A Conceptual and Systematics for Intelligent Power Management System-Based Cloud Computing: Prospects, and Challenges

This review describes a cloud-based intelligent power management system that uses analytics as a control signal and processes balance achievement pointer, and describes operator acknowledgments that must be shared quickly, accurately, and safely. The current study aims to introduce a conceptual and systematic structure with three main components: demand power (direct current (DC)-device), power mix between renewable energy (RE) and other power sources, and a cloud-based power optimization intelligent system. These methods and techniques monitor demand power (DC-device), load, and power mix between RE and other power sources. Cloud-based power optimization intelligent systems lead to an optimal power distribution solution that reduces power consumption or costs. Data has been collected from reliable sources such as Science Direct, IEEE Xplore, Scopus, Web of Science, Google Scholar, and PubMed. The overall findings of these studies are visually explained in the proposed conceptual framework through the literature that are considered to be cloud computing based on storing and running the intelligent systems of power management and mixing.

Development of an Intelligent Power Management System for Solar PV-Wind-Battery-Fuel-Cell Integrated System

The objective of this work is to develop a power management system that will control the power flow of an integrated renewable energy system with the focus on solar energy and wind energy and dual energy storage systems (batteries are used as the primary energy storage system for short to moderate storage term, whereas hydrogen fuel cell is used as a backup and long-term energy storage). These storage systems are needed to provide high reliability and control systems are necessary for the stable and optimal operation of the whole system. An Intelligent Power Management System (IPMS) is developed to handle various changes in power supply and power demand by managing erratic power and provide suitable control algorithm for the whole system. In order to test and validate the proposed IPMS model, simulations were conducted under various power supply and power demand using power system modeled in HOMER environment. The performed simulations confirm the ability of the IPMS to satisfy the load at all times using solar and wind power (which are unsteady renewables), through the support of batteries and hydrogen fuel cell without a reduction in the power quality or load supply.

A Multi-objective Optimization Algorithm for Multiple Home Users Intelligent Power Management and Control Based on Pareto and Nash Equilibrium Game

A multi-objective optimization model for multiple home users intelligent power management and control is proposed. A photovoltaic power model, an electric vehicle battery model and a load model are developed first, and then a strategy of home intelligent power management is presented based on battery operation and PV spontaneous self-use. Secondly, a multi-objective optimization model of multiple home users intelligent power management, including the user comfort, economy and optimization of load curve, is provided under the constraints. Then using a multi-objective optimization algorithm and Nash equilibrium game theory to solve the multi-objective problem. Finally, the 100-home power management and control simulation case show that the presented algorithm can improve the comfort and the economy of users effectively, but also help the power grid to peak load shifting.