Handling Multiple-Fuel Options in Economic Dispatch of Thermal Power Plants Through a Tight Model Applying Indicator Variables
Thermal power plants play a central role in present power systems because of their high efficiency, fast startup capability, and flexibility to integrate the variability of renewable generations. These thermal units can utilize various fuels, including coal, natural gas, and oil, which enhances both...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/etep/1572487 |
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| Summary: | Thermal power plants play a central role in present power systems because of their high efficiency, fast startup capability, and flexibility to integrate the variability of renewable generations. These thermal units can utilize various fuels, including coal, natural gas, and oil, which enhances both the economic performance and security of the overall energy system. Representing the fuel cost functions of these units with multiple fuels (MFs) as binary decisions involves adding binary variables to the economic dispatch (ED) problem. The inclusion of these binary variables and nonlinear cost functions results in a complex NP-hard mixed-integer nonlinear programming (MINLP). This paper provides another perspective on the ED, where indicator variables represent the MF options and determine which cost functions should be set to zero. Based on this perspective, the paper builds a tight model to handle the indicator variables and solve the MINLP ED. Moreover, the paper introduces an iterative solution method with a bound-tightening technique to speed up the solution process. We conducted experimental studies using eight ED case studies with MF options involving up to 1280 generating units. The optimal costs obtained from these case studies demonstrate the effectiveness of the tight model and the iterative solution method for solving the MINLP ED problem. Furthermore, the proposed approach generally outperforms earlier algorithms in terms of solution quality and robustness. Finally, the tight model can speed up the solution process by 18%–45% compared with the standard formulation in the adopted case studies. |
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| ISSN: | 2050-7038 |