Measuring the impact of innovation

Measuring the impact of innovation

Brussels, March 2011

2

Concept-Idea

The TWENTIES project aims at:“demonstrating by early 2014 through real life, large scale demonstrations, the benefits and impacts of several critical technologies required to improve the pan-European transmission network, thus giving Europe a capability of responding to the increasing share of renewable in its energy mix by 2020 and beyond while keeping its present level of reliability performance.”

To this extent it will be focused in removing several barriers which prevent:

• pan European electric system from welcoming more renewable generated electricity.• renewable-generated electricity from contributing more efficiently to the single

European electric market.

www.twenties-project.eu EWEA Conference 2011

3

Project objectives

Task force 1: Contributions of variable generation and flexible load to system services

Task force 2: Network operators needs to allow reliable off-shore wind development

Task force 3: Improvements in the transmission grid flexibility

6 high level demonstration

objectives


Demo 1 SYSERWIND Demo 3 DC-Grid Demo 5 NETFLEX

Demo 2 DERINT Demo 4 STORM MANAG. Demo 6 FLEXGRID

Control System

220 kV

Reactors

References

REE Research

and Execution

Measurement Measurement

Switching Devices

ABB Research

and Execution

Max

imum P

ositive

and

Neg

ative Dev

iations

Gain from

DLR

Loss from

GOPs

Initial

Positive

and

Neg

ative

Dev

iations

Gain from

PSTs

and HVDC-link

s

Initial Situation

Existing Margin

Created Margin

8 8 8

System

Plants

Processes

THERMALPOWER

WINDTURBINE

CHPPLANT

BOILER SUPER HEATER TUR

BINEM

CONTROL VALVE PUMP FEEDER

SYSTEMCONTROL

LEVEL 1

setpoint

PLANTCONTROL

setpoint

PROCESSCONTROL

setpoint

SERVOCONTROL

actuator signal

POWER PLANTPROCESSES

OBJECTIVES

LEVEL 2

LEVEL 3

LEVEL 4

4

Measuring impact & KPIs

Demo KPIs:Technical

performance at Demo1 level

WP #15 KPIs: Economic impact

at Demo & TF level

WP #16 KPIs: European impact at TF & Project

level

1 Including WP #17


Impact assessment

Measured data

Pre defined Methodology

Pre defined Methodology

5

Estimating the aggregated impact of the Demos

Objective: The objective of EU wide assessment is to provide an integrated global assessment of the impact that the task forces will have on the EU level. Thus, this (WP16) work complements the (WP15) analysis of the economic impact that the demonstrations have on a national level in the countries where they are performed.

Approach is to use existing simulation models to support the quantification of this impact. The impact will be included in the simulations mainly by changing input parameters of the models. Thus, model development will be avoided, although some minor adjustments will be needed to include the effect of the demonstrators.

The main barrier for the WP16 assessments is generally the limits in access to all relevant data for all Europe, but a relatively detailed market database including all Europe is available


6

WP #16. KPIs

The KPIs are selected to quantify the impact of the demonstrated technologies / solutions in terms of

Cost of energy CO2 emission Investment costs Need for transmission capacity Potential hydro power capacity Need for spinning reserves Wind power forecast errors Costs of ancillary services

KPIs are defined at Project level (4 KPIs) and at Task Force level (22 KPIs)


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OBJECTIVES Assess the local economic and/or technological impact of each demo. Perform an analysis of the joint impact for all the demos in the same task-

force. Identify the barriers, after the demonstrations but in a continuous way through

the project life that should be overcome to scale-up the outcomes of the three task-forces at a member-state or regional level.

Propose solutions to overcome the identified barriers in terms of new regulatory recommendations such as grid codes and electricity market designs.

Perform a transversal analysis of impact of all the demos to provide a reference point for replicating the results at a full scale EU27 level in some tasks of WP16

WP #15: Economic assesment


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IMPACTS & BARRIERS:

The identification of the barriers will be an outcome of this WP: which are the barriers that prevent from taking advantage of the new features demonstrated. An special emphasis will be done in regulatory barriers (market rules, products, etc.)

The assessment will be measured by a number of proposed KPIs. They are classified by Task Force

Besides these KPIs (directly connected to specific objectives of the project), WP15 will produce additional outcomes

KPI’s measurements

For each Demo, and jointly for each Tasks Force, the main impacts will be mearsured in economic terms (21 KPIs)

WP #15: Economic assesment


9

Thanks for your attention, [email protected]


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ANNEX 1. DEMO OBJECTIVES, BARRIERS & IMPACTS


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Demo #1: SYSERWINDOBJECTIVES:

To demonstrate that secondary frequency control and voltage control in the system can be performed effectively by existing aggregated wind farms through improving the control systems at EMS and local level.

