 288
24 =Peak and Off-Peak for each month
288 = 2 representative day (Week/W-E, bi-hourly)/month
Peak equation: additional demand constraint
With or without renewable contribution
Deterministic
Or multi-scenarios for one chosen year : testing with 4 annual scenarios
Continental Model with Investment loop
EDF R&D’s Elec Production Cost model
Electricity generation portfolio optimization
Stochastic simulation of hourly system
operation
Demand-generation balancing solved for one
year with hourly resolution
Interconnection constraints included
Stochastic parameters: (T°, hydro, wind, PV and
generation outages)
)()(max tDemtDem
OPTION 1 - REPRESENTATION OF FLEXIBILITY IN THE ENERGY MODEL :
COMPARISON MADONE-CONTINENTAL
| 10
89
90
89
90
90
91
90
91
91
67
63
65
40
68
71
68
70
63
77
90
106
125
90
86
92
88
96
0 50 100 150 200 250
Def 5k€_Seuil 5k€_200MW
Def 10k€_Seuil 5k€_200MW
Def 50k€_Seuil 5k€_200MW
Def 10k€_Seuil 5k€_MC1pt
Def 5k€_Seuil 5k€_MC2all
Def 20k€_Seuil 20k€_200MW
Def 20k€_Seuil 10k€_600MW
Def 20k€_Seuil 10k€_200MW
REF_Def 20k€_Seuil 5k€_200MW
Capacité installée globale : MAD Global (GW)
dCPTF dCCG dTAC
Base Mid-merit Peak
89
89
89
81
81
81
43
41
41
42
43
40
234
101
34
224
96
10
- 50 100 150 200 250 300 350
288p + peak ss EnR
288p + peak
288p
24p + peak ss EnR
24p + peak
24p
Base Mid-merit Peak
- Consistency of
base capacity needs
between the 2
models
- Mid-base capacity
underestimated
with TIMES model
- Peak capacity
dependant on peak
equation
calibration MADONE (TIMES)
CONTINENTAL
COMPARISON OF CONTINENTAL AND MADONE OPTIMAL THERMAL
GENERATION MIX
| 11
In the tests we made, a multi scenario approach helps reducing the gap for mid-
merit capacity but leads to a larger over-estimation of peak capacity
Choice of a (limited =4) set of scenario: how to select the right ones?
Calibration of peak equation could be a solution… but largely dependent on the system
studied
95
81
101
74
88
89
96
81
101
74
88
89
41
42
39
55
46
41
40
42
40
55
46
41
127
105
105
129
136
101
40
38
37
67
62
34
- 50 100 150 200 250 300
288p + peak monosénario 4
288p + peak monosénario 3
288p + peak monosénario 2
288p + peak monosénario 1
288p + peak multiscénario
288p + peak monoscénario moyen
288p monosénario 4
288p monosénario 3
288p monosénario 2
288p monosénario 1
288p mutiscénario
288p monoscénario moyen
Tirs multiscénario TIMES (GW) CPTF CCG TAC
89
90
89
90
90
91
90
91
91
67
63
65
40
68
71
68
70
63
77
90
106
125
90
86
92
88
96
0 50 100 150 200 250
Def 5k€_Seuil 5k€_200MW
Def 10k€_Seuil 5k€_200MW
Def 50k€_Seuil 5k€_200MW
Def 10k€_Seuil 5k€_MC1pt
Def 5k€_Seuil 5k€_MC2all
Def 20k€_Seuil 20k€_200MW
Def 20k€_Seuil 10k€_600MW
Def 20k€_Seuil 10k€_200MW
REF_Def 20k€_Seuil 5k€_200MW
Capacité installée globale : MAD Global (GW)
dCPTF dCCG dTAC
89
89
89
81
81
81
43
41
41
42
43
40
234
101
34
224
96
10
- 50 100 150 200 250 300 350
288p + peak ss EnR
288p + peak
288p
24p + peak ss EnR
24p + peak
24p
MULTI SCENARIOS SIMULATIONS
Madone
Continental
| 12
MADONE IS SUITABLE TO PROVIDE A “MERIT ORDER” BETWEEN
TECHNOLOGIES INCLUDING THE MIX AND GEOGRAPHIC DISTRIBUTION
OF RENEWABLES
Representing explicitly dynamic constraints in a long-term TIMES large planning model doesn’t seem realistic for the time being
Without modeling operation margin and reserve requirements & dynamic constraints the generation dispatch is not accurate
For instance, a peak equation imposes investment in back-up capacity, not its use.
