Outputs of a simulation¶

After optimization of an energy system, the MVS evaluates the simulation output. It evaluates the flows, costs and performance of the system. As a result, it calculates a number of key performance indicators (KPI), namely economic, technical and environmental KPI. Depending on the simulation settings, it can also generate different output files and figures of the results, including an automatic report in pdf or html format.

Overview of Key Performance Indicators¶

Technical KPI are calculated to assess the performance of a simulated energy system, ie. represent the technical system configuration and operation. They are calculated based on the asset capacities and asset dispatch. They should allow the comparision of different energy system topologies and different project sites with each other. These are the calculated technical KPI:

Economic KPI are calculated to assess the costs of a simulated energy system. They include the costs per asset as well as the system’s overall costs. Relative values like the levelized costs of supply allow a comparision to other investment options. These are the calculated economic KPI:

Annual operation, maintenance and dispatch expenses
Annuity
Costs attributed to a specific sector
Operation and maintenance costs
Dispatch costs
Investment costs
Operation, maintenance and dispatch costs
Net Present Costs (NPC)
Upfront investment costs
Levelized cost of throughput
Levelized costs of electricity equivalent
Replacement costs

Environmental KPI are calculated to assess the impact of a simulated energy system on the environment. These are the calculated environmental KPI:

Specific GHG per electricity equivalent
Total GHG emissions

Additionally to the KPI, the MVS can also generate a number of output files, which can be shared with other partners and used to vizualize the system’s behaviour and performance. These are the calculated files KPI:

Simulation report
Bar chart of optimal capacities
Excel file with all KPI
Excel file with dispatch timeseries
Simulation data after pre-processing (JSON)
Simulation data and results (JSON)
MVS logfile
Energy system model visualization
Pie charts of cost parameters
Dispatch of all assets on a bus
Input timeseries

In the sections economic, technical and environmental KPI, these indicators are further defined and in Files the possible exportable figures and files are presented. This takes place with the following structure:

Definition: Definition of the defined KPI, can be used as tooltips.
Type: One of Numeric, Figure, Excel File, JSON, Time series, Logfile or html/pdf
Unit: Unit of the KPI, multiple units possible if KPI can be applied to individual sectors (see also: Suffixes of KPI).
Valid Interval: Expected valid range of the KPI. Exceptions are possible under certain conditions.
Related indicators: List of indicators that are related to the described KPI, either because they are part of its calculation or can be compared to it.

Besides these parameters attributes, the underlying equation of a specific KPI may be presented and explained, or further hints might be provided for the parameter evaluation or for special cases.

Suffixes of KPI¶

The KPI of the MVS can be calculated per asset, for each sector or for the overall system.

KPI calculated per asset are not included in the scalar results of the automatic report or in the stored Excel file, but are displayed separately. They do not need suffixes, as they are always displayed in tables next to the respective asset.

KPI calculated for each vector are specifically these KPI that aggregate the dispatch and costs of multiple assets. For cost-related KPI, such aggregating KPI have the energy vector they are describing as a suffix. An example would be the attributed_costs of each energy vector - the attributed costs of the electricity and H2 sector would be called attributed_costs_electricity and attributed_costs_H2 respectively. For technical KPI, this suffix also applies, but additionally, due to the energy carrier weighting, they also feature the suffix electricity equivalent when the weighting has been applied. The energy demand of the system is an example: the demand per sector would be total_demand_electricity and total_demand_H2. To be able to aggregate these cost into an overall KPI for the system, the electricity equivalents of both values are calculated. They then are named total_demand_electricity_electricity_equivalent and total_demand_H2_electricity_equivalent.

KPI that describe the costs of the overall energy system do not have suffixes. Technical KPI often have the suffix electricity_equivalent to underline the energy carrier that the parameter is relative to.

Economic KPI¶

All the KPI related to costs described below are provided in net present value.

