Rational consumer decisions in a peak time rebate program

Vuelvas, José; Ruiz, Fredy

doi:10.1016/j.epsr.2016.11.001

Cited by 36 publications

(16 citation statements)

References 22 publications

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“…The consumer's expected marginal utility is a function of the baseline report f , because the consumption q is a function of f . For example, if the consumer is participating in the DR program and is signaled, then its consumption is q b (f, θ) which solves the implicit equation (12) and does depend on f . The following theorem establishes the optimality condition for the optimal baseline report f * in terms of M (f ), Theorem 1.…”

Section: B the Optimal Baseline Reportmentioning

confidence: 99%

A Minimal Incentive-Based Demand Response Program With Self Reported Baseline Mechanism

Muthirayan

Baeyens

Chakraborty

et al. 2020

IEEE Trans. Smart Grid

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In this paper, we propose a novel incentive based Demand Response (DR) program with a self reported baseline mechanism. The System Operator (SO) managing the DR program recruits consumers or aggregators of DR resources. The recruited consumers are required to only report their baseline, which is the minimal information necessary for any DR program. During a DR event, a set of consumers, from this pool of recruited consumers, are randomly selected. The consumers are selected such that the required load reduction is delivered. The selected consumers, who reduce their load, are rewarded for their services and other recruited consumers, who deviate from their reported baseline, are penalized. The randomization in selection and penalty ensure that the baseline inflation is controlled. We also justify that the selection probability can be simultaneously used to control SO's cost. This allows the SO to design the mechanism such that its cost is almost optimal when there are no recruitment costs or atleast significantly reduced otherwise. Finally, we also show that the proposed method of selfreported baseline outperforms other baseline estimation methods commonly used in practice.

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Section: B the Optimal Baseline Reportmentioning

confidence: 99%

A Minimal Incentive-Based Demand Response Program With Self Reported Baseline Mechanism

Muthirayan

Baeyens

Chakraborty

et al. 2020

IEEE Trans. Smart Grid

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“…Case r i = 0 follows from Definition 3. Case r i = 1 is derived from optimality conditions of problem (2) at each interval of G(q i ; b i ), and selecting the global maximizer (see Vuelvas and Ruiz (2017)).…”

Section: Problem Statementmentioning

confidence: 99%

“…There are different ways to active DR in the electric power. Broadly defined, controllable programs (Diaz et al, 2017) and indirect methods (Vuelvas and Ruiz, 2017) are found as DR solutions, which are tools implemented by system operator (SO) to balance the demand with power generation by means of load modification. In particular, indirect methods are performed by changing energy price or giving an incentive payment.…”

Section: Introductionmentioning

confidence: 99%

Limiting gaming opportunities on incentive-based demand response programs

2018

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Demand Response (DR) is a program designed to match supply and demand by modifying consumption profile. Some of these programs are based on economic incentives, in which, a user is paid to reduce his energy requirements according to an estimated baseline. Literature review and practice have shown that the counter-factual models of employing baselines are vulnerable for gaming. Classical solutions of mechanism design require that agents communicate their full types which result in greater difficulties for its practical implementation. In this paper, a novel contract is developed to induce individual rationality (voluntary participation) and asymptotic incentive-compatibility (truthfulness) through probability of call, where an agent does not require to report the marginal utility. In this approach, a consumer only announces the baseline and reduction capacity, given a payment scheme that includes cost of electricity, incentive price, and penalty caused by any deviation between self-reported and actual energy consumption. The aggregator decides randomly what users are called to perform the energy reduction. As result, asymptotic truth-telling behavior in incentive-based DR is managed by the aggregator through the probability of call for each agent. Mathematical proofs and numerical studies are provided to demonstrate the properties and advantages of this contract in limiting gaming opportunities and in terms of its implementation.

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“…Furthermore, this paradigm poses some problems of consumer acceptance [15]. On the other hand, indirect methods are based on prices or incentive signals aimed to influence consumer behaviour (EV owners’ decisions) [16, 17]. The main advantage of this approach is that the infrastructure costs can be reduced [18], since it is a unidirectional approach.…”

Section: Introductionmentioning

confidence: 99%