Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid

Samadi, Pedram; Mohsenian-Rad, Amir-Hamed; Schober, Robert; Wong, Vincent W. S.; Jatskevich, Juri

doi:10.1109/smartgrid.2010.5622077

Cited by 859 publications

(712 citation statements)

References 18 publications

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“…Several recent studies are on demand management for general electric devices [21,6,20,12,23]. Although these approaches can be applied to EV charging, they are not applicable to large populations of heterogeneous EVs.…”

Section: Related Workmentioning

confidence: 99%

Decentralised online charging scheduling for large populations of electric vehicles: a cyber-physical system approach

Jin

Wang

Zhang

2013

International Journal of Parallel, Emergent and Distributed Sys

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As the number of electric vehicles (EVs) grows, their electricity demands may have significant detrimental impacts on electric power grid when not scheduled properly. In this paper, we model an EV charging system as a cyber-physical system, and design a decentralised online EV charging scheduling algorithm for large populations of EVs, where the EVs can be highly heterogeneous and may join the charging system dynamically. The algorithm couples a clustering-based strategy that dynamically classifies heterogeneous EVs into mUltiple groups and a sliding-window iterative approach that schedules the charging demand for the EVs in each group in real time. Extensive simulation results demonstrate that our approach provides near-optimal solutions at significantly reduced complexity and communication overhead. It flattens the aggregated load on the power grid and reduces the costs of both the users and the utility.

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Section: Related Workmentioning

confidence: 99%

Decentralised online charging scheduling for large populations of electric vehicles: a cyber-physical system approach

Jin

Wang

Zhang

2013

International Journal of Parallel, Emergent and Distributed Sys

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“…The main objective of these DSM programs is to optimize and control the energy consumption automatically by considering scenarios with different energy suppliers and different energy users. A dual decomposition-based approach was successfully taken by some researchers [6,[23][24][25][26] for modeling these distributed optimization issues in an SG. Additionally, game-theoretic methods, including cooperative games, noncooperative games and evolutionary games, have been extensively employed as solution techniques for optimizing energy consumption by resolving the equilibriums of the games; because game-theoretic methods can capture the features of these interactions and study the possible outcomes and corresponding equilibriums [27].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Household Energy Management Based on a Double Cooperative Game Approach in the Smart Grid

et al. 2015

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Taking advantage of two-way communication infrastructure and bidirectional energy trading between utility companies and customers in the future smart grid (SG), autonomous energy management programs become crucial to the demand-side management (DSM). Most of the existing autonomous energy management schemes are for the scenario with a single utility company or the scenario with one-way energy trading. In this paper, an autonomous household energy management system with multiple utility companies and multiple residential customers is studied by considering the bidirectional energy trading. To minimize the overall costs of both the utility companies and the residential customers, the energy management system is formulated as a double cooperative game. That is, the interaction among the residential users is formulated as a cooperative game, where the players are the customers and the strategies are the daily schedules of their household appliances; and the interaction among the utility companies is also formulated as a cooperative game, where the players are the suppliers and the strategies are the proportions of the daily total energy they provide for the customers. Without loss of generality, the bidirectional energy trading in the double cooperative game is formulated by allowing plug-in electric vehicles (PEVs) to discharge and sell energy back. Two distributed algorithms will be provided to realize the global optimal performance in terms of minimizing the energy costs, which can be guaranteed at the Nash equilibriums of the formulated cooperative games. Finally, simulation results illustrated that the proposed double cooperative game can benefit both the utility companies and residential users significantly.

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“…Proof: It can be easily verified that r * (θ) and p * (θ) in (15) satisfy (14). Therefore, to prove Theorem 1, it suffices to show that r * (θ) in (15) is the solution to the optimization problem in (13).…”

Section: Theorem 1 For Regular Distributions the Optimal Contract Cmentioning

confidence: 98%

A contract-based approach for ancillary services in V2G networks: Optimality and learning

Gao

Chen

Wang

et al. 2013

2013 Proceedings IEEE INFOCOM

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Abstract-With the foreseeable large scale deployment of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technologies, it is possible to provide ancillary services to the power grid in a cost efficient way, i.e., through the bidirectional power flow of EVs. A key issue in such kind of schemes is how to stimulate a large number of EVs to act coordinately to achieve the service request. This is challenging since EVs are self-interested and generally have different preferences toward charging and discharging based on their own constraints. In this paper, we propose a contract-based mechanism to tackle this challenge. Through the design of an optimal contract, the aggregator can provide incentives for EVs to participate in ancillary services to power grid, match the aggregated energy rate with the service request and maximize its own profits. We prove that under mild conditions, the optimal contract-based mechanism takes a very simple form, i.e., the aggregator only needs to publish an optimal unit price to EVs, which is determined based on the statistical distribution of EVs' preferences. We then consider a more practical scenario where the aggregator has no prior knowledge regarding the statistical distribution and study how should the aggregator learn the optimal unit price from its interactions with EVs. Simulation results are shown to verify the effectiveness of the proposed contract-based mechanism.

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Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid

Cited by 859 publications

References 18 publications

Decentralised online charging scheduling for large populations of electric vehicles: a cyber-physical system approach

Decentralised online charging scheduling for large populations of electric vehicles: a cyber-physical system approach

Autonomous Household Energy Management Based on a Double Cooperative Game Approach in the Smart Grid

A contract-based approach for ancillary services in V2G networks: Optimality and learning

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