Pakistan is an energy-resourceful country with vast and untapped renewable energy sources (RESs). The wind, solar, and biomass of the country are practically capable of ending a power sector collapse caused by demand-supply variances. A significant percentage of Pakistan' s population resides in rural areas. For rural population, the lack of connection to the mainstream of national development is a direct consequence of frequent power blackouts and, in certain cases, a lack of grid connection altogether. Lucrative features of smart grid are not fully incorporated into the power network yet, but policy-makers are paying attention to increase RES reliance. A comprehensive study describing the renewable energy potential of Pakistan is of importance. This research work attempts to present a collective summary of Pakistan' s renewable energy potential. A statistical analysis of the proposed and installed projects in various districts are presented. This paper elaborates the pressing needs of renewable energy integration for resolving Pakistan' s energy crisis. Renewable energy projects are acclaimed in this paper for affording higher living standards and better job opportunities than the fossil fuel based industry in Pakistan. Integrating RESs into the national portfolio is guaranteed to offer profound socio-economic benefits to Pakistan' s rural population.

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Electric grid is vulnerable to power imbalance and inertia is the grid's response to overcome such disturbance. Augmentation of power electronic converter based renewable energy technologies like Photovoltaic Generators (PVG) and batteries in utility grid significantly reduces inertia. Inertia degradation is indicated by sharp Rate of Change of Frequency (ROCOF) events due to any grid component failure or imbalance. Fixed gain feedback Proportional Integral Derivative (PID) control is insufficient to deal with varying ROCOF events. This work proposes Sliding Mode (SM) robust droop control scheme assisted by Artificial Neural Network (ANN) algorithm for an islanded PVG integrated microgrid. Droop response is governed by swing equation that uses PVG Maximum Power Point (MPP) forecasted by ANN. ANN forecast is compared with optimized Gaussian process regression algorithm based on mean squared error and speed of training as key performance indicator. The algorithms are trained and validated based on climate dataset of Islamabad, Pakistan. SM control performance is compared with various PID gain settings and qualified as the most suitable against variable source, load and ROCOF scenarios. Finally, significance of accurate MPP forecast for droop control is established by comparing the ANN and deterministic forecaster assisted droop response in a microgrid case study.

The rapid growth in the population of major cities is causing an increase in energy consumption. Several approaches, including the use of recent technological advancements, are becoming increasingly important. IoT-based applications are becoming more common in addressing a variety of real-time issues. The central idea of the project in collaborating with this new technology is the human-machine interaction. Furthermore, the proposed architecture enables energy-saving applications that perform the following primary functions: estimating energy utilization of the home environment using metering devices; and applying user-defined limits that cut off boundaries providing if it exceeds energy limits. The Internet serves as an interactive medium between servers and home appliances, monitoring and controlling energy consumption remotely. For the purpose, android platform is used to design a mobile application. Raspberry Pi is dedicated for the server that maintains the database of each controllable unit. The proposed idea is successfully tested in the laboratory environment with synthetic load and in real-time over a running energy meter, to evaluate the efficiency and effectiveness.