| Autor: |
Kumar, Amit, Ghose, Tirthadip |
| Předmět: |
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| Zdroj: |
Electrical Engineering; Oct2024, Vol. 106 Issue 5, p5417-5429, 13p |
| Abstrakt: |
The increasing use of renewable energy sources (RESs) in distribution networks, combined with an ever-increasing load, makes balancing between generation and load difficult. Demand response programs (DRPs) and energy storage come together in a significant way for microgrid operators to avoid purchasing power at a higher cost from the upstream network and to move toward a self-reliant system. This work considers Electric vehicles (EVs), being the most common type of flexible load, can be a demand response (DR) resource for demand-side management (DSM). The work proposes a technique that decides SoC over the number of EV plugged-in chargers in a charging station with time. To cope with the large number of chargers, the work conceptualizes cluster control in dealing with a large population of EV chargers. To achieve the goal, the control concept is implemented by categorizing EV chargers into different bins or groups based on the SoC of plugged-in EVs. The proposed model forecasts EV state transitions using the Markov state transition approach. The probabilistic approach of the state transition matrix is determined to understand the status of the battery SoC of EVs. Hence the significant contribution of the proposed technique does not necessitate sending the SoC values of plugged-in EVs to the control room at sampled intervals. The work then proposed two control techniques based on identifying and prioritizing the chargers of various charging stations to redistribute the EV loads within the short span of the load curve. Two control concepts, on/off control and charging power control, have been developed and applied to the final state transition matrix as a part of the DRP. The results related to the load reduction show the justification of the concept of controlling the power consumption of chargers. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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