… but the objective is to have the « right » merit order between technologies investment decisions,
« right »= least cost + meeting capacity adequacy & flexibility adequacy system requirements
And then to assess, ex-post, if the generation mix calculated meets the electricity system constraints (Option 2)
Madone Continental model with
investment loop
Renewables mix per country:
F (cost/potential) with
sensitivity to interconnection
Flexibility and adequacy constraints
| 13
OPTION 2: CHAIN OF SIMULATION TOOLS FOR DETAILED FLEXIBILITY
ASSESSMENT OF CONTINENTAL MODEL SCHEDULING SOLUTIONS
Location of VG
Load factors (with resolution 1h or lower)
VG forecast errors
FlexAssessment
CONTINENTAL
Model
Reserves and flexibility adequacy
Economicalanalysis
Dynamic simulation model
Market prices and generation costs
Generation load factors
Interconnection load factors
Generation mix
Frequency stability
VG curtailment
Plant revenues
Investment / hourly dispatch
Investment loop
Detailed description of VG
Demand time series
Investment costs
Generation dynamic constraints
Fuels costs
CO2 price
Network transfer capacities
Input data
Madone/TIMES model
The model coupling can include multi-annual investment trajectories simulated with Times
complemented with annual snapshot simulations with Continental Model
| 14
-
100
200
300
400
500
600
0 30 60 90 120 150 180 210 240 270 300 330 360
-
100
200
300
400
500
600
0 30 60 90 120 150 180 210 240 270 300 330 360
-100
-
100
200
300
400
500
600
0 30 60 90 120 150 180 210 240 270 300 330 360
Net demand with 40 % VG penetration
Net demand with 15 % VG penetration
Demand
Difference between
31 weather years
(Δmax= 25% daily
net energy
demand)
Difference between
31 weather years
(Δmax= 90% of
daily net energy
demand)
Difference between
31 weather years
(Δmax= 7% of daily
energy demand)
-100
-
100
200
300
400
500
0 30 60 90 120 150 180 210 240 270 300 330 360-40
-30
-20
-10
-
10
20
30
40
50
60
70
80
90
100
Solaire
Eolien
Fatal
Hydraulique
Demande
DemandeRes
1 year
Scenario
Week For one single weather
year daily variation of
net demand and intra-
day variation requires
to take consider
forecasting errors &
margins
Intra-day variation of
net demand requires
to take into account
near term flexibilities
-40
-30
-20
-10
-
10
20
30
40
50
60
70
80
90
100
Solaire
Eolien
Fatal
Hydraulique
Demande
DemandeRes
Solar
Wind Biomas
s Hydro
Demand
Net Demand
VARIABLE GENERATION IMPACTS DEMAND-GENERATION
BALANCING FROM PLANNING TO OPERATION
Close to 100 scenarios (1 year with hourly resolution) created from synthetic demand,
wind and PV data for 31 weather years combined with generation availability
| 15
Va
ria
tio
n c
om
pa
red
to
no
n V
G r
ef ca
se
GW
-200
-100
-
100
200
300
400
500
600
700
0 1000 2000 3000 4000 5000 6000 7000 8000
European net load duration curve
European load duration curve
h GW
-200
-100
-
100
200
300
400
500
600
700
0 1000 2000 3000 4000 5000 6000 7000 8000
Peak power
needs increase
h
BASE
PEAK
BASE
PEAK
Comparison of the Generation mix transformation with VG
Load duration curve approach vs. optimized with the investment loop
Simulation of hourly scheduling leads to a mix with:
- similar base load
- increase of mid-merit plant
- significant increase of flexible peaking plant
compared to a load duration curve investment approach
