Optimal Price-maker Trading Strategy of Wind Power Producer Using Virtual Bidding

Xiao, Dongliang; AlAshery, Mohamed Kareem; Wang, Qiao

doi:10.35833/mpce.2020.000070

Cited by 36 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where M is a sufficiently large constant. Constraints ( 6) - (8) indicate that for each scenario w, z w is equal to 1 when π w ³ η b and 0 otherwise. By maximizing η b in the objective function (5), constraint (9) ensures that the probability of obtaining a profit equal to or higher than η b is no less than α s .…”

Section: B Proposed Risk-seeking Stochastic Optimization Model Based ...mentioning

confidence: 99%

“…Stochastic optimization [8], robust optimization [9], and information gap decision theory (IGDT) [10] have been widely used by electricity market participants facing risks. In stochastic optimization models, uncertain parameters are represented by the scenarios generated based on their probability distributions, and risk-averse participants can manage the risks in the worst-case scenarios by using risk measures such as value at risk (VaR) and conditional value at risk (CVaR).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical Measure for Risk-seeking Stochastic Wind Power Offering Strategies in Electricity Markets

Xiao

Chen

Wei

et al. 2022

Journal of Modern Power Systems and Clean Energy

Self Cite

View full text Add to dashboard Cite

This study proposes a statistical measure and a stochastic optimization model for generating risk-seeking wind power offering strategies in electricity markets. Inspired by the value at risk (VaR) to quantify risks in the worst-case scenarios of a profit distribution, a statistical measure is proposed to quantify potential high profits in the best-case scenarios of a profit distribution，which is referred to as value at best (VaB) in the best-case scenarios. Then, a stochastic optimization model based on VaB is developed for a risk-seeking wind power producer, which is formulated as a mixed-integer linear programming problem. By adjusting the parameters in the proposed model, the wind power producer can flexibly manage the potential high profits in the best-case scenarios from the probabilistic perspective. Finally, the proposed statistical measure and riskseeking stochastic optimization model are verified through case studies.

show abstract

Section: B Proposed Risk-seeking Stochastic Optimization Model Based ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Measure for Risk-seeking Stochastic Wind Power Offering Strategies in Electricity Markets

Xiao

Chen

Wei

et al. 2022

Journal of Modern Power Systems and Clean Energy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Reference [22] used the CVaR method to model uncertain risks. Reference [23] maximized the total expected profits of wind power and virtual bidding, while using CVaR for risk management. Some existing studies involved modelling based on the consideration of CVaR risk, but there are no similar articles on the risk assessment of aggregators' participation in peak shaving based on mixed CVaR.…”

Section: Introductionmentioning

confidence: 99%

Bargaining game based on psychological cost of electric vehicles and risk assessment of aggregators

Lin

Zhou

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

As electric vehicles (EVs) begin to participate in the peak‐shaving auxiliary service market, the question of how price aggregators can maximize the peak‐shaving capacity provided by EVs and maximize their own profit has become a major problem. This paper proposes a bargaining game pricing method based on the psychological cost of EVs and the risk assessment of aggregators. First, the comprehensive psychological cost of EV users is obtained based on the impact of users’ participation in peak shaving on the battery life of EVs and on users’ original travel plans and time, and the impact of aggregator pricing on users’ psychology. Then, based on users’ psychological cost and the law of gravitation, the evaluation scheme for the peak‐shaving capacity of EVs is obtained. On the basis of conditional value at risk (CVaR), the mixed CVaR is obtained by considering the risk‐chasing behaviour of users. Based on the mixed CVaR, the risk assessment of aggregators’ participation in the peak‐shaving auxiliary service market is carried out. According to the above information, the aggregator and EV groups are engaged in a bargaining game based on the peak‐shaving pricing problem, which is divided into complete information game and incomplete information game. Finally, the feasibility of the proposed method is verified by an example analysis which is run by the MATLAB R2019b. The method proposed in this paper provides aggregators with a complete peak‐shaving electricity pricing scheme, effectively improving the dispatching capacity of EVs. It is helpful to rationalize the pricing of aggregators and maximize their profits. EVs can also obtain satisfactory profits, attracting more EVs to participate in peak shaving, and reduce the pressure on power grid peak shaving.

show abstract

“…These technologies increase demand-side flexibility, reducing the need for supply-side adjustments to maintain energy balance. Financial instruments [23], [24] are another option to reduce operational wind-integration costs.…”

Section: Introductionmentioning

confidence: 99%

Using Interim Recommitment to Reduce the Operational-cost Impacts of Wind Uncertainty

Mansouri

Sioshansi

2022

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

Using wind-availability forecasts in day-ahead unit commitment can require expensive real-time operational adjustments. We examine the benefit of conducting interim recommitment between day-ahead unit commitment and real-time dispatch. Using a simple stylized example and a case study that is based on ISO New England, we compare system-operation costs with and without interim recommitment. We find an important tradeoff-later recommitment provides better wind-availability forecasts, but the system has less flexibility due to operating constraints. Of the time windows that we examine, hour-20 recommitment provides the greatest operational-cost reduction.

show abstract

Optimal Price-maker Trading Strategy of Wind Power Producer Using Virtual Bidding

Cited by 36 publications

References 0 publications

Statistical Measure for Risk-seeking Stochastic Wind Power Offering Strategies in Electricity Markets

Statistical Measure for Risk-seeking Stochastic Wind Power Offering Strategies in Electricity Markets

Bargaining game based on psychological cost of electric vehicles and risk assessment of aggregators

Using Interim Recommitment to Reduce the Operational-cost Impacts of Wind Uncertainty

Contact Info

Product

Resources

About