The renewable energy transition has seen an increased momentum globally in accordance with the Paris Agreement towards achieving the net-zero target by 2050. With Malaysia still heavily relying on fossils for its electricity generation, proper energy planning will be required to analyse various pathways of energy transition towards the future. This can be conducted through bottom-up national energy system modelling methodologies, such as in low-emission analysis platform (LEAP). With limited literature found in national-scale modelling of Malaysia’s energy transition, this paper modelled energy transition scenarios of Malaysia through long-term modelling approaches using LEAP based on the IRENA and NETR. These scenarios were evaluated in a comparative study according to the criterion of electrical generation, carbon dioxide emissions, carbon tax costs, and investment costs. Based on the criterion assessed, the findings indicated that the RE100S was the best scenario primarily due to its comparatively low-carbon emission when accounting for the entire lifecycle assessment of 23.5 MMT by 2050 and its respective carbon tax cost of 0.8 billion USD. This research provides quantifiable findings that can be valuable to policymakers and researchers in making strategic renewable energy development plans applied to Malaysia and other developing nations.

This work presents a roadmap for tuning field oriented control (FOC) for permanent magnet synchronous generator (PMSG)-based wind turbines. A detailed discussion about how to design meta-heuristic optimizers for this task is presented, providing systematic rules to ensure that the tuning procedure returns a set of gains that result in feasible optimized controllers. Ten popular nature-inspired optimizers are implemented at the core of the controller tuning method. The 10 optimized controllers are applied in the system considering a highly stochastic wind speed profile, generated using the Shinozuka method and Kaimal spectrum under intense turbulence. Among them, the ant lion optimizer (ALO) provided the best set of gains for FOC. Compared to the tenth algorithm in the rank, the ALO-based controller ensured a reduction of 96.78% and 90.27% of mean absolute error and root mean squared error, respectively. The findings of this work contribute to the automation of the controller tuning of PMSG‐based wind turbines, enhancing its power generation with more precise current tracking using optimized FOC.

In the world of electric vehicles energy recovery is a major concern for improving the mileage. One way of doing this can be recovery of road vibrations through regenerative damping. In this article, we propose a comparative analysis of 2 different types of regenerative dampers. One is Ball-screw damper and the other is Dual clutch rack and pinion damper on the basis of 2 characteristics. Firstly, analysis of impulse response to study the stability and filtration capability. Secondly, power optimization has been done to analyse the energy recovery and dissipation along with riding comfortability for different operational frequencies. The main identification parameter is the internal impedance of energy harvesting circuit. Mathematical analysis followed by simulation suggests that, in case of Dual clutch rack and pinion damper the average bandwidth to impedance ratio is 3.41 hertz-Ω−1 whereas for Ball-screw dampers is 0.22 hertz-Ω−1. This makes Dual clutch rack and pinion damper a good energy harvester at wider frequency ranges. However, magnitude of energy recovery is higher for Ball-screw damper dampers. The results obtained in this work would motivate other researchers to improve the functionality of both the dampers to enhance the dynamic stability and energy recovery of the vehicle.