Net Present Costs (NPC) (costs_total)¶

Definition: Net present costs of the system for the whole project duration, includes all operation, maintainance and dispatch costs as well as the investment costs (including replacements). Applied to a single asset, the costs can also be called present costs of the asset.
Type: Numeric
Unit: currency
Valid Interval: >=0
Related indicators: Operation and maintenance costs (costs_cost_om) | Dispatch costs (costs_dispatch) | Investment costs (costs_investment_over_lifetime) | Operation, maintenance and dispatch costs (costs_om_total) | Upfront investment costs (costs_upfront_in_year_zero)

The Net present costs (NPC) is the present value of all the costs associated with installation, operation, maintenance and replacement of energy assets within the optimized multi-vector energy system over the whole project lifetime, deducting the present value of the residual value of asset at project end and as well as all the revenues that it earns over the project lifetime. The capital recovery factor (CRF) is used to calculate the present value of the cash flows.

$NPC = \sum_i{(c_{specific} + c_{replacement} + c_{residual}) \cdot CAP_i} + \sum_i{\sum_t{E_i(t)} \cdot p_{dispatch}}$

Operation and maintenance costs (costs_cost_om)¶

Definition: Costs for fix annual operation and maintenance costs over the whole project lifetime, which do not depend on the assets dispatch but solely on installed capacity. An example would be the maintenance costs for cleaning the installed PV capacity.
Type: Numeric
Unit: currency
Valid Interval: >=0
Related indicators: Dispatch costs (costs_dispatch) | Investment costs (costs_investment_over_lifetime) | Operation, maintenance and dispatch costs (costs_om_total) | Net Present Costs (NPC) (costs_total) | Upfront investment costs (costs_upfront_in_year_zero)

Operation, maintenance and dispatch costs (costs_om_total)¶

Definition: Costs for annual operation and maintenance costs as well as dispatch of all assets of the energy system, for the whole project duration.
Type: Numeric
Unit: currency
Valid Interval: >=0
Related indicators: Operation and maintenance costs (costs_cost_om) | Dispatch costs (costs_dispatch) | Investment costs (costs_investment_over_lifetime) | Net Present Costs (NPC) (costs_total) | Upfront investment costs (costs_upfront_in_year_zero)

Dispatch costs (costs_dispatch)¶

Definition: Dispatch costs over the whole project lifetime including all expenditures that depend on the dispatch of assets (e.g. fuel costs, electricity consumption from the external grid, costs for operation and maintainance that depend on the throughput of an asset)
Type: Numeric
Unit: currency
Valid Interval: >=0
Related indicators: Operation and maintenance costs (costs_cost_om) | Investment costs (costs_investment_over_lifetime) | Operation, maintenance and dispatch costs (costs_om_total) | Net Present Costs (NPC) (costs_total) | Upfront investment costs (costs_upfront_in_year_zero)

Investment costs (costs_investment_over_lifetime)¶

Definition: Investment costs over the whole project lifetime, including all replacement costs.
Type: Numeric
Unit: currency
Valid Interval: >=0
Related indicators: Operation and maintenance costs (costs_cost_om) | Dispatch costs (costs_dispatch) | Operation, maintenance and dispatch costs (costs_om_total) | Net Present Costs (NPC) (costs_total) | Upfront investment costs (costs_upfront_in_year_zero) | Replacement costs (replacement_costs_during_project_lifetime)

Upfront investment costs (costs_upfront_in_year_zero)¶

Definition: The costs which will have to be paid upfront when project begins, ie. In year 0. These are the investment and fix project costs into the chosen configuration.
Type: Numeric
Unit: currency
Valid Interval: >=0
Related indicators: Operation and maintenance costs (costs_cost_om) | Dispatch costs (costs_dispatch) | Investment costs (costs_investment_over_lifetime) | Operation, maintenance and dispatch costs (costs_om_total) | Net Present Costs (NPC) (costs_total)

