BalmorelLite reflects an energy only market dispatching power plants according to their short run marginal costs. Hence, capital cost and fixed operation and maintenance costs for existing capacity are not considered in the simulations.
All simulations are performed with hourly time resolution.
The model can be used to determine new investments in additional capacity. Then capital cost, fixed operation and maintenance costs of new capacity is considered.
The technologies available for model investment are the following: wind, solar, biomass, coal, natural gas and oil. Nuclear and hydropower are excluded from the investment options since they largely depend on factors other than the economical one, which are difficult to capture in an optimization model. For example, hydropower development depends on availability of resource and environmental considerations, while nuclear power is linked to social acceptance and political support.
The model can invest in generation capacity if the value of the generated electricity exceeds the annualised cost of the investment plus fuel and O&M costs. The required rate of return is 5 % (real).
Technology data regarding new plants is mainly based on the Technology Catalogues  by the Danish Energy Agency. Investment cost of new units is showed below in €-2016:
|Technology||Investment cost [M€/MW]||Fixed O&M cost [k€/MW/year]|
Data related to power plants in input to the model is also derived from the Technology Catalogues  by the Danish Energy Agency. The following table expresses the assumptions regarding efficiency and O&M costs in €-2016.
|Technology||Average Efficiency [%]||Variable O&M cost [€/MWh]|
The model can be run with Unit Commitment (UC). The optimization becomes a relaxed Mix Integer Problem, which takes into account the cost to start-up a unit, a minimum production level and divides the total capacity in typical unit sizes to be scheduled. The optimization with UC increase the computation time.
If the option for Flexible power plants is enabled, the minimum production level is reduced, representing power plants with enanced flexibility to reduce their output level to periods with higher RE generation.
The following table express the relevant values for Unit Commitment. Start-up cost is expressed in €-2016. The sources considered for minimum loads are  and . Plant size is based on experience and start-up costs are based on , assuming biomass and nuclear plants have the same start-up cost as hard coal and oil plant a slightly lower cost than gas.
|Technology||Unit size [MW]||Start-up cost [€/MW]||Minimum level [%]||Minimum level flexible [%]|
The effect of UC on the results is both a different optimal dispatch due to the additional constraints and a modified price duration curve. Indeed, with the addition of the start-up costs to the optimization, the marginal cost of units is more diversified.
The user sets CO2- and fuel prices. The CO2-price is perceived as a real cost by the power plants as in the case of CO2 tax or CO2 quota system.
The fuel costs of nuclear power include both front-end and waste management costs.
The market simulations assume a price ceiling of EUR 3000 per MWh. At this price level consumers are disconnected to achieve balance between supply and demand. If load shedding occurs, the amount of MWh that is disconnected, is shown in the results overview. When calculating total system costs load shedding is also priced at EUR 3000 per MWh, as an indication of the value of lost load (VOLL).
The user can choose between a load curve for a cold climate location and a warm climate location. Both locations are on the Northern hemisphere.
The profiles of wind and solar are based on the conditions selected in Step 1. The wind generation profile depends on the size of the area and the quality of wind resource, while the solar one depends on climate and area size. In both cases, the effect of a larger area size is to smoothen the generation profiles, leading to higher production at low irradiation/wind speed thanks to the geographical spreading of the plants.
Wind generation is modelled through a wind speed profile and an aggregated power curve. Wind speed time series are derived from the EMHIRES dataset  for the year 2014. The three locations chosen to represent the differenct resource quality are Western Denmark (Very good wind resource, average of 8 m/s), Northern Germany (Good wind resource, average of 7 m/s) and Southern Germany (Pood wind resource, average of 6 m/s). All locations feature a higher wind resource in winter compared to summer. The standard technology, more suited for higher wind sites, is characterized by a specific power of 325 W/m2 and a hub height of 100 m. The capacity factor of the standard wind technology is equal to 0.33 for very good, 0.26 for good and 0.18 for poor wind resource.
If the option for Advanced specific turbines is selected under the system integration options, a technology with 180 W/m2 and 125 m of height is deployed. The deployment of lower specific power turbines (larger rotors for the same rated power) and higher hub heights enables to harvest a better wind resource and thus achieving higher capacity factors. In this case the capacity factors are equal to 0.5 for very good, 0.42 for good and 0.31 for poor wind resource.
|Wind Technology||Poor||Good||Very Good|
|Advanced (system integration measure)||0.31||0.42||0.50|
The solar profile is also derived from EMHIRES dataset  for 2014. The cold climate refers to Nortern Europe conditions and features a capacity factor of 0.11 (950 FLH), while warm climate is based on Southern Europe conditions, leading to a capacity factor of 0.17 (1500 FLH). Due to the location on the Northern hemisphere the generation is highest in summer time.
|Cold Climate||Warm Climate|
The hydro power run-of-river profile assumes a capacity factor of 38 %. Generation is highest in late spring.
The capacity factor of the hydro power plant with reservoir is 50 %. The inflow to the hydro power plant with reservoir follows the same profile as the run-of-river plant. The storage capacity of the reservoir can hold op to 66 % of the annual inflow to the reservoir with seasonal variations. The reservoir is in the middle of its bounderies at the beginning of the year and the model ensures that this is also the case at the end of year. The model optimizes electricity production from the hydro power plant with reservoir in order to maximize the value of the limited water inflow to the plant.
Thermal generators like coal and gas fired power plants are per se dispatchable units. However, in some jurisdictions where there are no actual markets for electricity, power plant owners often are provided with a guarantee to produce a certain number of MWhs annually. Such contracts are made to give investors certainty that they will be able to recover their capital costs, but at the same time they reduce the flexibility of the power system and its ability to integrate fluctuating renewable energy technologies.
In the simulation, minimum level of capacity factors can be set for dispatchable technologies, namely nucalear, coal, natural gas and biomass. Being generally a technology that covers the demand peaks, oil is not included as an option. The maximum value of capacity factor in input is set to 80%, since some technologies already have a seasonal capacity de-rate to take into account scheduled maintenance of units.
 Danish Energy Agency. Technology Data for Energy plants [Online]. Available: online. [Accessed: 20-Aug-2017].
 Agora Energiwende. The Danish Experience with Integrating Variable Renewable Energy - Lessons learned and options for improvement. 2015.
 Ecofys. Flexibility options in electricity systems. 2014.
 Agora Energiwende. Flexibility in thermal power plants – with a focus on existing coal-fired power plants. 2017.
 I. Gonzalez Aparicio, A. Zucker, F. Careri, F. Monforti, T. Huld, and J. Badger. EMHIRES dataset Part I: Wind power generation. 2016.
 I. Gonzalez Aparicio, T. Huld, F. Careri, F. Monforti, A. Zucker. EMHIRES dataset Part II: Solar power generation. 2017.