Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy
The accurate prediction of optimum load is crucial for electric utilities while planning and forecasting. Errors in the planning stage, regardless of their nature may result in operational loss of utility. The complex interplay of natural events, man-made factors, and global policies make forecastin...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025000891 |
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| author | Muhammad Shoaib Saleem Naeem Abas |
| author_facet | Muhammad Shoaib Saleem Naeem Abas |
| author_sort | Muhammad Shoaib Saleem |
| collection | DOAJ |
| description | The accurate prediction of optimum load is crucial for electric utilities while planning and forecasting. Errors in the planning stage, regardless of their nature may result in operational loss of utility. The complex interplay of natural events, man-made factors, and global policies make forecasting future power demand incredibly challenging, thus making prediction models to struggle with accurate predictions. Techniques used for electricity demand forecasting encompass artificial intelligence, artificial neural networks, trend line extrapolations, fuzzy logic, vector support machines, genetic algorithms and expert systems. Demand forecasting becomes even more difficult in polygeneration utilities with renewable energy sources integrated to meet the varying demands. This research work aims estimation of demand and line losses in utilities with generation deficiencies that implement demand side management. A polygeneration system integrating renewable energy system (RES) is dynamically tested under various electrical loading conditions. The generation comprises solar PV and wind turbine, each of 1 MW along with 1.5 MW auxiliary diesel engines is simulated in TRNSYS® to meet the varying demand under diverse weather conditions. Results show that RES can supply 77 % of energy demand, whereas 23 % of the load is met by fossil fuel (diesel). The wind turbine shows consistent performance and can replace traditional fossil fuel-based electricity generation system as a baseline supply. The polygeneration system converts surplus renewable energy into 5,000–7,000 kg of hydrogen for efficient storage and future electricity use, supporting sustainable energy needs. |
| format | Article |
| id | doaj-art-5b8290c674024d58942325921b0add61 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-5b8290c674024d58942325921b0add612025-01-23T05:27:39ZengElsevierResults in Engineering2590-12302025-03-0125104001Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energyMuhammad Shoaib Saleem0Naeem Abas1Department of Electrical Engineering, University of Gujrat, Hafiz Hayat Campus, Gujrat, Pakistan; Department of Electrical Engineering, University of Management and Technology Lahore, Sialkot Campus, Sialkot, PakistanDepartment of Electrical Engineering, University of Gujrat, Hafiz Hayat Campus, Gujrat, Pakistan; Corresponding author.The accurate prediction of optimum load is crucial for electric utilities while planning and forecasting. Errors in the planning stage, regardless of their nature may result in operational loss of utility. The complex interplay of natural events, man-made factors, and global policies make forecasting future power demand incredibly challenging, thus making prediction models to struggle with accurate predictions. Techniques used for electricity demand forecasting encompass artificial intelligence, artificial neural networks, trend line extrapolations, fuzzy logic, vector support machines, genetic algorithms and expert systems. Demand forecasting becomes even more difficult in polygeneration utilities with renewable energy sources integrated to meet the varying demands. This research work aims estimation of demand and line losses in utilities with generation deficiencies that implement demand side management. A polygeneration system integrating renewable energy system (RES) is dynamically tested under various electrical loading conditions. The generation comprises solar PV and wind turbine, each of 1 MW along with 1.5 MW auxiliary diesel engines is simulated in TRNSYS® to meet the varying demand under diverse weather conditions. Results show that RES can supply 77 % of energy demand, whereas 23 % of the load is met by fossil fuel (diesel). The wind turbine shows consistent performance and can replace traditional fossil fuel-based electricity generation system as a baseline supply. The polygeneration system converts surplus renewable energy into 5,000–7,000 kg of hydrogen for efficient storage and future electricity use, supporting sustainable energy needs.http://www.sciencedirect.com/science/article/pii/S2590123025000891Load forecastLine loss estimationOptimizationPolygeneration systemTRNSYSRenewable energy |
| spellingShingle | Muhammad Shoaib Saleem Naeem Abas Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy Results in Engineering Load forecast Line loss estimation Optimization Polygeneration system TRNSYS Renewable energy |
| title | Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy |
| title_full | Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy |
| title_fullStr | Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy |
| title_full_unstemmed | Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy |
| title_short | Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy |
| title_sort | optimization and loss estimation in energy deficient polygeneration systems a case study of pakistan s utilities with integrated renewable energy |
| topic | Load forecast Line loss estimation Optimization Polygeneration system TRNSYS Renewable energy |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025000891 |
| work_keys_str_mv | AT muhammadshoaibsaleem optimizationandlossestimationinenergydeficientpolygenerationsystemsacasestudyofpakistansutilitieswithintegratedrenewableenergy AT naeemabas optimizationandlossestimationinenergydeficientpolygenerationsystemsacasestudyofpakistansutilitieswithintegratedrenewableenergy |