KPI documentation

KPIs (Key Performance Indicators) are annual indicators. They can be used to analyse a context and to compare several contexts from the same study. NB: Most of them need computation results and will show no data until results are available.

The KPI view can be opened by clicking on this icon on the bottom-right of the screen :

context action view

Click here to access KPI view

A large number of KPIs can also be displayed directly on the map with a value by country or by interconnection. See the decorators documentation page.

Average production at peak demand (Wh)

Indexed by
  • scope
  • delivery point
  • energy
  • test case
  • technology

Average peak Production per delivery point and technology for a given energy

\[\small production_{dp, techno, energy} = \sum_{t \in peak period} production_{t, dp, techno, energy}\]

Border exchange surplus (euro)

Indexed by
  • scope
  • delivery point
  • main energy
  • test case
  • technology
  • asset name

This KPI will return No data on power contexts and gas contexts without import or export contracts as it only applies to gas import and export contracts.

A border exchange surplus is defined as the surplus of model outsiders, that is all actors which are not explicitly represented but are aggregated into flexible import/export assets. Flexible imports and exports are optimized during simulations along with the other supplies and consumptions: imports (respectively exports) level at each time step depend on the import costs (respectively export earnings) on one hand and the endogenous marginal supply cost on the other hand. The border exchange surplus represents the benefit that an outside actor receives for selling its product on the modeled network (for flexible imports) or from buying it from the network (for flexible exports).

The KPI thus computes the following:

For a flexible import asset:

\[\small borderExchangeSurplus_{dp, energy, asset} = \small \sum_t (prod_t^{dp, energy, techno}.(margCost_t^{dp, energy} - importPrice^{dp, energy, techno})\]

For a flexible export asset:

\[\small borderExchangeSurplus_{dp, energy, asset} = \small \sum_t (prod_t^{dp, energy, techno}.(exportPrice^{dp, energy, techno} - margCost_t^{dp, energy}\]

Capacity factor (%)

Indexed by
  • scope
  • delivery point
  • energy (electricity or gas)
  • test case
  • technology

Return the capacity factor, which is the mean annual usage of a given technology relative to its installed capacity in a given delivery point:

\[\small capacityFactor_{technology, dp} = \frac{mean_t(production_t^{technology, dp})}{installedCapacity^{technology, dp}}\]

CO2 emissions (t)

Indexed by
  • scope
  • delivery point
  • test case
  • technology
  • asset name

Return the annual volumes of CO2 emissions (tons) associated to the energy (electricity + reserve) production relative to a given technology

It is calculated as the sum over the hours, over the assets and over the energies, of the CO2 emissions/MWh per asset multiplied by the volume of produced energy:

\[\small CO2Emissions_{dp, techno, energy} = \sum_{t,assets} CO2perMWh_{t, dp, techno, energy}*producedEnergy_{t, dp, techno, energy}\]

CO2 emissions due the reserve activation (balancing) are not calculated by this KPI.

Congestion hours (h)

Indexed by
  • scope
  • energy (electricity or gas)
  • test case
  • transmission

The number of congestion hours corresponds to the number of hours over the year during which an interconnection is saturated with respect to its available capacity. An interconnection is considered saturated when it reaches 99.99% of its available capacity.

Congestion Rent (euro)

Indexed by
  • energy
  • transmission line
  • test case

The congestion rent is the benefit made when transferring energy from a zone to another. The congestion rent of a transmission line from zone \(Z_1\) to zone \(Z_2\) is defined as the cost saved from using a production from another zone to meet a local demand:

\[\small congestionRent_{Z_1 -> Z_2} = \sum_t (marginalCost_t^{Z_2} - marginalCost_t^{Z_1}).exchangedPower_t^{Z_1 -> Z_2}\]

It is called congestion rent, because at time steps when a line is not saturated, marginal costs in the two extremities of the line converge and the surplus is 0. In other words, the rent is only derived from congestions.

Since the result is given for each transmission, and that transmissions are directional (e.g. transmision \(Z_1 -> Z_2\) is different from transmision \(Z_2 -> Z_1\)), one must sum the results given for each direction in order to have the total congestion rent associated to a transmission between two zones:

\[\small congestionRent_{Z_1, Z_2} = congestionRent_{Z_1 -> Z_2} + congestionRent_{Z_2 -> Z_1}\]

Consumer Surplus (euro)

Indexed by
  • scope
  • delivery point
  • energy
  • test case

A consumer surplus occurs when the consumer is willing to pay more for a given product than the current market price.

