Optimal stochastic scheduling of thermal‐wind GENCOs trading in the day‐ahead market including bilateral contracts with BSS

Gong, Naiwei; Luo, Xizhao; Ma, Honglian; Wang, Qi; Wang, Kaiyan

doi:10.1049/iet-gtd.2018.0060

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…Gong et al [27] developed the optimum bidding model in which the thermal-wind hybrid system signs bilateral contracts with the battery swapping station and participates in the DA market. [27][28][29][30][31], EVPLs and line capacities are not considered in these studies. Also, bilateral contracts were not included in studies other than [27].…”

Section: Relevant Backgroundmentioning

confidence: 99%

“…[27][28][29][30][31], EVPLs and line capacities are not considered in these studies. Also, bilateral contracts were not included in studies other than [27]. While risk management is covered only in [28,30], portfolio structure is only considered in [28,29,31] Aliasghari et al [33] proposed a structure where the renewable microgrid in the presence of plug-in EVs participates in the power market minimizing the operating cost.…”

Section: Relevant Backgroundmentioning

confidence: 99%

“…Since it is considered that taking the average of the power at t and (t + 1) instead of P wind,prod w,k,t power in the processes related to the power generated from WPPs gives more accurate results, the trapezoidal rule for charging power is applied in Eq. (27). , ∀k, s, h (24)…”

Section: Armentioning

confidence: 99%

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Risk-averse optimal bidding strategy for a wind energy portfolio manager including EV parking lots for imbalance mitigation

Çiçek¹,

Erdinç²

2021

Turk J Elec Eng & Comp Sci

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In this study, an optimal bidding strategy for a wind energy portfolio manager (WEPM) including electric vehicle parking lots (EVPLs) that aims to maximize profits by trading in the day-ahead (DA) market and balancing market (BM) and through bilateral contracts, taking into account line capacities and risk management is proposed. The mentioned structure is modeled in Mixed-Integer Linear Programming (MILP) framework, and the uncertainties regarding electric vehicle (EV) behavior, electricity market data, wind power generation are captured via a stochastic approach. To demonstrate the effectiveness of the model, several case studies are carried out considering Sweden and Turkey electricity market prices with different risk aversion factors and bilateral contract situations. In this manner, the important results as well as useful findings regarding the economic impacts of the proposed concept are analyzed in detail.

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Section: Relevant Backgroundmentioning

confidence: 99%

Section: Relevant Backgroundmentioning

confidence: 99%

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Risk-averse optimal bidding strategy for a wind energy portfolio manager including EV parking lots for imbalance mitigation

Çiçek¹,

Erdinç²

2021

Turk J Elec Eng & Comp Sci

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“…Furthermore, in [9], a methodology to maximize Plug-in EV aggregator profit taking decisions in DA and balancing markets with considering risk aversion has been developed without considering the preferences of EV owners and responsive loads. Other risk-averse instruments such as bilateral contracts are also designed to reduce the effects of uncertainties [10]. In bilateral contracts, the aggregator may sign a long term contract with generating companies to purchase energy, which points out buying power quantity through a pre-specified time period [11].…”

Section: Ch D T E / mentioning

confidence: 99%

A Regret-Based Stochastic Bi-Level Framework for Scheduling of DR Aggregator Under Uncertainties

Rashidizadeh‐Kermani

Vahedipour‐Dahraie

Shafie‐khah

et al. 2020

IEEE Trans. Smart Grid

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A regret-based stochastic bi-level framework for optimal decision making of a demand response (DR) aggregator to purchase energy from short term electricity market and wind generation units is proposed. Based on this model, the aggregator offers selling prices to the customers, aiming to maximize its expected profit in a competitive market. The clients' reactions to the offering prices of aggregators and competition among rival aggregators are explicitly considered in the proposed model. Different sources of uncertainty impressing the decisions made by the aggregator are characterized via a set of scenarios and are accounted for by using stochastic programming. Conditional value-at-risk (CVaR) is used for minimizing the expected value of regret over a set of worst scenarios whose collective probability is lower than a limitation value. Simulations are carried out to compare CVaR-based approach with value-at-risk (VaR) concept and traditional scenario based stochastic programming (SBSP) strategy. The findings show that the proposed CVaR strategy outperforms the SBSP approach in terms of making more risk-averse energy biddings and attracting more customers in the competitive market. The results show that although the aggregator offers the same prices in both CVaR and VaR approaches, the average of regret is lower in the VaR approach.

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Grid integration of battery swapping station: A review

Revankar

Kalkhambkar

2021

Journal of Energy Storage

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Optimal stochastic scheduling of thermal‐wind GENCOs trading in the day‐ahead market including bilateral contracts with BSS

Cited by 7 publications

References 27 publications

Risk-averse optimal bidding strategy for a wind energy portfolio manager including EV parking lots for imbalance mitigation

Risk-averse optimal bidding strategy for a wind energy portfolio manager including EV parking lots for imbalance mitigation

A Regret-Based Stochastic Bi-Level Framework for Scheduling of DR Aggregator Under Uncertainties

Grid integration of battery swapping station: A review

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