MICOES-Europe and MICOES-Barometer are fundamental electricity market models. MICOES-Europe models the European electricity market in detail and provides scenarios of future day-ahead electricity prices, taking into account various well-founded assumptions. MICOES-Barometer focuses on the German control reserve market and calculates fundamental power and work prices for primary, secondary and minute reserve (FCR, aFRR and mFRR).
Software: GAMS
Model type: Techno-economic, deterministic
Field of application: Scenarios of future electricity prices (day-ahead), balancing power prices, optimal dispatch of electricity suppliers and demanders
Model description
Background
Prices on the wholesale electricity market will change in the future due to the further expansion of renewable energies and the increasing penetration of sector coupling technologies. For investment decisions in new electricity generation or consumption plants, knowledge of the future development of electricity prices in terms of revenues or costs is central. Due to the changing power plant fleet, however, historical prices cannot simply be extrapolated into the future, as their structure is subject to fundamental changes.
Modelling objective
The fundamental models MICOES-Europe and MICOES-Barometer start at this point and explicitly take into account the techno-economic properties of the power plant fleet as well as of flexible and inflexible sector coupling technologies. This allows their cost-optimal use on the electricity market (day-ahead and for balancing power) to be determined for future scenarios. As a result, the models provide scenarios of future day-ahead electricity prices as well as fundamental power and labour prices for FCR, aFRR and mFRR.
Approach
Both models use a detailed database of the European and German power plant fleet with its techno-economic parameters. In addition, hourly resolved time series of the conventional electricity demand as well as of heat pumps and electric vehicles are taken into account. The weather-dependent feed-in of renewable energies is fundamentally determined on the basis of regional weather data. Both models calculate the cost-optimal plant deployment as a mixed-integer optimisation problem and deliver both the plant deployment and prices for electricity on the wholesale market or power and work prices on the balancing power market as a result. Strategic behaviour is not taken into account in the models.
Benefits
The scenarios of future prices for electricity on the wholesale market or on the balancing power market can be used to support strategic decisions. In particular, by varying the scenarios examined for their effects, sensitivities to certain parameters can be estimated in advance.
