The number of battery energy storage systems (BESSs) installed in the United Kingdom and worldwide is growing rapidly due to a variety of factors, including technological improvements, reduced costs and the ability to provide various ancillary services. The aim of this paper is to carry out a comprehensive literature review on this technology, its applications in power systems and to identify potential future developments. At first, the main BESSs projects in the UK are presented and classified. The parameters provided for each project include rated power, battery technology and ancillary services provided, if any. In the next section, the most commonly deployed ancillary services are classified and described. At the same time, the nomenclature found in the literature is explained and harmonised. The second part of the paper focuses on future developments and research gaps: ancillary services that currently are not common but that are likely to be deployed more widely in the future will be described, and more general research topics related to the development of BESSs for power system applications will be outlined.

This work deals with the steady-state operation of wind energy conversion systems (WECSs) used simultaneously as active filters (AFs) and power generators. This application is motivated by the increasing number of non-linear loads (NLLs) on the power grid and by the possibility to offer an economical alternative to the installation of dedicated filters that sink the harmonic currents injected by such loads. The main contributions of this paper are as follows: (i) three different control systems that allow the simultaneous use of the WECS as AF and power generator are presented; (ii) compensation of triplen harmonics is addressed; (iii) additional power losses due to AF operation, leading to the derating of the WECS, are quantified; (iv) results based on numerical simulations are provided.

The voluminous amount of raw waveform data recorded by triggerless power quality monitors contain conspicuous and inconspicuous disturbance events. Data reduction and detection techniques are needed to efficiently extract useful information hidden in the raw data and identify power quality disturbances. The overall objective of this study is to use step changes in the rms voltage profile as an alternative triggering feature for automatically detecting switching events. The full characterization of the event is based on processing a small portion of the voltage waveform selected around the detected rms voltage step change. A filtering method is proposed to smooth out rapid fluctuations in the rms voltage profile during steady-state operation, while preserving the sharp edges caused by rms voltage step changes. Once the rms voltage profile has been filtered, adaptive limits based on the median absolute deviation are computed for detecting rms voltage step changes. The effectiveness of the proposed technique is evaluated using triggerless voltage waveforms to detect capacitor switching events. The use of the filtered rms voltage profile allows accurate detection of capacitor energizing and de-energizing events, while more than 50% of the detections in the unfiltered profile correspond to false-positives.

The growing adoption of decentralised renewable energy generation (such as solar photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain experienced by the distribution networks in the near future. In such a scenario, energy storage is becoming a key alternative to traditional expensive reinforcements to network infrastructure, due to its flexibility, decreasing costs and fast deployment capabilities. In this work, an end-to-end data-driven solution to optimally design the control of a battery unit with the aim of reducing the peak electricity demand is presented. The proposed solution uses state-of-the-art machine learning methods for forecasting electricity demand and PV generation, combined with an optimisation strategy to maximise the use of photovoltaic energy to charge the energy storage unit. To this end, historical demand, weather, and solar energy generation data collected at the Stentaway Primary substation near Plymouth, UK, and at other six locations were employed.