Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithm
Considering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose optimal...
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| Published in: | IET generation, transmission & distribution Vol. 18; no. 4; pp. 809 - 833 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Wiley
01.02.2024
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| Subjects: | |
| ISSN: | 1751-8687, 1751-8695 |
| Online Access: | Get full text |
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| Summary: | Considering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose optimal energy management for MMGs in the DN. Different objective functions have been taken into account in this optimization, including network cost, pollution reduction, and distribution network power losses. To design the multi‐objective optimization problem, a fuzzy method has been adopted for simultaneous multi‐objective calculations. Furthermore, the effect of the placement of distributed generations (DGs) and microgrids (MGs) is considered to reduce the distribution network power losses. Information gap decision theory (IGDT) has formulated uncertainties about renewable sources and consumers. To solve this optimization problem, a new method of the modified Harris Hawk optimization (MHHO) algorithm has been implemented, compared with the original HHO and genetic algorithm (GA). Finally, the proposed method has been analysed under the IEEE 33‐bus distribution network for a 24‐hour time horizon, including three MGs considering different renewable energy sources (RESs). The simulation results have demonstrated the high performance of the allocated network with the MHHO algorithm compared to the other scenarios.
Here, to design the multi‐objective optimization problem, a fuzzy method has been adopted for simultaneous multi‐objective calculations. Furthermore, the effect of the placement of distributed generations and microgrids is considered to reduce the distribution network power losses. Information gap decision theory has formulated uncertainties about renewable sources and consumers. To solve this optimization problem, a new method of the modified Harris Hawk optimization (MHHO) algorithm has been implemented, compared with the original HHO and genetic algorithm. |
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| ISSN: | 1751-8687 1751-8695 |
| DOI: | 10.1049/gtd2.13116 |