The real-world energy supply system is a complex entity that is influenced by a variety of factors. These influences (such as technological developments, consumption patterns, weather conditions, raw materials and potentials) are made increasingly more complex by political, social and economic interests.
These influences also have a national impact, due to the close connection between individual locations across the energy networks. Our wide range of modeling instruments is perfectly suited to detecting all the essential causal relationships in the energy markets and for forecasting. As a result, we obtain realistic forecasts and possible scenarios for the further development of the energy system.
We continuously develop our models – including in the context of research and industrial projects - so that we can analyze new questions on the basis of current model findings.
The European electricity market model developed by r2b is used to simulate the individual decisions of economic agents (businesses and consumers) on competitively organized electricity markets - taking into account economic and political conditions.
Our model maps the investment and disinvestment decisions of market participants – as well as their short-term, hourly decisions on the supply and demand of electricity – to inform our medium to long-term forecasts. Our electricity market model can predict the minimal development cost of a future power generation system. These predications correspond to the requirements of future developments called fundamental variables, such as the price of fuels and CO2 certificates or annual electricity demand. In this instance, r2b takes into account both the composition of the generation park as well as hourly usage on all relevant markets.
Determine the power plant park of the future
Our model of the European power plant dispatch simulates operational decisions for conventional, as well as for storage and pumped storage power plants, with a precise breakdown by plant unit. We simulate the competitive market outcome of the electricity market in an outstanding way. We do this by considering the minimal operation cost of the plant park on the wholesale electricity market and balancing energy markets. Basically, our power plant dispatch model takes into account all generating capacity (in order of their short term marginal costs), the start-up processes required to meet the demand for each hour, and the provision of balancing power and energy.
We calculate the profit-maximizing hourly production and marketing structure on the basis of predefined hourly electricity and balancing energy market prices, as well as the variable costs of the power plant.
In addition to marketing on the spot market, the model also calculates a maximum profit bid strategy on all balancing energy markets, taking account of energy and power prices as well as the stochastic demand of balancing energy.
The European Renewable Energy Model predicts the future development of renewable energies in the electricity sector; taking into account technical, economic and political conditions.
Premium models and quota models can be modeled in addition to fixed-price remuneration systems, in combination with our European fundamental electricity market model.
Our forecasting model for the development of electricity demand is a bottom-up model. Based on the relevant factors, it updates sector-specific electricity consumption in the context of different scenarios. Foreseeable future developments (such as in the field of energy efficiency, electric mobility, climate, population growth and economic growth) are analyzed and predicted, based on single hourly consumption profiles for the individual sectors. Thus, in addition to the development of annual electricity consumption, hourly load time series for the individual sectors can also be derived.
We model European consistent feed-in profiles for onshore wind, offshore wind and photovoltaic technologies, based on our extensive and high-resolution weather, potential and plant data. The feed-in profiles can be generated for years with contrasting weather. The data has been validated in extensive analyzes so we can realistically depict the feed-in structures of European renewable technologies.
We take account of technological developments such as the increase in hub heights or power densities of new wind turbines to forecast future feed-in structures. The feed-in profiles generated are used for all quantitative analyzes of the development of the German and European power system.
The determination of the future demand for reserves and the demand probabilities for balancing energy of different qualities is subject to complex circumstances. Therefore, we represent them in a model specially developed for this purpose. The calculation of the target values using the Graf-Haubrich Process is at the core of this model.
This takes account of the probability of a given extent of imbalances on the basis of a probabilistic approach. This takes into account the inevitable differences between planned and actual feeds into the grid, and between planned and actual consumption quantities from the network. There are various causes for the inevitable imbalances considered in the calculation. Relevant factors include the type of balancing energy and its usage duration, any failures in conventional power plants, renewables, load and power plant noise as well as any forecast error regarding renewable feed and load.
Our model for controlling balancing energy dimensioning is used, among its other applications, as an important input interface into quantitative simulations of electricity market modeling or in the context of security of supply considerations.
The issue of security of supply is of increasingly greater importance in the ongoing changes in an electricity supply system characterized more and more by energy generated from renewables. r2b energy consulting employs a probabilistic approach to calculate the 'guaranteed capacity' of a power generation park. What does this mean? Our approach determines the total generating capacity available at the time of annual peak load in a production system using a certain probability. This takes into account the likelihood of unplanned, indispensable and the supply-dependent non-availabilities of individual generating facilities.
We estimate short-term fundamental factors - such as the wind power feed, the amount of consumption load or the temperature curve - based on advanced econometric models, across different electricity tariff structures (spot market, intraday and balancing energy).
We use the results of this to create HPFCs, for example. They can be corrected for stochastic effects (the effect of contrasting weather years) or the construction of additional renewable energy plants.