Pakistan is experiencing an undersupply of electricity, causing load shedding several hours per day due to the adherence to conventional energy resources having quantitative and environmental limitations. Fossil fuels generate more than half of the country’s total electricity, but they will ultimately run out due to their limited supply. Their combustion emits greenhouse gases, posing environmental threats. Since the world is tending toward efficient and sustainable alternative methods for harvesting energy from nature, Pakistan has also been investigating an elevated deployment of renewable energy projects. This paper presents a critical analysis of the present energy sector of Pakistan along with global scenarios. Pakistan relies on mainly thermal, hydro, and nuclear energy for power generation. National solar, wind, geothermal, and biomass resources have not been extensively explored and implemented. This paper provides an insight into the potential of these resources in Pakistan to generate electricity for the national grid on a large scale. It focuses on biomass energy, which can be harnessed from bagasse, poultry waste, and municipal waste for power production, and biomass-based fuel for industries and transportation. It concludes that biomass is the most sustainable, available, implementable, and environment-friendly resource that can be utilized to lessen the energy demand and supply gap in Pakistan.

In this paper, we explore learning methods to improve the performance of the open-circuit fault diagnosis of modular multilevel converters (MMCs). Two deep learning methods, namely, convolutional neural networks (CNN) and auto encoder based deep neural networks (AE-based DNN), as well as stand-alone SoftMax classifier are explored for the detection and classification of faults of MMC-based high voltage direct current converter (MMC-HVDC). Only AC-side three-phase current and the upper and lower bridges’ currents of the MMCs are used directly in our proposed approaches without any explicit feature extraction or feature subset selection. The two-terminal MMC-HVDC system is implemented in Power Systems Computer-Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC) to verify and compare our methods. The simulation results indicate CNN, AE-based DNN, and SoftMax classifier can detect and classify faults with high detection accuracy and classification accuracy. Compared with CNN and AE-based DNN, the SoftMax classifier performed better in detection and classification accuracy as well as testing speed. The detection accuracy of AE-based DNN is a little better than CNN, while CNN needs less training time than the AE-based DNN and SoftMax classifier.

The increasing penetration of large-scale Renewable Energy Sources (RESs) has raised several challenges for power grid operation. Power management solutions supporting the integration of RESs, such as those based on energy storage technologies, are generally costly. Alternatively, promoting a more proactive role of the Distribution System Operator (DSO) to successfully manage RESs’ uncertainty, and take advantage of their flexible resources for the provision of ancillary services, can avoid installing expensive devices in the network and reduce costs. In this line, improved coordination between Transmission System Operators (TSOs) and DSOs is highly desirable. In this paper, the feasibility of solving different aspects of the integration of RESs through an improved TSO/DSO coordination is evaluated. In particular, a Systematic Literature Review (SLR) is conducted to study the most relevant TSO/DSO coordination approaches, exclusively focused on integrating distributed RESs, currently available in the literature. Their main operational, managerial, economic, and computational challenges, advantages, and disadvantages are discussed in detail to identify the most promising research trends and the most concerning research gaps to pave the way for future research toward developing a solid TSO/DSO coordination mechanism for integrating RESs efficiently. The main results of the SLR show a clear trend in implementing decentralized TSO/DSO coordination models since they provide efficient facilitation of RESs’ services, while reducing computational burden and communication complexity and, consequently, reducing operative costs. In addition, while different aspects of the TSO/DSO coordination implementation, such as reactive power and voltage regulation, operational cost minimization, operational planning, and congestion management, have been thoroughly addressed in the literature, further research is needed regarding data exchange mechanisms and RESs’ uncertainty modeling and prediction. In this line, the development of standardized communication solutions, based on the Common Grid Model Exchange Standard (CGMES) of the International Electrotechnical Commission (IEC), has shown promising interoperability results, whereas the use of learning-based approaches to predict RESs’ uncertain behavior and distribution networks’ responses, using only historical data, which relieves the need for access to commercially sensitive and proprietary network data, has also shown itself to be a promising research direction.

