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| Format: | Recurso digital |
| Language: | English |
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2026
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| Online Access: | https://doi.org/10.5281/zenodo.19847207 |
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| author | Dr.M. Sri Suresh A. Sai Pranav Redd B. Prem Kumar P. Ganesh |
| author_facet | Dr.M. Sri Suresh A. Sai Pranav Redd B. Prem Kumar P. Ganesh |
| contents | The escalating environmental pollution have become major issues that require novel and eco-friendly methods. Integration of renewable energy is one of the possible solutions. resources (RERs) and effective energy. management strategies. Energy management aims at minimizing. and as well as operating, maintenance and generation costs. enhancing system performance by means of methods like minimization of power losses, stability improvement, and emission. reduction. In this respect, the energy management of mi-crogrids has played out to be a major concern in the modern. power systems. An optimi-zation is presented in this paper. model of a multi-objective problem of a renewable. Mul-ti-microgrid (MMG) system is based on energy. The system consisting of three intercon-nected microgrids, all equipped. and wind turbines (WT) and photovoltaic (PV) panels, became part of the IEEE 33-bus distribution system. The model takes into consideration the variation in PV and WT output, load Sporadic demand, and real-time prices of elec-tricity. Three objective functions are designed in a way that they reduce the overall. cost/year, deviation of voltage, and voltage stability index- developing a cost-performance multi-objective collectively. optimization problem. With the assistance of the, the issue is considered. Particle Swarm Optimization (PSO) algorithm, both with and and without RERs.. Additionally, a comparative analysis is conducted using two other optimization techniques: Mountain Gazelle Optimization (MGO) and Gorilla Troop Optimization (GTO). Simulation results demonstrate that the proposed approach significantly reduces system costs and enhances overall performance. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19847207 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Energy Management System On Multi-Microgrid System Using Metaheuristic Algorithms Dr.M. Sri Suresh A. Sai Pranav Redd B. Prem Kumar P. Ganesh The escalating environmental pollution have become major issues that require novel and eco-friendly methods. Integration of renewable energy is one of the possible solutions. resources (RERs) and effective energy. management strategies. Energy management aims at minimizing. and as well as operating, maintenance and generation costs. enhancing system performance by means of methods like minimization of power losses, stability improvement, and emission. reduction. In this respect, the energy management of mi-crogrids has played out to be a major concern in the modern. power systems. An optimi-zation is presented in this paper. model of a multi-objective problem of a renewable. Mul-ti-microgrid (MMG) system is based on energy. The system consisting of three intercon-nected microgrids, all equipped. and wind turbines (WT) and photovoltaic (PV) panels, became part of the IEEE 33-bus distribution system. The model takes into consideration the variation in PV and WT output, load Sporadic demand, and real-time prices of elec-tricity. Three objective functions are designed in a way that they reduce the overall. cost/year, deviation of voltage, and voltage stability index- developing a cost-performance multi-objective collectively. optimization problem. With the assistance of the, the issue is considered. Particle Swarm Optimization (PSO) algorithm, both with and and without RERs.. Additionally, a comparative analysis is conducted using two other optimization techniques: Mountain Gazelle Optimization (MGO) and Gorilla Troop Optimization (GTO). Simulation results demonstrate that the proposed approach significantly reduces system costs and enhances overall performance. |
| title | Energy Management System On Multi-Microgrid System Using Metaheuristic Algorithms |
| url | https://doi.org/10.5281/zenodo.19847207 |