Replacement costs (replacement_costs_during_project_lifetime)¶

Definition: Costs for replacement of assets which occur over the project lifetime.
Type: Numeric
Unit: currency
Valid Interval: >=0
Related indicators: Investment costs (costs_investment_over_lifetime)

Costs attributed to a specific sector (attributed_costs)¶

Definition: Costs attributed to supplying the demand of a specific sector, based on the net present costs (NPC) of the energy system and the share of the sector demand compared to the overall system demand.
Type: Numeric
Unit: currency
Valid Interval: >=0
Related indicators: Net Present Costs (NPC) (costs_total)

A multi-vector energy system connects energy vectors into a joined energy system and the system is then designed to have an optimial, joined operation. With other systems, the costs associated to each individual energy vector would be used to calculate the costs to supply the individual sector. With the multi-vector system, this could lead to distorted costs - for example if there is a lot of PV (electricity sector), which in the end is only supplying an electrolyzer (H2 sector). The investment and operational costs of the electricity sector assets would thus turn out to be very high, which could be considered unfair as the electricity from PV is solely used to provide the H2 demand. Therefore, we define the attributed costs of each energy vector, to determine how much of the overall system costs should be attributed to one sector, depending on the energy demand it has compared to the other sectors. To be able to compare the demands of different energy carriers, energy carrier weighting is applied.

Annuity (annuity_total)¶

Definition: Annuity of the assets costs over the project lifetime or the energy system’s net present costs (NPC) .
Type: Numeric
Unit: currency/a
Valid Interval: >=0
Related indicators: Annual operation, maintenance and dispatch expenses (annuity_om) | Net Present Costs (NPC) (costs_total)

Annual operation, maintenance and dispatch expenses (annuity_om)¶

Definition: Annuity of the operation, maintenance and dispatch costs of the asset or energy system, i.e. ballpark number of the annual expenses for asset or system operation.
Type: Numeric
Unit: currency/a
Valid Interval: >=0
Related indicators: Annuity (annuity_total) | Operation, maintenance and dispatch costs (costs_om_total)

Levelized costs of electricity equivalent (levelized_costs_of_electricity_equivalent)¶

Definition: Levelized cost of energy of the sector-coupled energy system, calculated from the systems annuity and the total system demand in electricity equivalent.
Type: Numeric
Unit: currency/kWheleq
Valid Interval: >=0
Related indicators: Net Present Costs (NPC) (costs_total) | Energy demand (total_demand)

Specific electricity supply costs, eg. levelized costs of electricity are commonly used to compare the supply costs of different investment decisions or also energy provider prices to local generation costs. However, the a multi-vector energy system connects energy vectors into a joined energy system and the optimization objective of the MVS then is to minimize the overall energy costs, without distinguising between the different sectors. This sector-coupled energy system is then designed to have an optimial, joined operation. With other systems, the costs associated to each individual energy vector would be used to calculate the levelized costs of energy (LCOEnergy). With the multi-vector system, this could lead to distorted costs - for example if there is a lot of PV (electricity sector), which in the end is only supplying an electrolyzer (H2 sector). The LCOE of electricity would thus turn out to be very high, which could be considered unfair as the electricity from PV is solely used to provide the H2 demand. Therefore, we define the attributed costs of each energy vector, to determine how much of the overall system costs should be attributed to one sector, depending on the energy demand it has compared to the other sectors. To be able to compare the demands of different energy carriers, energy carrier weighting is applied.

Therefore the levelized costs of energy (LCOEnergy) for energy carrier $i$ are defined based on the annuity of the attributed costs, the CRF and the demand of one energy sector $E_{dem,i}$ :

$LCOEnergy_i = \frac{Attributed~costs \cdot CRF}{\sum_{t} E_{dem,i}(t)}$

The LCOEnergy are are calculated for each sector (resulting in the levelized costs of electricity, heat, H2…), but also for the overall energy system. For the overall energy system, the levelized costs of electricity equivalent are calculated, as this system may supply different energy vectors.

