Applications of BESS in Electrical Distribution Network With Cascading Failures Study: A Review - Abhay Chhetri; Devender Kumar Saini; Monika Yadav (IEEE Access)
The rise in the growth of renewables in the distribution network introduces several challenges, such as voltage & frequency fluctuation, while the digitization of the network introduces cyber threats and risks like false data injection (FDI), resulting in a cascading network failure. By determining the optimal sitting, size and cost of the Battery Energy Storage Systems (BESS), these power quality issues & threats can be overcome as BESS can rapidly inject or absorb power as needed. In case of any cyber threat, it can supply energy to critical areas, reducing the cascading effect and increasing the survivability of the distribution network. Therefore, this paper provides a review of the existing methodologies used for the optimal placement, sizing, and cost of BESS in distributed networks involving Distributed Energy Resources and Electric Vehicles. Additionally, it provides insights into cascading effects in power systems due to cyber-attack, especially FDI attacks or component failure. After a thorough literature survey of the existing methods utilized for placement, sitting, costing, and modelling techniques, the paper concludes by providing a framework to enhance the grid resiliency against such power quality issues by improving the quality and reducing the minimum disruption area in case of any FDI attack. The proposed framework differs from the existing methodologies as it provides adaptive ancillary services and dynamic BESS response to address the operational and security challenges, based on this framework EV load penetration is estimated. Further to this EV load will be integrated into the dynamic distribution grid to analyze grid parameters including frequency, voltage, and line losses considered to be the future research work of the authors. However, the proposed framework has some challenges, including data dependency for stochastic modelling, computational challenges, and high quality for reliable optimization results.