Renewable energy generation projects are often measured by their peak capacity. A wind farm rated at 25 MW will generate 25 MW of power under the right circumstances. This peak capacity is reached very little in practice. However, these generators are forced to purchase grid operation infrastructure that can handle this peak generation event. The high voltage grid connections are expensive and increasingly difficult to receive permits for. This work presents a solution in which the high voltage grid connection is undersized in comparison to the renewable energy generator. A battery energy storage system is installed in the local grid to solve the issue of excess energy generation (congestion).

A simulation of the local network has been built that models a battery energy storage system (BESS), the network and uses data from a solar park. A case study in which a 19 MW solar park is connected to the high voltage grid with a transformer of only 14 MW as well as a 14 MW | 30 MWh BESS on the network is investigated in the rest of the work. Furthermore a BESS control strategy for price arbitrage on the TenneT imbalance market is presented and encoded such that it can be optimised.

Four heuristics are presented that time and size the congestion issue in a manner the control strategy of the BESS can prepare for and solve congestion when necessary. These heuristics are tested against strategies optimized for revenue maximisation through price arbitrage. While the most aggressive strategies did not solve all the congestion events in these simulations, we found that the heuristic that takes the average generation of the solar park into account performs the best while remaining appropriately conservative.

A basic evolutionary algorithm is presented that optimizes a BESS control strategy for price arbitrage when the BESS is not needed on the local network to solve congestion. Although the strategies earn ~33% less revenue due to the congestion related limitations, the optimisation surrounding congestion does improve revenue by 2.58%.

The results presented in this work suggest that this setup of a local grid can be economically viable and that the BESS can solve the congestion issue when steered with an appropriate control strategy. We hope to inspire parties that battery energy storage systems can earn substantial revenue aside from solving issues on a (local) grid.

Amsterdam Airport Schiphol has 5 runways, each of which can be used for take-off or landing of aeroplanes. The weather heavily influences which runway configuration air traffic control might pick. Airport Forecasting Service (AFOS) predicts which configuration of runways works most efficiently given a set of expected weather conditions and the standard deviations of wind components. These standard deviations give the system an indication of the accuracy of the weather forecasts.

Currently, the KNMI (Royal Netherlands Meteorological Institute) is the only meteorological institute that provides these standard deviations along with the weather forecast. This raises the main research question of this report: Is it possible to make accurate enough estimations of the standard deviation of wind direction and wind speed using historical data and future weather expectations. Estimating these standard deviations has been researched with two different approaches: a statistical method approach and a machine learning approach.

Statistical Methods Four fitting methods have been researched in search of the best statistical model to estimate the standard deviation of wind direction and speed: the Maximum Likelihood Method (MLM) and three Least Square Method implementations of a Weibull, Minimum Weibull and Double Weibull distribution. The performance of aggregates on the outcome of these four methods was also researched. One case takes the minimum standard deviation of the four, the other takes the mean.

MLM not only performs the best but also performs most consistently of the four fitting methods. Taking into account aggregates, MLM is more consistent than the minimum method but the minimum method outperforms it. Neither of these methods managed to meet the success criteria.

Machine Learning In regards to machine learning, the problem of estimating the standard deviations of wind direction and wind speed is a regression problem. The following machine learning models have been researched for Estimatic: MLPN, LSTM RNN, ERNN and RBFN.

LSTM RNNs outperform MLPNs, RBFNs and ERNNs for both wind direction and speed standard deviation estimation. LSTM RNN performance did not meet the success criteria.

The research concludes that it is not possible to make accurate enough estimations of the standard deviation of wind components using the historical data and future weather expectations available for Amsterdam Airport Schiphol.