In classic models, consumers’ benefit equals the value of loss of load (VoLL), leading to the following formulation:

\[\small consumerSurplus_{dp, energy} = \sum_t (VoLL^{dp, energy} - marginalCost_t^{dp, energy}) \times demand_t^{dp, energy}\]

For a given demand, the term \(\small \sum_t (VoLL.demand_t)\) is a constant, which does not affect comparisons between two scenarios (that have the same demand). As the VoLL is arbitrary, those comparisons are usually more interesting than absolute values. The consumer surplus indicator therefore uses the following formula:

\[\small consumerSurplus_{dp, energy} = \sum_t - marginalCost_t^{dp, energy} \times demand_t^{dp, energy}\]

NB : The KPI should thus only be used in comparison mode (between scenarios) in order to have a proper meaning.

In the formula above, \(\small demand\) is the realized demand. For non-flexible demand assets, the realized demand equals the raw demand. For flexible demand assets, which may also produce energy, we have :

\[\small realizedDemand_t^a = consumption_t^a - production_t^a \ ,\forall \ demand \ asset \ a\]

In case of loss of load, part of this realized demand was not really matched. But it is included in the formula to substract to the consumer surplus the cost of not matching this demand.

So when properly including flexible demands and the costs of load management, the consumer surplus becomes :

\[\small consumerSurplus_{dp, energy} = \sum_{a \ demand \ asset \ with [dpCons, energyCons]==[dp, energy]} production_t^a \times marginalCost_t^{dpProd, energyProd} - consumption_t^a \times marginalCost_t^{dpCons, energyCons} - assetCost_t^a\]

Consumption peak (Wh)

Indexed by
  • scope
  • delivery point
  • energy (including fuels)
  • technology
  • test case

Return the peak of energy demand for a given technology or contract. We here consider the flexible demand after optimization (using the consumption of the corresponding assets). This demand can be negative for asset “Voluntary load curtailment”.

Consumption (Wh)

Indexed by
  • scope
  • delivery point
  • energy (including fuels)
  • test case
  • technology
  • asset name

Return the annual volumes of energy demand for a given technology or contract. We here consider the flexible demand after optimization (using the consumption of the corresponding assets).

Curtailment cost (euro)

Indexed by
  • scope
  • delivery point
  • energy (electricity or gas)
  • test case
  • technology
  • asset name

The curtailement cost represents the cost, for the system, associated to curtailing or not using RES energy production. The KPI thus computes the total cost associated to the sum of annual volumes of Renewable Energy Sources (RES) production that is curtailed and annual volumes of RES production that is not used, per technology.

This cost is calculated by multplying the volume of RES production that is curtailed by the value for the system associated to this RES production, which, in the case of RES assets, is the difference between the incentives and the production cost.

The formula used is thus the following:

\[\small curtailmentCost_{RES technology} = \small \sum_t installedCapa_{t}^{RES technology}*availability_{t}^{RES technology} - \small \sum_t prod_{t}^{RES technology})* \small RESproductionValue^{RES technology}\]

Where:

\[\small RESproductionValue_{RES technology} = incentive_{RES technology} - productionCost_{RES technology}\]

Results are given for electricity and gas production

Curtailment (Wh)

Indexed by
  • scope
  • delivery point
  • energy
  • test case
  • technology
  • asset name

Return the sum of annual volumes of production that is curtailed and annual volumes of production that is not used, per technology. It is calculated as the difference between the possible RES production (i.e. the installed capacity times the availability) and the actual RES production, per delivery point, per technology:

\[\small curtailment_{technology} = \sum_{RES asset} (\sum_t installedCapacity_{t, asset}^{technology}*availability_{t, asset}^{RES asset} - \sum_t production_{t, asset}^{technology})\]

Demand peak (W)

Indexed by
  • scope
  • delivery point
  • test case
  • energy
  • demand type

Return the maximum value of the demand over the year, for a given scope, delivery point, energy, test case and the demand asset type. NB : In case of flexible demand, we here consider realized demand (after optimization)

Demand (Wh)

Indexed by
  • scope
  • delivery point
  • test case
  • energy
  • demand type
  • asset name

Return the annual volumes of energy demand for a given technology or contract. We here consider the flexible demand after optimization (using the consumption of the corresponding assets). This demand can be negative for asset “Voluntary load curtailment”. For asset “Electric Vehicles” with behavior Vehicule to grid, the demand is the difference between consumption and generation of the vehicles.

