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Multi-objective optimal scheduling of microgrid with electric vehicles
Citation
Mei, Y and Li, B and Wang, H and Wang, X and Negnevitsky, M, Multi-objective optimal scheduling of microgrid with electric vehicles, Energy Reports, 8 pp. 4512-4524. ISSN 2352-4847 (2022) [Refereed Article]
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DOI: doi:10.1016/j.egyr.2022.03.131
Abstract
With the increasing global attention to environmental protection, microgrids with efficient usage of renewable energy have been widely developed. Currently, the intermittent nature of renewable energy and the uncertainty of its demand affect the stable operation of a microgrid. Additionally, electric vehicles (EVs), as an impact load, could severely affect the safe dispatch of the microgrid. To solve these problems, a multi-objective optimization model was established based on the economy and the environmental protection of a microgrid including EVs. The linear weighting method based on two-person zero-sum game was used to coordinate the full consumption of renewable energy with the full bearing of load, and balance the two objectives better. Moreover, the adaptive simulated annealing particle swarm optimization algorithm (ASAPSO) was used to solve the multi-objective optimization model, and obtain the optimal solution in the unit. The simulation results showed that the multi-objective weight method could diminish the influence of uncertainty factors, promoting the full absorption of renewable energy and full load-bearing. Additionally, the orderly charging and discharging mode of EVs could reduce the operation cost and environmental protection cost of the microgrid. Therefore, the improved optimization algorithm was capable of improving the economy and environmental protection of the microgrid.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | microgrid, electric vehicles, multi objective optimization, two-person zero-sum game, adaptive simulated annealing particles, swarm optimization algorithm |
| Research Division: | Engineering |
| Research Group: | Mechanical engineering |
| Research Field: | Numerical modelling and mechanical characterisation |
| Objective Division: | Energy |
| Objective Group: | Energy storage, distribution and supply |
| Objective Field: | Energy systems and analysis |
| UTAS Author: | Wang, X (Professor Xiaolin Wang) |
| UTAS Author: | Negnevitsky, M (Professor Michael Negnevitsky) |
| ID Code: | 149452 |
| Year Published: | 2022 |
| Deposited By: | Engineering |
| Deposited On: | 2022-03-31 |
| Last Modified: | 2022-05-03 |
| Downloads: | 0 |
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