BARRIERS:

Technical: New tools at turbine and wind farm level to be implemented. It might even mean modifying the existing software of the PLC of the turbines and the algorithms of the local SCADA system of each wind farm. The communication systems speed has to be enhanced considerably to coordinate the different wind farms. The forecast tools have to be enhanced for providing short-term forecast.

Regulatory: the current market structure for secondary regulation is itself a barrier from the forecast tools point of view.

Economical: the current wind farm components may be affected in their performance, in terms of loss of profit, CAPEX and OPEX, electrical infrastructure losses, life of components, etc. at Wind Farm and Wind Turbine level


12

Demo #1: SYSERWINDIMPACT:

Through the new active power control services tested (secondary frequency control), a lower system power reserve due to wind energy penetration might be needed, and a new approach to wind energy market integration could be addressed.

Through the new reactive power control services tested, the wind energy could participate in the grid voltage control in a similar way as the conventional power plants. It will lower the power losses and the voltage control complexity for the TSO.

As a result of the two new services provided by wind energy, the cost of the system operation will be reduced, and it will allow an increase in the wind energy penetration by preserving the grid security and stability.

KPI’s measurements:

Dynamic Response of both active and reactive power regulators (fullfills the same requirements that conventional generators) (KPI.D1.1.2, KPI.D1.2.2, KPI.D1.2.4,

KPI.D1.2.6) Accuracy and Reliability of the offered services

(KPI.D1.1.1, KPI.D1.1.3, KPI.D1.2.1, KPI.D1.2.3,

KPI.D1.2.5)www.twenties-project.eu EWEA Conference 2011

13

Demo #2: DERINTOBJECTIVES:

To show at large scale that aggregating wind production with flexible DER and loads within appropriate regulatory schemes lead to a more secure and efficient electricity system having high scalability potential.

To create an alternative to deliver ancillary services from the traditional fossil fired power plants.

BARRIERS:

Technical: Scalability of the IT platform and optimization algorithms. Transform LU capabilities into ancillary services. Providing a standardized solution for LU control

Regulatory: The TSO may not create the necessary ancillary market to handle consumption units thereby making it possible for the VPP to act on the market. The TSO may not be willing to allow an aggregation of small units to deliver ancillary services.

Economical: Customers may not be willing to participate if cannot measure the economic benefits of joining the VPP. The LU may technically not be suitable for connecting to the VPP. Potential poor business case for connecting a LU.


14

Demo #2: DERINTIMPACT:

Physical outcomes: A 100 MW VPP system operating in the Danish power system. The VPP platform should be applicable to other technical and market environments, e.g. Spain

Intellectual outcomes: New optimization models, etc. manifested as academic papers and/or patent applications. Analyses, documentation and guidelines that facilitates the implementation of a VPP solution in a new technical and market environment, e.g. Spain.

Financial outcomes: To demonstrate commercial incentives for the TSO, for the ancillary service provider and for the local asset owner. This is exemplified in any amount of income to DONG Energy’s VPP solution in Denmark, its TSO customer (Energinet.dk) and its energy customers (local asset owners, both B2B and B2C) that demonstrates the value of the VPP concept.

Environmental outcomes: The increase in supply of ancillary services will decrease the price, thereby lowering the overall electricity cost for the consumer. The VPP provides a flexible solution that allows larger amounts of fluctuating renewable resources


15

Demo #2: DERINTKPI’s measurements:

Number of Units integrated into the VPP (KPI.D2.1) Number of different technologies in VPP portfolio (KPI.D2.2) Active capacity made available by VPP in Spot Market and for Ancillary

Services (KPI.D2.3) Actual energy delivered by VPP in Spot Market and for Ancillary Services

(KPI.D2.4) Reactive capacity made available by VPP to grid operator (KPI.D2.5)


16

Demo #3: DCGRIDOBJECTIVES:

Show that the long term increase of off-shore wind generation will request innovative solutions in transmission topologies more complex and more adapted to RES features

Set up economic and technical requirements for designing and operating future off-shore grids in parallel with AC mainland grid

Propose from the already existing connections and available technologies, a step-by-step roadmap for off-shore network development for several scenarios of target

BARRIERS:

The technical feasibility and operability of MTDC grids with wind generators in normal but also disturbed conditions is not proven yet, as no MTDC grid with wind farm connections is operated over the world today and no standards are available up to now

The lack of a DC breaker at appropriate voltage level is commonly identified as a drawback today; moreover the complete protection plan design is still missing and its feasibility should be proven as well (detection, selection, clearance).

The high cost of HVDC technology and wind inherent intermittency make it difficult to settle economic criteria for developing off-shore networks “just for” wind


17

IMPACT:

Technological impact: It will be highlighted the most feasible grid topologies at the 2030 scenario based on existing HVDC technology and/or development of components (e.g. breakers and converters). Optimal and secure operation by TSO’s of the DC/AC System

Economic impact: The drivers for developing HVDC off-shore networks on an European basis and taking into account on-shore conditions will be analysed in detail through probabilistic methods. Capacity increase among countries and capacity sharing with wind generation will be analysed as a key issue for market applications.


