Risk-Averse Optimal Bidding Strategy for Demand-Side Resource Aggregators in Day-Ahead Electricity Markets Under Uncertainty

This paper first presents a generic model to characterize a variety of flexible demand-side resources (e.g., plug-in electric vehicles and distributed generation). Key sources of uncertainty affecting the modeling results are identified and are characterized via multiple stochastic scenarios. We the...

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Bibliographic Details
Published in:IEEE transactions on smart grid Vol. 8; no. 1; pp. 96 - 105
Main Authors: Xu, Zhiwei, Hu, Zechun, Song, Yonghua, Wang, Jianhui
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aggregates
Aggregator
Benchmarks
Computational modeling
Computer simulation
conditional value-at-risk (CVaR)
Decision theory
Demand side management
Distributed generation
Electric power demand
Electric vehicles
Electricity consumption
Electricity supply industry
Energy consumption
Generators
Mathematical models
optimal day-ahead bidding
regret
Risk levels
risk-averse
Robustness (mathematics)
Stochastic processes
Strategy
Uncertainty
value-at-risk (VaR)
ISSN:1949-3053, 1949-3061
Online Access:Get full text
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Summary:This paper first presents a generic model to characterize a variety of flexible demand-side resources (e.g., plug-in electric vehicles and distributed generation). Key sources of uncertainty affecting the modeling results are identified and are characterized via multiple stochastic scenarios. We then propose a risk-averse optimal bidding formulation for the resource aggregator at the demand side based on the conditional value-at-risk (VaR) theory. Specifically, this strategy seeks to minimize the expected regret value over a subset of worst-case scenarios whose collective probability is no more than a threshold value. Our approach ensures the robustness of the day-ahead (DA) bidding strategy while considering the uncertainties associated with the renewable generation, real-time price, and electricity demand. We carry out numerical simulations against three benchmark bidding strategies, including a VaR-based approach and a traditional scenario based stochastic programming approach. We find that the proposed strategy outperforms the benchmark strategies in terms of hedging high regret risks, and results in computational efficiency and DA bidding costs that are comparable to the benchmarks.
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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2015.2477101