To Overconsume or Underconsume: Baseline Manipulation in Demand Response Programs

Wang, Xiaochu; Tang, Wenyuan

doi:10.1109/naps.2018.8600558

Cited by 8 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The alternative approaches that bypass statistical methods are in fact addressing the weaknesses of conventional BLP estimation methods. In flexibility markets at the distribution level, BL calculation based on averaging or interpolation leads to concerns about manipulation [79] and inflating, whereas methods such as those based on regression add complexity and administrative costs, suggesting that BL are not suitable for those markets [80].…”

Section: Programs Driven By Market Incentives and Manipulation Risksmentioning

confidence: 99%

Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load

et al. 2022

View full text Add to dashboard Cite

Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (“baseline”) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost.

show abstract

Section: Programs Driven By Market Incentives and Manipulation Risksmentioning

confidence: 99%

Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Thus, they were unable to manipulate their rebate baseline consumption of the normal week (January 17−23). While baseline-based rebates provide customers with incentives to reduce electricity use during event days, they may also create undesired incentives for customers to manipulate their baseline consumption (Wang and Tang, 2018). This "baseline manipulation" has often been observed in RCT field experiments (Wolak, 2007) and causes significant errors in the prediction of demand and treatment effects.…”

Section: Treatmentsmentioning

confidence: 99%

Heterogeneous Treatment Effects of Nudge and Rebate: Causal Machine Learning in a Field Experiment on Electricity Conservation

Murakami

Shimada

Ushifusa

et al. 2022

Int Economic Review

View full text Add to dashboard Cite

This study investigates the different impacts of monetary and nonmonetary incentives on energy-saving behaviors using a field experiment conducted in Japan. We find that the average reduction in electricity consumption from the rebate is 4%, whereas that from the nudge is not significantly different from zero. Applying a novel machine learning method for causal inference (causal forest) to estimate heterogeneous treatment effects at the household level, we demonstrate that the nudge intervention's treatment effects generate greater heterogeneity among households. These findings suggest that selective targeting for treatment increases the policy efficiency of monetary and nonmonetary interventions.

show abstract

“…Also presented in [17] is a cross validation-based method to determine the error magnitudes in baseline parameter estimation. In the absence of carefully designed mechanisms, baseline manipulation can improve a demand response provider's revenue, and [18] derives an optimal baseline reporting strategy for a demand response provider. Reference [19] proposes a demand response exchange for trading demand response capacity among DR providers and DR buyers.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Mechanism for Demand Response Markets

Bharadwaj¹,

Roozbehani²,

Dahleh³

2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

To Overconsume or Underconsume: Baseline Manipulation in Demand Response Programs

Cited by 8 publications

References 25 publications

Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load

Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load

Heterogeneous Treatment Effects of Nudge and Rebate: Causal Machine Learning in a Field Experiment on Electricity Conservation

A Two-Stage Mechanism for Demand Response Markets

Contact Info

Product

Resources

About