Performance of the DC breaker prototype (KPI.D3.1) Number of basic topologies fully investigated (KPI.D3.2) Control and protection design for 2 HVDC technologies and 2 converter architectures

(KPI.D3.3) Volume and location of off-shore wind generation on European shore (KPI.D3.4) Capacity of the off-shore network to transmit power after a disturbance on the DC side

(KPI.D3.5)

Demo #3: DCGRID


18

Demo #4: STORM MANAGEMENTOBJECTIVES:

Improve assessment of offshore storm forecasting Improve wind turbine flexibility Explore different operational management strategies and test some of them Operate the HVDC interconnection with respect to real-time balancing Define hydro power control strategies Investigate consequences for the Nordic system caused by this demonstration for

the different solution methods proposed, Explore reasonable compensation measures for forced regulations under stormy

conditions

IMPACT:

Impact on wind turbine performance Impact on balancing management by storm forecasting Impact of hydro system control Impact of coordinated action


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Maximum power forecast error on wind farm level (KPI.D4.1)

Improved wind turbine flexibility (KPI.D4.2)

Management strategies for a storm (KPI.D4.3)

Operating the HVDC interconnection with respect to real time balancing

(KPI.D4.4)

Hydro power control strategies (KPI.D4.5)

Demo #4: STORM MANAGEMENT


20

Demo #5: NETFLEXOBJECTIVES:

Demonstrate the enhanced grid flexibility• By forecasting overhead line dynamic thermal ratings (DLR)• By coordinating Phase Shifter Transformers (PSTs) and HVDC-links actions• While monitoring the stability of this flexible system

facilitates further integration of wind generation

BARRIERS: Technical in order to be able to forecast security margins accurately enough Strategic because of the cross-country impact of larger interconnection capacity

IMPACTS: Enhanced use of the system

• Through forecasting line ratings (1)• Through coordination among ‘active’ grid devices (2)• By combining the previous results: I(1+2) ≥ I(1) + I(2)

With out compromising system stability


21


Manageable wind uncertainty (KPI.D5.Smart-PFC.2)

Additional wind integration• Gain from DLR (1) (KPI.D5.NETFLEX.2)• Gain from optimising the coordination of

PSTs and HVDC-links (2)

(KPI.D5.NETFLEX.3)• Loss from stability constraints (3)

(KPI.D5.NETFLEX.4) Gain from the set of technologies

(KPI.D5.NETFLEX.4)• I(1+2) ≥ I(1) + I(2)• I(3) very small

Demo #5: NETFLEX

Max

imum

Posi

tive

and

Neg

ativ

e D

evia

tions

Gai

n from

DLR

Loss from

GO

Ps

Initia

l

Posi

tive

and

Neg

ativ

e

Dev

iations

Gai

n from

PS

Ts

and

HV

DC

-link

s

Initial Situation

Existing Margin

Created Margin

µ = 5%

µ = 5%

µ = ±10%

µ = -2%

µ = 0%


22

Demo #6: FLEXGRIDOBJECTIVES:

Reduce the number of times when curtailment in wind production is needed due to overloads in transmission lines

Make use of the maximum transmission capacity without taking over high risks Provide the suitable information to allow real time decision by the operator in a

scenario with high level of wind production Better management of bottlenecks in transmission lines with wind power Maximize wind integration without compromising system integrity

BARRIERS: Technical:

• RTTR: To be able to forecast accurately the actual capacity and correlate it with wind production, implementation of novel technologies (OPPC)

• FACTS: Outstanding detail engineering works to ensure successful implementation, stability of the electrical signal and proper durability of the device

Regulatory (FACTS): Singular devices should be fitted into the Network planning and find a suitable economic framework


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IMPACT:

RTTR models and new algorithms in order to expand the network capacity to evacuate more wind energy and to improve security

Allow more flexible operation of the lines to improve the operation in N and N-X situations

New alternative device to use in the network planning Reduction of profit losses for wind generators Significant increase of network capacity and the integration of renewable

energy


Correlation between RTTR and Wind Generation (KPI.D6.1) Capacity Gain using RTTR and FACTS (KPI.D6.2Y; KPI.D6.4Y) Errors in the forecast of the Thermal Rating (KPI.D6.3) Cost and profitability of RTTR and FACTS (KPI.D6.5.1;

KPI.D6.5.1; KPI.D6.5.1; KPI.D6.6)

Demo #6: FLEXGRID


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WP #17: Smart licensing for off-shore interconnectorsOBJECTIVES:

To address the issues of smart licensing of subsea interconnectors Focus on submarine interconnectors with and without wind parks in the North

Sea and Baltic Sea To draw applicable conclusions about common licensing barriers To propose concrete regulatory measures to anticipate licensing problems

BARRIERS:

Regulatory: harmonization of the regulation for several countries

IMPACT

Reduction of cost and time for applying interconnector projects

KPI’s measurements

Reduction of cost and time of applying interconnector projects (KPI.WP17.1) Effectiveness of the smart licensing process (KPI.WP17.2)


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ANNEX 2. DETAILED KPIs LIST


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DEMO #1 KPI’s Code KPI Description Target Values Units

KPI.D1.1.1Active power availability for upwards and downwards secondary power reserve.

