Quantification of efficiency improvements from integration of battery energy storage systems and renewable energy sources into domestic distribution networks

Due to the increasing use of renewable, non-controllable energy generation systems energy storage systems (ESS) are seen as a necessary part of future power delivery systems. ESS have gained research interest and practical implementation over the past decade and this is expected to continue into the future. This is due to the economic and operational benefits for both network operators and customers, battery energy storage system (BESS) is used as the main focus of this research paper. This paper presents an analytical study of the benefits of deploying distributed BESS in an electrical distribution network (DN). The work explores the optimum location of installing BESS and its impact on the DN performance and possible future investment. This study provides a comparison between bulk energy storage installed at three different locations; medium voltage (MV) side and low voltage (LV) side of the distribution transformer (DT) and distributed energy storage at customers’ feeders. The performance of a typical UK DN is examined under different penetration levels of wind energy generation units and BESS. The results show that the minimum storage size is obtained when BESS is installed next to the DT. However, the power loss is reduced to its minimum when BESS and wind energy are both distributed at load busbars. The study demonstrates that BESS installation has improved the loss of life factor of the distribution transformer

Point estimate method for voltage unbalance evaluation in residential distribution networks with high penetration of small wind turbines

Voltage unbalance (VU) in residential distribution networks (RDNs) is mainly caused by load unbalance in three phases, resulting from network configuration and load-variations. The increasing penetration of distributed generation devices, such as small wind turbines (SWTs), and their uneven distribution over the three phases have introduced difficulties in evaluating possible VU. This paper aims to provide a three-phase probabilistic power flow method, point estimate method to evaluate the VU. This method, considering the randomness of load switching in customers’ homes and time-variation in wind speed, is shown to be capable of providing a global picture of a network’s VU degree so that it can be used for fast evaluation. Applying the 2m + 1 scheme of the proposed method to a generic UK distribution network shows that a balanced SWT penetration over three phases reduces the VU of a RDN. Greater unbalance in SWT penetration results in higher voltage unbalance factor (VUF), and cause VUF in excess of the UK statutory limit of 1.3%

Electroporation for water disinfection: a proof of concept experimentation

This paper is a proof of concept showing the effectiveness of using irreversible electroporation (IRE) as a stage of water disinfection in the water treatment process. The IRE process essentially requires relatively high voltage pulses to pose a pulsed electric field across harmful microorganisms. In this paper, a laboratory-based solid-state Marx generator was built for this purpose and untreated water samples have been used to test the effectiveness of applying variable pulse width, magnitude and rate. All the pulses are unipolar rectangular. The tested samples are all from the same water source with the same coliform count. After performing the electroporation disinfection process the coliform count reached zero proving the effectiveness of IRE

On the UK smart metering system and value of data for distribution system operators.

The Smart Metering Implementation Programme (SMIP) is an ongoing energy infrastructure upgrade that is delivering 53 million smart electricity and gas meters for homes and small businesses in the UK. The programme is expected to deliver economic benefits for customers, energy suppliers and the national grid. The programme is also enabling the transition to a more efficient, and flexible smart grid as well as the decarbonisation of the energy sector to achieve the Net Zero carbon emissions goal by 2050. However, with the immense data generated by smart meters connected to the low voltage distribution networks, further technical benefits can be unlocked. This paper provides an overview of the smart meter system in the UK with its originally intended benefits. Then the physical, functional, interface and data specifications of the smart meters are detailed to give an idea of the possible uses of these data. Finally, the paper discusses the technical benefits that are possible from combining the smart meters’ data with industry 4.0 technologies such as decision support systems for network reinforcement and investment, active monitoring and management of the network and its assets, and data-driven digital twins of the distribution networks

Open circuit fault diagnosis technique for inverter switches and gate drive malfunction

Open circuit faults (OCFs) in voltage source inverters (VSIs) can significantly affect their performance and reliability. In this paper, a novel fault diagnosis technique (FDT)is presented for the detection and classification of two types of OCFs in VSIs: gate drive malfunction (GDM) and open switch fault (OSF). the effect of these OCFs on the output current of the VSI is analysed, this shows that they can be identified and distinguished using the average and root mean square (RMS) ratio of the current parameters. The proposed FDT is simple to implement and can identify switch faults with quick response, without the need for additional equipment. In this work the authors adopted the ensemble bagged tree classification method to detect and classify the GDM and OSF, the results show the credibility of the proposed technique in identifying different open circuit faults

Fault detection and localisation in LV distribution networks using a smart meter data-driven digital twin

Modern solutions for precise fault localization in Low Voltage (LV) Distribution Networks (DNS) often rely on costly tools such as micro-Phasor Measurement Unit (μPMU), potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μPMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. Using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables' Currents Symmetrical Component (CSC). Since SMS do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate cables' currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by industry partner Scottish Power Energy Networks (SPEN). Results show that the current estimation regressor significantly improves fault localization and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, enabling highly accurate fault detection using SM voltage-only data, with further refinement through estimation of CSC. The proposed DT offers automated fault detection, enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μPMU on the densely-noded distribution network