Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch
In this paper, Chaotic Artificial Ecosystem-based Optimization Algorithm (CAEO) is proposed and utilized to determine the optimal solution which achieves the economical operation of the electrical power system and reducing the environmental pollution produced by the conventional power generation. He...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9380276/ |
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| author | Mohamed H. Hassan Salah Kamel Sinan Q. Salih Tahir Khurshaid Mohamed Ebeed |
| author_facet | Mohamed H. Hassan Salah Kamel Sinan Q. Salih Tahir Khurshaid Mohamed Ebeed |
| author_sort | Mohamed H. Hassan |
| collection | DOAJ |
| description | In this paper, Chaotic Artificial Ecosystem-based Optimization Algorithm (CAEO) is proposed and utilized to determine the optimal solution which achieves the economical operation of the electrical power system and reducing the environmental pollution produced by the conventional power generation. Here, the Combined Economic Emission Dispatch (CEED) problem is represented using a max/max Price Penalty Factor (PPF) to confine the system’s nonlinearity. PPF is considered to transform a four-objective problem into a single-objective optimization problem. The proposed modification of AEO raises the effectiveness of the populations to achieve the best fitness solution by well-known 10 chaotic functions and this is valuable in both cases of the single and multi-objective functions. The CAEO algorithm is used for minimizing the economic load dispatch and the three bad gas emissions which are sulfur dioxide (SO2), nitrous oxide (NOx), and carbon dioxide (CO2). To evaluate the proposed CAEO, it is utilized for four different levels of demand in a 6-unit power generation (30-bus test system) and 11-unit power generation (69-bus test system) with a different value of load demand (1000, 1500, 2000, and 2500MW). Statistical analysis is executed to estimate the reliability and stability of the proposed CAEO method. The results obtained by CAEO algorithm are compared with other methods and conventional AEO to prove that the modification is to boost the search strength of conventional AEO. The results display that the CAEO algorithm is superior to the conventional AEO and the others in achieving the best solution to the problem of CEED in terms of efficient results, strength, and computational capability all over study cases. In the second scenario of the bi-objective problem, the Pareto theory is integrated with a CAEO to get a series of Non-Dominated (ND) solutions, and then using the fuzzy approach to determine BCS. |
| first_indexed | 2024-04-12T04:58:20Z |
| format | Article |
| id | doaj.art-9416d0c74d05415aabdb57db7ae5e2aa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T04:58:20Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9416d0c74d05415aabdb57db7ae5e2aa2022-12-22T03:47:03ZengIEEEIEEE Access2169-35362021-01-019511465116510.1109/ACCESS.2021.30669149380276Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission DispatchMohamed H. Hassan0https://orcid.org/0000-0003-1754-4883Salah Kamel1https://orcid.org/0000-0001-9505-5386Sinan Q. Salih2Tahir Khurshaid3https://orcid.org/0000-0001-6113-123XMohamed Ebeed4https://orcid.org/0000-0003-2025-9821Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, EgyptDepartment of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, EgyptComputer Science Department, Dijlah University College, Baghdad, IraqDepartment of Electrical Engineering, Yeungnam University, Gyeongsan, South KoreaDepartment of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag, EgyptIn this paper, Chaotic Artificial Ecosystem-based Optimization Algorithm (CAEO) is proposed and utilized to determine the optimal solution which achieves the economical operation of the electrical power system and reducing the environmental pollution produced by the conventional power generation. Here, the Combined Economic Emission Dispatch (CEED) problem is represented using a max/max Price Penalty Factor (PPF) to confine the system’s nonlinearity. PPF is considered to transform a four-objective problem into a single-objective optimization problem. The proposed modification of AEO raises the effectiveness of the populations to achieve the best fitness solution by well-known 10 chaotic functions and this is valuable in both cases of the single and multi-objective functions. The CAEO algorithm is used for minimizing the economic load dispatch and the three bad gas emissions which are sulfur dioxide (SO2), nitrous oxide (NOx), and carbon dioxide (CO2). To evaluate the proposed CAEO, it is utilized for four different levels of demand in a 6-unit power generation (30-bus test system) and 11-unit power generation (69-bus test system) with a different value of load demand (1000, 1500, 2000, and 2500MW). Statistical analysis is executed to estimate the reliability and stability of the proposed CAEO method. The results obtained by CAEO algorithm are compared with other methods and conventional AEO to prove that the modification is to boost the search strength of conventional AEO. The results display that the CAEO algorithm is superior to the conventional AEO and the others in achieving the best solution to the problem of CEED in terms of efficient results, strength, and computational capability all over study cases. In the second scenario of the bi-objective problem, the Pareto theory is integrated with a CAEO to get a series of Non-Dominated (ND) solutions, and then using the fuzzy approach to determine BCS.https://ieeexplore.ieee.org/document/9380276/Combined economic and emission dispatchartificial ecosystem-based optimizationgreenhouse gasesPareto frontprice penalty factorchaotic AEO |
| spellingShingle | Mohamed H. Hassan Salah Kamel Sinan Q. Salih Tahir Khurshaid Mohamed Ebeed Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch IEEE Access Combined economic and emission dispatch artificial ecosystem-based optimization greenhouse gases Pareto front price penalty factor chaotic AEO |
| title | Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch |
| title_full | Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch |
| title_fullStr | Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch |
| title_full_unstemmed | Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch |
| title_short | Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch |
| title_sort | developing chaotic artificial ecosystem based optimization algorithm for combined economic emission dispatch |
| topic | Combined economic and emission dispatch artificial ecosystem-based optimization greenhouse gases Pareto front price penalty factor chaotic AEO |
| url | https://ieeexplore.ieee.org/document/9380276/ |
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