HYBRID – introduction 2
© EMD International A/S • www.emd.dk • windPRO 4.1 • September 2024
15.1 HYBRID – introduction
Introduction to HYBRID
The windPRO HYBRID module makes it possible to evaluate the technical and financial feasibility of
energy plants with power production. The module handles time-varying prices against fluctuations in
production and can include storage. The OPTI-Storage functionality can from windPRO 3.6 handle
arbitrage (charge when price is low, discharge when price is high) and optimal handling of grid limits
(minimize the grid limit curtailment).
Time varying production calculations within windPRO for Wind and PV are smoothly loaded from
HYBRID module. Price time series concurrent to production data must be imported to a METEO object,
similar for demand time series if relevant. (Export all to grid does not require demand time series).
To deliver renewable production directly to the demands will often give the extra benefit that taxes
and tariff costs are saved, which is what often makes a Hybrid plant feasible. Demands connected
to the production units can be handled as either INSIDE or OUTSIDE the system, which is
conveniently named the “MicroGrid”.
The module can handle scenarios from very simple systems, like if it is feasible to invest in a Solar-
PV plant, and how big the plant shall be based on a given demand, to highly complex systems with
wind, PV, other green and black production interacting together, as well as storage, external grid
and an internal demand. The grid limitations can be set and can be given a cost to expand the grid
capacity and find out if this investment is feasible, or investing in, for example, storage would be
better.
Interaction with an external grid with time-varying prices is a core feature, but it is also possible not
to have any interactions with an external grid; the so-called “island operation”. Here a special feature
is the “shedding filler”, where a green or black production plant can be set to fill the gaps, when the
other production units cannot fulfil the demand, and “load shedding” would be the solution without
the “filler”.
The basis within the module is an energy balance simulation for one year where production, price
and demand time-series must be available for the same year. This simulation is repeated for all years
in the financial simulation period, where changes during the simulation period are included. Changes
could be that the Solar-PV plant degrades year by year, energy price levels increase or some plants
or storage are brought in or taken out of the system during the simulation period. All developments
by year can be controlled by freely definable indices.
For the power price simulation there is a high flexibility. Subsidies, tariffs and energy taxes can be
included dependent on which plant makes the power, and where it is used. There might be a tariff
just for the exported part of a specific wind plants production, while a subsidy is given to all PV
production. Any possible mix can be handled.
Curtailments can be based on grid capacity as well as price. If the price is negative, the production
can be shut down, like it will be curtailed when the grid limit is reached. For both curtailment types
a value can be given per MWh. Shutting down by negative prices often is compensated while the
plant helps the grid balance. If curtailment is due to grid limits, the power might be used for heating
and assigned an appropriate value is an option. Load shedding can also be given a value. If the
demand cannot be fulfilled in some hours, this has a cost, which could be the reason for investing in
storage. If the load shedding is not assigned a cost, the storage would probably not be feasible.
Scaling factors for the different technologies are part of the simulation. It is easy to see the impact
on the lifetime net cost when having e.g., a 50% larger PV-plant or storage. Here the cost functions
are a very important new tool in windPRO. Plant costs and productions are automatically scaled based
on size (simple scaling). Some plant cost types are based on formulas. For a wind park the turbine
costs depend on specific power as well as MW, rotor area and hub height. The cabling and road costs
depend on the inter-array distance, automatically calculated from the layout (i.e., this is a