-100%
-50%
0%
50%
100%
150%
200%
Base Mid Merit PeakBase generation decreases
in the order of the energy
provided by VG
MID-MERIT
MID-MERIT
With flexibility constraints
Load duration curve
VARIABLE GENERATION REDUCES THE NEED FOR BASE-LOAD AND
INCREASES THE NEED FOR FLEXIBLE PEAKING PLANT
Example with 40% VG penetration in Europe
| 16
Continental provides generation investment solutions that ensure sufficient
flexibility to manage annual, seasonal, weekly… to hourly variability
However solving a multi-zone hydro-thermal investment and operation
optimization, at the European system scale, is a complex problem and
approximations are required:
Different technologies are represented by clusters of identical units (nuclear, coal,CCGT, OCT) with
aggregated dynamic constraints => MSG, Min-up and down times, ramp-rates, start and stop times,
etc. (EU:1640 units and 169 clusters)
The annual optimization is performed by successive optimizations using a sliding window with a
perfect foresight of stochastic values (units failure, demand, wind and PV) are known for the duration
of the window.
In order to simulate short term system operation two layers are added to the
approach:
FlexAssessment – tests the robustness of the dispatch solutions considering all aspects of the
stochastic behavior of demand and generation.
European synchronous system dynamic model–assessment of the dynamic frequency stability
for every dispatch period
INTEGRATION OF A DETAILED FLEXIBILITY ASSESSMENT INTO
CONTINENTAL MODEL
| 17
FLEXASSESSMENT BUILDING BLOCKS AND FLOWCHART
Generation scheduling
-hourly generation schedule
-for several annual scenarios
Frequency distribution of operation margins
- Upward and downward requirements for different lead times (eg: day-ahead,1h, 2h, 4h, etc)
Available margins
- Upward and downward available margins for different lead times
Flexibility assessment : required Vs available operation margins
- Upward and downward flex adequacy for different lead times represented as a
Probability of Insufficient operation margin = f(direction, lead-time)
2-MarginAssessment
3-OPIUM 1-
EnhanceDispatch
-40
-30
-20
-10
-
10
20
30
40
50
60
70
80
90
100
Solaire
Eolien
Fatal
Hydraulique
Demande
DemandeRes
-40
-30
-20
-10
-
10
20
30
40
50
60
70
80
90
100
Solaire
Eolien
Fatal
Hydraulique
Demande
DemandeRes?
Calculation of operation
margin requirements
Lead
time
Quantification of
the technical
flexibility available
| 18
OPIUM generates for each time step t and for each lead time T (1h, 2h, day-
ahead, etc) a probabilistic density function of difference between demand
and generation in t+T.
Convolution:
generation excess generation deficit
Example of distribution of each source of uncertainty for
one period
Example of the distribution of demand-
generation balance
e.g. n % risk used to
define reserve
requirements
Probability to need a
margin lower than x MW
OPIUM – PROBABILISTIC TOOL FOR THE CALCULATION OF OPERATION
MARGINS AND RESERVE REQUIREMENTS
| 19
Flexibility sources are stacked by
decreasing flexibility of each source:
« spinning component»
Start OCT (gaz or oil)
Start Offline CCGT
Start Offline Coal and nuclear
Load shedding.
Required margin and available
margin comparison outputs for each
flexibility source a saturation
probability.
