EV charging and discharging scheduling will result in additional challenges within power grids. With the growing adoption of EVs and RESs such as PVs and WP in SGs, there is an increasing need for accurate predictions and joint scheduling optimization to improve system stability and reliability.
In discharging mode, the control system is supposed to limit the battery current and avoid over-discharging throughout the time that battery regulates the DC voltage by the control of energy discharge.
LP has been mainly used for obtaining the optimal charging and discharging schedule , , , searching the optimal solutions of electricity price, feed-in tariff, and battery modeling parameters to reduce the overall cost , and EV charging rate .
The proposed method adapts the battery energy storage system (BESS) to employ the same control architecture for grid-connected mode as well as the islanded operation with no need for knowing the micro-grid operating mode or switching between the corresponding control architectures.
Discharging activity can benefit the EV customers and households with PV systems, but it impacts the battery lifetime . Frequent discharging will lead to quick battery degradation; one has to make a trade-off between battery life and the discharging profits. An MOO setting is the best to address this issue.
Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network.