Time during the maximum/minimum active power regulation level is mantained

The maximum/minimum active power level is maintained at least 15 minutes Min

KPI.D1.1.2Dynamic response of the wind

energy AGC regulator.

Achievement of the dynamic requirements to participate in the Spanish secondary frequency control of the system, assessed every 4 seconds:

Constant time of the AGC regulator

Active power increase/reduction according the set point requested

AGC regulator dynamic response should be:An equivalent first order system with a 100 seconds time constant minimum (63% of

the set point value is achieved in less than that time).The total active power value produced will not be higher (power increase) or lower (power decrease) than the required secondary frequency control power, by each

sampling cycle (4 seconds).

MW / seconds

KPI.D1.1.3Availability of the offer for upwards and downwards secondary power reserve.

Percentage of achievement of the active power set point requested each 4 seconds 90% of the requests received during each hour achieved. % (MW)

KPI.D1.2.1Accuracy of the available

reactive power reported to the TSO dispatch centre.

Percentage of produced reactive power samples, during one hour, within a certain band around the maximum and minimum available reactive

power reported to the TSO.

75 % of the produced reactive power samples, measured during one hour, within ± 10 % around the reported available reactive power value. % (MVAr)

KPI.D1.2.2

Dynamic performance and accuracy of the reactive power

regulation according to the requirements for the

conventional generation.

Regulation band and settling time (ts) of the cluster reactive power regulator since a set point is received.

Steady-state error within ± 10 % around the set point value (if achievable) and settling time less than 5 minutes.

% (MVAr)

KPI.D1.2.3

Accuracy of the voltage set point achievement, or reactive power saturation if the set point

is not achievable

Percentage of cluster voltage samples, during one hour, within a certain band around the requested set point value.

75 % of the voltage samples, measured during one hour, within ± 2.5 kV around the requested set point.

If the set point is not achievable, the reactive power support from the cluster must equal to the saturation value.

% (kV) or

YES/NO if set point

not achievabl

e

KPI.D1.2.4

Dynamic performance of the voltage regulation according to

the requirements for the conventional generation

Settling time (ts) of the cluster voltage regulator since a set point is received in the cluster or since the perturbation occurs.

The settling time is defined as the time to reach the ± 5 % band around the set point.

Settling time less than 5 minutes seconds

KPI.D1.2.5Accuracy in the requested

voltage profile achievement.Voltages at the buses according to the issued set points, assessed in a

one hour period.

75% of the voltage samples during one hour, that the voltage is reached within ± 2.5 kV around the requested set point.

If the set point is not achievable, the reactive power support from the cluster equals to the saturation value.

% (kV) or

YES/NO if set point

not achievable

KPI.D1.2.6

Dynamic performance of the wide area voltage regulation

according to the requirements for the conventional

generation.

Settling time (ts) of the voltage network buses since the set point is received by CORE.

Settling time less than 5 minutes. Minutes


27

DEMO #2 KPI’s

Code KPI Description Target Values Units

Year 1 Year 2 Year 3

KPI.D2.1Units

aggregatedNumber of units integrated into VPP 10 total

40 total20 B2C

50 in cluster sites

>100 total (B2B)2,000 B2C

devices300 in cluster

sites

#

KPI.D2.2Technologies incorporated

Number of different technologies in VPP portfolio

4 6 8 #

KPI.D2.3Available active

capacity

Active capacity made available by VPP in Spot Market and for Ancillary Services

respectively15/ 7.5 50/ 25 100 / 50 MW

KPI.D2.4Energy

delivered

Actual energy delivered by VPP in Spot Market and for Ancillary Services

respectively150 / 30 1,000/ 200 10,000/ 2,000 MWh

KPI.D2.5Available reactive capacity

Reactive capacity made available by VPP to grid operator

0 3 MVAr 10 MVAr MWAr


28

DEMO #3 KPI’s


Initial Value Target value

KPI.D3.1DC breaker prototype

performance

C1: open stateC2: closed stateC3: peak fault reductionC4: fault current interruption delay

(0 0 1 x) (1 1 1.2 1) #

KPI.D3.2 HVDC grid cases Number and complexity of basic topologies

1 structure + 2 nodes

(PTPC)3 structure + >4 nodes #

KPI.D3.3Control and protection

design / HVDC technologyNumber of technologies and

convertersNone 2+2 #

KPI.D3.4Off-shore wind integration in the economic analysis

Number of geographical areas

Installed capacity

1

2

3

40

#

GW

KPI.D3.5

Power transmission through the HVDC grid

under contingency conditions

% of the power transmitted in normal conditions

0% (for a PTPC) >0% by steps of 10 % %


29


Initial Value Target value

KPI.D4.1Maximum power forecast error on wind farm level

The power forecast error is defined as the difference between the forecasted and produced power. The power is obtained from the

forecasted and measured wind speeds, using the wind turbine power curve.

