Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

Kang, Jimyung; Lee, Jee-Hyong

doi:10.3390/en10101537

Cited by 9 publications

(9 citation statements)

References 47 publications

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“…The DSRP also distributes incentives to its customers, exclusive of the commission (ⓒ). More details on the structure of the considered demand response system can be found in the literature [4,5]. Other price-based models such as time of use (TOU) [6], critical peak pricing (CPP) [7], and real time pricing (RTP) [8] are not considered here because the incentive-based system is more common and widespread in real applications; moreover, the real data we acquired are from incentive-based demand response systems.…”

Section: Introductionmentioning

confidence: 99%

“…However, in current and emerging demand response systems, this assumption is no longer valid. Rather, the actual load reductions of customers Energies 2018, 11,2905 3 of 14 are highly uncertain [4]. A customer who provided a reduction of 10 kWh one day might provide only 1 kWh another day.…”

Section: Introductionmentioning

confidence: 99%

“…For example, a demand response customer only received three to five reduction requests in the year 2016 in a real operating energy market. As a result, the prediction of load curtailment has been discussed by a few studies [4,[11][12][13]. In Chelmis's work, the necessity regarding the prediction of reduced electricity consumption is first presented and a simple weighting method with the consideration of recent consumption patterns is proposed [11].…”

Section: Introductionmentioning

confidence: 99%

“…However, in this study, the prediction is conducted based on recent experience data that are only five minutes old and the focus of their work is not on individual load but on aggregated load. The uncertainty of load curtailment is also mentioned in the another article [4]; however, only an optimization framework is proposed to determine the dispatch schedule of demand response without much consideration of the detailed prediction of load curtailment.…”

Section: Introductionmentioning

confidence: 99%

“…The structure of the considered demand response system. (a) Overall market structure; (b) detailed transaction procedure; Adapted with permission from[4], Jimyung Kang and Jee-Hyong Lee, Energies, MDPI, 2017. DRSP: demand response service provider.…”

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confidence: 99%

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Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System

Kang

Lee

2018

Energies

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Demand response, in which energy customers reduce their energy consumption at the request of service providers, is spreading as a new technology. However, the amount of load curtailment from each customer is uncertain. This is because an energy customer can freely decide to reduce his energy consumption or not in the current liberalized energy market. Because this uncertainty can cause serious problems in a demand response system, it is clear that the amount of energy reduction should be predicted and managed. In this paper, a data-driven prediction method of load curtailment is proposed, considering two difficulties in the prediction. The first problem is that the data is very sparse. Each customer receives a request for load curtailment only a few times a year. Therefore, the k-nearest neighbor method, which requires a relatively small amount of data, is mainly used in our proposed method. The second difficulty is that the characteristic of each customer is so different that a single prediction method cannot cover all the customers. A prediction method that provides remarkable prediction performance for one customer may provide a poor performance for other customers. As a result, the proposed prediction method adopts a weighted ensemble model to apply different models for different customers. The confidence of each sub-model is defined and used as a weight in the ensemble. The prediction is fully based on the electricity consumption data and the history of demand response events without demanding any other additional internal information from each customer. In the experiment, real data obtained from demand response service providers verifies that the proposed framework is suitable for the prediction of each customer’s load curtailment.

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