Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm
The development of efficient energy management for nanogrid (NG) systems, while reducing both the carbon dioxide (CO2) emissions and power generation cost, is achievable through the effective utilization of available energy sources. This paper proposes a multi-objective optimal energy management str...
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2025
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| author | Jamal S. Pasupuleti J. Rahmat N.A. Tan N.M.L. |
| author2 | 57265080900 |
| author_facet | 57265080900 Jamal S. Pasupuleti J. Rahmat N.A. Tan N.M.L. |
| author_sort | Jamal S. |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The development of efficient energy management for nanogrid (NG) systems, while reducing both the carbon dioxide (CO2) emissions and power generation cost, is achievable through the effective utilization of available energy sources. This paper proposes a multi-objective optimal energy management strategy for grid-connected NG systems, which incorporates PV arrays and battery storage devices (BSDs), to reduce operating costs and CO2 emission simultaneously over a 24-hour scheduling period. This strategy, which is based on the improved pelican optimization algorithm (IPOA), involves the development of a multi-objective optimization (MOA) equation with several constraints, while taking into account the Malaysian grid purchasing and selling prices. An innovative IPOA-derived technique is developed to facilitate the NG's optimal energy management operation in multi-objective situations. The proposed algorithm is tested on three distinct scenarios to affirm its efficacy. It is assumed that (a) power exchange between the NG and the main grid is limitless, (b) power interchange between the NG and main grid has a predetermined limit and (c) operating at the maximum capacity of PV array. In order to demonstrate the effectiveness of the proposed algorithm, The outcomes of the simulation are juxtaposed with results obtained from the initial Pelican Optimisation Algorithm (POA), the Bat Algorithm, and the Improved Differential Evolutionary (IDE) Algorithm. The simulation reveals that the suggested IPOA algorithm exhibited the most economical performance and the lowest CO2 emissions. Moreover, in the second scenario, operational costs decreased by 9.5%, and CO2 emissions were reduced by 15%. Conversely, in Scenario 3, there was a 2% decrease in cost and 23% reduction in CO2 emissions as against the first scenario. ? 2013 IEEE. |
| format | Article |
| id | my.uniten.dspace-37133 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
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| spelling | my.uniten.dspace-371332025-03-03T15:47:50Z Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm Jamal S. Pasupuleti J. Rahmat N.A. Tan N.M.L. 57265080900 11340187300 55647163881 24537965000 Carbon dioxide Cost effectiveness Cost reduction Emission control Energy efficiency Energy management Multiobjective optimization Operating costs Power markets Virtual storage > emission CO<sub xmlns:ali=" Cost effective Improved pelican optimization algorithm Microgrid Multi objective Multi-objective energy management Nanogrids Optimisations Optimization algorithms Xmlns:mml=" Xmlns:xlink=" Xmlns:xsi=" Genetic algorithms The development of efficient energy management for nanogrid (NG) systems, while reducing both the carbon dioxide (CO2) emissions and power generation cost, is achievable through the effective utilization of available energy sources. This paper proposes a multi-objective optimal energy management strategy for grid-connected NG systems, which incorporates PV arrays and battery storage devices (BSDs), to reduce operating costs and CO2 emission simultaneously over a 24-hour scheduling period. This strategy, which is based on the improved pelican optimization algorithm (IPOA), involves the development of a multi-objective optimization (MOA) equation with several constraints, while taking into account the Malaysian grid purchasing and selling prices. An innovative IPOA-derived technique is developed to facilitate the NG's optimal energy management operation in multi-objective situations. The proposed algorithm is tested on three distinct scenarios to affirm its efficacy. It is assumed that (a) power exchange between the NG and the main grid is limitless, (b) power interchange between the NG and main grid has a predetermined limit and (c) operating at the maximum capacity of PV array. In order to demonstrate the effectiveness of the proposed algorithm, The outcomes of the simulation are juxtaposed with results obtained from the initial Pelican Optimisation Algorithm (POA), the Bat Algorithm, and the Improved Differential Evolutionary (IDE) Algorithm. The simulation reveals that the suggested IPOA algorithm exhibited the most economical performance and the lowest CO2 emissions. Moreover, in the second scenario, operational costs decreased by 9.5%, and CO2 emissions were reduced by 15%. Conversely, in Scenario 3, there was a 2% decrease in cost and 23% reduction in CO2 emissions as against the first scenario. ? 2013 IEEE. Final 2025-03-03T07:47:50Z 2025-03-03T07:47:50Z 2024 Article 10.1109/ACCESS.2024.3377250 2-s2.0-85188437564 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188437564&doi=10.1109%2fACCESS.2024.3377250&partnerID=40&md5=e1de1881cdb7e4987001ba5e44337cd8 https://irepository.uniten.edu.my/handle/123456789/37133 12 41954 41966 All Open Access; Gold Open Access Institute of Electrical and Electronics Engineers Inc. Scopus |
| spellingShingle | Carbon dioxide Cost effectiveness Cost reduction Emission control Energy efficiency Energy management Multiobjective optimization Operating costs Power markets Virtual storage > emission CO<sub xmlns:ali=" Cost effective Improved pelican optimization algorithm Microgrid Multi objective Multi-objective energy management Nanogrids Optimisations Optimization algorithms Xmlns:mml=" Xmlns:xlink=" Xmlns:xsi=" Genetic algorithms Jamal S. Pasupuleti J. Rahmat N.A. Tan N.M.L. Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm |
| title | Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm |
| title_full | Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm |
| title_fullStr | Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm |
| title_full_unstemmed | Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm |
| title_short | Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm |
| title_sort | multi-objective optimal energy management of nanogrid using improved pelican optimization algorithm |
| topic | Carbon dioxide Cost effectiveness Cost reduction Emission control Energy efficiency Energy management Multiobjective optimization Operating costs Power markets Virtual storage > emission CO<sub xmlns:ali=" Cost effective Improved pelican optimization algorithm Microgrid Multi objective Multi-objective energy management Nanogrids Optimisations Optimization algorithms Xmlns:mml=" Xmlns:xlink=" Xmlns:xsi=" Genetic algorithms |
| url_provider | http://dspace.uniten.edu.my/ |