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Hamidreza Arasteh

Publications and source records attributed to Hamidreza Arasteh.

2 recordsLinked to original sources

Quantifying Realizable Flexibility Limits in Fast and Ultra-Fast EV Charging Using Real-World Data

The rapid growth of electric vehicles (EVs) is increasing the need to accurately quantify their flexibility as a resource for power system operation. However, most existing approaches rely on simplified or power-controllable models that overlook the intrinsic constraints of fast and ultra-fast DC charging. In practice, flexibility is fundamentally shaped by battery management system (BMS) behavior, connection time availability, and battery-protection limits. This paper introduces a trajectory-aware data-driven framework to quantify EV charging flexibility as an energy-bounded and time-constrained process. Based on 252 real charging sessions, 141 representative Power-SoC profiles are reconstructed to capture real-world charging dynamics. Unidirectional flexibility is defined through bounds on the maximum shiftable charging energy, while bidirectional flexibility is quantified as the bounds of the maximum extractable discharge energy under feasibility constraints. Results show that flexibility depends on charging state and connection time. Charging beyond 80% SoC increases duration with limited gains, while higher charger power saturates due to BMS limits. Charging time in the 20%-80% range drops by over 60%, and mean power increases by up to 40%. The maximum extractable bidirectional energy can exceed twice its value depending on the point at which flexibility is activated. These results highlight that EV flexibility is not a controllable resource, but a bounded and time-dependent capability. As such, the proposed framework provides actionable limits that can be directly used by system operators and aggregators for scheduling, peak shaving, and short-duration flexibility services.

eess.SY↗

Demand Response Requirements from the Cultural, Social, and Behavioral Perspectives

Demand-side response programs which also called Demand Response (DR) are interesting ways to attract consumers' participation in order to improve electric consumption patterns. DR programs motivate customers to change consumption patterns in response to price changes. This could be done by paying incentives or considering penalties either when wholesale market prices are high or when network reliability is at risk. The overall purpose of implementing DR programs is to improve reliability of the networks, while reducing the operation costs. Successful implementation of these DR programs requires prerequisites, which without them, there is no guarantee of success. Different disciplines have provided various scientific solutions for creating optimal power consumption behavior in customers (such as utilizing intelligent home appliances and big data techniques). Although each of these solutions might be efficient and effective, they could not cover all the aspects of the solutions. The results of studies conducted by many researchers show that in addition to the technical and economic issues, social, cultural, and behavioral factors are of great importance in DR implementation. Therefore, in this paper, these aspects, as one of the vital requirements for better implementation of DR, are investigated and analyzed in detail.

physics.soc-ph↗