This paper provides the result of a techno-economic study of potential energy storage technologies deployable at wind farms to provide short-term ancillary services such as inertia response and frequency support. Two different scenarios are considered including a single energy storage system for the whole wind farm and individual energy storage for each wind turbine (located at either the dc or the ac side of its grid-side converter). Simulations are introduced to check the technical viability of the proposal with different control strategies. Power and energy capability requirements demanded by both specific services are defined for each studied case based on present and future grid code needs. Based on these requirements, the study compares a wide range of energy storage technologies in terms of present-day technical readiness and properties and identifies potential candidate solutions. These are flywheels, supercapacitors, and three chemistries out of the Li-ion battery family. Finally, the results of a techno-economic assessment (mainly based on weight, volume, lifetime, and industry-confirmed costings) detail the advantages and disadvantages of the proposed solutions for the different scenarios under consideration. The main conclusion is that none of the candidates are found to be clearly superior to the others over the whole range of scenarios. Commercially available solutions have to be tailored to the different requirements depending on the amount of inertia, maximum Rate of Change of Frequency and maximum frequency deviation to be allowed.

Inertial response from grid‐followers (GFLs) is deemed to be “synthetic” due to a slow response. In contrast, grid‐forming (GFM) inertial response is deemed to be faster and therefore “true” and more useful for frequency stability. This paper explores the differences and similarities between an established example of a GFM and a GFL inertial controller by carrying out parametric sweeps at different operating conditions. The analysis aims to assist the ongoing efforts to quantify grid stabilising phenomena, particularly the recent adaptation of the British grid code to incorporate GFM converters. The optimal tuning configurations are identified, showing that some configurations of the GFL can achieve fast inertial provision on strong grids. These configurations are shown to contain the grid frequency as effectively as the GFM, despite the opposing consensus in the literature. The results also highlight the importance of voltage‐source behaviours in determining the initial evolution of grid frequency. Although a blanket inclusion of all GFL inertial configurations is not appropriate, equally, the existing blanket disqualification could limit the assets available to support GFMs (who will certainly be required to stabilise the grid in a fundamental sense) and could inhibit the rate that the net zero transition can occur.

It is imperative to increase the connectable capacity (i.e., hosting capacity) of distributed generation in order to decarbonise electricity distribution networks. Hybrid generation that exploits complementarity in resource characteristics among different renewable types potentially provides value for minimising technical constraints and increasing the effective use of the network. Tidal, wave and wind energy are prominent offshore renewable energy sources. It is of importance to explore their potential complementarity for increasing network integration. In this work, the novel introduction of these distinct offshore renewable resources into hosting capacity evaluation enables the quantification of the benefits of various resource combinations. A scenario reduction technique is adapted to effectively consider variation of these renewables in an AC optimal power flow-based nonlinear optimisation model. Moreover, the beneficial impact of active network management (ANM) on enhancing the renewable complementarity is also investigated. The combination of complementary hybrid generation and ANM, specifically where the maxima of the generation profiles rarely co-occur with each other and with the demand minimum, is found to make the best use of the network components.

This paper describes a new approach used by Hydro-Quebec to optimize the settings of automatic devices installed in its main power plants to maintain secure operation under extreme contingencies. An example of application of this approach is given to illustrate how to apply data mining techniques for the setting of the automatic generator tripping and load shedding system (RPTC: rejet de production et teledelestage de charges in French) installed at the Churchill Falls hydroelectric power plant (5500 MW) in Labrador. Real time snapshots of the Hydro-Quebec power system collected over a 5 year period have been used to generate large amounts of results (database) by transient stability simulations. The database is processed by the data mining software developed by the University of Liege to construct the decision trees. This approach gives the most relevant parameters and finds optimal settings for the RPTC system at the Churchill Falls, minimizing the number of generator tripping while maintaining the same performance in terms of security coverage. New operation rules have thus been established.

Wind turbine (WT) control is being adapted to enable inertia provision that supports network frequency on short timescales. Measuring the inertia contribution from wind turbines is critical to asses the provision of the service as well as understand the WT operation. However, inertia measurement methods disagree on the impact of the wind and how to approximate its effects. This paper uses data from a ScottishPower Renewables (SPR) test of a grid connected wind farm to highlight that wind can impact inertia provision and that external network power measurements are unable to measure the inertia. Two proposals are made to improve inertia measurements. First, the International Electrotechnical Commission (IEC) industrial standard for WT inertia measurement is adapted, and secondly, an alternate method using system identification is proposed that considers characteristics of the WT’s dynamic response. The measurement methods from the literature and the proposals are assessed using the output of time-domain WT models to find the sensitivity of their accuracies to variations in the wind, frequency, and control-setting conditions. The methods from the literature are inaccurate during variable wind conditions but the proposed approaches improve the accuracy. The findings of the sensitivity study are then validated by applying the measurement methods to the SPR wind farm experimental data and confirm that the proposed system identification method is the most accurate measurement approach.