Power Trading Strategy and Risk Management for Electricity Retailers Considering Interruptible Load

Yu, Xin; Lyu, Xiaofan; Luo, Shuhan; Zhao, Xuan; Xu, Weiting; Yang, Meng

doi:10.1109/peac.2018.8590500

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…In this paper, the retailer offers incentive payments for customers' SL resources instead of the IL resources investigated in [30][31][32]34], and the case study results suggest that the SL is another important DR resource the retailer can deploy to improve its profit and reduce the risk caused by the market price uncertainty. In [31][32][33], the retailer's incentive prices for DR resources are specified according to predetermined stepwise reward-based DR curves, however, in this paper the SL incentive prices are specified by solving a bi-level model which can better model the characteristics of customers, e.g., customers' load-shift capability and customers' willingness to participate in the DRP. In addition, in [33], a RO-based decision-making model is proposed to maximize retailer's benefit in the worst case, however, over-conservative strategies may be obtained if the uncertainty set is not constructed properly.…”

Section: Discussionmentioning

confidence: 99%

“…In [31][32][33], the retailer's incentive prices for DR resources are specified according to predetermined stepwise reward-based DR curves, however, in this paper the SL incentive prices are specified by solving a bi-level model which can better model the characteristics of customers, e.g., customers' load-shift capability and customers' willingness to participate in the DRP. In addition, in [33], a RO-based decision-making model is proposed to maximize retailer's benefit in the worst case, however, over-conservative strategies may be obtained if the uncertainty set is not constructed properly. In [37], incentive strategies for the SL resources are studied but the market price uncertainty is neglected.…”

Section: Discussionmentioning

confidence: 99%

“…Retailers' energy allocation in the wholesale market, contracts market and short-term DR bidding market is investigated in [32] based on the RO to minimize the electricity procurement cost. In [33], a CVaR-based bi-level optimization model for retailers' trading strategy with multi-segment IL contracts offered to customers is developed. The proposed strategy can help retailers to gain more market share and enhance their competitiveness.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since constraints in (33) are non-linear complementary constraints, the Fortuny-Amat McCarl linearization method in [40] is adopted to transform these constraints into linear constraints for calculation convenience. The model after reformulation is a mixed-integer non-linear programming (MINLP) problem that can be solved using SBB solver under GAMS optimization software.…”

Section: Mathematical Reformulation Of the Bi-level Modelmentioning

confidence: 99%

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Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand

Zhang

Wang

et al. 2020

Energies

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This paper investigates the problem of how to deploy customers’ shiftable load (SL) for electricity retailers’ risk management under uncertainty of the day-ahead (DA) wholesale market price. The robust profit (RP) and the conditional robust profit (CRP) are introduced for a risk-averse retailer’s risk-reward trade-off analysis in its decision-making of electricity procurement from various options. A CRP-based bi-level optimization model is proposed for the risk-averse retailer to determine its electricity procurement strategy taking into consideration customers’ shiftable load. In the upper problem, the retailer decides its electricity procurement from various options and the SL incentive prices to maximize its CRP under a given confidence level, and in the lower problem, the customers shift their load according to the SL incentive prices to minimize their comprehensive costs including the discomfort cost caused by rescheduling electricity consumption. Finally, a case study is used to verify the effectiveness of this model. It is shown that the retailer can achieve larger profit and less risk by utilizing customers’ SL and the retailer’s risk-aversion level has an important impact on its electricity procurement and SL incentive strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Mathematical Reformulation Of the Bi-level Modelmentioning

confidence: 99%

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Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand

Zhang

Wang

et al. 2020

Energies

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“…Altogether, these circumstances create a major challenge to the industry in the near future. To be competitive it is essential to deal efficiently with losses due to energy theft in distribution grids 9,10 . On top of the economic burden for companies, energy frauds also bring a series of negative consequences such as the increase of energy consumption fares, civil lawsuits based on proven frauds and health risks.…”

Section: Introductionmentioning

confidence: 99%

Gradient boosting and Shapley additive explanations for fraud detection in electricity distribution grids

Santos¹,

Yamouni²,

Albiero³

et al. 2021

Int Trans Electr Energ Syst

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Fraud in electrical energy consumption represents a critical economic burden for utility companies around the world. Despite systematic efforts to mitigate electricity theft, this practice persists mostly in developing countries where companies rely on traditional detection methods. In Brazil it is estimated that around 7% of the total electrical energy available for consumption in 2016 was lost due to frauds. Here we describe an efficient and scalable system to predict fraudulent behavior and guide in loco inspections. We compared the performances of several machine learning algorithms using consumption and inspection data provided by CPFL Energia. We show that proper feature engineering and boosted classification trees trained with XGBoost are able to extract patterns related to fraud occurrence and to achieve predictive power of practical consequences. Moreover, we demonstrate how Shapley additive explanation (SHAP) values can be employed to build user friendly explanations. Together, the proposed model and its explainers contribute not only to reveal potentially fraudulent behavior but also to understand root causes, what can be used to devise robust mitigation strategies.

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Joint decision on service level of chain electricity retailers

Yao

Shu²,

Jian³

et al. 2022

Computers & Industrial Engineering

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Power Trading Strategy and Risk Management for Electricity Retailers Considering Interruptible Load

Cited by 6 publications

References 8 publications

Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand

Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand

Gradient boosting and Shapley additive explanations for fraud detection in electricity distribution grids

Joint decision on service level of chain electricity retailers

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