Dispatchable power generation capacity (W)

Indexed by
  • scope
  • delivery point
  • energy (electricity and gas)
  • test case
  • technology

Return the annual mean dispatchable capacity per delivery point and technology, i.e. the mean capacity of assets that can be switched on at will (i.e. the mean capacity of assets other than “must-run” assets). The mean dispatchable capacity is calculated as the sum over the dispatchable assets, of the installed capacity times the mean availability:

\[\small meanDispatchCapa_{dp} = \sum_{asset} installedCapa_{asset}^{dp}*mean_t(availability_{t, asset})\]

Where \(\small meanDispatchCapa_{dp}\) is the mean dispatchable capacity for a given dp and \(\small installedCapa_{asset}^{dp}\) is the installed capacity of a dispatchable asset.

Expected Unserved Demand (%)

Indexed by
  • scope
  • delivery point
  • energy
  • test case

The Expected Unserved Demand is a metric used to measure security of supply. This is the amount of electricity, gas or reserve demand that is expected not to be met by the production means during the year. It is calculated as the Loss Of Load volumes (LOL) expressed relatively to the corresponding annual demand volumes, in percentage. It can be calculated for each energy independently:

\[\small EENS_{dp, energy} = \frac {LOL_{dp, energy}}{demand_{dp, energy}} (\%)\]

See the ‘Loss of load’ KPI for further documentation about the loss of load.

Expected Unserved Energy (Wh)

Indexed by
  • scope
  • delivery point
  • energy
  • test case

The Expected Unserved Energy is the annual volume of energy (including reserves) that is not served, i.e. the annual volume of a given energy that is needed but is not delivered due to a lack of generation.

Exports and imports (Wh)

Indexed by
  • scope
  • delivery point
  • energy (electricity or gas)
  • test case
  • technology
  • asset name
  • data type (exports or imports)

Return the annual volumes of energy imported and exported by a given delivery point.

Flow (Wh)

Indexed by
  • scope
  • energy (electricity or gas)
  • test case
  • transmission

Return the annual volumes flowing through a considered transmission line (monodirectionnal)

Import capacity (W)

Indexed by
  • scope
  • delivery point
  • energy (electricity or gas)
  • test case

This KPI will return No data on power contexts and gas contexts without import or export contracts as it only applies to gas import and export contracts.

Return the import capacity of a given delivery point for a given energy and a given test case.

Installed capacities (W)

Indexed by
  • scope
  • delivery point
  • energy (electricity or gas)
  • test case
  • technology
  • asset name

Return the installed capacity of a given technology, for a given delivery point and a given test case.

Investment Analysis (euro)

Indexed by
  • scope
  • energy
  • test case
  • production asset

The Investment Analysis calculates the economic profitability of a given production asset in a given delivery point, defined as the difference between the producer surplus and the investment costs for this specific asset.

The producer surplus is calculated as the benefit the producer receives for selling its product on the market. The investment costs is the sum of the Capital Expenditure (CAPEX) and Fixed Operating Cost (FOC):

\[\small investmentAnalysis_{asset} = \small \sum_t producerSurplus_{t, asset} - investmentCosts_{asset}\]

with:

\[\small investmentCosts_{asset} = \small \sum_t installedCapacity^{asset}*(CAPEX^{asset} + FOC^{asset})\]

and:

\[\small producerSurplus_{asset} = \small \sum_t (production_{t, asset}.marginalCost_{t, dp, energy}) - productionCost_{asset}\]

Investment costs (euro)

Indexed by
  • scope
  • delivery point
  • energy (electricity or gas)
  • test case
  • technology
  • asset name

Return the investment costs per technology, i.e. the cost associated to building a given technology. It is equal to the technology installed capacity times the sum of the CAPEX and Fixed Operating Cost (FOC):

\[\small investmentCosts_{technology, energy} = installedCapacity^{technology, energy}*(CAPEX^{technology, energy} + FOC^{technology, energy})\]

Load payment (euro)

Indexed by
  • scope
  • delivery point
  • energy
  • test case

The load payment corresponds to the price consumers must pay for the energy consumed over the year, in a given delivery point. It is calculated as the sum, over the year, of the hourly marginal cost of the delivery point for the corresponding energy times the hourly demand for this energy in the delivery point.