1 p.u. 0.2 p.u. p.u.

KPI.D4.2Improved wind

turbine flexibility

The wind turbine manufacturer will improve the control to enable a more flexible operation during storm passages, i.e. operation also above 25 m/s, possibly at a reduced power. The new flexibility will be tested and certified on a wind turbine on land before it is implemented in all Horns Rev 2 wind

turbines. This new flexibility should ensure a reliability of maximum 1 sudden shutdown per 200 year of operating time in Denmark, measured in

February 2013.

2 turbinesAll turbines at Horns Rev 2 (Available for all

Siemens Variable Speed turbines)#

Year 1 Year 2 Year 3

KPI.D4.3Management

strategies for a storm

To indicate the performance of Horns Rev 2 wind farm during a storm the amount of imbalance will be used.

The imbalance will be defined as the difference between the scheduled wind power, which is handed in half an hour before the operational hour,

and the actual measured production

Base case will be established and the imbalance will be

calculated. In addition there will be an evaluation of how the improvement

should be calculated.

The imbalance will be reduced by 15%

The 15% improvement shall be

well documented, thereby enabling a better up scaling of

the results.

%

KPI.D4.4

Operating the HVDC

interconnection with respect to

real-time balancing

The KPI for operating the HVDC interconnection is measured through the time response of balance power supplied as a cause of storm event

through the HVDC connection

To determine which coordinated control system between the

HVDC and hydro power plant is most suitable for

the demo

The time response to supply regulating power through the

HVDC line is expected to be

tHVDC_storm<900s (<15 minutes).

The time response to supply regulating power through the HVDC line is then

expected to be optimized

seconds

KPI.D4.5Hydro power

control strategiesThe KPI for operating the hydro power is measured through the time

response of balance power supplied as a cause of storm event.

To determine which coordinated control system between the

HVDC and hydro power plant is most suitable for

the demo

The time response to supply regulating

power by the hydro power station as a

cause of storm incident is expected to be: thydro_storm<900s

(<15 minutes)

The time response to supply regulating

power by the hydro power station as a

cause of storm event is expected to be

optimized

seconds

DEMO #4 KPI’s


30


KPI.D5.DLR.1 Ampacity Ratios(Distribution of the) ratios between seasonal ampacity and measured

ampacityMu = 20%

P(<sa) < 0.05%

KPI.D5.DLR.2 Increase on wind power installation

the ratio of wind power that could be installed on site with or without DLR information

30.00% %

KPI.D5.DLR.3 Accuracy of the ampacity forecast

(distribution of the) error between the measured ampacity and forecasted ampacity

Mu = 5%P(>10%) < 0.05

MW

KPI.D5.Smart-PFC.1 Additional transverse power flow

(distribution of the) ratio between the additional transverse power flow through the system considering controllable devices and the transverse

flow withoutMu = 25.00% %

KPI.D5.Smart-PFC.2 Manageable uncertainty(distribution of the) ratio between the positive and negative deviations and

the wind uncertaintyMu = 120% %

KPI.D5.Smart-PFC.3 Number of maneuvers distribution of the change in settings -3< x <3 # taps

KPI.D5.Smart-PFC.4 Stability of the settingsdistribution of the ratio between the change in positive and negative

deviations and the wind uncertaintyMu = 20% %

KPI.D5.WAMS.1 Damping factor (distribution of the) damping factor of the dominant modes Sigma = 5.00% %

KPI.D5.WMAS.2 Robustness of GOPs Robustness of GOPs < 3/yFrequency of revision

KPI.D5.NETFLEX.1 Increase of wind integration

distribution of the current capability in accommodating additional wind generation

Mu = 0% %

KPI.D5.NETFLEX.2 Gain from DLR distribution of the gain from DLRs Mu = 5% %

KPI.D5.NETFLEX.3 Gain from PFC distribution of the gain from Smart-PFC Mu = 5% %

KPI.D5.NETFLEX.4 Loss from GOPs distribution of the loss from GOPs Mu = -2% %

KPI.D5.NETFLEX.4 Combined Gain distribution of the combined gain of the technologies Mu = 10% %


31


KPI.D6.1 Correlation between RTTR and Wind Generation

KPI.D6.1will be 1 if a significant and usable correlation between the Real Time Thermal Rating of one or several transmission lines properly chosen and Wind Generation level in the area is found. If a good enough correlation is found, a new concept that could be called Statistical RTTR (SRTTR) will be defined and used in

the project.