Proba ( , , , )saturation flexSource t T way
OCGT upward 1h 01/01-19h
0 1000 2000 3000 4000 50000
0.1
0.2
0.3
0.4
0.5
0.6
Power (MW)
Upward Activation Probability by Technology, for lead time = 2 h
Head Room Margin
OCT Margin
CCGT Margin
Load Shedding
1 h
FLEXIBILITY ADEQUACY: EXAMPLE OF REQUIRED VS AVAILABLE
UPWARD MARGIN
| 20
flexibility source
annual indicators
number of period with insufficient flexibility
mean hourly power deployment (MW)
generation headroom 400,8 702,1
quick plant start 1 100,5 150,8
quick plant start 2 0,0 60,1
slow plant start 0,0 0,0
load shedding 0,0 0,0
Flexibility indicators that can be obtained: example for lead time =
1 h, upward direction
Not deployable
Insufficient 1H margin probability =0
Means that the probability of deployment
of flexibility higher than « head room » +
quick plant start 1-2 within in less than 1 h
is 0 zero risk to run out to upward
margin
Annual utilization of different
flexibility sources
Average 1h upward flexibility
deployment = 913 MW
For this example the deterministic scheduling provides sufficient
flexibility to cover for all possible scenarios of 1h variability
During 400,8 h we need to
resort to OCGT to
manage 1h fluctuations
Not desired
INDICATORS REGARDING SHORT TERM FLEXIBILITY ISSUES
| 21
Renewable capacity is based on the geographic distribution of wind and solar resources. Its
development involves a necessary adaptation of the existing electrical and energy system and
drives additional costs.
The development of renewables increases the need for flexibility and significant work has been
done to model the need for flexibility in planning models.
Coupling of investment and operation models seems to be the state of the art practice for realistic
size systems. This notion is currently being extended to multi-energy systems.
EDF R&D is studying the possibility of coupling the energy model MADONE with the electricity
system planning tool Continental Model. Similar type of work has been done by other institutions
as :
Coupling PRIMES – Plexos - DSIM used in the DG study « Energy integration of renewable energy in
Europe « (Kema, Imperial College)
Coupling TIMES-Plexos (or other) envisaged by the JRC
Hierarchical approaches permit having a good representation of both the energy and the
electricity system but the solutions obtained are not necessarily optimal.
Further research is needed either concerning the development of unified models or to define the
type of information exchanges between models if chains of models are used.
CONCLUSIONS AND NEXT STEPS
APPENDIX
| 23
Flexibility is mostly connected with operation decisions and
represents the ability of a system to adapt its to both predictable
and unpredictable fluctuating conditions, either on the demand or
generation side, at different time scales, within economical
boundaries.
WHAT IS ADEQUACY ? WHAT IS FLEXIBILITY ?
Adequacy is connected with the issues of investment decisions and
is used as a measure of long term ability of a system to match
demand and supply with an accepted level of risk. This is a measure
that internalizes the stochastic fluctuations of the aggregate
demand and supply.
| 24
ELECTRICITY SYSTEM FUNCTIONS AND FLEXIBILITY
Time scale Domain Elements affected Flexibility sources
Close to real
time horizon
Seconds
Frequency regulation:
Frequency containment
reserves (FCR)
Dynamic frequency
stability FCR reserve providers
Minutes
Frequency regulation:
Frequency restoration
reserves (FRR)
Frequency FRR reserve providers
Scheduling
and dispatch
horizon
Minutes to
hour
Replacement reserves
(RR) and balancing
Economic dispatch
Follow net load variation
and FCR and FRR
Observability and Forecasting
Increase reserves
Ramping capability
Quick start plant
Hours to
days
Generation scheduling
Day-ahead and intra-
day markets
Generation dispatch
Transmission and
distribution operation
Wind utilisation
Observability and Forecasting
Efficient market design
Scheduling flexibility
Planning
horizon Years Expansion planning
Generation adequacy
Flexibility adequacy
Transmission and
distribution reinforcement
Optimise generation mix
Coordination between generation
and network investment
| 25
BUT HOW IS IT BEING SOLVED…
Single optimisation problem
minimises investement and
operation decisions
Generation investement
loops connected to
production cost models
Investement loops +
production cost model+
operational flexibility
assessment
Unit construction and
Commitment algorithm (J. Ma,
V. Silva, 2011)
DSIM (Imperial College
London, 2012)
Plexos with flexibility offline
loop (EPRI, UC Dublin)
Maximizing future flexibility in
electric generation
Portfolios (Giraldo & McCalley,
2013)
FESTIV (NREL, 2013)
CONTINENTAL +
FLEXASSESSMENT (EDF
R&D, 2014)
MEPO-UC (Palmintier, Mort,
2013)
CONTINENTAL (EDF R&D,
2009 and 2011) … ect
Best solution but difficult to
solve => use of simplifications Best solution to identify
the critical constraints but
not to obtain the optimal
investement solution
Requires significant
simplification on
operation constraints
| 26
IMPACT OF VARIABLE GENERATION ON THE NET DEMAND
ADDRESSED TO CONVENTIONAL GENERATION
Source: H. Holtinnent et all, « Flexibility in the 21 century power systems», 21st century Power
Partnership project
| 27
MADONE: a bottom-up TIMES model
Industry:
• 18 main sectors
• 47 sub-sectors
• 53 energy using technos.