Levelized cost of throughput (levelized_cost_of_energy_of_asset)¶

Definition: Cost per kWh throughput through an asset, based on the assets costs during the project lifetime as well as the total throughput through the asset in the project lifetime. For generation assets, equivalent to the levelized cost of generation.
Type: Numeric
Unit: currency/kWh
Valid Interval: >=0
Related indicators: Annuity (annuity_total) | Aggregated flow (annual_total_flow)

This KPI measures the cost of generating 1 kWh for each asset in the system. It can be used to assess and compare the available alternative methods of energy production. The levelized cost of energy of an asset ( $LCOE~ASSET_i$ ) is usually obtained by looking at the lifetime costs of building and operating the asset per unit of total energy throughput of an asset over the assumed lifetime [currency/kWh].

Since not all assets are production assets, the MVS distinguishes between the type of assets. For assets in energyConversion and energyProduction the MVS calculates the $LCOE~ASSET_i$ by dividing the total annuity $a_i$ of the asset $i$ by the total flow $\sum_{t} E_i(t)$ .

$LCOE~ASSET_i = \frac{a_i}{\sum_{t} E_i(t)}$

For assets in energyStorage, the MVS sums the annuity for storage capacity $a_{i,sc}$ , input power $a_{i,ip}$ and output power $a_{i,op}$ and divides it by the output power total flow $\sum{t} E_{i,op}(t)$ .

$LCOE~ASSET_i = \frac{a_{i,sc} + a_{i,ip} + a_{i,op}}{\sum_{t}{E_{i,op}(t)}}$

If the total flow is 0 in any of the previous cases, then the $LCOE~ASSET$ is set to None.

$LCOE~ASSET_i = None$

For assets in energyConsumption, the MVS outputs 0 for the $LCOE~ASSET_i$ .

$LCOE~ASSET_i = 0$

Technical KPI¶

Optimal additional capacity (optimizedAddCap)¶

Definition: Capacity added to installed capacity for optimal economic system performance.
Type: Numeric
Unit: kW, kWh, kWp, …
Valid Interval: >=0
Related indicators: Peak flow (peak_flow)

Dispatch of an asset (flow)¶

Definition: Optimized dispatch of an asset in the optimized energy system, ie. its generation or thoughput.
Type: Time series (with time stamps and values)
Unit: kW,kgH2,…
Valid Interval: NA
Related indicators: Dispatch of all assets on a bus | Peak flow (peak_flow) | Aggregated flow (annual_total_flow) | Average flow (average_flow)

Peak flow (peak_flow)¶

Definition: Peak of the dispatch of an asset.
Type: Numeric
Unit: kW
Valid Interval: >=0
Related indicators: Average flow (average_flow) | Aggregated flow (annual_total_flow)

Average flow (average_flow)¶

Definition: Average value of the assets dispatch. The ratio of average dispatch to peak dispatch indicates how much the asset is used in comparison to its actual installed capacity.
Type: Numeric
Unit: kWh
Valid Interval: >=0
Related indicators: Aggregated flow (annual_total_flow) | Peak flow (peak_flow)

Aggregated flow (annual_total_flow)¶

Definition: Dispatch of the asset over a year, aggregated generation, demand or throughput.
Type: Numeric
Unit: kWh
Valid Interval: >=0
Related indicators: Average flow (average_flow) | Peak flow (peak_flow)

Energy demand (total_demand)¶

Definition: Demand of energy in local energy system over a the project lifetime.
Type: Numeric
Unit: kWh, kWheleq, …
Valid Interval: >=0
Related indicators: None

Energy export (total_feedin)¶

Definition: Feed-in of energy into external grid.
Type: Numeric
Unit: kWh, kWheleq, …
Valid Interval: >=0
Related indicators: Onsite energy fraction (onsite_energy_fraction)

