An Analytical Approach of Integrating Automated Load Scheduling to a Smart Energy Meter using Differential Evolution Algorithm

Abstract

Leading-edge power systems which are employed in present-day scenario are significantly complex, extensively distributed and populous. Rising costs of generating power, ever-increasing demand for electricity and shortage of resources have led to introduction of applications of optimal power systems. Hence Smart Energy Meters (SEMs) and highly efficient algorithms are required to facilitate Automated Load Scheduling (ALS). The latest optimization methods for ALS which works on the principle of genetics and natural selection has been presented in the paper. Differential Evolution (DE) is a simple numerical optimization approach that is easy to implement and significantly faster and robust. This article presents a novel mathematical modelling approach for the minimization of the energy consumption. The paper proposes an in depth modelling approach for ALS considering numerous aspects of the DE: initialization, mutation, crossover and selection. Case-studies have been made which include multiple aspects of non-linearities like prohibited zones of operation and transmission losses. The performance of DE based ALS algorithm was implemented using Python and evaluated at the test site in New Delhi which integrates a local renewable energy source (Local PV Grid) with an SEM. The Peak-to-Average (PAR) Ratio was used as a measure of evaluation. The proposed algorithm saved up to 19.42% power in the best-case scenario.

Improved Particle Swarm Optimization Algorithm in Site Selection and Capacity of Distributed Power Supply

“…At the same time, it also brings significant challenges to distribution network planning. Therefore, power grid designers must consider the impact of DG grid connection on the distribution network when choosing the best solution [5][6].…”