To achieve the goals on greenhouse gas emissions, the energy supply and demand is in transition. Distribution power grids therefore are increasingly reaching their capacity limits due to electrification and the vast increase of distributed energy resource (DER) connection requests with large peak power output. Increasing physical grid capacity is a costly operation and take lots of time to realise. Grid operators are therefore allowed to connect additional energy resources to the power grid at the cost of N-1 reserve capacity. Having N-1 reserve capacity means that any grid component can go out of service, without causing overloading of another grid component. When this security principle is abandoned, coupled preventive and corrective control measures might be necessary to preserve security of energy supply. In this thesis, commissioned by the Dutch distribution grid operator Stedin Netbeheer, a preventive-corrective contingency control method based on differential evolution (DE) is designed to increase the maximum admissible DER generation on a distribution grid. For the contingency analysis process in this contingency control method, the full AC power flow method is compared to the method based on line outage distribution factors. The resulting DE-based preventive-corrective control method using the full AC power flow contingency analysis method allows for significant extra DER generation capacity to be connected to the study case distribution power grid, without requiring expensive grid expansions. The case study of this thesis work is the Stedin Middelharnis distribution power grid. Due to the computational complexity of the preventive-corrective contingency control method and the sparse connectivity of power system data, convolutional neural networks are deployed to reduce the computational time. In the first approach, the convolutional neural network is used to perform contingency analysis. Due to this neural network approach, the time to compute the preventive-corrective control actions is reduced by 40%. However, the accuracy of the control is significantly reduced due to the inaccuracy of the used contingency analysis method. In the second approach, a neural network is trained to determine the coupled preventive control actions. The performance of a neural network with and without convolutional neural networks is compared in this approach. Results show that the convolutional neural network outperforms the neural network without convolutional layers. This second convolutional neural network approach is satisfactory accurate and the computational efficiency of this control method is greatly increased compared to the control method based on DE, making realtime preventive-corrective control possible.

The renewable generation capacity, particularly solar and wind power installations, has increased steadily in the Netherlands over the course of recent years. Due to the local, small-scale nature of these power plants (compared to conventional power plants), a large share of this generation capacity is installed into the medium- and low-voltage distribution grids. This trend acts as the source of several challenges for distribution system operators (DSOs) such as Stedin Netbeheer B.V. (a DSO in the Netherlands, supporting this project). One of the main problems is the fact that voltage limit violations, particularly upper voltage limit violations, become more frequent. Conventional voltage control schemes assume a unidirectional power flow (i.e., consumption only) in distribution grids and are unable to keep the limits in case a large share of generation capacity is installed. This is further complicated by the fact that underground cable networks, typical in Western European electricity distribution grids, have lines with large R/X ratios, which reduces the effectiveness of reactive power injection-based voltage control methods. This MSc project intends to solve the issue of voltage limit violations with a model predictive control (MPC) policy. The considered control actions coordinated by the model predictive controller are the switching of the on-load tap changer (OLTC) mechanism mounted to the primary substation’s transformer, setpoint adjustments of the low-level OLTC control relay, and the active power curtailment of larger photovoltaic plants. A linear, sensitivity-matrix-based model is used for the grid’s state prediction; and the sensitivity values are re-calculated at each sampling time step of the MPC. To avoid the curtailment of photovoltaic (PV) plants when not justified, a conditional curtailment logic is incorporated into the MPC policy: PV plants are only allowed to be curtailed if their local voltage magnitude is above a tuneable threshold. This logic is described and incorporated into the model predictive controller’s optimization problem with the help of binary variables and mixed-integer linear (MIL) constraints. The benefit of incorporating knowledge about future disturbances (load, generation, and external grid voltage profiles) is also tested, in order to assess the potential benefit DSOs could get from forecasting these quantities. A case study was conducted in which the considered controllers were tested on a section of a Stedin grid that carries the characteristics of a typical Western European medium voltage distribution grid: large R/X ratios and a large installed PV generation capacity both in the form of household generation and larger PV plants. All controllers were simulated in 4 different test cases: a typical summer day, a summer day with the external grid’s overvoltage, a summer day with 2 out of 3 large PV plants not operating, and a typical winter day. All test cases have the control goals of mitigating limit violations and ensuring that the nodal voltage magnitudes are as close as possible to the nominal 1 per unit throughout the day. The winter day test case has the additional goal of avoiding excessive curtailment as PV energy is worth considerably more during these days. All profile data is based on real Stedin measurements. The designed model predictive control policies are compared to two simple control schemes: current compounding, i.e. when the primary substation’s automatic voltage control relay’s setpoint is adjusted based on the active power delivery through the substation’s transformer, and another scheme when current compounding is combined with local active power curtailment controllers for large PV plants. The most important metrics used for comparison are voltage root mean square error (RMSE), the total voltage limit violation area, the percentage of curtailed PV energy, and the number of tap changes over the considered day. The simulations were carried out using Python and DIgSILENT PowerFactory. The simulation results show that the MPC policy can perform better than the simple control schemes but only when exact knowledge of future profiles is available. In this case, the MPC results in lower voltage RMSE, smaller violation areas, and lower curtailment percentage values, at the expense of using more tap changes in all 4 test cases. In 3 of the 4 test cases, MPC was completely able to eliminate voltage limit violations, showing clearly the advantages of good quality forecasts on future disturbances. Since this exact knowledge about the near future fluctuations is quite ideal, more realistic MPC policies were also tested with no future knowledge and tightened voltage constraints. These simulations brought mixed results when compared with the simple schemes, performing better in terms of voltage RMSE, but worse in terms of the total voltage limit violation area.