Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi Mohammed
In a generation operating system planning, avoided utility cost (AUC) is customarily implemented to attain the optimal economic benefits in a generating system by taking into account intriguing issues on the energy efficiency, renewable energy sources or conservation programs. In this thesis a new a...
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| Format: | Book Section |
| Language: | English |
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Institute of Graduate Studies, UiTM
2017
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| Online Access: | https://ir.uitm.edu.my/id/eprint/19731/ |
| _version_ | 1848804839325171712 |
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| author | Sasi Mohamme, Daw Saleh |
| author_facet | Sasi Mohamme, Daw Saleh |
| author_sort | Sasi Mohamme, Daw Saleh |
| building | UiTM Institutional Repository |
| collection | Online Access |
| description | In a generation operating system planning, avoided utility cost (AUC) is customarily implemented to attain the optimal economic benefits in a generating system by taking into account intriguing issues on the energy efficiency, renewable energy sources or conservation programs. In this thesis a new approaches of optimal dispatch of limited energy unit (ODLEU) and demand side management (DSM) using computational intelligence approach is proposed for AUC improvement. Contrary to the conventional approaches, which mainly rely on dispatching of each limited energy unit (LEU) in sequential order, the proposed algorithm comprising with optimization technique is used as an alternative for performing LEU dispatch; which has a tangible impact to improve and increase the AUC value. In order produce a global optimal solution of AUC, the self-adaptive strategy was proposed to serve as a new mutation technique responsible to provide a new population for discrete artificial bee colony. The newly designed algorithm is termed as the discrete artificial bee colony associated with selfadaptive strategy (DABCSAS). The AUC is originated from the summation of avoided energy cost, avoided expected cycle cost and avoided capacity cost of the generating system… |
| first_indexed | 2025-11-14T22:01:31Z |
| format | Book Section |
| id | uitm-19731 |
| institution | Universiti Teknologi MARA |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T22:01:31Z |
| publishDate | 2017 |
| publisher | Institute of Graduate Studies, UiTM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uitm-197312018-06-07T06:33:27Z https://ir.uitm.edu.my/id/eprint/19731/ Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi Mohammed Sasi Mohamme, Daw Saleh Malaysia In a generation operating system planning, avoided utility cost (AUC) is customarily implemented to attain the optimal economic benefits in a generating system by taking into account intriguing issues on the energy efficiency, renewable energy sources or conservation programs. In this thesis a new approaches of optimal dispatch of limited energy unit (ODLEU) and demand side management (DSM) using computational intelligence approach is proposed for AUC improvement. Contrary to the conventional approaches, which mainly rely on dispatching of each limited energy unit (LEU) in sequential order, the proposed algorithm comprising with optimization technique is used as an alternative for performing LEU dispatch; which has a tangible impact to improve and increase the AUC value. In order produce a global optimal solution of AUC, the self-adaptive strategy was proposed to serve as a new mutation technique responsible to provide a new population for discrete artificial bee colony. The newly designed algorithm is termed as the discrete artificial bee colony associated with selfadaptive strategy (DABCSAS). The AUC is originated from the summation of avoided energy cost, avoided expected cycle cost and avoided capacity cost of the generating system… Institute of Graduate Studies, UiTM 2017 Book Section PeerReviewed text en https://ir.uitm.edu.my/id/eprint/19731/1/ABS_DAW%20SALEH%20SASI%20MOHAMMED%20TDRA%20VOL%2011%20IGS%2017.pdf Sasi Mohamme, Daw Saleh (2017) Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi Mohammed. (2017) In: The Doctoral Research Abstracts. IGS Biannual Publication, 11 (11). Institute of Graduate Studies, UiTM, Shah Alam. |
| spellingShingle | Malaysia Sasi Mohamme, Daw Saleh Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi Mohammed |
| title | Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi Mohammed |
| title_full | Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi Mohammed |
| title_fullStr | Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi Mohammed |
| title_full_unstemmed | Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi Mohammed |
| title_short | Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi Mohammed |
| title_sort | computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / daw saleh sasi mohammed |
| topic | Malaysia |
| url | https://ir.uitm.edu.my/id/eprint/19731/ |