Depletion of fossil fuels and petroleum products due to population explosion has created a tremendous demand for renewable energy sources. Non-conventional loads such as electric vehicles and smart residential systems are increasing daily, creating additional load to conventional utility grids. The extra energy demand is filled mainly by energy generated from renewable energy sources such as solar, wind and geothermal energy sources. This has meant that load distribution and power flow management have emerged as the most significant challenges for electrical engineers. Therefore, advanced power management systems must be designed to operate the present distribution system smoothly. The fourth industrial revolution has broken down the walls between the physical, digital and biological worlds. Advancements in artificial intelligence, big data, machine learning, the Internet of Things (IoT), genetic engineering, and quantum computing have made the interface between machines and users very easy. The fourth industrial revolution has brought a drastic revolution for users, from controlling battery charging to planning a suitable control technique for fabricated electrical equipment. Smooth load sharing between grid and renewable energy sources, power management as per the availability of generating sources, and circumventing the sag and swell of utility grids to operate equipment smoothly is facilitated by advanced artificial intelligent techniques. The progressive machine learning approach enables the smooth operation of machines. Overall, the fourth industrial revolution has brought enormous advantages to help electrical users. The work presented in this book deals with the advanced design methods adopted by electrical researchers to facilitate smooth utilization of the fourth industrial revolution. The content of the book includes but is not limited to the following research areas: * Topological improvement of electrical equipment to facilitate smooth user interfaces. * Improvement of techniques to tackle advanced power system problems such as sag, swell, reactive power imbalance and power flow management. * Advanced practices to facilitate smooth electric vehicle charging systems. * Grid to smart residence (G2S) and smart residence to grid (S2G) operation of the utility grid. * Stability analysis of the utility grid amid non-conventional loading. * Artificial intelligence, big data and machine learning applications to power system problems. * Intelligent controllers for an advanced residential system. * Intelligent storage systems for residential buildings.