Energy import (total_consumption_from_energy_provider)¶

Definition: Aggregated energy imports into the local energy system from the provider.
Type: Numeric
Unit: kWh, kWheleq, …
Valid Interval: >=0
Related indicators: None

Total non-renewable local generation (total_internal_non-renewable_generation)¶

Definition: Aggregated amount of non-renewable energy generated within the energy system.
Type: Numeric
Unit: kWheleq
Valid Interval: >=0
Related indicators: Total local generation (total_internal_generation) | Total renewable local generation (total_internal_renewable_generation)

Total renewable local generation (total_internal_renewable_generation)¶

Definition: Aggregated amount of renewable energy generated within the energy system.
Type: Numeric
Unit: kWheleq
Valid Interval: >=0
Related indicators: Total local generation (total_internal_generation) | Total non-renewable local generation (total_internal_non-renewable_generation)

Total local generation (total_internal_generation)¶

Definition: Aggregated amount of energy generated within the energy system.
Type: Numeric
Unit: kWheleq
Valid Interval: >=0
Related indicators: Total non-renewable local generation (total_internal_non-renewable_generation) | Total renewable local generation (total_internal_renewable_generation)

Energy excess (total_excess)¶

Definition: Excess of energy, ie. unused energy in local energy system.
Type: Numeric
Unit: kWh, kWheleq, …
Valid Interval: >=0
Related indicators: None

Total renewable energy use (total_renewable_energy_use)¶

Definition: Aggregated amount of renewable energy used within the energy system (ie. Including local generation and external supply).
Type: Numeric
Unit: kWheleq
Valid Interval: >=0
Related indicators: Total non-renewable energy use (total_non-renewable_energy_use)

Total non-renewable energy use (total_non-renewable_energy_use)¶

Definition: Aggregated amount of non-renewable energy used within the energy system (ie. Including local generation and external supply).
Type: Numeric
Unit: kWheleq
Valid Interval: >=0
Related indicators: Total renewable energy use (total_renewable_energy_use)

Renewable factor (renewable_factor)¶

Definition: Describes the share of the energy influx to the local energy system that is provided from renewable sources. This includes both local generation as well as consumption from energy providers.
Type: Numeric
Unit: Factor
Valid Interval: [0,1]
Related indicators: Renewable share of local generation (renewable_share_of_local_generation) | Onsite energy fraction (onsite_energy_fraction) | Onsite energy matching (onsite_energy_matching)

Describes the share of the energy influx to the local energy system that is provided from renewable sources. This includes both local generation as well as consumption from energy providers.

$RF &=\frac{\sum_i {E_{rgen,i} \cdot w_i + RES \cdot E_{grid}}}{\sum_j {E_{gen,j} \cdot w_j}+\sum_k {E_{grid} (k) \cdot w_k}} \text{with } rgen &\text{: Renewable generation} gen &\text{: Renewable and non-renewable generation} i, j &\text{: Assets 1,2,…} RES &\text{: Renewable energy share of energy provider} k &\text{: Energy provider 1,2…} w_i, w_j, w_k &\text{: Energy carrier weighting factor for output of asset i/j/k}$

Example

An energy system is composed of a heat and an electricity side. Following are the energy flows:

100 kWh from a local PV plant
0 kWh local generation for the heat side
100 kWh consumption from the electricity provider, who has a renewable factor of 50%

Again, the heat sector would have a renewable factor of 0% when considered separately, and the electricity side would have an renewable factor of 75%. This results in a system-wide renewable share of:

$RF = \frac{ 100 kWh(el)\cdot \frac{kWh(eleq)}{kWh(el)} +50 kWh(el) \cdot \frac{kWh(eleq)}{kWh(el)}}{200 kWh(el) \cdot \frac{kWh(eleq)}{kWh(el)}} = 3/4 = \text{75 \%}$

The renewable factor, just like the Renewable share of local generation (renewable_share_of_local_generation), cannot indicate how much renewable energy is used in each of the sectors. In the future, it might be possible to get a clearer picture of the flows between the sectors with the proposed degree of sector-coupling.

