PV-Load Decoupling Based Demand Response Baseline Load Estimation Approach for Residential Customer With Distributed PV System

Xuan, Zhiming; Gao, Xue; Li, Kangping; Wang, Fei; Ge, Xinxin; Hou, Yunhe

doi:10.1109/tia.2020.3014575

Cited by 77 publications

(18 citation statements)

References 38 publications

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“…In [34], the authors utilize temporal features from the history of the target customer and spatial features from the control group that was formed by K-means clustering and load patterns. In [35], the CBL estimation problem is converted into two sub-problems: the estimation of actual load power and the estimation of distributed photovoltaic system (DPVS) output power. First, the actual load power of DR customers is estimated based on the load power of the control group customers (using matching nighttime usage).…”

Section: ) Control Group Methodsmentioning

confidence: 99%

“…For the successful operation of the residential DR program, incentives need to be assured for both DR operators and customers, failing which, nobody would join the residential DR program. In this respect, we may have two problematic cases: overestimation and underestimation of CBL [35]. Overestimation (i.e., DR_Cap ISO t < DR_Cap OP t ) may attract more residential customers to the residential DR programs, but the interest of the DR operator to run the residential DR programs will diminish.…”

Section: ) Descriptionsmentioning

confidence: 99%

“…Most of the existing work focuses on CBL calculation methods for residential customers and operation strategies for DR operators. The CBL calculation methods include direct CBL calculation such as averaging methods [9], regression [13], [14], deep learning [15]- [21], and probabilistic methods [7], [22]- [25] and indirect CBL calculation methods such as control group methods [24], [26]- [35]. The operation strategies for DR operators include optimal bidding strategy [38]- [47] and modeling of residential customers [8], [23], [48]- [53].…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: ) Descriptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Estimating DPVS output correctly has direct implications on the BLP estimation. ML techniques have proved to provide successful support, which has been explored with the SVR-based model approach [114] and k-means clustering algorithms combined with decupling-based BLP estimation for residential customers [115]. Clustering by k-means adopted alone, used to categorise consumers based on their load profile, has shown to have a good performance on the accuracy, bias, variability, and reliability in terms of prediction [116].…”

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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“…In [22] the author estimate the BtM penetration in the large power system from data that collected from multiple site. In [23] and [24], BtM PV generation is directly decoupled from load demand pattern by comparing load demand data pairs of different weather conditions. This strategy is more suitable for the situation when load demand data is collected from small grid.…”

Section: Introductionmentioning

confidence: 99%

Stochastic forecast error estimation of high PV penetration system considering net‐load/PV decoupling for microgrid operation

Yoo

Jang

Lee

et al. 2022

IET Renewable Power Gen

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The increase of distributed Photovoltaic (PV) generators in the power system changes the load demand pattern as some of them are located behind the meter (BtM). As known as BtM PV generators, the impact on netload demand pattern increases the uncertainty of scheduling and balancing of the power system. The distortion of load demand by BtM PV must be decoupled to make an accurate forecast model for the load. In this paper, the impact of BtM PV on stochastic forecast error (SFE) is analyzed and verified how they change the efficiency of microgrid operation. The net load pattern and solar irradiation data are collected and used to decouple BtM PV distortion by using K-mean clustering and support vector machine regression analysis. Then, the baseload demand and solar irradiation forecast model is built using recurrent neural networks with external input parameters. Using the forecast model, the correlation between distributions of forecast and forecast error is built as a joint probability distribution. The SFE is modeled as conditional forecast error in a form of a probability density function and used for microgrid scheduling optimization. Case studies are based on data acquired from global data assimilation and prediction system (GDAPS) and local transmission system operator (TSO) at Jeju island in Korea.

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PV-Load Decoupling Based Demand Response Baseline Load Estimation Approach for Residential Customer With Distributed PV System

Cited by 77 publications

References 38 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

Stochastic forecast error estimation of high PV penetration system considering net‐load/PV decoupling for microgrid operation

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