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Electrical power systems are the lifeblood of modern civilization, providing the essential energy infrastructure that powers our homes, industries, and technologies. As our world increasingly relies on electricity, and modern power systems incorporate renewable energy sources, the challenges have become more complex, necessitating advanced forecasting and optimization to ensure effective operation and sustainability. This review paper provides a comprehensive overview of electrical power systems and delves into the crucial roles that forecasting and optimization play in ensuring future sustainability. The paper examines various forecasting methodologies from traditional statistical approaches to advanced machine learning techniques, and it explores the challenges and importance of renewable energy forecasting. Additionally, the paper offers an in-depth look at various optimization problems in power systems including economic dispatch, unit commitment, optimal power flow, and network reconfiguration. Classical optimization methods and newer approaches such as meta-heuristic algorithms and artificial intelligence-based techniques are discussed. Furthermore, the review paper examines the integration of forecasting and optimization, demonstrating how accurate forecasts can enhance the effectiveness of optimization algorithms. This review serves as a reference for electrical engineers developing sophisticated forecasting and optimization techniques, leading to changing consumer behaviors, addressing environmental concerns, and ensuring a reliable, efficient, and sustainable energy future.

Abstract The purpose of this paper is to examine cyber security issues related to smart grids (SGs), such as prior cyber‐attacks, possible vulnerability issues, and enhanced cyber‐security procedures for SGs on a technical and management basis. When designing public policies for SGs, it is important to consider both the motivations and obstacles to cyber protection and socio‐economic conditions. Furthermore, the importance of suggested policies is viewed from various perspectives. The present paper provides an assessment of a group of policies specified by various stakeholders as having the potential for developing SG cyber security. The methodology for the formation of such a group of policies includes a perfect examination of global experience and meetings with experts from various fields. Subsequently, such policies are evaluated using a Delphi questionnaire aimed at assessing their advantages in pursuing investment goals for cyber security. The first result is that every policy is considered a positive effect of all goals, different in the priority assigned to each of them. The policies that received the most attention were: “regulatory changes policy to foster innovation in cyber security of SGs,” “regulation of new business models to improve cyber security and detect malicious attacks” and “establish a cyber‐security governance strategy.”

Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell–CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal MG operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.

This study proposes a new four-level model to enhance the resilience of power systems against human attacks. The model considers human attacks such as intelligent attackers and power system planners as defenders. The attackers want to maximize the impact of actions, while the defender tries to avoid imposed costs and damages due to budget limitations. The classic resilience model is a three-level defense-attack-defense (DAD) model, which includes the hardening measure, human attack, and recovery levels. In this study, power system planning, as a new defensive layer, was added prior to the conventional DAD. The proposed method becomes a four-level defense-defense-attack-defense (DDAD) model. To this end, a new four-level defense-defense-attack-defense (DDAD) model is proposed, in which a new defense layer is added to the common three-level defense-attack-defense (DAD) model. Looking at the model more closely, new power plants and substations are added to the power system to improve its resilience, and the most important power plants and substations are determined. Subsequently, a conventional three-level model was applied. In this research, the defender and attacker have strategies with their own costs for every power system component, such as power plants and substations, and their own interactions. The power system model includes the load, power plant and substation, and substation priority, which are defined as a combination of the values of the load and network topology. The proposed model was applied to the IEEE-30 bus test system, and the results indicated the effectiveness of the proposed method.

This paper proposes a new framework for the optimal operation of a microgrid aggregator (MGA) that participates in an oligopoly electricity market. This aggregator obtains an optimal bidding (power and price) strategy for a multigrid (MG) system, i.e., a community MG. Consequently, the granted quantity, i.e., power in the electricity market, is deployed to optimally schedule the MG’s resources to meet demand. As such, as per three-level optimization, the independent system operator (ISO) clears the market with the goal of maximizing the social welfare in the first stage and determining the hourly market price as well as players’ credited power. In the second-level optimization process, the players select the optimal coefficient supply function equilibrium according to the power granted from the market. In third-level optimization, an optimal scheduling for MGs’ resources and demand would be obtained according to the won power in the market to maximize the aggregator profit. In addition, a price-taker MGA is simulated for comparison with the price–maker MGA to highlight the advantage of the proposed technique. Furthermore, a bidding strategy based on game theory is proposed to obtain the optimal price and power of the oligopoly market players and maximize all players’ profits. Finally, a test system including three generators is created to evaluate the performance of the devised bidding strategy. The results show that the proposed bidding strategy can optimally calculate the focal point of the Nash equilibrium (NE) in the oligopoly electricity market.