KPI.D6.1will be 0 if the correlation between the above mentioned variables is not significant enough

KPI.D6.1 = 1 #

KPI.D6.2YLine Capacity Gain obtained

by using RTTR or SRTTR (MVA or %)

Hourly ValuesFor each monitored line, KPI2 at hour h is defined as: KPI.D6.2.1h=C1–C2 (MVA)KPI.D6.2.2h = (C1 – C2) / C2 (%)Where C1 is the line capacity at hour h using RTTR or SRTTR and C2 is the seasonal line rating.

Yearly ValueFor each monitored line, the yearly value of KPI2 is defined as the addition of KPI2h for all hours h of the year with positive gain divided by 8760 hours (average gain)KPI.D6.2Y = KPI.D6.2.2h / 8760 (for all h with KPI.D6.2.2h) > 0

KPI.D6.2Y >= 115% %

KPI.D6.3 Errors in the forecast of the Thermal Rating

For each monitored line, the error in the forecast of the line capacity at hour h is: Error h = (CA – CB) / CB (%)Where CA is the line capacity at time t forecasted 2 hours ahead (2HATR) and CB is the line rating (or line capacity) at time t evaluated in real time (RTTR).

KPI.D6.3 is defined as the standard deviation of all the errors obtained in one year.

KPI.D6.3 <= 35% %

KPI.D6.4Y Capacity Gain due to the use of the Device

Capacity Gain due to the use of the Device: energy evacuation capacity (in MW or in %) KPI.D6.4h. Hourly values A- With the Device installed: The result will be denominated CwiB- Without the Device: The result will be denominated CwoThe calculation of Cwi and Cwo will be done always using the same criteria, which will correspond to the n and n-1 security criteria KPI.D6.4 is defined as: KPI.D6.4.1h = Cwi – Cwo (MW); KPI.D6.4.2h = (Cwi – Cwo) / Cwo (%)

KPI.D6.4Y. Yearly value: The yearly value of this KPI is defined as the addition of KPI.D6.4.2h for all hours h of the year or it can be estimated using a reduced number of hourly values and appropriate weight: KPI.D6.4Y = KPI.D6.4.2h / 8760

KPI.D6.4Y >= 110% %

KPI.D6.5.1 Cost of the re-dispatch of conventional generation

If in one particular system state there is a need to increase the energy evacuation capacity from one zone of the grid but the Device is not available, has not been installed in a location that would allow increasing that capacity, the System Operator will use some of the following alternative solutions:1.-Re-dispatch of the conventional generation 2.-Topological changes 3.-Renewable production curtailmentThen, three KPIs are defined for each of these alternative measures:- KPI.D6.5.1 = Cost of the re-dispatch of conventional generation (€)- KPI.D6.5.2 = Number of realized switching operations (n)- KPI.D6.5.3 = MWh of renewable generation curtailed (MWh)Despite this KPI’s are not directly related with the demo performance they will provide an essential input for WP 15 and 16 subsequent assessments so they have been kept but assuming that no target values can be ex ante considered. KPI.D6.5Y. Annual values: The yearly values of the indicators KPI.D6.5.1, KPI.D6.5.2 y KPI.D6.5.3 are obtained respectively by the addition of their 8760 hourly values of the year. Alternatively, they can be estimated using a reduced number of values of the hourly values and appropriate weights

€

KPI.D6.5.2 Number of realized switching operations

#

KPI.D6.5.3Renewable generation

curtailed MWh

Target Value

KPI.D6.6 Cost and profitability

KPI6 is the annual cost of the Device. The total cost of the installation will be estimated and it will be annualised using appropriate accounting techniques. The O&M costs will be added.

Besides the operational alternative solutions, there will be alternative planning solutions, potentially more efficient in the long run. The annual cost of the potential planning alternative solutions that might exist (for example the repowering of a transmission line or the construction of a new one) will be evaluated and the profitability of the proposed system will then be estimated by comparing both costs although it has to be taken into account that being very different

solutions, a complete comparison cannot be made in terms of costs only.

30% of Cost Reduction %


32

WP #17 KPI’s


KPI.WP17.1

Improvements in future interconnector projects

-efficiency

Interconnector projects applying improved measures (best practices) would have got a 20% reduction of

costs and time of licensing

20% of reduction in cost and time %

KPI.WP17.2

Improvements in future interconnector projects-

effectiveness

Improvements (best practices) are targeting 30% of the typical measures (number of permits and consents by

competent authorities) for new licensing offshore interconnectors.

30% of measures %


33

WP #15 KPI’s

KPI.15.TF1.1Cost savings in the Spanish system where wind power generators are able to control their active power and to provide frequency control: [Euro/year] for installed wind generation capacity in 2013 and prospective analysis for future scenarios up to 2020.

