Microgrid energy management system for optimum energy scheduling based on combination of swarm intelligent and cuckoo search algorithm

• Published in: AIP conference proceedings Vol. 3072; no. 1
• Main Authors: Gupta, Anuj, Aryan, Nakhale
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 19.03.2024
• Subjects: Algorithms; Distributed generation; Electrical loads; Energy consumption; Energy management; Evolutionary algorithms; Feedback; Optimization techniques; Particle swarm optimization; Performance enhancement; Power dispatch; Power management; Scheduling; Search algorithms; Smart grid; Swarm intelligence
• ISSN: 0094-243X; 1935-0465; 1551-7616; 1551-7616
• DOI: 10.1063/5.0198676

To control power dispatch and meet load demand in a microgrid made up of distributed energy sources (DERs), a power management/dispatch system is necessary whether it is grid-connected or islanded to set up a bilateral contact negotiation between suppliers and customers. At the tertiary control level of a typical microgrid, an optimal scheduling mechanism is utilized to manage power generation from local DERs, energy consumption by the load, and energy drawn from the grid. This study suggests a new hybrid optimization technique for day-ahead scheduling in a smart-grid. The proposed technique employs a Hybrid Feedback Particle Swarm Optimization-Modified Cuckoo (PSO-MCS) algorithm which combines swarm intelligence and cuckoo search to improve performance and achieve a cost-effective solution for a microgrid prosumer. The PSO much like other evolutionary algorithms, initializes a swarm (a set of candidate solutions) and then searches for the best possible global optimum. This algorithm utilizes Levy flights instead of basic isotropic random-walks to enhance its performance. The standard CSA employs the following three critical rules in solving an optimization problem. To compare the performance of the Hybrid Feedback PSO-MCS algorithm with PSO and modified CS (MCS) algorithm, a comparison has been made. The algorithm is implemented in both MATLAB/Simulink and Python IDE platforms to compare their execution time.