In view of scarcity of traditional energy resources and environmental issues, renewable energy resources (RERs) are introduced to fulfill the electricity requirement of growing world. Moreover, the effective utilization of RERs to fulfill the varying electricity demands of customers can be achieved via demand response (DR). Furthermore, control techniques, decision variables and offered motivations are the ways to introduce DR into distribution network (DN). This categorization needs to be optimized to balance the supply and demand in DN. Therefore, intelligent algorithms are employed to achieve optimized DR. However, these algorithms are computationally restrained to handle the parametric load of uncertainty involved with RERs and power system. Henceforth, this paper focuses on the limitations of intelligent algorithms for DR. Furthermore, a comparative study of different intelligent algorithms for DR is discussed. Based on conclusions, quantum algorithms are recommended to optimize the computational burden for DR in future smart grid.

In association with the development of intermittent renewable energy generation (REG), dynamic multiobjective dispatch faces more challenges for power system operation due to significant REG uncertainty. To tackle the problems, a day‐ahead, optimal dispatch problem incorporating energy storage (ES) is formulated and solved based on a robust multiobjective optimization method. In the proposed model, dynamic multistage ES and generator dispatch patterns are optimized to reduce the cost and emissions. Specifically, strong constraints of the charging/discharging behaviors of the ES in the space‐time domain are considered to prolong its lifetime. Additionally, an adaptive robust model based on minimax multiobjective optimization is formulated to find optimal dispatch solutions adapted to uncertain REG changes. Moreover, an effective optimization algorithm, namely, the hybrid multiobjective Particle Swarm Optimization and Teaching Learning Based Optimization (PSO‐TLBO), is employed to seek an optimal Pareto front of the proposed dispatch model. This approach has been tested on power system integrated with wind power and ES. Numerical results reveal that the robust multiobjective dispatch model successfully meets the demands of obtaining solutions when wind power uncertainty is considered. Meanwhile, the comparison results demonstrate the competitive performance of the PSO‐TLBO method in solving the proposed dispatch problems.

The green innovations in the energy sector are smart solutions to meet the excessive power requirements through renewable energy resources (RERs). These resources have forwarded the revolutionary relief in control of carbon dioxide gaseous emissions from traditional energy resources. The use of RERs in a heuristic manner is necessary to meet the demand side management in microgrids (MGs). The pricing scheme limitations hinder the profit maximization of MG and their customers. In addition, recent pricing schemes lack mechanistic underpinning. Therefore, a dynamic electricity pricing scheme through linear regression is designed for RERs to maximize the profit of load customers (changeable and unchangeable) in MG. The demand response optimization problem is solved through the particle swarm optimization (PSO) technique. The proposed dynamic electricity pricing scheme is evaluated under two different scenarios. The simulation results verified that the proposed dynamic electricity pricing scheme sustained the profit margins and comforts for changeable and unchangeable load customers as compared to fixed electricity pricing schemes in both scenarios. Hence, the proposed dynamic electricity pricing scheme can readily be used for real microgrids (MGs) to grasp the goal for cleaner energy production.

Renewable energy resources (RERs) play a vital role in reducing greenhouse gases, as well as balancing the power generation demand in daily life. Due to the high penetration of RERs and non-linear loads into utility power systems, various power quality issues arise, i.e., voltage drop, harmonic distortion, reactive power demand, etc. In order to handle these power quality issues, there is a need for smart flexible alternating current transmission system (FACTS) devices. In this paper, a super capacitor energy storage system (SCESS)-based static synchronous compensator (STATCOM) is designed in order for the grid-connected photovoltaic (PV) system to overcome the abovementioned power quality issues. A voltage controller and a d-q axis controller are used for the efficient performance of the STATCOM. In order to show the superiority of the supercapacitor, a detailed comparison is made between a battery energy storage system (BESS)-based STATCOM and a SCESS-based STATCOM. Four scenarios are studied to evaluate the performance of the proposed STATCOM design. The proposed SCESS-based STATCOM not only boosts the voltage but also stabilizes it from 368 V to 385 V (Ph-Ph_rms). The simulated results have confirmed that the proposed design is not only superior to a BESS-based STATCOM but also has the capability to overcome the power quality issues as well.

The paper introduces advanced aggregation operators based on Hamacher operational laws for Bipolar Fermatean Fuzzy sets, addressing complex decision-making problems. In addition, a score function is utilized to evaluate and rank alternatives within the Bipolar Fermatean Fuzzy decision framework, offering a quantitative measure of each alternative’s performance based onthe aggregation results. This score function helps simplify decision-making, particularly in cases involving complex uncertainty and multiple criteria. The proposed operators include the Bipolar Fermatean Fuzzy-Hamacher weighted average, the Bipolar Fermatean Fuzzy-Hamacher Ordered Weighted Average, and the Bipolar Fermatean Fuzzy-Hamacher Hybrid Weighted Average. These operators are designed to integrate membership, nonmembership, and hesitation degrees, making them highly effective in Bipolar Fermatean Fuzzy environments. To demonstrate the practicality of the proposed approach, a real-world group decision-making scenario is applied to managing electronic waste. The results show the proposed methodology’s superior efficiency, flexibility, and precision compared to existing approaches. The findings highlight the robustness of operators in handling uncertainty and enhancing the accuracy of decision-making.

Coronary Heart Disease (CHD) continues to affect close to 145 million males and 110 million females across the globe, taking close to nine million lives each year. A new evolving framework incorporating IoT-edge computing, Explainable AI, and blockchain technology is in the process of development in order to create a secure, privacy-preserving, and interpretable automated machine learning environment to predict chronic diseases. The IoT-based system utilizes medical sensors and assistive devices for real-time monitoring of the patient. The patient information is transmitted securely using blockchain architecture so that various health practitioners have access to it with the guarantee of data integrity, confidentiality, and access control. The use of Explainable AI (XAI) enables interpretable predictions in machine learning for clinicians and fosters confidence and transparency. Physicians are able to make evidence-based decisions beyond traditional methods since XAI provides the reasons for predictions.