Structure of Energy Systems

Energy systems in the sense of the Spreadseet Energy System Model Generator are designed according to the specifications of the oemof library. Accordingly, energy systems can be represented with the help of mathematical graph theory. Thus, energy systems are exemplified as “graphs” consisting of sets of “vertices” and “edges”. In more specific terms, vertices stand for components and buses while directed edges connect them. The status variable of the energy flow indicates which amount of energy is transported between the individual nodes at what time. Possible components of an oemof energy system are

  • sources,
  • sinks,
  • transformers, and
  • storages.

Buses furthermore form connection points of an energy system. The graph of a simple energy system consisting of each one source, one transformer, one sink, as well as two buses, could look like the example displayed in the following figure.

Bus-Example

Graph of a simple energy system, consisting of one source, two buses, one transformer, and one a sink.

An oemof energy system must be in equilibrium at all times. Therefore sources must always provide exactly as much energy as the sinks and transformer losses consume. In turn, the sink must be able to consume the entire amount of energy supplied. If there is no balance, oemof is not able to solve the energy system.

Buses

The modelling framework oemof does not allow direct connections between components. Instead, they must always be connected with a bus. The bus in turn can be connected to other components, so that energy can be transported via the bus. Buses can have any number of incoming and outgoing flows. Buses can not directly be connected with each other. They do not consider any conversion processes or losses.

Sources

Sources represent the provision of energy. This can either be the exploitation of of an energy source (e.g. gas storage reservoir or solar energy, no energy source in physical sense), or the simplified energy import from adjacent energy systems. While some sources may have variable performances, depending on the temporary needs of the energy system, others have fixed performances, which depend on external circumstances. In the latter case, the exact performances must be entered to the model in form of time series. With the help of oemofs “feedinlib” and “windpowerlib”, electrical outputs of photovoltaik (pv)-systems and wind power plants can be generated automatically. In order to ensure a balance in the energy system at all times, it may be useful to add a “shortage” source to the energy system, which supplies energy in the event of an energy deficit. In reality, such a source could represent the purchase of energy at a fixed price.

Photovoltaic Systems

The following Figure sketches the fractions of radiation arriving at a PV-module as well as further relevant parameters.

pv_systems

Radiation on a photovoltaic module.

The global radiation is composed of direct and diffuse radiation. The “direct horizontal irradiance” dirhi is the amount of sun radiation as directly received by a horizontal surface. The “diffuse horizontal irradiance” dhi is the share of radiation, which arrives via scattering effects on the same surface. A part of the global radiation is reflected on the ground surface and can thus cause an additional radiation contribution on the photovoltaic module. The amount of the reflected part depends on the magnitude of the albedo of the ground material. Exemplary albedo values are listed in the following table.

Wind Turbines

For the modelilng of wind turbines, the weather data set must include wind speeds. The wind speeds must be available for a measurement height of 10 m in the unit m/s.

The system data of the wind turbine to be modelled are obtained from the “oedb” database.

Sinks

Sinks represent either energy demands within the energy system or energy exports to adjacent systems. Like sources, sinks can either have variable or fixed energy demands. Sinks with variable demands adjust their consumption to the amount of energy available. This could for example stand for the sale of surplus electricity. However, actual consumers usually have fixed energy demands, which do not respond to amount of energy available in the system. As with sources, the exact demands of sinks can be passed to the model with the help of time series.

In order to ensure a balance in the energy system at all times, it may be appropriate to add an “excess” sink to the energy system, which consumes energy in the event of energy surplus. In reality, this could be the sale of electricity or the give-away of heat to the atmosphere.

Standard Load Profiles

Oemof’s sub-library demandlib can be used for the estimation of heat and electricity demands of different consumer groups, as based on German standard load profiles (SLP). The following electrical standard load profiles of the Association of the Electricity Industry (VDEW) can be used:

Profil Consumer Group
H0 households
G0 commercial general
G1 commercial on weeks 8-18 h
G2 commercial with strong consumption in the evening
G3 commercial continuous
G4 shop/hairdresser
G5 bakery
G6 weekend operation
L0 agriculture general
L1 agriculture with dairy industry/animal breeding
L2 other agriculture

The following heat standard load profiles of the Association of Energy and Water Management (BDEW) can be used:

Profile House Type
EFH single family house
MFH multi family house
GMK metal and automotive
GHA retail and wholesale
GKO Local authorities, credit institutions and insurance companies
GBD other operational services
GGA restaurants
GBH accommodation
GWA laundries, dry cleaning
GGB horticulture
GBA bakery
GPD paper and printing
GMF household-like business enterprises
GHD Total load profile Business/Commerce/Services

In addition, the location of the building and whether the building is located in a “windy” or “non-windy” area are taken into account for the application of heat standard load profiles. The following location classes may be considered:

Stochastic Load Profiles (Richardson Tool)

The use of standard load profiles has the disadvantage that they only represent the average of a larger number of households (> 200). Load peaks of individual households (e.g. through the use of hair dryers or electric kettles) are filtered out by this procedure. To counteract this, the Spreadsheet Energy System Model Generator offers the possibility to generate stochastic load profiles for residential buildings. These are generated on the basis of Richardsonpy. Thereby, an arbitrary number of different realistic load profiles is simulated under consideration of statistic rules. The mean value of a large-enough number of profiles should, again, result in the standard load profile. However, if calculations are continued using the individual values before averaging – as in the above calculation of costs – different values are obtained than when calculating with SLPs.

Transformers

Transformers are components with one ore more input flows, which are transformed to one or more output flows. Transformers may be power plants, energy transforming processes (e.g., electrolysis, heat pumps), as well as transport lines with losses. The transformers’ efficiencies can be defined for every time step (e.g., the efficiency of a thermal powerplants in dependence of the ambient temperature).

Currently only Generic Transformers can be used within the Spreadsheet Energy System Model Generator. These may have one or more different outputs, e.g., heat and electricity. For the modelling, the nominal performance of a generic transformer with several outputs, the respective output ratios, and an efficiency for each output need to be known.

Heat Pumps

For the modelilng of heat pumps, different heat sources are considered so the weather data set must include different temperatures. The efficiency of the heat pump cycle process can be described by the Coefficient of Performance (COP). The heat pump automatically creates a heat source and a low temperature bus (see red bubble). So only a transformer and a electricity bus needs to be created. An example is shown in the following figure.

HeatPump-Example

Graph of a heat pump system.

At the moment it is possible to use ground water, soil (vertical heat exchanger), surface water and ambient air as a heat source.

The heat pumps are implemnted by using “oemof.thermal” .

Storages

Storages are connected to a bus and can store energy from this bus and return it to a later point in time.

Investment

The investment costs help to compare the costs of building new components to the costs of further using existing components instead. The annual savings from building new capacities should compensate the investment costs. The investment method can be applied to any new component to be built. In addition to the usual component parameters, the maximum installable capacity needs to be known. Further, the periodic costs need to be assigned to the investment costs. The periodic costs refer to the defined time horizon. If the time horizon is one year, the periodical costs correspond to the annualized capital costs of an investment.

Non-Convex-Investments: While a linear programming approach is used for normal investment decisions, a mixed integer variable is defined for non-convex investment decisions. The model can thus decide, for example, whether a component should be implemented FULL or NOT. Mixed-integer variables increase the computational effort significantly and should be used with caution.