Load Scheduling and Dispatch for Aggregators of Plug-In Electric Vehicles

Wu, Di; Aliprantis, Dionysios; Lü, Ying

doi:10.1109/tsg.2011.2163174

Cited by 326 publications

(165 citation statements)

References 15 publications

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“…Maximum charge power (MW) 0.5 Initial capacity (MWh) 1.0 Total capacity (MWh) 5.5 We consider that load aggregators (LAs) [39][40][41] operate in our research district. LA provides a significant load reduction capacity to power network, according to dispatching commands by implementing the corresponding control measures to electricity consumers with thermostatically controllable devices.…”

Section: Parameters Valuesmentioning

confidence: 99%

Optimal Dispatching of Active Distribution Networks Based on Load Equilibrium

Han

Zhou

et al. 2017

Energies

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This paper focuses on the optimal intraday scheduling of a distribution system that includes renewable energy (RE) generation, energy storage systems (ESSs), and thermostatically controlled loads (TCLs). This system also provides time-of-use pricing to customers. Unlike previous studies, this study attempts to examine how to optimize the allocation of electric energy and to improve the equilibrium of the load curve. Accordingly, we propose a concept of load equilibrium entropy to quantify the overall equilibrium of the load curve and reflect the allocation optimization of electric energy. Based on this entropy, we built a novel multi-objective optimal dispatching model to minimize the operational cost and maximize the load curve equilibrium. To aggregate TCLs into the optimization objective, we introduced the concept of a virtual power plant (VPP) and proposed a calculation method for VPP operating characteristics based on the equivalent thermal parameter model and the state-queue control method. The Particle Swarm Optimization algorithm was employed to solve the optimization problems. The simulation results illustrated that the proposed dispatching model can achieve cost reductions of system operations, peak load curtailment, and efficiency improvements, and also verified that the load equilibrium entropy can be used as a novel index of load characteristics.

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Section: Parameters Valuesmentioning

confidence: 99%

Optimal Dispatching of Active Distribution Networks Based on Load Equilibrium

Han

Zhou

et al. 2017

Energies

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“…In [8], Xu and Wong proposed a coordinated charging control method that uses approximate dynamic programming to minimize the charging cost and reduce the power losses. Wu et al proposed algorithms that help the aggregator to determine the purchase of energy in the day-ahead market and to distribute the purchased energy to EVs [9]. Among these works, many of them have studied the use of EVs for ancillary services.…”

Section: Related Workmentioning

confidence: 99%

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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“…In the ABDTF, PEV load is estimated using a stochastic formulation that takes into account spatial and temporal diversity [15]. When developing the PEV power consumption, various factors are modeled, for example, PEV fleet characteristics such as charge-depleting range and fraction of tractive energy from electricity in charge-depleting mode [16], [17], charging circuits [18], travel patterns [10], [17], and PEV load control and management strategies [19]- [21]. Similar to the A/C system, the PEV aggregator entity (or entities) could be modeled as a separate agent (or agents) in the wholesale market simulation.…”

Section: B Plug-in Electric Vehiclesmentioning

confidence: 99%

Development of an agent-based distribution test feeder with smart-grid functionality

Jahangiri

et al. 2012

2012 IEEE Power and Energy Society General Meeting

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Abstract-This paper reports on the development of an agentbased distribution test feeder with smart-grid functionality. The test feeder is based on an actual distribution feeder with various additional features incorporated, including rooftop photovoltaic generation and price-responsive loads (e.g., plug-in electric vehicles and intelligent air-conditioning systems). This work aims to enable the integrated study of wholesale electric power markets coupled with detailed representations of the retail-side distribution systems.

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Load Scheduling and Dispatch for Aggregators of Plug-In Electric Vehicles

Cited by 326 publications

References 15 publications

Optimal Dispatching of Active Distribution Networks Based on Load Equilibrium

Optimal Dispatching of Active Distribution Networks Based on Load Equilibrium

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

Development of an agent-based distribution test feeder with smart-grid functionality

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