Integration of the Chimp Optimization Algorithm and Rule-Based Energy Management Strategy for Enhanced Microgrid Performance Considering Energy Trading Pattern DOI Open Access
Mukhtar Fatihu Hamza, Babangida Modu, Sulaiman Z. Almutairi

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(10), P. 2037 - 2037

Published: May 16, 2025

The increasing integration of renewable energy into modern power systems has prompted the need for efficient hybrid solutions to ensure reliability, sustainability, and economic viability. However, optimizing design systems, particularly those incorporating both hydrogen battery storage, remains challenging due system complexity fluctuating trading conditions. This study addresses these gaps by proposing a novel framework that combines Chimp Optimization Algorithm (ChOA) with rule-based management strategy (REMS) optimize component sizing operational efficiency in grid-connected microgrid. proposed integrates photovoltaic (PV) panels, wind turbines (WT), electrolyzers (ELZ), fuel cells (FC), storage (BAT), while accounting seasonal variations dynamic trading. Each contribution Research Contributions section directly critical limitations previous studies, including lack advanced metaheuristic optimization, underutilization hydrogen-battery synergy, absence practical control strategies management. Simulation results show ChOA-based model achieves most cost-effective configuration, PV capacity 1360 kW, WT 462 164 kWh BAT 138 H2 tanks, 571 kW ELZ, 381 FC. configuration yields lowest cost (COE) at $0.272/kWh an annualized (ASC) $544,422. Comparatively, Genetic (GA), Salp Swarm (SSA), Grey Wolf Optimizer (GWO) produce slightly higher COE values $0.274, $0.275, $0.276 per kWh, respectively. These findings highlight superior performance ChOA offer scalable, adaptable support future deployment smart grid development.

Language: Английский

Implementation of real-time optimal load scheduling for IoT-based intelligent smart energy management system using new decisive algorithm DOI Creative Commons
Challa Krishna Rao, Sarat Kumar Sahoo, Franco Fernando Yanine

et al.

Journal of Electrical Systems and Information Technology, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 14, 2025

Abstract This paper presents the implementation of a real-time optimal load scheduling system for an IoT-based intelligent smart energy management (SEMS) using novel decisive algorithm. The increasing use electrical equipment by consumers often leads to mismatch between demand and supply, posing significant challenges sector. proposed addresses these optimizing distribution enhancing efficiency through advanced demand-side techniques. By leveraging data from IoT sensors incorporating user preferences, new algorithm dynamically adjusts power consumption avoid peak-hour overloads, thus preventing widespread outages. Experimental results demonstrate that effectively reduces overall while maintaining comfort costs. innovative approach controlled partial shedding based on consumer priorities ensures balanced resilient supply. study highlights potential algorithms in transforming practices providing sustainable solutions future.

Language: Английский

Citations

2
Development of a Smart Cloud-based Monitoring System for Solar Photovoltaic Energy Generation DOI Creative Commons
Challa Krishna Rao, Sarat Kumar Sahoo, Franco Fernando Yanine

et al.

Unconventional Resources, Journal Year: 2025, Volume and Issue: unknown, P. 100173 - 100173

Published: March 1, 2025

Language: Английский

Citations

1
Designing an intelligent smart energy monitoring system for optimizing the utilization of PV energy DOI
Challa Krishna Rao, Sarat Kumar Sahoo, Franco Fernando Yanine

et al.

Energy Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

Citations

4
A smart solar PV monitoring system using internet of things (IoT) DOI
Rita Pimpalkar, Anil Kumar Sahu, Anindita Roy

et al.

Concurrent Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 14, 2025

This paper proposes the implementation of a Smart Solar PV Monitoring System that utilizes Internet Things (IoT) technology integrated with Nodemcu microcontroller and Blynk application for real-time data visualization. The system aims to evaluate impact dust fly ash on solar photovoltaic (PV) systems performance. By integrating monitoring app, monitoring, analysis effects efficiency becomes possible. Data 18 days operation an experimental 5.5 W panel is collected analyzed establish correlation between accumulation performance systems. Experimental investigations were performed using artificial viz., normal in study duration quantities 2, 4 6 g. It was found ash’s average loss 49.16%, 17.46%. findings this research point IoT-based smart effect efficiency.

Language: Английский

Citations

0
A Comprehensive Review of Smart Energy Management Systems for Photovoltaic Power Generation Utilizing the Internet of Things DOI Creative Commons
Challa Krishna Rao, Sarat Kumar Sahoo, Franco Fernando Yanine

et al.

Unconventional Resources, Journal Year: 2025, Volume and Issue: unknown, P. 100197 - 100197

Published: April 1, 2025

Language: Английский

Citations

0
Integration of the Chimp Optimization Algorithm and Rule-Based Energy Management Strategy for Enhanced Microgrid Performance Considering Energy Trading Pattern DOI Open Access
Mukhtar Fatihu Hamza, Babangida Modu, Sulaiman Z. Almutairi

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(10), P. 2037 - 2037

Published: May 16, 2025

The increasing integration of renewable energy into modern power systems has prompted the need for efficient hybrid solutions to ensure reliability, sustainability, and economic viability. However, optimizing design systems, particularly those incorporating both hydrogen battery storage, remains challenging due system complexity fluctuating trading conditions. This study addresses these gaps by proposing a novel framework that combines Chimp Optimization Algorithm (ChOA) with rule-based management strategy (REMS) optimize component sizing operational efficiency in grid-connected microgrid. proposed integrates photovoltaic (PV) panels, wind turbines (WT), electrolyzers (ELZ), fuel cells (FC), storage (BAT), while accounting seasonal variations dynamic trading. Each contribution Research Contributions section directly critical limitations previous studies, including lack advanced metaheuristic optimization, underutilization hydrogen-battery synergy, absence practical control strategies management. Simulation results show ChOA-based model achieves most cost-effective configuration, PV capacity 1360 kW, WT 462 164 kWh BAT 138 H2 tanks, 571 kW ELZ, 381 FC. configuration yields lowest cost (COE) at $0.272/kWh an annualized (ASC) $544,422. Comparatively, Genetic (GA), Salp Swarm (SSA), Grey Wolf Optimizer (GWO) produce slightly higher COE values $0.274, $0.275, $0.276 per kWh, respectively. These findings highlight superior performance ChOA offer scalable, adaptable support future deployment smart grid development.

Language: Английский

Citations

0