Truthful, Practical and Privacy-Aware Demand Response in the Smart Grid via a Distributed and Optimal Mechanism

Τσαούσογλου, Γεώργιος; Steriotis, Konstantinos; Efthymiopoulos, Nikolaos; Makris, Prodromos; Varvarigos, Emmanouel

doi:10.1109/tsg.2020.2965221

Cited by 42 publications

(25 citation statements)

References 45 publications

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“…Thus, eq. (34) becomes equivalent to (26), which contradicts the fact that max n∈Ω b {c * n } is the optimal solution to (11).…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 93%

“…which, by ( 36) and ( 37), is equivalent to (26), and therefore contradictory. Thus, the aggregator cannot benefit from false declarations.…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 99%

“…Another family of studies in the smart gird literature, leverages the (DSIC and optimal) Vickrey-Clarke-Groves (VCG) mechanism for achieving truthfulness and optimality. Examples include [24] and [25], while in [26], the authors propose a distributed implementation of VCG to achieve truthfulness along with scalability and privacy in a Demand Response scheme.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Since the studies mentioned above ( [23]- [26]) , examine different use cases, each one proposes a different payment rule. However, the underlying objective in all of them is social welfare maximization, which, although formulated differently in different use cases, means that the allocation rules are similar in nature: minimizing the average agent cost.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Mechanism Design for Fair and Efficient DSO Flexibility Markets

Τσαούσογλου

Giraldo

Paterakis

2021

IEEE Trans. Smart Grid

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“…Thus, eq. (34) becomes equivalent to (26), which contradicts the fact that max n∈Ω b {c * n } is the optimal solution to (11).…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 93%

“…which, by ( 36) and ( 37), is equivalent to (26), and therefore contradictory. Thus, the aggregator cannot benefit from false declarations.…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Mechanism Design for Fair and Efficient DSO Flexibility Markets

Τσαούσογλου

Giraldo

Paterakis

2021

IEEE Trans. Smart Grid

Self Cite

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“…Towards making dispatch decisions within an EC, local electricity markets have been the topic of several studies. Mech- anism design approaches (e.g., [2], [3]) have been proposed, while many studies (e.g., [4], [5]) exploit duality to construct a price-based control scheme. However, in settings with high penetration of DERs, the local economic dispatch problem involves the energy management of numerous small entities, and also needs to be solved online and under uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Managing Distributed Flexibility under Uncertainty by Combining Deep Learning with Duality

Τσαούσογλου¹,

Mitropoulou²,

Steriotis³

et al. 2021

Preprint

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<div>In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We model multiple sources of uncertainty such as RES, wholesale electricity prices as well as the arrival times and energy needs of a set of Electric Vehicles. The economic dispatch problem is formulated as a multi-agent Markov Decision Process. The difficulties lie in the curse of dimensionality and in guaranteeing the satisfaction of constraints under uncertainty.</div><div>A novel method, that combines duality theory and deep learning, is proposed to tackle these challenges. In particular, a Neural Network (NN) is trained to return the optimal dual variables of the economic dispatch problem. By training the NN on the dual problem instead of the primal, the number of output neurons is dramatically reduced, which enhances the performance and reliability of the NN. Finally, by treating the resulting dual variables as prices, each distributed agent can self-schedule, which guarantees the satisfaction of its constraints. As a result, our simulations show that the proposed scheme performs reliably and efficiently.</div>

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