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This study proposes a novel operation strategy for wind farms' optimal Frequency Containment Reserve (FCR) provision that simultaneously distributes FCR and optimally controls wake formation. The power reserve allocation is dynamically decided at the wind farm supervisory control level, considering the intermittent wind power and direction, grid frequency stochasticity, and the aerodynamic complexity of the wake. A two-stage stochastic programming approach supports decision-making for an optimal contribution to day-ahead energy/FCR markets considering sub-hourly wind power and grid frequency uncertainty. Moreover, a novel method is used to reduce the computational complexity by employing a data-driven surrogate model of wake formation in the optimizer. This surrogate model consists of a neural network trained on the Gauss-Curl-Hybrid wake model in FLORIS. This deep learning approach allows fast estimation of the wake control parameters, i.e., the optimal yaw angles and axial induction factors. Then, a coevolutionary-based multi-objective particle swarm optimization searches for the optimal deloading of the WTs and maximizes the total power production and kinetic energy while minimizing wake. The performance of the proposed algorithm is evaluated on the C-Power wind farm layout in the North Sea. Simulation results demonstrate its effectiveness in improving the wind farm's overall performance for different operational conditions.

Electricity markets around the world are opening up to a greater contribution from wind power producers (WPPs). In this regard, WPPs are incentivised to participate in both energy and reserve market floors while being responsible for real-time deviations from their submitted bids. Therefore, despite uncertainties in wind speed and system frequency, effective control systems should be developed to enable WPPs to respond reliably concerning their committed day-ahead bids, as flexible conventional power plants do. However, designing a control system for WPP to regulate their capacity margin and output power as per the offered reserve bid is challenging, as a fast response with respect to the offered balancing service is required. This paper proposes an effective control system that allows WPP to regulate their set-points so as to provide the committed reserve power while considering the real-time wind variations. A machine-learning algorithm based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to predict the wind speed of the following instances, to be used as input to the control system. Several wind profiles are generated to simulate a practical case study, including real and predicted cases with varying levels of turbulence. Finally, the effectiveness of proposed control strategies on the WPP's profit is evaluated.

Goal: An optimal operation strategy for wind farms to maximize total energy production, minimize wake interaction, and provide grid balancing services to transmission system operators.

An LVDC backbone with aggregated PV and BESS is a promising grid architecture leading to a considerable reduction of the energy losses. Additionally, previous analysis showed that the replacement of the multiple small DC/AC inverters in a traditional LVAC grid by one central DC/AC inverter creates many other scale-related advantages. The operating voltage level is an important parameter therein as it has a direct impact on the cable and conversion losses. As the PV and BESS voltages and powers exhibits a high variability due to the natural intermittency of solar irradiance, potential benefits emerge when a dynamic backbone voltage optimisation is applied instead of a static voltage. Moreover, by extending the objective of the optimization with the maximization of the produced PV power, the DC/DC converter —which guarantees the maximum power point (MPP) operation of the PV system —, could be eliminated. The dynamic voltage strategy will hence be driven by a multi-objective optimization algorithm in order to define the Pareto front between minimizing the cable and conversion losses and maximizing the PV yield. The elimination of the DC/DC converters would lead to a reduction of the cost, the space and the complexity of the installation. The obtained results exhibits a clear decrease of 53% in energy losses when applying a dynamic voltage strategy with MPP tracker (MPPT). This is mainly caused by the decrease in cable losses and conversion losses in the second stage DC/DC of the central grid inverter. Eliminating the MPPT leads to a slight increase of the losses which is a consequence of the compromise between the predefined objectives. However, this slight loss increase combined with the 1% curtailment loss opens perspectives to simplify the PV installation and to reduce the balance of system cost.