Euro/year

KPI.15.TF1.2Additional economic benefit, compared with the default case, for a wind power producer participating in the Spanish secondary reserve market: [Euro/year/Installed MW]

Euro/year/Installed MW

KPI.15.TF1.3Energy losses avoided thanks to the voltage control in wind farms (and clusters): [GWh/year] for installed wind generation capacity in 2013 and prospective analysis for future scenarios up to 2020.

GWh/year

KPI.15.TF1.4Economic value of the losses avoided thanks to the voltage control in wind farms (and clusters): [Euro/year] for installed wind generation capacity in 2013 and prospective analysis for future scenarios up to 2020.

Euro/year

KPI.15.TF1.5CO2 emissions avoided in the Spanish system with respect the default case due to the new services provided by wind power generators: [tonne CO2/year] for installed wind generation capacity in 2013 and prospective analysis for future scenarios up to 2020

tonne CO2/year

KPI.15.TF1.6Additional wind energy that could be generated in the Spanish system thanks to the new capabilities tested in Demo 1. [GWh/year]

GWh/year

KPI.15.TF1.7Marginal (operating) costs for providing and utilizing the services, as defined in the Demo 2 DERINT KPIs, from the VPP within each demand/technical/regulation/market scenario in Denmark

[Euro]/ [MW], [MWh], [MVA], [MVAr], [tonne] range

KPI.15.TF1.8Existing approaches for providing the services, as defined in the Demo 2 DERINT KPIs, in Denmark / What are the marginal (operating) costs within each demand/technical/regulation/market scenario for providing these services? For each existing approach the following measurements/calculations will be made

[Euro]/ [MW], [MWh], [MVA], [MVAr], [tonne] range

KPI.15.TF1.9Marginal (operating) cost benefit of the VPP when providing the services within each demand/technical/regulation/market scenario in comparison with the existing approaches in Denmark This will be measured in [€] and [tons of CO2 ]

reduction/increase for each of the services provided.

[€] and [tons of CO2]

KPIs for Task-force 1


34

KPI.15.TF3.1Gain in transfer capacities (between the countries in the Central Western Europe area) with network flexibility with respect to the reference case [%/border] and [MW/border]

[%/border] and [MW/border]

KPI.15.TF3.2Expected increase in net transfer capacities (between the countries in the Central Western Europe area) with network flexibility with respect to the reference case [%/border] and [MW/border]

[%/border] & [MW/border]

KPI.15.TF3.3Extra amount of wind generation that network operation flexibility allows to be transmitted [GWh/year] and reduction of wind curtailment [%] with respect the default case.

GWh/year] and [%]

KPI.15.TF3.4 Economic value (benefits minus costs) at a central European level of increasing the network operation flexibility, [Euro/year] Euro/year

KPI.15.TF3.5Impact on the market shares of the conventional generation technologies in the central European countries [GWh/year/(technology & country)], and in the cross-border interchanges [GWh/year/border]

GWh/year/border

KPI.15.TF3.6 CO2 emissions that could be avoided at a central European level in 2020 due to this network operation flexibility, [tonne CO2/year]. tonne CO2/year

KPI.15.TF3.7Potential wind power integration increase in the Spanish system obtained by identifying the latent capacity of the network using Real Time Thermal Rating (RTTR), and by operating the line at maximum capacity by means of Overload Line Controllers (OLC), [GWh/year]

GWh/year

KPI.15.TF3.8Economic impact (benefits and costs) of scaling-up the RTTR and the OLC in the Spanish power system (the deferral of network investment and lines re-powering, renewable generation increase as the system can accommodate larger amounts of wind power, etc.), [Euro/year]

Euro/year

KPI.15.TF2.1 Amount of offshore renewable energy that could be securely transmitted by the new HVDC network, [GWh/year] GWh/year

KPI.15.TF2.2Ratio between the expected benefit to the system for integrating this energy from of offshore renewable power in the system, and the expected incurred cost to deploy the new components, [Euro / Euro].

Euro / Euro

KPI.15.TF2.3 CO2 emissions that could be avoided in Europe 2020 due to this offshore renewable power, [tonne CO2/year]. tonne CO2/year

KPI.15.TF2.4 Reduced reserve requirement to operate the Danish 2020 and 2030 power system securely in storm situations [MW. MW



WP #15 KPI’s


35

WP #16 KPI’s

Code KPI / Description Units

- KPI.16.P1 Potential reduction in operational costs in the European power system by 2020 utilising the solutions demonstrated in TWENTIES

EUR/year

- KPI.16.P2 Potential reduction in investment costs in the European power system by 2020 utilising the solutions demonstrated in TWENTIES

EUR/year

- KPI.16.P3 Potential reduction of CO2 in the European power system by 2020 utilising the solutions demonstrated in TWENTIES

tonnes CO2/year

- KPI.16.P4 Reduction in incremental cost of wind power by using all the techniques demonstrated in TWENTIES

EUR / MW

Project level KPIs


36

WP #16 KPI’s KPIs for Task-force 1

KPI.16.TF1.1 Cost savings in the German system where wind power generators are able to control their active power and to provide frequency control: [Euro/year] for installed wind generation capacity in 2011 and future scenarios.