Degree of sector-coupling (DSC)¶

To assess how much an optimized multi-vector energy system makes use of the potential of sector-coupling, it is planned to introduce the degree of sector-coupling in the future. This level of interconnection is to be calculated with the ratio of energy flows in between the sectors (ie. those, where energy carriers are converted to another energy carrier) to the energy demand supplied:

$DSC & =\frac{\sum_{i,j}{E_{conversion} (i,j) \cdot w_i}}{\sum_i {E_{demand} (i) \cdot w_i}} \text{with } i,j &\text{: Electricity,H2…}$

Note

This feature is currently not implemented.

Onsite energy fraction (onsite_energy_fraction)¶

Definition: Onsite energy fraction is also referred to as self-consumption. It describes the fraction of all locally generated energy that is consumed by the system itself.
Type: Numeric
Unit: Factor
Valid Interval: [0,1]
Related indicators: Onsite energy matching (onsite_energy_matching)

Onsite energy fraction is also referred to as “self-consumption”. It describes the fraction of all locally generated energy that is consumed by the system itself. (see [1] and [2]).

An OEF close to zero shows that only a very small amount of locally generated energy is consumed by the system itself. It is at the same time an indicator that a large amount is fed into the grid instead. A OEF close to one shows that almost all locally produced energy is consumed by the system itself.

$OEF &=\frac{\sum_{i} {(E_{generation} (i) - E_{gridfeedin}(i)) \cdot w_i}}{\sum_{i} {E_{generation} (i) \cdot w_i}} &OEF \epsilon \text{[0,1]}$

Onsite energy matching (onsite_energy_matching)¶

Definition: The onsite energy matching is also referred to as self-sufficiency. It describes the fraction of the total demand that can be covered by the locally generated energy.
Type: Numeric
Unit: Factor
Valid Interval: [0,1]
Related indicators: Onsite energy fraction (onsite_energy_fraction) | Energy export (total_feedin)

The onsite energy matching is also referred to as “self-sufficiency”. It describes the fraction of the total demand that can be covered by the locally generated energy (see [1] and [2]).

An OEM close to zero shows that very little of the demand can be covered by locally produced energy. Am OEM close to one shows that almost all of the demand can be covered with locally generated energy. Per definition OEM cannot be greater than 1 because the excess generated energy would automatically be fed into the grid or an excess sink.

$OEM &=\frac{\sum_{i} {(E_{generation} (i) - E_{gridfeedin}(i) - E_{excess}(i)) \cdot w_i}}{\sum_i {E_{demand} (i) \cdot w_i}} &OEM \epsilon \text{[0,1]}$

Note

The feed into the grid should only be positive.

Degree of Autonomy (degree_of_autonomy)¶

Definition: A degree of autonomy close to zero shows high dependence on the energy provider, while a degree of autonomy of 1 represents an autonomous or net-energy system and a degree of autonomy higher 1 a surplus-energy system.
Type: Numeric
Unit: Factor
Valid Interval: [0,1]
Related indicators: Energy demand (total_demand)

The degree of autonomy describes the overall energy consumed minus the energy consumed from the grid divided by the overall energy consumed. Adapted from this definition [3].

A degree of autonomy close to zero shows high dependence on the grid operator, while a degree of autonomy of one represents an autonomous system. Note that this key parameter indicator does not take into account the outflow from the system to the grid operator (also called feedin). As above, we apply a weighting based on Electricity Equivalent.

