A smart electricity markets for a decarbonized microgrid system.
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| Title: | A smart electricity markets for a decarbonized microgrid system. |
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| Authors: | Alhasnawi, Bilal Naji1 (AUTHOR) bilalnaji11@yahoo.com, Zanker, Marek2 (AUTHOR) marek.zanker@uhk.cz, Bureš, Vladimír2 (AUTHOR) vladimir.bures@uhk.cz |
| Source: | Electrical Engineering. May2025, Vol. 107 Issue 5, p5405-5425. 21p. |
| Subjects: | Institute of Electrical & Electronics Engineers, Optimization algorithms, Electricity markets, Energy industries, Carbon emissions, Renewable energy sources |
| Abstract: | Demand response (DR) programs are potentially powerful tools to support renewable energy integration, ensure power balance and update electricity market mechanism. Based on the existing work, in this paper propose a day-ahead a smart electricity markets for a decarbonized microgrid system with the DR program. The proposed system aims to minimize the operating cost, and carbon emission. An IEEE 33-bus system is used as an illustrative example to validate the application of the proposed smart electricity market model in the real large system. The proposed unit utilizes the African Vultures Optimization Algorithm (AVOA) which is used to optimize the cost of operation based on current load demand, energy prices and generation capacities. Also, a comparison between the optimization outcomes obtained results is implemented using Artificial Rabbits Optimization Algorithm (AROA), and Grasshopper Optimization Algorithm (GOA). The simulation results reveal that energy costs and PAR can be reduced energy cost, and carbon emission, whereas the Discomfort Index (DI) is maintained at a minimum value. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Demand response (DR) programs are potentially powerful tools to support renewable energy integration, ensure power balance and update electricity market mechanism. Based on the existing work, in this paper propose a day-ahead a smart electricity markets for a decarbonized microgrid system with the DR program. The proposed system aims to minimize the operating cost, and carbon emission. An IEEE 33-bus system is used as an illustrative example to validate the application of the proposed smart electricity market model in the real large system. The proposed unit utilizes the African Vultures Optimization Algorithm (AVOA) which is used to optimize the cost of operation based on current load demand, energy prices and generation capacities. Also, a comparison between the optimization outcomes obtained results is implemented using Artificial Rabbits Optimization Algorithm (AROA), and Grasshopper Optimization Algorithm (GOA). The simulation results reveal that energy costs and PAR can be reduced energy cost, and carbon emission, whereas the Discomfort Index (DI) is maintained at a minimum value. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09487921 |
| DOI: | 10.1007/s00202-024-02699-9 |
