A battery management approach to improve steady state voltage performance of an LV distribution feeder

In recent times, distribution system operators in Australia have introduced several guidelines for the connection of small-scale solar photovoltaic (PV) units. Such guidelines are necessary for the operators to efficiently manage a system; however, this may sometimes impose a restriction on the growth of PV. In this paper, the voltage rise phenomenon of a realistic LV distribution network is investigated with new PV inverters. Even though prospective inverters are connected according to the existing guideline, voltage rise can still be a concern. This issue could be solved with battery storage rather than putting a limit on the PV capacity. In this context, this research work proposes an approach to control the charge and discharge of a battery to mitigate the voltage rise in the studied network. A thumb rule is established to determine the required number of storages to ensure an acceptable voltage performance

Investigation of post-fault voltage recovery performance with battery-based energy storage system in a microgrid

Robust and resilient delivery are two principal features of future smart grids and energy storage can play a significant role in meeting these challenges. With increased renewable penetration, energy storage can improve the operating capabilities of hybrid microgrids. However, capability of storage system to support voltage and frequency in microgrids largely depends on its proper placement and size. In this paper, battery-based energy storage system (BESS) has been placed in a renewable integrated microgrid depending on the voltage sensitivity factors of the network buses. Then, impact of such a placement on post-fault voltage recovery in a 43 bus industrial microgrid has been investigated. Results show that distributed placement of BESS across highly sensitive buses results in grid compatible and faster voltage recovery in comparison to centralised placement

A tool to estimate maximum arbitrage from battery energy storage by maintaining voltage limits in an LV network

Modern electricity distribution networks are facilitated with a high share of renewable generation, especially solar photovoltaics (PV). PV source results in fluctuating power injection and bi-directional power flow in a system, which can introduce overvoltage problem in low voltage (LV) networks. Using battery energy storages can mitigate this problem. This paper proposes a tool, which searches for an optimum daily operation strategy and size of batteries so that owners get maximum arbitrage benefit while maintaining voltage constraints. A time-series optimal power flow is formulated and solved in Generic Algebraic Modelling System (GAMS) platform. Day-ahead rooftop PV power profile over a year is studied and categorized by using k-means clustering algorithm. Seasonal load patterns and clustered PV power patterns are then used to execute optimal power flow. The resulting payback period of PV-battery system is also estimated

Investigation of voltage performance of an LV distribution network for improving rooftop photovoltaic uptake in Australia

Recently, Queensland state of Australia has seen a rapid growth in rooftop solar photovoltaic (PV) installations. However, the growth can be throttled due to voltage violation constraints at the distribution feeder level. Consequently, distribution utilities in Queensland have set a guideline for new PV inverter connections. During light load condition and under high availability of PV power, voltage rise may exceed its acceptable range in some buses even after the PV inverters are operated at specified lagging power factor given by the aforementioned guideline. To address the above-mentioned issue, this paper investigates the impact of upcoming PV units on voltage performance of a Queensland distribution system. This study also determines an allowable power export limit of a new PV inverter by satisfying standard voltage margins. This limit is utilised to develop a battery control strategy. This strategy is then applied to explore the implication of PV-battery on voltage enactment of the network

Evaluation of technical and financial benefits of battery-based energy storage systems in distribution networks

Prompted by technical issues that have arisen due to the widespread deployment of distributed intermittent renewable generators, rapidly rising peak demand and reductions in battery price, the use of battery-based energy storage systems in power networks is on the rise. While battery-based energy storage has the potential to deliver technical benefits, the best possible sizing, location and usage govern the financial viability. The objective of this study is twofold. Firstly, a generalised approach is proposed to model network upgrade deferral as a function of load growth rate, renewable generation penetration and peak shave fraction. This model is then used for the formulation of an optimisation problem which benefits from multi-period power flow analysis to co-optimise battery size, location, charge/discharge profile for a pre-specified number of units to be deployed in a given distribution network. The proposed approach is implemented using the generic algebraic modelling system platform and validated on an Australian medium voltage distribution network under multiple practical and potential future scenarios

Ensemble learning based transmission line fault classification using phasor measurement unit (PMU) data with explainable AI (XAI).

A large volume of data is being captured through the Phasor Measurement Unit (PMU), which opens new opportunities and challenges to the study of transmission line faults. To be specific, the Phasor Measurement Unit (PMU) data represents many different states of the power networks. The states of the PMU device help to identify different types of transmission line faults. For a precise understanding of transmission line faults, only the parameters that contain voltage and current magnitude estimations are not sufficient. This requirement has been addressed by generating data with more parameters such as frequencies and phase angles utilizing the Phasor Measurement Unit (PMU) for data acquisition. The data has been generated through the simulation of a transmission line model on ePMU DSA tools and Matlab Simulink. Different machine learning models have been trained with the generated synthetic data to classify transmission line fault cases. The individual models including Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (K-NN) have outperformed other models in fault classification which have acquired a cross-validation accuracy of 99.84%, 99.83%, and 99.76% respectively across 10 folds. Soft voting has been used to combine the performance of these best-performing models. Accordingly, the constructed ensemble model has acquired a cross-validation accuracy of 99.88% across 10 folds. The performance of the combined models in the ensemble learning process has been analyzed through explainable AI (XAI) which increases the interpretability of the input parameters in terms of making predictions. Consequently, the developed model has been evaluated with several performance matrices, such as precision, recall, and f1 score, and also tested on the IEEE 14 bus system. To sum up, this article has demonstrated the classification of six scenarios including no fault and fault cases from transmission lines with a significant number of training parameters and also interpreted the effect of each parameter to make predictions of different fault cases with great success

A methodology to prevent cascading contingencies using BESS in a renewable integrated microgrid

One of the key features of a microgrid is its capability to operate in an islanded mode when required. However, due to limited resources and bidirectional power flows, a microgrid in islanded mode is susceptible to different contingencies with extensive effects, e.g., cascading loss of distributed generators (also known as Distributed Energy Resources – DERs) due to unacceptable post-fault voltage recovery performance. In a renewable integrated microgrid, conventionally Battery Energy Storage System (BESS) is placed at the point of common coupling of distributed generators. However, such a placement approach does not prevent the cascading tripping of DERs following a fault, especially in the presence of a large share of induction motor loads. To address this challenge, this paper proposes a new placement methodology of BESS based on reactive power margin to prevent cascading contingencies and a subsequent blackout in a microgrid. The proposed methodology deploys an iterative technique. If the voltages at DERs terminals after a fault fail to recover within the stipulated time suggested by the grid code, BESS is placed at the bus with the lowest reactive power margin. It is known as the centralised placement approach of BESS. If this approach does not avert the DER tripping, the location of BESS is changed using the distributed placement scheme. In this scheme, several BESSs are placed on multiple buses, which have relatively lower values of reactive power margin. In a specific microgrid, either the centralised or the distributed BESS placement approach is eventually adopted. To explore the effectiveness of the proposed method, the IEEE 43 bus industrial test system integrated with renewable energy resources is considered as an islanded microgrid for simulations. It is found that the proposed approach results in the most appropriate placement of BESS to make the microgrid immune to cascading tripping of DERs

Representation of the identified changes in voltage phases in terms of different simulated circumstances.

(A) Voltage phases under no fault condition. (B) Voltage phases under single line fault condition. (C) Voltage phases under double line fault condition. (D) Voltage phases under double line to ground fault condition. (E) Voltage phases under three phase fault condition. (F) Voltage phases under three phase to ground fault condition.</p

Observations of transmission lines in terms of symmetrical fault circumstances.

(A) Short circuit between three phase conductors. (B) Short circuit between three phase conductors and ground.</p