Customer Baseline Load Bias Estimation Method of Incentive-Based Demand Response Based on CONTROL Group Matching

Wang, Xinkang; Li, Kagping; Gao, Xue; Wang, Fei; Mi, Zengqiang

doi:10.1109/ei2.2018.8582122

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Then, the optimal cluster with the most similar daily load pattern for the target residential customer is determined based on Euclidean distance. In [32], the authors utilize the bias information of control customers not participating in the DR program but displaying a bias distribution similar to the DR group on the historical days prior to the DR event day, to estimate the bias of the DR group on the DR event day. In [33], the authors propose the concept of a virtual control group.…”

Section: ) Control Group Methodsmentioning

confidence: 99%

Novel Single Group-Based Indirect Customer Baseline Load Calculation Method for Residential Demand Response

Lee

Jang²,

et al. 2021

IEEE Access

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Demand response (DR) is a voluntary program that encourages related stakeholders, in this case electricity consumers, to cut down on usage during periods of high electricity load. One key to fully exploiting DR is to encourage residential customers to join the DR program. Unlike in the DR programs for commercial and industrial customers, for the successful operation of the residential DR program, several issues have to be addressed, one of which is to provide a group-level incentive to participating customers. In particular, the issue comes up when the incentive calculated for a group is not equal to the aggregated incentives for each customer (i.e., non-equal incentive problem). The non-equal incentive problem deteriorates the successful operation of residential DR by decreasing the motivation of DR operators and customers. We first prove the non-equal incentive problem through mathematical and experimental methods. We then propose the novel single group-based indirect incentive calculation method. The basic idea of our approach is to indirectly calculate the incentive for each customer not using the customer's data but using the data of other customers of the same DR group. Through experiments involving the electricity usage data of 42,193 households and the real DR events in Korea, we show that our method solves the non-equal incentive problem in most cases. Furthermore, our method improves the accuracy of the baseline estimation (used for calculating the contribution).

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Section: ) Control Group Methodsmentioning

confidence: 99%

Novel Single Group-Based Indirect Customer Baseline Load Calculation Method for Residential Demand Response

Lee

Jang²,

et al. 2021

IEEE Access

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“…Another interesting proposal is the control group method [122,123]. It uses historical data of the non-DR customers who exhibit the most similar load patterns to the DR participants [124] and sometimes applies a clustering technique [122,125].…”

Section: Novel Tools For Estimationmentioning

confidence: 99%

“…That is valuable, for example, in the case of a new customer. For instance, in [123] authors obtain the data of the control group, selecting from the entire group monitored only the users who are not participating in DR, for the round considered. Other methods are to include costumers not participating at all in the program, or creating a virtual control group and then using a difference-in-difference approach for comparisons.…”

Section: Novel Tools For Estimationmentioning

confidence: 99%

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

et al. 2022

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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.

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FCM based Demand Response Baseline Load Estimation Using Smart Meter Data

Zhang

Bie

et al. 2021

2021 IEEE 4th International Electrical and Energy Conference (CIEEC)

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Customer Baseline Load Bias Estimation Method of Incentive-Based Demand Response Based on CONTROL Group Matching

Cited by 6 publications

References 22 publications

Novel Single Group-Based Indirect Customer Baseline Load Calculation Method for Residential Demand Response

Novel Single Group-Based Indirect Customer Baseline Load Calculation Method for Residential Demand Response

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

FCM based Demand Response Baseline Load Estimation Using Smart Meter Data

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