Residential sectors:
• 8 types of dwelling
• 11 energy needs (heating, HW, cook., light…)
• 11heating+hot water technologies
• 3 cooking technos
• 16 other electric appliances
Service sectors:
• 7 main sectors
• 2 types of dwelling for each sector
• 7 energy needs (heating, HW, light, computers…)
• 6 heating+HW technos
Transport sectors:
• Passengers and freight
• 9 transports modes
• 23 transport means
Agriculture:
• 6 energy uses
Oil & solid fuel supply
Gas supply (Eurasian area)
• Native gas production
• Pipeline transportation
• Gas storage
• LNG
Biomass-waste supply
• Primary ressources (17 types)
• Conversion technologies (13)
• Final bio-energy products (7)
Uranium supply
Non energy uses:
• 6 energy uses
FINAL ENERGY PRIMARY ENERGY
Wind, solar, hydro supply • availabilty factors (country + on/off shore + wind speed (9) + hours differenciation) • Ressources limits: areas X capacity density hyp. Ex: wind off –shore: km2 available per country according to wind speed (9), distance to coast (2) and depth (3) Ex: solar PV: m2 of roofs available, land available…
Electricty & steam production:
• >50 power generation technos
• Cogeneration: 21 technos
• District heating: 7 technos
• Industrial boilers
•Interconnections among countries
Refineries
Industrial transformations
( efficiency rates of direct
consumption processes)
TRANSFORMATION
PROCESSES
Energy sector consumption:
• Grid losses, ancillary conso etc.
= linked to production CO2 storage potentials: Saline aquifers, DGOF on & off-shore
| 28
Generation reliability model to access generation availability
• Thermal generation uncertainty represented using forced outage rates (ORR) and failure to synchronize (Ps). A capacity outage probability table (COPT) is built for each dispatch period.
• Hydro generation uncertainty represented using an non-biased normal distribution
Probabilistic model of wind forecast errors
• Forecast uncertainty depends on lead-time and forecasted load factor.
• Wind uncertainty is modeled using empirical distributions
• Shape and dispersion of the distribution depend on the forecasted load factor
Probabilistic model of PV forecast errors
• Separate representation of small rooftop installations and PV farms
• PV forecast errors are represented by empirical distributions that depend on the hour of the day and the month of the year
Probabilistic model of demand forecast errors model
• Demand forecast errors are represented by non-biased normal distributions
• The standard deviation of the distribution depends on the hour of the day and the month of the year
-3000 -2000 -1000 0
0
0.05
0.1
0.15
0.2
0.25
0.3
Actual - committed generation (MW)
Pro
babili
ty
0
50
0204060801000
5
FLF (%IC)LF (%IC)
De
ns
ity
-20 -10 0 10 20
0
0.02
0.04
0.06
0.08
Deviation (%IC)
De
nsity
Hour
PV Farms
Roof-top PV
OPIUM – PROBABILISTIC TOOL FOR THE CALCULATION OF OPERATION
MARGINS AND RESERVE REQUIREMENTS