$Degree of Autonomy = \frac{\sum_{i} E_{demand,i} \cdot w_i - \sum_{j} E_{consumption,provider,j} \cdot w_j}{\sum_{i} E_{demand,i} \cdot w_i}$

Degree of Net Zero Energy (degree_of_nze)¶

Definition: The degree of net zero energy describes the ability of an energy system to provide its aggregated annual demand through local sources.
Type: Numeric
Unit: Factor
Valid Interval: >=0
Related indicators: Energy export (total_feedin) | Energy import (total_consumption_from_energy_provider)

The degree of net zero energy describes the ability of an energy system to provide its aggregated annual demand though local sources. For that, the balance between local generation as well as consumption from and feed-in towards the energy provider is compared. In a net zero energy system, demand can be supplied by energy import, but then local energy generation must provide an equally high energy export of energy in the course of the year. In a plus energy system, the export exceeds the import, while local generation can supply all demand (from an aggregated perspective). To calculate the degree of NZE, the margin between grid feed-in and grid consumption is compared to the overall demand.

Some definitions of NZE systems require that the local demand is solely covered by locally generated renewable energy. In MVS this is not the case - all locally generated energy is taken into consideration. For information about the share of renewables in the local energy system checkout Renewable share of local generation (renewable_share_of_local_generation).

A degree of NZE lower than 1 shows that the energy system can not reach a net zero balance, and indicates by how much it fails to do so, while a degree of NZE of 1 represents a net zero energy system and a degree of NZE higher 1 a plus-energy system.

As above, we apply a weighting based on Electricity Equivalent.

$Degree of NZE &= 1 + \frac{\sum_{i} {(E_{grid feedin}(i) - E_{grid consumption} (i) )\cdot w_i}}{\sum_i {E_{demand, i} \cdot w_i}}$

Environmental KPI¶

Total GHG emissions (total_emissions)¶

Definition: Total greenhouse gas emissions in kg.
Type: Numeric
Unit: kg GHGeq
Valid Interval: >=0
Related indicators: Renewable factor (renewable_factor) | Specific GHG per electricity equivalent (specific_emissions_per_electricity_equivalent)

The total emissions of the MES in question are calculated with all aggregated energy flows from the generation assets including energy providers and their subsequent emission factor:

$Total\_emissions &= \sum_i {E_{gen} (i) \cdot emission\_factor (i)} \text{with~} &i \text{: generation assets 1,2,…}$

The emissions of each generation asset and provider are also calculated and displayed separately in the outputs of MVS.

Specific GHG per electricity equivalent (specific_emissions_per_electricity_equivalent)¶

Definition: Specific GHG emissions per supplied electricity equivalent.
Type: Numeric
Unit: kg GHGeq/kWh
Valid Interval: >=0
Related indicators: Total GHG emissions (total_emissions)

The specific emissions per electricity equivalent of the MES are calculated in $\text{kg/kWh}_{eleq}$ :

$Specific\_emissions &= \frac{Total\_emissions}{total\_demand_{eleq}}$

Emissions can be of different nature: CO2 emissions, CO2 equivalents, greenhouse gases, …

Currently the emissions do not include life cycle emissions of energy conversion or storage assets, nor are they calculated separately for the energy sectors. For the latter, the problem of the assignment of assets to sectors arises e.g. emissions caused by an electrolyser would be counted to the electricity sector although you might want to count it for the H2 sector, as the purpose of the electrolyser is to feed the H2 sector. Therefore, we will have to verify whether or not we can apply the energy carrier weighting also for this KPI.

Files¶

Bar chart of optimal capacities¶

Definition: A bar chart to compare the optimized additional capacities for each asset to be installed in the energy system. Please be aware that the units of the capacities may different.
Type: Figure
Unit: kWh, kWp, kW, …
Valid Interval: NA
Related indicators: Optimal additional capacity (optimizedAddCap)

An example of a bar chart of recommended additional asset capacities is shown below. Note that currently kWp are displayed on the same scale as kW (or kWh or gkH2), which is not ideal.

