Demand response for residential electric vehicles with random usage patterns in smart grids
Abstract
Electric vehicles (EVs) are expected to become widespread in future years. Thus, it is foreseen that EVs will become the new high-electricity-consuming appliances in the households. The characteristics of the extra power load that they impose on the distribution grid follow the patterns of people's random usage behaviors. In this paper, we seek to provide answers to the following question: assigning real-world randomness to the EVs' availability in the households and their charging requirements, how can EVs' demand response (DR) help to minimize the peak power demand and, in general, shape the aggregated demand profile of the system? We present a general demand-shaping problem applicable for limit order bids to a day-ahead (DA) energy market. We propose an algorithm for distributed DR of the EVs to shape the daily demand profile or to minimize the peak demand. Additionally, we put these problems in a game framework. Extensive simulations show that, for certain practical distributions of EVs' usage, it is possible to accommodate EVs for all the users in the system and yet achieve the same peak demand as when there is no EV in the system without any changes in the users' commuting behaviors.
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Reducible loads cannot be modulated continuously; continuous modulation is currently only feasible for inverter-connected devices or large industrial loads.
“Secondly, the reducible loads cannot be modulated continuously; this is currently only feasible for inverterconnected devices [18], [19] or large industrial loads [20], [21].”
From Congestion mitigation in unbalanced residential networks with OPF-based demand management (2022) · cites this paper as [19]
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References 27 · Cited by 197
- Authors
- Rassaei, F., Soh, W.-S., & Chua, K.-C.
- Affiliations
- National University of Singapore (SG)
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2015-10
- Volume
- 6
- Issue
- 4
- Pages
- 1367-1376
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W1523739524
- Cited by
- 197
- References
- 27
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 21 · 2023: 19 · 2022: 22 · 2021: 15 · 2020: 23 · 2019: 20 · 2018: 21 · 2017: 18 · 2016: 15 · 2015: 3
- FWCI
- 9.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Sustainable Energy (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- electric vehicles, demand response, residential charging, peak shaving, demand shaping, smart grid, day-ahead market, game-theoretic model, distribution networks
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