Resumen:
This paper presents a new framework for the scheduling of microgrids and distribution feeder reconfiguration (DFR), taking into consideration the uncertainties due to the load demand, market price, and renewable power generation. The model is implemented on the modified IEEE 118-bus test system, including microgrids and smart homes. The problem has been formulated as a two-stage model, which at the first stage, the day-ahead self-scheduling of each microgrid is carried out as a two-objective optimization problem. The two objectives include the minimization of the total operating cost and maximization of the consumer's comfort index. Then, the solution, obtained from the first stage is delivered to the distribution system operator (DSO). Then, at the second stage, the DSO determines the optimal configuration of the system with the aim of minimizing operating costs of the main grid and the penalty of deviating from microgrid scheduling. Note that the penalty is due to the difference in power exchange requested by the microgrids from the power exchange finalized by the DSO. The presented two-stage optimization problem is modeled in a mixed-integer linear programing (MILP) framework with four case studies, and solved in GAMS by using the GURUBI solver. The simulation results show that in the cases the DSO is able to reconfigure the system, the deviation from the optimal scheduling of microgrids would be considerably lower than the cases with fixed system configuration.
Palabras Clave: Multi-objective optimization; Distribution feeder reconfiguration; Microgrids; Renewable energy resources; Smart homes; Consumers' comfort index
Índice de impacto JCR y cuartil WoS: 9,000 - Q1 (2022)
Referencia DOI:
https://doi.org/10.1016/j.energy.2022.123228
Publicado en papel: Abril 2022.
Publicado on-line: Enero 2022.
Cita:
S.A. Mansouri, A. Ahmarinejad, E. Nematbakhsh, M.S. Javadi, A. Esmaeel Nezhad, J.P.S. Catalão, A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources. Energy. Vol. 245, pp. 123228-1 - 123228-26, Abril 2022. [Online: Enero 2022]
