10 results found
Saint-Drenan Y-M, Besseau R, Jansen M, et al., 2020, A parametric model for wind turbine power curves incorporating environmental conditions, Renewable Energy, Vol: 157, Pages: 754-768, ISSN: 0960-1481
A wind turbine’s power curve relates its power production to the wind speed it experiences. The typical shape of a power curve is well known and has been studied extensively. However, power curves of individual turbine models can vary widely from one another. This is due to both the technical features of the turbine (power density, cut-in and cut-out speeds, limits on rotational speed and aerodynamic efficiency), and environmental factors (turbulence intensity, air density, wind shear and wind veer). Data on individual power curves are often proprietary and only available through commercial databases. We therefore develop an open-source model for pitch regulated horizontal axis wind turbine which can generate the power curve of any turbine, adapted to the specific conditions of any site. This can employ one of six parametric models advanced in the literature, and accounts for the eleven variables mentioned above. The model is described, the impact of each technical and environmental feature is examined, and it is then validated against the manufacturer power curves of 91 turbine models. Versions of the model are made available in MATLAB, R and Python code for the community.
Jansen M, Staffell I, Kitzing L, et al., 2020, Offshore wind competitiveness in mature markets without subsidy, Nature Energy
© 2020, The Author(s), under exclusive licence to Springer Nature Limited. Offshore wind energy development has been driven by government support schemes; however, recent cost reductions raise the prospect of offshore wind power becoming cheaper than conventional power generation. Many countries use auctions to provide financial support; however, differences in auction design make their results difficult to compare. Here, we harmonize the auction results from five countries based on their design features, showing that offshore wind power generation can be considered commercially competitive in mature markets. Between 2015 and 2019, the price paid for power from offshore wind farms across northern Europe fell by 11.9 ± 1.6% per year. The bids received in 2019 translate to an average price of €51 ± 3 MWh−1, and substantially different auction designs have received comparably low bids. The level of subsidy implied by the auction results depends on future power prices; however, projects in Germany and the Netherlands are already subsidy-free, and it appears likely that in 2019 the United Kingdom will have auctioned the world’s first negative-subsidy offshore wind farm.
Jansen M, Staffell I, Green R, 2018, Daily marginal CO2Emissions eeductions from wind and solar generation, 15th Conference on the European Energy Market (EEM), Publisher: IEEE, ISSN: 2165-4093
This paper estimates the half-hourly and daily CO 2 emissions from electricity generation in Britain, and the influence that wind and solar output has on these. Emissions are inferred from the output of individual plants and their expected efficiency, accounting for the penalty of part-loading thermal generators. Empirical Willans lines are created for typical coal, oil and combined-cycle gas generators from the US CEMS database, giving the first fully-empirical treatment of the British power system. We compare regressions of half-hourly and daily emissions to estimate the impact of plant start-ups, which may not occur in the specific hours when wind and solar output drops, and thus may be mis-identified in half-hourly regressions. Our preliminary findings show that dynamic plant efficiency may reduce the carbon savings from wind by 5-12% and for solar by 0-6%. The effect is strengthening with increasing penetration.
Green R, Jansen M, Staffell I, et al., 2018, Electricity, Wind and Carbon: What determines the emissions savings from wind power in Great Britain?, Conference on Renewable Energy and Electricity Markets
Jansen M, 2016, Economics of control reserve provision by fluctuating renewable energy sources
The provision of control reserve, and therefore contributing to the secure operation of the power system, is paramount in a future energy system with increasing shares of fluctuating renewable energy sources. This doctoral thesis proves that fluctuating renewable energy sources, such as onshore and offshore wind farms as well as photovoltaic systems, are capable of providing control reserve at the same level of reliability as conventional generators. It is shown that the introduction of fluctuating renewables to the control reserve market can access a welfare gain that could be realized as additional income by the new market participants or as cost saving potential of the control reserve procurement. The dependency analysis between the welfare gain and the regulatory framework leads to recommendations for the development of the control reserve market.
Jansen M, 2016, Economics of control reserve provision by fluctuating renewable energy sources, 2016 13th International Conference on the European Energy Market (EEM), Publisher: IEEE, ISSN: 2165-4077
The delivery of control reserve by fluctuating renewable energy sources (RES) generators will be important in an energy system with high RES penetration. This paper extends a previously introduced methodology to quantify the possible additional income of different pools of fluctuating RES generators in the negative secondary and tertiary control reserve market in Germany. The updated methodology allows concluding on the ideal market conditions by comparing different pool types and years. The development of the results over a long assessment period allows extrapolating the market value of the new market participants into the future. Results show a high dependency of the possible additional income on the overall market size and the market conditions and regulations.
Richts C, Jansen M, Siefert M, 2015, Determining the economic value of offshore wind power plants in the changing energy system, 12th Deep Sea Offshore Wind R and D Conference (EERA DeepWind), Publisher: ELSEVIER SCIENCE BV, Pages: 422-432, ISSN: 1876-6102
Hennig T, Loewer L, Faiella LM, et al., 2014, Ancillary Services Analysis of an Offshore Wind Farm Cluster - Technical Integration Steps of a Simulation Tool, EERA 11th Deep Sea Offshore Wind R and D Conference (DeepWind), Publisher: ELSEVIER SCIENCE BV, Pages: 114-123, ISSN: 1876-6102
Jansen M, Speckmann M, 2013, Participation of photovoltaic systems in control reserve markets
Photovoltaic (PV) systems have not provided control reserve until now in Germany although the installed capacity is increasing constantly. This paper will give an overview of how PV systems could deliver control reserve to the system. A new proof method for the offering of control reserve provision is presented. Results show an economic opportunity for PV systems if they opt to offer negative control reserves. The cost saving potentials under realistic conditions can reach up to 6.5 % in the tertiary control reserve market and up to 3.9 % in the secondary control reserve market.
Jansen M, Speckmann M, 2013, Wind turbine participation on control reserve markets, Pages: 1432-1439
Under current frame work conditions wind turbines are not able to participate in control reserve markets, although they are contributing an increasing share of the electricity in the power system. The introduction of an "optional market premium" in the revision of the German Renewable Energies Act (EEG) by January 2012 has set the legislative framework for the integration of wind farms into the existing market structures. This led to the successful integration in power exchange markets, whereas wind farms have not yet participated in control reserve markets in Germany, due to the lack of proper regulations. Currently the proof is done by comparing the planned power production and the real power production. The difference has to match the control reserve power. Applying this method to wind farms will enforce them to stick to a schedule. Instead of this a method is shown and demonstrated which is suitable for wind farms and for all three kinds of control reserve (primary, secondary and tertiary control). By comparing the available active power with the real power production the delivery of energy from control reserve can be proven. The available active power is the power that would have been produced if the wind farms had not been down-regulated. The creation of bids for the control reserve markets will utilize probabilistic forecasts. Wind farms are capable of providing control reserve on very high security levels. They can compete economically with conventional generation. The cost saving potentials can reach up to 24 % in the tertiary control reserve market with the most suitable conditions.
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