With the rapid proliferation of cyber-physical systems (CPSs) in various sectors, including critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing need for robust cybersecurity mechanisms to protect these systems from cyberattacks. A cyber-physical system is a combination of physical and cyber components, and a security breach in either component can lead to catastrophic consequences. Cyberattack detection and mitigation methods in CPSs involve the use of various techniques such as intrusion detection systems (IDSs), firewalls, access control mechanisms, and encryption. Overall, effective cyberattack detection and mitigation methods in CPSs require a comprehensive security strategy that considers the unique characteristics of a CPS, such as the interconnectedness of physical and cyber components, the need for real-time response, and the potential consequences of a security breach. By implementing these methods, CPSs can be better protected against cyberattacks, thus ensuring the safety and reliability of critical infrastructure and other vital systems. This paper reviews the various kinds of cyber-attacks that have been launched or implemented in CPSs. It reports on the state-of-the-art detection and mitigation methods that have been used or proposed to secure the safe operation of various CPSs. A summary of the requirements that CPSs need to satisfy their operation is highlighted, and an analysis of the benefits and drawbacks of model-based and data-driven techniques is carried out. The roles of machine learning in cyber assault are reviewed. In order to direct future study and motivate additional investigation of this increasingly important subject, some challenges that have been unaddressed, such as the prerequisites for CPSs, an in-depth analysis of CPS characteristics and requirements, and the creation of a holistic review of the different kinds of attacks on different CPSs, together with detection and mitigation algorithms, are discussed in this review.

The increase in the demand for a reliable electricity supply by the utilities and consumers has necessitated the evaluation of the reliability of power systems. A reliable electricity supply is characterized by no or minimal duration and frequency of supply outages. This has triggered the necessity of using renewable energy sources (RESs) with optimization methods for reliability improvement of electricity systems and reduction of greenhouse gas (GHG) emissions. The main objective of this study is to optimize micro-grid systems operations, improve reliability, reduce emissions and balance the demand and supply of energy through RESs and battery energy storage system (BESS). The adaptive model predictive control (AMPC) method is used to address the issues of micro-grid operation. The AMPC algorithm solves the optimization problem of disturbance prediction in a micro-grid with different types of RESs and BESS integration. This optimization problem considers different constraints for minimum operating costs in different case scenarios. The financial viability of the proposed method is investigated. Solar photovoltaic (Solar PV), wind plant (WP) and BESSs are used with the AMPC method to investigate the impacts of annual real interest rates on cost and emission parameters, quantification of the emission oxides from different case scenarios, reduction of the cost of electricity (Ccoe), and power system reliability improvement. A modified Roy Billinton Test System (RBTS) is used to confirm the reliability enhancement and financial feasibility of the system. Case studies are used to confirm the proposed methods using climatic data for the city of Pietermaritzburg (29.37° S and 30.23° E), South Africa. The results obtained establish that the incorporation of RESs and BESSs using the AMPC method gives satisfactory outcomes.

An increase in the weather events can result in an increase in the frequency of grid faults, thereby leading to a decrease in electricity supply reliability. To develop a better performance model for outage forecasting, it is essential to consider various types of events for different complex weather conditions and constraints. A conventional event-driven forecasting algorithm is only appropriate for a single type of event scenario and unable to adapt effectively to multiple types of event scenarios concurrently. A multiple-constraints event-driven outage model that depends on a maximum entropy function, which transforms a complex event-driven outage problem into two continuous differential single-objective sub-problems using the collaborative neural network (CONN) algorithm is proposed in this paper. The CONN event-driven forecasting algorithm, validated by many experiments, successfully resolves the problem of the difficulty in obtaining very large complex weather events and outages data.

The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) and multiple linear regression (MLR) models. The results obtained from the ANN model are found to be satisfactory when compared to those obtained from MLR and ES. The results demonstrate that artificial neural networks are robust and can be used to predict electricity outages with regards to faults caused by severe weather conditions.

The sudden increase in global energy demandefor renewable energy resources. The global transition to renewable energy has emphasized the need for efficient, sustainable and cost-effective hybrid renewable energy system in the conventional power system. This study focuses on the optimization of HRES witeeh the aim objective of improving energy efficiency, sustainability and affordability. The proposed HRES which consists of standby diesel generator, wind turbines, battery storage system and photovoltaic system is designed to satisfy energy demands while reducing dependency on fossil fuels. The optimization of the power system is implemented with the eevolutionary computation techniques governed by particle swarm optimization and genetic algorithm in the MATLAB environment to coordinate the optimal power flow among several components of HRES. The techniques present in this research are based on the optimization of the total cost of the system and cost of energy of DG/PV/WT/BSS hybrid energy system. The hybridization of WT, PV and BSS in a single power system provides uninterrupted power supply to consumers at minimum C_T of $11399 and $10906 as well as minimum COE of $0.1369/kWh and $0.1316/kWh by using GA and PSO. The findings show that the computational time to solve the problem by PSO is significantly less than that provided by GA. The optimal configuration has 72 PV panels (8.64 kW), 1 unit of WT (3 kW) and 76 battery systems (159.6 kWh) with computational time of 0.146831 s. The outcomes of the study demonstrate that HRES is a cost-effective solution to satisfy the power demand of the selected location and other regions based on similar meteorological data. The results obtained from the study align with Sustainable Development Goals by promoting clean energy access and fostering sustainable infrastructure development.