../_images/example_optimal_additional_capacities.png

Pie charts of cost parameters¶

Definition: Displays the share of individual asset costs on different economical parameters of the overall system
Type: Figure
Unit: Percentages
Valid Interval: NA
Related indicators: Net Present Costs (NPC) (costs_total) | Operation, maintenance and dispatch costs (costs_om_total) | Annuity (annuity_total)

An examplary pie chart is displayed below, in this case for the operation and management costs of an energy system.

Input timeseries¶

Definition: Vizualization of timeseries provided as input data, eg. PV generation timeseries.
Type: Figure
Unit: kW, kgH2, …
Valid Interval: NA
Related indicators: None

An example of the graph created from the timeseries, eg. specific generation timeseries, provided by the input files is shown below.

Dispatch of all assets on a bus¶

Definition: Visualization of the dispatch of all assets of a specific energy bus, ie. all inflows and outflows of a specific bus. Generated for every single energy bus in the energy system. If relevant, a plot of the state of charge is also displayed.
Type: Figure
Unit: kW, kgH2, …
Valid Interval: NA
Related indicators: Dispatch of an asset (flow)

An example of the graph displaying the asset dispatch on a specific bus is shown below.

Energy system model visualization¶

Definition: Plot of the energy system model in oemof-solph topology. This graph also includes the automatically generated components, ie. the sub-assets of energy providers and an energy excess sink on each energy bus. The model therefore appears different than in the Energy Planning Application (EPA).
Type: Figure
Unit: NA
Valid Interval: NA
Related indicators: None

An example of the created energy system model graphs is shown below.

Excel file with all KPI¶

Definition: Excel sheet with all calculated KPI, both for the individual assets, the sectors and the overall energy system.
Type: Excel file
Unit: NA
Valid Interval: NA
Related indicators: None

The file is named scalars.xlsx. An example is shown below.

Excel file with dispatch timeseries¶

Definition: Excel sheet with the dispatch of all assets of the energy system. Each tab represents one energy bus.
Type: Excel file
Unit: NA
Valid Interval: NA
Related indicators: Dispatch of an asset (flow) | Dispatch of all assets on a bus

The file is named timeseries_all_busses.xlsx. An example is shown below.

MVS logfile¶

Definition: Logfile of the MVS simulation including a number of log entries: Debug, information, warning and error messages. Helpful to debug the energy system simulation.
Type: Logfile
Unit: NA
Valid Interval: NA
Related indicators: None

The file is named mvs_logfile.log. An example is shown below.

Simulation report¶

Definition: Automatically generated simulation report, including the most important input data as well as all output data. The html can be browsed interactively, while the pdf can be shared with partners.
Type: html or pdf
Unit: NA
Valid Interval: NA
Related indicators: None

MVS has a feature to automatically generate a PDF report that contains the main elements from the input data as well as the simulation results’ data. The report can also be viewed as a web app on the browser, which provides some interactivity.

MVS version number, the branch ID and the simulation date are provided as well in the report, under the MVS logo. A commit hash number is provided at the end of the report in order to prevent the erroneous comparing results from simulations using different versions.

It includes several tables with project data, simulation settings, the various demands supplied by the user, the various components of the system and the optimization results such as the energy flows and the costs. The report also provides several plots which help to visualize the flows and costs.

Please, refer to the report section for more information on how to setup and use this feature, or type

mvs_report -h

in your terminal or command line.

A screenshot of the example report header is displayed below. The full examplary report can be accessed on github in docs/model/images/example_simulation_report.pdf.

Simulation data after pre-processing (JSON)¶

Definition: This file includes all data that is used to setup the energy system model, including all the pre-processing performed within the module C0. It is mostly used by developers.
Type: JSON
Unit: NA
Valid Interval: NA
Related indicators: None

Simulation data and results (JSON)¶

Definition: This file includes all simulation data and also results of the energy system optimization. With mvs_report this file can be used to create a report without re-simulating the energy system. This file is also only used by developers, and also the file used to provide the EPA with the simulation results.
Type: JSON
Unit: NA
Valid Interval: NA
Related indicators: None