For a given energy, the load payment differs depending on the scope, the load payment is computed as follows:

\[\small loadPayment_{dp, energy} = \sum_t marginalCost_t^{dp, energy}*demand_t^{dp, energy}\]

Loss of load cost (euro)

Indexed by
  • scope
  • delivery point
  • energy
  • test case

The loss of load cost is the cost associated to loss of load (LoL) in an energy system. In classic models, this cost is directly indexed on the value of loss of load (VoLL), which is the amount customers would be willing to pay in order to avoid a disruption in their electricity service. This leads to the following formulation:

\[\small LoLcost_{dp} = \sum_t VoLL^{dp}.LoL_t^{dp}\]

Loss Of Load (h)

Indexed by
  • scope
  • delivery point
  • energy
  • test case

Loss Of Load (LOL), also called Loss Of Load Expectation (LOLE) represents the number of hours per year in which there is a situation of loss of load (i.e. that supply does not meet demand). It takes into account the fact that if the LOL is “too large”, then additional capacities (power plants or batteries) will be built. See KPI “Loss of load” for more details.

\[\small LOL_{zone} = \sum_t \mathbf 1_{\{supply_t < demand_t\}}\]

Marginal costs statistics (euro/MWh)

Indexed by
  • scope
  • delivery point
  • energy (electricity, reserve or gas)
  • test case
  • statistics (min, max, average or demand average)

Computes the minimum, maximum and average value of the marginal cost over the year for a given delivery point and energy.

The KPI also computes the demand weighted (demand average) marginal cost:

\[\small demandWeightedMarginalCost_{zone, energy} = \frac{\sum_t marginalCost_t^{zone, energy}.demand_t^{zone, energy}}{\sum_t demand_t^{zone, energy}} \]

The marginal cost of a given energy and a given delivery point is the variable cost of the production unit that was last called (after the costs of the different technologies were ordered in increasing order) to meet the energy demand in the delivery point.

Marginal costs (euro/MWh)

Indexed by:
  • scope
  • delivery point
  • energy (electricity, reserve or gas)
  • test case

The marginal cost of a given energy and a given delivery point is the variable cost of the production unit that was last called (after the costs of the different technologies were ordered in increasing order) to meet the energy demand in the delivery point.

This KPI computes the average value of the marginal cost over the year for a given delivery point and energy. Additional statistics can be calculated in Marginal costs statistics KPI

Minimum unused production capacity (Wh)

Indexed by
  • scope
  • delivery point
  • energy
  • test case
  • technology

Minimal margin between available capacity and energy production for a given delivery point, for a given energy and a given technology:

\[\small minMargin_{dp, techno, energy} = \min_t availability_{t, dp, techno, energy} imes pmax_{t, dp, techno, energy} - production_{t, dp, techno, energy}\]

Net demand peak (W)

Indexed by
  • scope
  • zone
  • energy (electricity, gas)
  • test case

Return the maximum value over the year of the net demand, which is defined as the difference between the realized energy demand and the available capacity of flexible renewable energy at a given delivery point:

\[\small netDemandPeak_{testCase, zone} = max_t(demand_t^{testCase, zone} - availableRenewableCapacity_t^{testCase, zone})\]

Net Production (Wh)

Indexed by
  • scope
  • delivery point
  • energy
  • test case
  • technology
  • asset name

Return the total energy production in a given delivery point, a given energy and a given technology minus the consumption on the same period.

\[\small production_{dp, energy, techno} = \sum_t (production_{t, dp, energy, techno} - consumption_{t, dp, energy, techno})\]

Producer surplus (euro)

Indexed by
  • scope
  • reference delivery point
  • test case
  • technology
  • asset name

A producer surplus occurs when the producer is paid more for a given product than the minimum amount it is willing to pay for its production. It represents the benefit the producer receives for selling its product on the market.

The KPI thus computes the difference between the amount an energy producer receives and the minimum amount the producer is paying for the energy it produces, i.e. the production times the marginal cost (the amount the producer receives) minus the production cost and the marginal consumption cost if there is one (the amount the producer has paying). And to compute the producer revenue, we need to consider all its “primary energy productions” and consumptions (electricity, reserve energies,…), but not the secondary ones (fuel, co2,…).

For each scope \(s\), test case \(c\), delivery points \(d\) and technology \(T\) :

\[\begin{split}\small productionSurplusKpi_{s, c, d, T} = \small \sum_{asset\ a\ |\ refDp(a)==d\ \\and\ technology(a)==T} ( \sum_{time\ step\ t} (\sum_{primary\ produced\ \\energy\ e\ of\ a} production_{s, c, t, a, e} \times margCost_{s, c, t, e, prodDp(a,e)} - \sum_{primary\ consumed\ \\energy\ e'\ of\ a} consumption_{s, c, t, a, e'} \times margCost_{s, c, t, e', consumptionDp(a,e')}) \\ \small - productionCost_{s, c, a})\end{split}\]

Where \(\small productionCost_{s, c, a}\) is the total production cost of the asset for given scope and test case (cf the “Production costs” KPI documentation).

