Optimal energy management applying load elasticity integrating renewable resources

Urban growth aimed at developing smart cities confronts several obstacles, such as difficulties and costs in constructing stations and meeting consumer demands. These are possible to overcome by integrating Renewable Energy Resources (RESs) with the help of demand side management (DSM) for managing generation and loading profiles to minimize electricity bills while accounting for reduction in carbon emissions and the peak to average ratio (PAR) of the load. This study aims to achieve a multi-objective goal of optimizing energy management in smart cities which is accomplished by optimally allocating RESs combined with DSM for creating a flexible load profile under RESs and load uncertainty. A comprehensive study is applied to IEEE 69-bus with different scenarios using Sea-Horse Optimization (SHO) for optimal citing and sizing of the RESs while serving the objectives of minimizing total power losses and reducing PAR. SHO performance is evaluated and compared to other techniques such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Whale Optimization (WO), and Zebra Optimization (ZO) algorithms. The results show that combining elastic load shifting with optimal sizing and allocation using SHO achieves a global optimum solution for the highest power loss reduction while using a significantly smaller sized RESs than the counterpart.