Microgrids are an emerging technology that offers many benefits compared with traditional power grids, including increased reliability, reduced energy costs, improved energy security, environmental benefits, and increased flexibility. However, several challenges are associated with microgrid technology, including high capital costs, technical complexity, regulatory challenges, interconnection issues, maintenance, and operation requirements. Through an in-depth analysis of various research areas and technical aspects of microgrid development, this study aims to provide valuable insights into the strategies and technologies required to overcome these challenges. By assessing the current state of microgrid development in Pakistan and drawing lessons from international best practices, our research highlights the unique opportunities microgrids present for tackling energy poverty, reducing greenhouse gas emissions, and promoting sustainable economic growth. Ultimately, this research article contributes to the growing knowledge of microgrids and their role in addressing global sustainability issues. It offers practical recommendations for policymakers, industry stakeholders, and local communities in Pakistan and beyond.

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class’s main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach’s advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions.

In today's electrical grid, frequency cannot be ignored. The proliferation of renewable energy sources into a power system degrades its frequency; hence, the need for frequency regulation has been increased as a result. The second major factor is the perturbation in the load that originates the frequency fluctuation which needs to be suppressed in minimum time to avoid any system collapse. The designing of an optimal controller is indispensable because of increasing power system complexity. In order to maintain the stability of the power system, this paper presents the cascaded design of the model predictive controller with fractional order PID controller (MPC‐FOPIDN) to mitigate the frequency oscillations due to disruption in load. The combination of the predictive capabilities of model predictive control (MPC) and fractional order control enhances control abilities, making it an optimal control strategy for load frequency control (LFC). The grasshopper optimization algorithm (GOA) is applied to obtain optimal gains values for the FOPID controller. The efficacy of the controller has effectively mitigated frequency fluctuations caused by a change in demand or any uncertainty in the power system.

Photovoltaic (P.V.) systems have become an emerging field for power generation by using renewable energy (RE) sources to overcome the usage of conventional combustible fuels and the massive release of dangerous gases. The efficient operation of the PV system is vital to extracting the maximum power from the PV source. For this, a maximum power point tracking (MPPT) algorithm works with a DC–DC converter to extract maximum power from the P.V. system. Two main issues may arise with the involvement of a converter: (1) to locate M.P.P and (2) the performance of the PV model in varying weather conditions. Therefore, designing any converter gain has the utmost significance; thus, the proposed work is on non-isolated boost converters. To calculate the values of specific parameters such as input capacitor, output capacitor, and inductor, the averaging state-space modeling typically uses governing equations. In this research, the formula of the input capacitor is derived through the average state-space modeling of the boost converter, which signifies the relation between input and output capacitors. From the results, it has been proven that the input capacitor efficiently performs when the input capacitor is half of the output capacitor. At an irradiance level of 1000 W/m², the system shows stable behavior with a fast convergence speed of 0.00745 s until the irradiance falls to a value of 400 W/m². The system is less stable during the morning and the evening when irradiance falls are very low.