CVaR-Constrained Optimal Bidding of Electric Vehicle Aggregators in Day-Ahead and Real-Time Markets

Yang, Hongming; Zhang, Sanhua; Qiu, Jing; Qiu, Duo; Lai, Mingyong; Dong, Zhaoyang

doi:10.1109/tii.2017.2662069

Cited by 107 publications

(33 citation statements)

References 28 publications

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“…Decision‐making strategies for retailer participating in electricity markets have also been investigated in several research works. Some of these works address decision‐making problem of EVs aggregator as a retailer to participate in energy market . For example, a bi‐level programming approach has been proposed in Lujano‐Rojas et al based on the Stackelberg game model.…”

Section: Introductionmentioning

confidence: 99%

“…[11][12][13][14][15] For example, a bi-level programming approach has been proposed in Lujano-Rojas et al 10 based on the Stackelberg game model. In Yang et al, 12 a bidding strategy for EV aggregator has been proposed to minimize the expected electricity costs considering price volatility. Khojasteh and Jadid 13 present a fuzzy information gap decision theory-based framework for electricity retailers to determine the energy acquisition strategy.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic risk-constrained decision-making approach for a retailer in a competitive environment with flexible demand side resources

Rashidizadeh‐Kermani

Vahedipour‐Dahraie

Anvari‐Moghaddam

et al. 2018

Int Trans Electr Energ Syst

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Summary This paper presents a risk‐averse stochastic bi‐level programming approach to solve decision‐making of a retailer in a competitive market under uncertainties. The retailer decides the level of involvement in day‐ahead (DA) and regulation markets by making an optimal bidding strategy with the goal of expected profit maximization. Uncertainties associated with DA prices, up/down regulation market prices, customers' demand, and rival retailers' behaviors are tackled through a stochastic programming model. In the proposed model, responsive loads and electric vehicles (EVs) track the real‐time prices and choose the proper retailer to minimize their payments in the competitive trading floor. The obtained nonlinear stochastic model is transformed into an equivalent linear single‐level program by replacing the lower‐level problem with its Karush‐Kuhn‐Tucker optimality conditions and using duality theory. Finally, the proposed methodology is evaluated by applying to a realistic case study, and the results demonstrate the effectiveness of the proposed framework.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic risk-constrained decision-making approach for a retailer in a competitive environment with flexible demand side resources

Rashidizadeh‐Kermani

Vahedipour‐Dahraie

Anvari‐Moghaddam

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

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“…Constraints are related with power balances and power traded in both DA and real time market (RT) and are described in previous researches [11], [12] as follows:…”

Section: Constraints: Power Balancesmentioning

confidence: 99%

“…Studies shown in [8]- [12] were based on different sources of travel information, batteries and market price data. Therefore, in order to compare and unify the research, an analysis of multiple vehicle fleet patterns was carried out, considering the analysis of travel patterns as distributed probability of departure and arrival time [14], which will be considered with trip time and average speed scenarios.…”

mentioning

confidence: 99%

Electric Vehicles Aggregator Participation in Energy Markets Considering Uncertainty Travel Patterns

Villanueva¹,

Delgado²,

Carbajal³

2019

IJITEE

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This research studies a general modeling to evaluate different scenarios of travel patterns and their impact on the daily cost negotiated in the Real Time and Day-Ahead market, using the GAMS methodology in a MILP model, evaluating also a characterization of the PQP market (price quantity probability). The purpose of this characterization is to determine the behavior of the electric energy market, considering also the deterioration of batteries and the negotiations of it in real time in situations of shortage and overload, optimizing in this way the effects of the analysis of the cost of the application of the battery on the different travel patterns, consequently triggering the emergence of the development of the local electric transport aggregator industry.

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“…While the flexibility of charging power (kW) are taken into account in many papers, the flexibility of the charging energy (kWh), however, is ill-considered. In the previous business model [11][12][13][14][15][16][17][18][19], the EVA optimizes the charging profiles for PEVs on the premise of satisfying the charging demands determined by PEV owners but ignoring the elasticity of charging demands. In fact, if the energy prices are sufficiently high, PEV owners may reduce their charging demands.…”

Section: Introductionmentioning

confidence: 99%

Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model

2019

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Realizing the full potential of plug-in electric vehicle (PEVs) in power systems requires the development of business models for PEV owners and electric vehicle aggregators (EVAs). Most business models neglect the significant economic potential of PEV demand response. This paper addresses this challenge by proposing a novel business model to optimize the charging energy of PEVs for maximizing the owners’ profits. The proposed business model aims to overcome the opportunity cost neglect for PEV owners, whose charging energy and charging profiles are optimized with full consideration of the demand curves and market conditions. Lagrangian relaxation technology is used for the relaxation of the constraint of satisfying the charging demand, and as a result, the optimization potential becomes greater. The bidding/offering strategy is formulated as a two-stage stochastic optimization problem, considering the different market prices and initial and target state of energy (SOE) of the PEVs. By case studies and analyses, we demonstrate that the proposed business model can effectively overcome the opportunity cost neglect and increase the PEV owners’ profits. Furthermore, we demonstrate that the proposed business model is incentive-compatible. The PEV owners will be attracted by the proposed business model.

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CVaR-Constrained Optimal Bidding of Electric Vehicle Aggregators in Day-Ahead and Real-Time Markets

Cited by 107 publications

References 28 publications

Stochastic risk-constrained decision-making approach for a retailer in a competitive environment with flexible demand side resources

Stochastic risk-constrained decision-making approach for a retailer in a competitive environment with flexible demand side resources

Electric Vehicles Aggregator Participation in Energy Markets Considering Uncertainty Travel Patterns

Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model

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