A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints
Dynamic economic load dispatch (DELD), which determines the best generation scheduling for different power plants during the next 24 hours in order to meet the electricity demand as well as minimize the total energy cost, is a highly complex non-linear and non-convex problem. This study proposes an...
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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/9490257/ |
| _version_ | 1818455962301759488 |
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| author | Mohana S. Alanazi |
| author_facet | Mohana S. Alanazi |
| author_sort | Mohana S. Alanazi |
| collection | DOAJ |
| description | Dynamic economic load dispatch (DELD), which determines the best generation scheduling for different power plants during the next 24 hours in order to meet the electricity demand as well as minimize the total energy cost, is a highly complex non-linear and non-convex problem. This study proposes an efficient modified teaching–learning–based optimization algorithm, called MTLBO, to effectively solve the DELD optimization problem in a power system containing thermal generations and wind power. The planning problem comprises the total fuel cost function with valve-point loading effect and the transmission power losses. Also, the uncertainties of wind power and load demand along with the various equality and inequality operational constraints such as power generation limits, ramp rate limits, prohibited operating zones and power balance are considered in the problem. Additionally, in the proposed MTLBO algorithm, the learning phase is properly integrated into the teaching phase to improve the convergence characteristic of the original TLBO. Moreover, in order to enhance the feature of local optima avoidance, interaction of up to five students are incorporated into the learning phase to improve the knowledge of each student. To exhibit the effectiveness of the proposed approach, the algorithm is applied into the 14 real test functions. In addition, several cases with 10 and 30 unit test systems are investigated over the planning period. Compared to the main TLBO algorithm which is mostly used optimization algorithms proposed in prior studies, the simulation results demonstrate the efficiency and superiority of the proposed optimization approach in terms of consistency, robustness, convergence rate and finding better plausible optimal solutions. |
| first_indexed | 2024-12-14T22:19:06Z |
| format | Article |
| id | doaj.art-c88290176591464eac4e16c0b350b7a5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T22:19:06Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c88290176591464eac4e16c0b350b7a52022-12-21T22:45:32ZengIEEEIEEE Access2169-35362021-01-01910166510168010.1109/ACCESS.2021.30979859490257A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational ConstraintsMohana S. Alanazi0https://orcid.org/0000-0003-0191-7059Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, Saudi ArabiaDynamic economic load dispatch (DELD), which determines the best generation scheduling for different power plants during the next 24 hours in order to meet the electricity demand as well as minimize the total energy cost, is a highly complex non-linear and non-convex problem. This study proposes an efficient modified teaching–learning–based optimization algorithm, called MTLBO, to effectively solve the DELD optimization problem in a power system containing thermal generations and wind power. The planning problem comprises the total fuel cost function with valve-point loading effect and the transmission power losses. Also, the uncertainties of wind power and load demand along with the various equality and inequality operational constraints such as power generation limits, ramp rate limits, prohibited operating zones and power balance are considered in the problem. Additionally, in the proposed MTLBO algorithm, the learning phase is properly integrated into the teaching phase to improve the convergence characteristic of the original TLBO. Moreover, in order to enhance the feature of local optima avoidance, interaction of up to five students are incorporated into the learning phase to improve the knowledge of each student. To exhibit the effectiveness of the proposed approach, the algorithm is applied into the 14 real test functions. In addition, several cases with 10 and 30 unit test systems are investigated over the planning period. Compared to the main TLBO algorithm which is mostly used optimization algorithms proposed in prior studies, the simulation results demonstrate the efficiency and superiority of the proposed optimization approach in terms of consistency, robustness, convergence rate and finding better plausible optimal solutions.https://ieeexplore.ieee.org/document/9490257/Dynamic economic load dispatchmodified teaching learning based optimizationload demandoperational constraintsoptimal solutionsreal-world optimization problems |
| spellingShingle | Mohana S. Alanazi A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints IEEE Access Dynamic economic load dispatch modified teaching learning based optimization load demand operational constraints optimal solutions real-world optimization problems |
| title | A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints |
| title_full | A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints |
| title_fullStr | A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints |
| title_full_unstemmed | A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints |
| title_short | A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints |
| title_sort | modified teaching x2014 learning based optimization for dynamic economic load dispatch considering both wind power and load demand uncertainties with operational constraints |
| topic | Dynamic economic load dispatch modified teaching learning based optimization load demand operational constraints optimal solutions real-world optimization problems |
| url | https://ieeexplore.ieee.org/document/9490257/ |
| work_keys_str_mv | AT mohanasalanazi amodifiedteachingx2014learningbasedoptimizationfordynamiceconomicloaddispatchconsideringbothwindpowerandloaddemanduncertaintieswithoperationalconstraints AT mohanasalanazi modifiedteachingx2014learningbasedoptimizationfordynamiceconomicloaddispatchconsideringbothwindpowerandloaddemanduncertaintieswithoperationalconstraints |