This paper endeavors a probabilistic framework to ascertain optimal operation of a microgrid with a special focus on challenges that storage systems bring about. The objective function optimizes the operation of renewable energy resources and startup/shutdown costs of nonrenewable energy resources along with the main grid and storage units’ costs as a mixed-integer nonlinear programming (MINLP) problem. The optimization problem is solved based on a modified bird mating optimization (MBMO) algorithm and a novel cumulative mutation process. In order to capture the high uncertainties associated with the market price, Photovoltaic, Wind Turbine output powers, and load demands, a reduced unscented transformation (RUT) method has been exploited. The RUT method can effectively model the correlation of variables using (m+2) sampling points. The framework presented in this paper has considered five case studies with multiple seasonal and property features. Implementing the proposed framework on a typical test microgrid and a real large-scale microgrid proves its effectiveness and accuracy through various operational conditions, changing the storage units’ structure and characteristics, RESs’ correlation modeling, and avoiding convergence to local minimums by adopting a newly mutated population. This extensive analysis provides options for MG operation by studying compound cases and providing solutions for every scenario. Promising results regarding the execution time, cost function and its SD values as well as head-to-head points for battery investment costs have been found.

In this paper, an intrusion detection system is introduced that uses data mining and machine learning concepts to detect network intrusion patterns. In the proposed method, an artificial neural network (ANN) is used as a learning technique in intrusion detection. The metaheuristic algorithm with the swarm-based approach is used to reduce intrusion detection errors. In the proposed method, the Grasshopper Optimization Algorithm (GOA) is used for better and more accurate learning of ANNs to reduce intrusion detection error rate. The role of the GOAMLP algorithm is to minimize the intrusion detection error in the neural network by selecting useful parameters such as weight and bias. Our implementation in MATLAB software and using the KDD and UNSW datasets show that the proposed method detects abnormal, malicious traffic and attacks with high accuracy. The GOAMLP method outperforms and is more accurate than the existing state-of-the-art techniques such as RF, XGBoost, and embedded learning of ANN with BOA, HHO, and BWO algorithms in network intrusion detection.

Cyber Security is a total business problem. Creating meaningful learning experiences to prepare students for complex cyber situations in an organization is no small undertaking. The higher education is ill prepared in meeting the challenge of providing learning experiences that provide both breadth and depth. To fill the void and meet the fast-growing demands in cybersecurity workforce, the authors of the paper propose consulting experience as a strategy to create student-driven learning. Student-led consulting will assist students in systematically assessing organizational security problems and designing and implementing organizational interventions. We describe why college students should be equipped to conduct consulting projects with faculty supervision and how it can be done.

The authors have designed a platform for research and consulting through a high-level collaborative seminar series to promote networking in proactive artificial intelligence (AI) for cybersecurity (SPAIC). The primary objective is to cover a wide range of techniques in cyber threat intelligence gathering from various social media to dark-net and deep-net, hacker forum discussions, and malicious hacking. The secondary objective is to bring together researchers and consultants in the field to come up with automated and advanced methods of attack vector recognition and isolation using AI and machine learning (ML). In most cases, the hidden nature of security issues makes it hard for fixes in real time. Advanced AI techniques have proven to be superior to the current static methods in cyber threat detection. There have been numerous recent advances in the field of AI, especially in algorithmic approaches such as Speech and Signal Processing, Machine and Deep Learning, Computer Vision, Robotics, Data Mining, Augmented/Virtual Reality, Blockchain, and Cognitive Computing. These highly advanced methods provide tremendous opportunities for behavior/trend based automated analysis, detection, and prevention of cyber attacks/threats. In addition to the potential of development of concepts and whitepapers for a large-scale center, the seminar series will result in identification and recruitment of industrial, academic and/or government partnerships in support of initiatives and research and consulting collaborations as well as creation and support of resources such as research consortia, collaboration sites or social networking tools to facilitate large-scale inter-university research programs in AI and ML in cybersecurity.

Students' attitudes towards Science, Technology, Engineering and Math (STEM) has been a topic of enduring interest in the field of STEM education over the past decade – but why? After all, there is no sense in which people are concerned about students' attitudes towards the learning of English or history. So what drives the interest in these topics? Previous research has suggested a relationship between teachers' and students' attitudes towards a subject area. In order to increase student representation in STEM, we need teachers who have positive attitudes and can serve as role models to encourage their students to pursue STEM careers. In this paper we argue that in order to engage students' interest in the technical career paths, we must start with inspiring and training teachers to instill such interest in their students at early age. We present our experiences and outcomes from a teacher training program. Through the training, we were able to help teachers explore ways to incorporate programming into their curriculum, gain STEM career awareness and develop an understanding of importance of soft skills in STEM. The results show that teachers were able to gain confidence in incorporating their subject matter into Scratch-based classroom activities and teaching this material in a classroom setting and gain a better understanding of careers in STEM.