Optimal Bidding Strategy for Electric Vehicle Aggregators in Electricity Markets

Vagropoulos, Stylianos I.; Bakirtzis, Anastasios G.

doi:10.1109/tpwrs.2013.2274673

Cited by 394 publications

(224 citation statements)

References 21 publications

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“…Constraint (11) discusses the contribution of each aggregator to supply EVs and considers the EVs switching between the aggregators. Equation (12) explains the total expected demand of EVs at each hour and Equation (13) expresses that all of the EVs' demand should be supplied by all of the aggregators. The last two constraints explain the limitation for the variables.…”

Section: Lower Level: Ev Owners' Viewpointmentioning

confidence: 99%

A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets

et al. 2017

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This paper proposes a stochastic bi-level decision-making model for an electric vehicle (EV) aggregator in a competitive environment. In this approach, the EV aggregator decides to participate in day-ahead (DA) and balancing markets, and provides energy price offers to the EV owners in order to maximize its expected profit. Moreover, from the EV owners' viewpoint, energy procurement cost of their EVs should be minimized in an uncertain environment. In this study, the sources of uncertainty-including the EVs demand, DA and balancing prices and selling prices offered by rival aggregators-are modeled via stochastic programming. Therefore, a two-level problem is formulated here, in which the aggregator makes decisions in the upper level and the EV clients purchase energy to charge their EVs in the lower level. Then the obtained nonlinear bi-level framework is transformed into a single-level model using Karush-Kuhn-Tucker (KKT) optimality conditions. Strong duality is also applied to the problem to linearize the bilinear products. To deal with the unwilling effects of uncertain resources, a risk measurement is also applied in the proposed formulation. The performance of the proposed framework is assessed in a realistic case study and the results show that the proposed model would be effective for an EV aggregator decision-making problem in a competitive environment.

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Section: Lower Level: Ev Owners' Viewpointmentioning

confidence: 99%

A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets

et al. 2017

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“…Participants in the DAM submit supply-offers/demand-bids to the system operator. These participants also submit supplyoffers for the regulation capacity, and they may later submit revised regulation quantities, which are different from dayahead offer quantities, without any penalty imposed [17]. The time framework for the DAM is the whole day d, which is cleared at 10am of the day (d-1).…”

Section: Market Frameworkmentioning

confidence: 99%

“…In [5], EV aggregators did not participate in the regulation market. In [16,17], it has been shown that the highest benefits for EV owners are expected through participation in regulation markets.…”

Section: Introductionmentioning

confidence: 99%

Energy Exchange Between Electric Vehicle Load and Wind Generating Utilities

Tavakoli

Negnevitsky

Nguyen³

et al. 2016

IEEE Trans. Power Syst.

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This paper proposes a novel approach to energy exchange between electric vehicle (EV) load and wind generation utilities participating in the day-ahead energy, balancing, and regulation markets. An optimal bidding/offering strategy model is developed to mitigate wind energy and EV imbalance threats, and optimize EV charging profiles. A new strategy model is based on optimizing decision making of a wind generating company (WGenCO) in selecting the best option among the use of the balancing or regulation services, the use of the energy storage system (ESS) and the use of all of them to compensate wind power deviation. Energy imbalance is discussed using conventional systems, ESS, and EV-Wind coordination; results are compared and analyzed. Stochastic intra-hour optimization is solved by mixed-integer linear programming (MILP). Uncertainties associated with wind forecasting, energy price, and behavior of EV owners based on their driving patterns, are considered in the proposed stochastic method and validated through several case studies. Abstract-This paper proposes a novel approach to energy exchange between electric vehicle (EV) load and wind generation utilities participating in the day-ahead energy, balancing, and regulation markets. An optimal bidding/offering strategy model is developed to mitigate wind energy and EV imbalance threats, and optimize EV charging profiles. A new strategy model is based on optimizing decision making of a wind generating company (WGenCO) in selecting the best option among the use of the balancing or regulation services, the use of the energy storage system (ESS) and the use of all of them to compensate wind power deviation. Energy imbalance is discussed using conventional systems, ESS, and EV-Wind coordination; results are compared and analyzed. Stochastic intra-hour optimization is solved by mixed-integer linear programming (MILP). Uncertainties associated with wind forecasting, energy price, and behavior of EV owners based on their driving patterns, are considered in the proposed stochastic method and validated through several case studies.Index Terms-Electricity market, energy exchange, electric vehicles, energy storage, stochastic optimization. NOMENCLATURE Indices

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“…The trade contract is made one-day advance under the control of the V2G trader broker running in the aggregator [12]. It first estimates the hour-by-hour electricity demand of the microgrid according to its own demand forecast model, which is usually built using the previous records [13]. Considering the electricity price, the amount of electricity it wants to buy during each slot is decided.…”

Section: Related Workmentioning

confidence: 99%

Revenue Analysis of a Lightweight V2G Electricity Trader Based on Real-Life Energy Demand Patterns

Lee

Park

2015

IJMUE

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Optimal Bidding Strategy for Electric Vehicle Aggregators in Electricity Markets

Cited by 394 publications

References 21 publications

A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets

A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets

Energy Exchange Between Electric Vehicle Load and Wind Generating Utilities

Revenue Analysis of a Lightweight V2G Electricity Trader Based on Real-Life Energy Demand Patterns

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