EUR/year

KPI.16.TF1.2

Quantified estimation of the impact on the power systems where wind power generators are able to control their active power and to provide frequency control: - [MW] applicable reserve allocation on wind generation over the France-Benelux-Germany area,- [Euro/year] cost reduction in France for future scenarios up to 2020

[MW], [EUR/year]

KPI.16.TF1.3 Cost savings in the German system thanks to the voltage control in wind farms (and clusters): [€/year] for installed wind generation capacity in 2011 and for future scenarios

EUR/year

KPI.16.TF1.4 Marginal (operating) costs for providing and utilizing the services, as defined in the Demo 2 DERINT KPIs, from the VPP within each demand/technical/regulation/market scenario in Germany

[EUR]/ [MW], [MWh], [MVA], [MVAr], [tonne]

range

KPI.16.TF1.5Existing approaches for providing the services, as defined in the Demo 2 DERINT KPIs, in Germany / What are the marginal (operating) costs within each demand/technical/regulation/market scenario for providing these services? For each existing approach the following measurements/calculations will be made

[EUR]/ [MW], [MWh], [MVA], [MVAr], [tonne]

range

KPI.16.TF1.6:Marginal (operating) cost benefit of the VPP when providing the services within each demand/technical/regulation/market scenario in comparison with the existing approaches in Germany. This will be measured in [€] and [tons of CO2 ] reduction/increase for each of the services provided.

[EUR] and [tonnes of CO2 ]

KPI.16.TF1.7 Economic impact of a applying the VPP concept [Euro/year] in 2011 and for future scenarios. EUR/year

KPI.16.TF1.8 CO2 emissions avoided in the German system due to the new services provided by wind power generators and virtual power plants: [tonne CO2/year] for installed capacity in 2011 and for future scenarios


37


WP #16 KPI’s

KPI.16.TF2.1 Reduction in ”worst case” forecast errors in the European power system by 2020 and 2030 with new storm control compared to old storm control [MW].

MW

KPI.16.TF2.2 Reduction in the need for spinning reserves in the European power system by 2020 and 2030 with new storm control compared to old storm control [MW * hours/year].

MWxhours/year

KPI.16.TF2.3 Increased wind power production with the new storm control compared to the old storm control.

KPI.16.TF2.4 Potential for increased hydro power generation capacity in the Nordic synchronous system by 2020 and 2030[MW].

MW

KPI.16.TF2.5Economic benefit in the European power system by 2020, utilizing the potential contribution of added HVDC connections and added Nordic hydro capacity, to the large-scale integration of wind power in northern Europe [Euro/year]

EUR/year

KPI.16.TF2.6 Reduction in the needed transmission capacity if an offshore grid combines wind farm grid connections with area interconnectors, under the condition of optimal use of Nordic hydro [km×MW]

km×MW

KPI.16.TF2.7 Potential alpine hydro capacity with 2020 time horizon [MW]. MW

KPI.16.TF2.8Economic benefit in the European power system by 2020 utilising the potential contribution of the European grid and alpine hydro capacity to the large-scale integration of wind power in northern Europe [Euro/year] (T16.2.2).

EUR/year

KPI.16.TF2.9CO2 emission benefit in the European power system by 2020 utilising the potential contribution of the European grid and alpine hydro capacity to the large-scale integration of wind powerin northern Europe [tonnes CO2/year] (T16.2.2).

tonnes CO2/year

KPI.16.TF2.10Reduction in operational costs in the European power system by 2020 and 2030 assuming new storm control and recommended grid reinforcement to utilise hydro in Nordic system and the Alps, compared to old storm control and only already planned grid development [Euro/year]

EUR/year

KPI.16.TF2.11Reduction in CO2 emissions in the European power system by 2020 and 2030 assuming new storm control and recommended grid reinforcements to utilise hydro in Nordic system and the Alps, compared to old storm control and only already planned grid development [tonne CO2/year].

tonnes CO2/year


38


WP #16 KPI’s

KPI.16.TF3.1Potential reduction in operational costs at the European level of increasing the network operation flexibility by applying demo 5 and demo 6 technologies to optimise the capacity of the existing transmission system [Euro/year]

EUR/year

KPI.16.TF3.2 Investment costs at the European level of increasing the network operation flexibility by applying demo 5 and demo 6 technologies to optimise the capacity of the existing transmission system

EUR

KPI.16.TF3.3Potential reduction of CO2 emission at the European level of increasing the network operation flexibility by applying demo 5 and demo 6 technologies to optimise the capacity of the existing transmission system

tonnes CO2/year


Documents

Measuring the impact of innovation