The transport sector significantly contributes to global greenhouse gas emissions, making electromobility crucial in the race toward the United Nations Sustainable Development Goals. In recent years, the increasing competition among manufacturers, the development of cheaper batteries, the ongoing policy support, and people’s greater environmental awareness have consistently increased electric vehicles (EVs) adoption. Nevertheless, EVs charging needs—highly influenced by EV drivers’ behavior uncertainty—challenge their integration into the power grid on a massive scale, leading to potential issues, such as overloading and grid instability. Smart charging strategies can mitigate these adverse effects by using information and communication technologies to optimize EV charging schedules in terms of power systems’ constraints, electricity prices, and users’ preferences, benefiting stakeholders by minimizing network losses, maximizing aggregators’ profit, and reducing users’ driving range anxiety. To this end, accurately forecasting EV charging demand is paramount. Traditionally used forecasting methods, such as model-driven and statistical ones, often rely on complex mathematical models, simulated data, or simplifying assumptions, failing to accurately represent current real-world EV charging profiles. Machine learning (ML) methods, which leverage real-life historical data to model complex, nonlinear, high-dimensional problems, have demonstrated superiority in this domain, becoming a hot research topic. In a scenario where EV technologies, charging infrastructure, data acquisition, and ML techniques constantly evolve, this paper conducts a systematized literature review (SLR) to understand the current landscape of ML-based EV charging demand forecasting, its emerging trends, and its future perspectives. The proposed SLR provides a well-structured synthesis of a large body of literature, categorizing approaches not only based on their ML-based approach, but also on the EV charging application. In addition, we focus on the most recent technological advances, exploring deep-learning architectures, spatial-temporal challenges, and cross-domain learning strategies. This offers an integrative perspective. On the one hand, it maps the state of the art, identifying a notable shift toward deep-learning approaches and an increasing interest in public EV charging stations. On the other hand, it uncovers underexplored methodological intersections that can be further exploited and research gaps that remain underaddressed, such as real-time data integration, long-term forecasting, and the development of adaptable models to different charging behaviors and locations. In this line, emerging trends combining recurrent and convolutional neural networks, and using relatively new ML techniques, especially transformers, and ML paradigms, such as transfer-, federated-, and meta-learning, have shown promising results for addressing spatial-temporality, time-scalability, and geographical-generalizability issues, paving the path for future research directions.

Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To directly classify the raw sensor data without certain feature extraction and classifier design, a long short-term memory (LSTM) neural network is proposed and used for seven states of the MMC-HVDC transmission power system simulated by Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC). It is observed that the LSTM method can detect faults with 100% accuracy and classify different faults as well as provide promising fault classification performance. Compared with a bidirectional LSTM (BiLSTM), the LSTM can get similar classification accuracy, requiring less training time and testing time. Compared with Convolutional Neural Networks (CNN) and AutoEncoder-based deep neural networks (AE-based DNN), the LSTM method can get better classification accuracy around the middle of the testing data proportion, but it needs more training time.

Electric vehicles (EVs) encounter substantial obstacles in effectively managing energy, particularly when faced with varied driving circumstances and surrounding factors. This study aims to evaluate the performance of three different control systems in a fully operational hybrid energy storage system (HESS) installed in the Nissan Leaf. The objective is to improve the performance of EVs by focusing on optimising energy management in response to different global environmental and driving circumstances. This study utilises an analytical strategy by developing a distinct energy management system model using MATLAB/Simulink. This model is specifically designed for optimising the integration and control of batteries and supercapacitors (SCs) in a fully active HESS. This model mimics the performance of the controllers under three different driving cycles—Artemis rural, Artemis motorway, and US06. The findings demonstrate notable progress in managing the battery state of charge (SOC) and the system’s responsiveness, especially when employing the radial basis function (RBF) controller. This study emphasises the capacity of HESSs to enhance the effectiveness and durability of EVs, therefore promoting wider acceptance and progress in electric transportation technology.