Production costs (euro)

Indexed by
  • scope
  • reference delivery point
  • test case
  • technology
  • asset name

The KPI computes the total production costs in a given delivery point and a given technology.

So for each scope \(s\), test case \(c\), asset \(a\) and time step \(t\), asset costs fall in the various categories below :

\[\small variableCost_{s, c, t, a} = production_{s, c, t, a, mainEnergy} \times (productionCost_{s, c, t, a} \times \delta_{notActive(BH\_CLUSTER)} + variableCost_{s, c, t, a} \times \delta_{isActive(BH\_CLUSTER)})\]
\[\small runningCapacityCost_{s, c, t, a} = runningCapacityCost_{s, c, t, a} \times runningBound_{s, c, t, a}\]
\[\small startUpCost_{s, c, t, a} = startUpIndexedCost_{s, c, t, a} \times startingCapacity_{s, c, t, a}\]
\[\small fuelCost_{s, c, t, a} = \small \sum_{fuelEnergy} fuelConsumption_{s, c, t, a, fuelEnergy} \times marginalCost_{s, c, t, fuelEnergy, fuelDP(fuelEnergy, a)}\]

where \(\small fuelDP(fuelEnergy, a)\) is the delivery point where the asset \(a\) consumes the fuel energy \(\small fuelEnergy\)

\[\small consumptionCost_{s, c, t, a} = consumption_{s, c, t, a, mainEnergy} \times consumptionIndexedCost_{s, c, t, a}\]
\[\small co2EmissionsCost_{s, c, t, a} = co2Emissions_{s, c, t, a, co2Energy} \times marginalCost_{s, c, t, co2Energy, co2DP(co2Energy,a)}\]

where \(\small co2DP(co2Energy,a)\) is the delivery point where the asset \(a\) produces the fuel energy \(\small co2Energy\)

\[\small storageCost_{s, c, t, a} = storageLevel_{s, c, t, a} \times storageLevelIndexedCost_{s, c, t, a}\]

For each reserve energy :

\[\small reserveCost_{s, c, t, a, reserveEnergy} = production_{s, c, t, a, reserveEnergy, RUNNING} \times reserveProdCost_{s, c, t, a, reserveEnergy}\]
\[\small reserveNotRunningCost_{s, c, t, a} = production_{s, c, t, a, mfrrUp, NOT\_RUNNING} \times notRunningReserveCost_{s, c, t, a, mfrrUp}\]

So we can define the total production cost of an asset as :

\[\small productionCost_{s, c, a} = \sum_t variableCost_{s, c, t, a} + runningCapacityCost_{s, c, t, a} + startUpCost_{s, c, t, a} + fuelCost_{s, c, t, a} + consumptionCost_{s, c, t, a} + co2EmissionsCost_{s, c, t, a} + storageCost_{s, c, t, a}\]
\[\small + \sum_{reserveEnergy} reserveCost_{s, c, t, a, reserveEnergy} + reserveNotRunningCost_{s, c, t, a}\]

NB : All costs of a given asset must always be considered together, since some costs are shared. For instance, the runningCapacityCost is linked to the electricity production as much as to the reserve production. Therefore, indexing these costs by energy would be misleading. It is also true for the indexing by delivery points : an asset could produce or consume at several delivery points, but divide its cost on several DP would be misleading and wrong. For practicality purposes, we just index this KPI by reference DP, each asset having a single reference DP (classically the DP where the asset produces its main energy production).

So for each scope \(s\), test case \(c\), delivery points \(d\) and technology \(T\), we have :

\[\begin{split}\small productionCostKpi_{s, c, d, T} = \sum_{time\ step\ t} \sum_{asset\ a\ |\ refDp(a)==d\ \\and\ technology(a)==T} productionCost_{s, c, a}\end{split}\]

Production revenue (euro)

Indexed by
  • scope
  • delivery point
  • energy
  • test case
  • technology
  • asset name

Return the annual revenue received by a given technology in a given delivery point, for the energy it produces.

