A Contract Design Approach for Phantom Demand Response

Dobakhshari, Donya G.; Gupta, Vijay

doi:10.1109/tac.2018.2830502

Cited by 19 publications

(6 citation statements)

References 22 publications

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“…Since our formulation does not assume any specific form for the cost functions, or assume baselines and costs to be deterministic, it is considerably more general than the formulations in [12], [13], [20], [21]. References [22]- [26] are a few other papers on mechanism design for DR.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Mechanism for Demand Response Markets

Bharadwaj¹,

Roozbehani²,

Dahleh³

2022

Preprint

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We present a two-stage mechanism for creating markets for demand response. Demand response involves system operators using incentives to modulate electricity consumption around peak hours or when faced with an incidental supply shortage. However, system operators typically have imperfect information about their customers' counterfactual consumption, that is, their consumption had the incentive been absent. The standard approach to estimate the reduction in a customer's electricity consumption then is to estimate their counterfactual baseline. However, this approach is not robust to estimation errors or strategic exploitation by the consumers and can potentially lead to overpayments to customers who do not reduce their consumption and underpayments to those who do. In addition, the incentive payments are often designed based on models of consumer behavior or other ad-hoc rules that are only partially accurate at best. The two-stage mechanism proposed in this paper circumvents the aforementioned issues. In the dayahead market, the participating loads submit the probability distribution of their next-day consumption. In real-time, if and when called upon for demand response, the loads report the realization of their baseline demand and receive credit for reductions below their reported baseline. The mechanism guarantees ex post incentive compatibility of truthful reporting of the probability distribution in the day-ahead market and truthful reporting of the realized baseline demand in real-time. The mechanism can be viewed as an extension of the celebrated Vickrey-Clarke-Groves mechanism augmented with a carefully crafted second-stage penalty for deviations from the day-ahead bids.

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Section: Related Workmentioning

confidence: 99%

A Two-Stage Mechanism for Demand Response Markets

Bharadwaj¹,

Roozbehani²,

Dahleh³

2022

Preprint

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“…The Scheduled Load Reduction Program (SLRP) operated by Pacific Gas & Electric Company (PG&E) calculates the baseline by averaging the load demand of the selected time periods in the ten previous normal operating days [12]. Bottom-up approaches, on the other hand, require demand response participants, the "provider" of demand response, to report baselines a-prior to the dispatch period, accompanied with supporting tariff mechanisms that encourage participants to truthfully report baselines base on the best of their knowledge [9], [13].…”

Section: A Baseline Demand Estimationmentioning

confidence: 99%

End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning

Shi,

2021

Preprint

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This paper proposes a novel end-to-end deep learning framework that simultaneously identifies demand baselines and the incentive-based agent demand response model, from the net demand measurements and incentive signals. This learning framework is modularized as two modules: 1) the decision making process of a demand response participant is represented as a differentiable optimization layer, which takes the incentive signal as input and predicts user's response; 2) the baseline demand forecast is represented as a standard neural network model, which takes relevant features and predicts user's baseline demand. These two intermediate predictions are integrated, to form the net demand forecast. We then propose a gradientdescent approach that backpropagates the net demand forecast errors to update the weights of the agent model and the weights of baseline demand forecast, jointly. We demonstrate the effectiveness of our approach through computation experiments with synthetic demand response traces and a large-scale real world demand response dataset. Our results show that the approach accurately identifies the demand response model, even without any prior knowledge about the baseline demand.

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“…Competition among data aggregators is studied in [10], where aggregators are coupled in their costs and data sources are coupled in their compensation. Payment coupling is also considered in [11] in a demand response problem. However, the issue of collusion has received much less attention in the literature.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Contract Design for Multi-Agent Systems with Collusion Detection

Aguiar,

Venkitasubramaniam,

Gupta

2021

Preprint

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In applications such as participatory sensing and crowd sensing, self-interested agents exert costly effort towards achieving an objective for the system operator. We study such a setup where a principal incentivizes multiple agents of different types who can collude with each other to derive rent. The principal cannot observe the efforts exerted directly, but only the outcome of the task, which is a noisy function of the effort. The type of each agent influences the effort cost and task output. For a duopoly in which agents are coupled in their payments, we show that if the principal and the agents interact finitely many times, the agents can derive rent by colluding even if the principal knows the types of the agents. However, if the principal and the agents interact infinitely often, the principal can disincentivize agent collusion through a suitable data-driven contract.

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A Contract Design Approach for Phantom Demand Response

Cited by 19 publications

References 22 publications

A Two-Stage Mechanism for Demand Response Markets

A Two-Stage Mechanism for Demand Response Markets

End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning

Data-Driven Contract Design for Multi-Agent Systems with Collusion Detection

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