Optimal participation of an electric vehicle aggregator in day-ahead energy and reserve markets

Sarker, Mushfiqur R.; Dvorkin, Yury; Ortega‐Vazquez, Miguel A.

doi:10.1109/pesgm.2016.7741311

Cited by 32 publications

(45 citation statements)

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“…An additional constraint determines the absolute loading of the transformer while considering the base consumer load TX base t and the net EV power consumption, which is (18). Note that TX base t is the sum of the base load of all consumers in each period t, i.e., P base =TX base .…”

Section: A Centralized Strategy: Aggregator Co-optimization Modelmentioning

confidence: 99%

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Co-Optimization of Distribution Transformer Aging and Energy Arbitrage Using Electric Vehicles

Sarker

Olsen

Ortega‐Vazquez

2017

IEEE Trans. Smart Grid

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The advent of electric vehicles (EVs) will bring forth increases in power transmitted over longer periods of time through the distribution power grid. Such an effect will result in accelerated loss-of-life of distribution grid assets including poletop transformers. As preventive and corrective measures, the charging of the set of EVs connected to a particular pole-top transformer can be centrally managed (e.g., by a distribution system operator or independent aggregator). This paper proposes a centralized model to co-optimize the transformer lossof-life with the benefits for EVs' owners on charging/discharging management. The proposed model is compared against a decentralized optimization model in which EVs' owners optimize their benefits, while ignoring the effect on the transformer. Results show the benefit of the centralized strategy in maintaining the grid assets, while modestly reducing consumers' arbitrage benefits.

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Section: A Centralized Strategy: Aggregator Co-optimization Modelmentioning

confidence: 99%

“…Such centralized co-optimization was performed with EVs considering distribution system losses [15], voltage limits [15]- [17], and market participation [18], [19]. However, limited work (i.e., [20] and [21]) exists on cooptimizing the transformer aging with EVs.…”

Section: Introductionmentioning

confidence: 99%

Co-Optimization of Distribution Transformer Aging and Energy Arbitrage Using Electric Vehicles

Sarker

Olsen

Ortega‐Vazquez

2017

IEEE Trans. Smart Grid

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“…It is claimed that the probability density function of the amount of reserve deployment requirements is hard to build due to the characteristic of regulation and reserve markets. A probability‐based model is applied in [27] to assess the aggregator's capability in providing ancillary services to power grids. Two types of probabilities (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the impact of the reserve deployment on EVs charging/discharging is less discussed, such as in [26, 29, 31]. Finally, most existing studies simplicity focused on the EVs charging/discharging from a single stakeholder's viewpoint but neglect the economic relationship between different stakeholders, such as in [27, 32]. The aggregator profits are maximised while the EV owners economic benefits are sacrificed (charging fee increase).…”

Section: Introductionmentioning

confidence: 99%

Day‐ahead electric vehicle aggregator bidding strategy using stochastic programming in an uncertain reserve market

Han

Xue

et al. 2019

IET Generation, Transmission & Distribution

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“…Recently, increasing peak load due to economic development and replacing fossil fuels with electricity power has resulted in serious challenges to the power grid, such as increasing the operation costs and decreasing the reliability of power grid [1,2]. To cope with these challenges, day-ahead scheduling, which is widely recognized as an indirectly and effective approach for costs reducing and peak load shaving, has been intensively discussed [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimal Day-Ahead Scheduling in a Smart Grid: A Trade-Off among Consumers, Power Suppliers, and Transmission Owners

Xiong

Liu

2017

Complexity

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To cope with the challenges due to increasing peak load, an optimal day-ahead scheduling problem for social welfare maximization is proposed, in which not only the comfort level of consumers and costs of power suppliers but also the power losses in transmission and operation costs of transmission owners are taken into account. Then this optimal day-ahead scheduling problem is reformulated and solved via the alternating direction method of multipliers (ADMM), by which fast convergence is guaranteed and the privacy of participants is ensured, in a distributed manner. Specifically, in the proposed distributed optimal day-ahead scheduling, the hourly prices for consumers are divided into hourly supply prices and hourly delivery prices, which will be updated by the independent system operator based on the hourly demand-supply situations and hourly demand-delivery situations, respectively. And the consumers, power suppliers, and transmission owners make their individual optimal day-ahead scheduling based on their individual hourly prices, hourly supply prices, and hourly delivery prices, respectively, until the hourly demand-supply balances and hourly demand-delivery balances are achieved. Effectiveness of the proposed distributed optimal day-ahead scheduling is verified by the cases studied.

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Optimal participation of an electric vehicle aggregator in day-ahead energy and reserve markets

Cited by 32 publications

References 0 publications

Co-Optimization of Distribution Transformer Aging and Energy Arbitrage Using Electric Vehicles

Co-Optimization of Distribution Transformer Aging and Energy Arbitrage Using Electric Vehicles

Day‐ahead electric vehicle aggregator bidding strategy using stochastic programming in an uncertain reserve market

Distributed Optimal Day-Ahead Scheduling in a Smart Grid: A Trade-Off among Consumers, Power Suppliers, and Transmission Owners

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