It is calculated as the sum over the year of the production of the technology times the marginal cost within the considered delivery point:

\[\small revenue_{technology, dp} = \sum_t production_t^{technology, dp}.marginalCost_t^{dp}\]

Production (Wh)

Indexed by
  • scope
  • delivery point
  • energy
  • test case
  • technology
  • asset name

Total energy production in a given delivery point, for a given energy and a given technology:

\[\small production_{dp, techno, energy} = \sum_t production_{t, dp, techno, energy}\]

Raw demand (Wh)

Indexed by
  • scope
  • delivery point
  • test case
  • energy
  • demand type

Return the annual volumes of unoptimized energy demand for a given technology or contract. We here consider the flexible demand before optimization (using the objective demand of the corresponding assets).

This demand will be 0 for asset “Voluntary load curtailment” since it is price activated and as no consumption objectives. For asset “Electric Vehicles” with behavior Vehicule to grid, this demand does not consider any production, since V2G is price activated too.

Reserve Sizing (W)

Indexed by
  • scope
  • delivery point
  • reserve energy
  • test case

Return reserve requirements per delivery point in W (annual average values instead of annual volumes).

Scarcity Price Hours (h)

Indexed by
  • scope
  • delivery point
  • energy
  • test case

Return the number of hours where marginal costs of a delivery point is higher than a certain value reflecting scarcity of production capacity at said hours.

Share of production in national demand (%)

Indexed by
  • scope
  • delivery point
  • energy
  • test case
  • technology
  • asset name

Return, for each technology and for each energy, the share (in %) of the energy demand procured by the technology.

\[\small share_{dp, technology, energy} = \frac{\sum_t production_{t}^{dp, technology, energy}}{\sum_t demand_{t}^{dp, energy}}\]

Because of transmissions, the result can be greater than 100% (if the delivery point is a net exporter) or lower than 100% (if the delivery point is a net importer).

Storage capacity (Wh)

Indexed by
  • scope
  • delivery point
  • energy (electricity, gas and water)
  • test case
  • technology
  • asset name

Return the storage capacity per delivery point, per energy and technology (in Wh).

Storage cycles (cycles)

Indexed by
  • scope
  • delivery point
  • energy (electricity)
  • test case

For a given delivery point, computes the equivalent number of full discharge cycles of all the cycling storage units within the delivery point, based on their annual production and their production capacity.

Supply (Wh)

Indexed by
  • scope
  • delivery point
  • energy (any energy, fuel and reserve type)
  • test case
  • technology
  • asset name

Return for a given delivery point, the annual volumes of production per technology, as well as the imports to the delivery point through the transmissions and the fuel supply. It is similar to the KPI ‘Production (detailed)’ but it also includes imports and fuel supply.

The KPI is particularly adapted for gas models as national gas demand is in large parts satisfied by imports.

Total costs (euro)

Indexed by:
  • scope
  • delivery point
  • test case

Return the total costs associated to satisfying the demand (for electricity and reserve), for a given delivery point and scope, as the sum of the production costs, the loss of load costs, the loss of reserve costs and the curtailment costs:

\[\begin{split}\small totalCosts{dp,scope} & = \small productionCost_t^{scope,dp} + \small lossOfLoadCost^{scope,dp} \\ & + \small lossOfReserveCost^{scope,dp} + \small CurtailmentCost^{scope,dp} \end{split}\]

Transmission capacities (W)

Indexed by
  • scope
  • delivery point
  • energy (electricity or gas)
  • test case
  • transmission

Return the capacity of each single transmission.

Transmission usage (%)

Indexed by
  • scope
  • delivery point (dummy)
  • energy
  • test case
  • transmission

The instant transmission usage of an interconnection is the ratio of electricity or gas flowing through the transmission over its capacity. The KPI computes the yearly average value of instant transmission usage, for a given transmission:

\[transmissionUsage_{transmission} = \small \frac{mean(instantTransmissionUsage^{transmission})}{installedCapacity^{transmission}} (\%)\]

Welfare (euro)

Indexed by
  • scope
  • delivery point
  • test case

The welfare for a given delivery point is the sum of its consumer surplus, its producer surplus, the border exchange surplus and half of the congestion rent for power transmission lines connected to the delivery point:

\[\small welfare_{scope, dp, tc} = consumerSurplus_{scope, dp, tc} + producerSurplus_{scope, dp, tc} + exchangeSurplus_{scope, dp, tc} + \frac{1}{2} \sum_{transmission\ t \in dp} congestionRent_{scope, t, tc}\]