Smart Grid

Advanced Metering Infrastructure is prone to cyber threats and Distributed Denial of Service is renowned attack that threatens critical infrastructure. Intrusion detection schemes proposed in earlier works consider the network specific parameters for intrusion detection rather than physical layer parameters. Hence, the proposal considers the physical layer parameters in detecting distributed denial of service attack through abnormal energy consumption pattern of the Data concentrator in the advanced metering infrastructure.

Modern power systems face growing risks from cyber-physical attacks, necessitating enhanced resilience due to their societal function as critical infrastructures. The challenge is that defense of large-scale systems-of-systems requires scalability in their threat and risk assessment environment for cyber-physical analysis including cyber-informed transmission planning, decision-making, and intrusion response. Hence, we present a scalable discrete event simulation tool for analysis of energy systems, called DESTinE.

This dataset contains synthetic smart meter data with simulated cyber attacks, designed to support research in anomaly detection, cybersecurity, and energy consumption analysis. The dataset is based on 159 users from the Smart Meters in London dataset, selected for their regular consumption patterns. This larger dataset can be found in

https://www.kaggle.com/datasets/jeanmidev/smart-meters-in-london,

which is a refactorised version of the data found in

https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households.

This dataset contains South Korea's Jeju Island SMP (System Marginal Price) information for 2023. The data is organized chronologically, providing hourly SMP values for each day of the year. SMP represents the marginal cost of generating electricity and is a crucial factor in determining electricity prices. For Jeju Island, which has a separate power grid from mainland Korea, this data reflects local supply and demand dynamics, including the impact of renewable energy sources.

The recent developments in the field of the Internet of Things (IoT) bring alongside them quite a few advantages. Examples include real-time condition monitoring, remote control and operation and sometimes even remote fault remediation. Still, despite bringing invaluable benefits, IoT-enriched entities inherently suffer from security and privacy issues. This is partially due to the utilization of insecure communication protocols such as the Open Charge Point Protocol (OCPP) 1.6. OCPP 1.6 is an application-layer communication protocol used for managing electric vehicle chargers.

Introduction

As electric vehicles (EVs) gain popularity, the demand for efficient and convenient charging solutions has surged. Inductive Power Transfer (IPT) technology offers a promising solution, allowing for wireless charging without the need for physical connections. Among the various advancements in this field, the development of self-aligning capability in IPT pads stands out.

One of the major challenges of microgrid systems is the lack of comprehensive Intrusion Detection System (IDS) datasets specifically for realistic microgrid systems' communication. To address the unavailability of comprehensive IDS datasets for realistic microgrid systems, this paper presents a UNSW-MG24 dataset based on realistic microgrid testbeds. This dataset contains synthesized benign network traffic from different campus departments, network flow of attack activities, system call traces, and microgrid-specific data from an integrated Festo LabVolt microgrid system.

This is the snapshot of the directories included in this dataset, which are load, price, pv, and wind. Within each directory, there is an .xlsx file that list the detailed data in that category. These data are used in an article titled "Consensus-based distributed reinforcement learning with primal-dual update for networked microgrids on-line coordination."