Case study of customer baseline(CBL) load determination in Korea based on statistical method

Won, Jong-Ryul; Yoon, Yong-Beum; Choi, Junho; Yi, Kyung-Keug

doi:10.1109/td-asia.2009.5356907

Cited by 9 publications

(11 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Won et al . () classified load management programs in Korea into two classes: one is related to load management equipment and the other is related to the incentive for load reduction by consumers’ willingness. The equipment includes a demand controller, remote control air conditioner and thermal storage systems.…”

Section: Customer Base‐linementioning

confidence: 99%

“…Won et al . () indicate that most utilities use the statistical averaging method with weather adjustment. The prior days are selected as: 5 prior days averaging, 5 highest daily usage days from a pool of 10 days, average of the 3 highest energy use days of the past 10 days and average of 5 weekdays out of 10 days, all with weather adjustments.…”

Section: Customer Base‐linementioning

confidence: 99%

“…As previously mentioned, Won et al . () tested several methods of domestic industrial loads in Korea. Assuming a number of conditions, CBL error analysis was conducted for 10 industrial customers where 8 days in November 2008 are considered for 3 CBL determination methods.…”

Section: Customer Base‐linementioning

confidence: 99%

“…The study by Won et al . () sheds light on load management for industry customers. Through the comprehensive sensitivity analysis, they found that the 10 prior weekdays averaging method, which uses an adjustment for CBL determination, is appropriate for industry customers.…”

Section: Customer Base‐linementioning

confidence: 99%

See 3 more Smart Citations

Demand, Customer Base‐line and Demand Response in the Electricity Market: A Survey

Heshmati

2013

Journal of Economic Surveys

View full text Add to dashboard Cite

The increasing use of demand‐side management, as a tool to reliably meet electricity demands at peak time, has stimulated interest among researchers, consumers and producer organiza‐tions, managers, regulators and policymakers. This research reviews the growing literature on models which are used to study demand, customer base‐line (CBL) and demand response in the electricity market. After characterizing the general demand models, the CBL, based on which the demand response models are studied, is reviewed. Given the experience gained from the review and existing conditions, the study combines an appropriate model for each case for a possible application to the electricity market; moreover, it discusses the implications of the results. In the literature, these aspects are studied independently. The main contribution of this survey is attributed to the treatment of the three issues as sequentially interdependent. The review is expected to enhance the understanding of the demand, CBL and demand response in the electricity market and their relationships. The objective is conducted through a combination of demand and supply side managements in order to reduce demand through different demand response programs during peak times. This enables electricity suppliers to save costly electricity generation and at the same time reduce energy vulnerability.

show abstract

Section: Customer Base‐linementioning

confidence: 99%

Section: Customer Base‐linementioning

confidence: 99%

Section: Customer Base‐linementioning

confidence: 99%

Section: Customer Base‐linementioning

confidence: 99%

See 2 more Smart Citations

Demand, Customer Base‐line and Demand Response in the Electricity Market: A Survey

Heshmati

2013

Journal of Economic Surveys

View full text Add to dashboard Cite

show abstract

“…Won et al (2009) have used statistical methods to calculate the value of CBL in South Korea. In Coughlin et al (2009) a statistical analysis on the performance of different models is performed for the calculation of CBL of buildings that participate in DR programs.…”

Section: Introductionmentioning

confidence: 99%

Customer baseline load models for residential sector in a smart-grid environment

2016

View full text Add to dashboard Cite

Demand side management (DSM) Customer baseline load (CBL) Building load coefficient (BLC) Building insulation Flexible load (FL) Non-flexible load (NFL) a b s t r a c tDemand response (DR) can expand the customer participation in the electricity market and lead by changing its pattern from a simple function to an interactive relation. There are various methods to evaluate the successful implementation of DR program, the most important of which is determination of customer baseline load (CBL). In fact, CBL is the expected pattern of customer consumption in the absence of DR programs. Few works have been done in the field of calculation of CBL in residential sector, while most of them have paid little attention to the impact of changes in weather conditions on these calculations.In this paper, a new method is presented for the calculation of CBL for customers in residential sector in the context of a smart grid, considering the impact of weather changes. The results clearly show the high impact of changes in weather conditions on the calculation of CBL, and also show the extent of effect of buildings' improved insulation on this parameter. It is also indicated that implementing DR programs can increase the willingness of customers in residential sector to improve the insulations of their buildings.

show abstract

Calculation of Consumer Baseline Load for Residential Sectors in the Context of Smart Grid

Sharma

Saxena

Kumar

et al. 2023

Electric Power Components and Systems

View full text Add to dashboard Cite

Case study of customer baseline(CBL) load determination in Korea based on statistical method

Cited by 9 publications

References 1 publication

Demand, Customer Base‐line and Demand Response in the Electricity Market: A Survey

Demand, Customer Base‐line and Demand Response in the Electricity Market: A Survey

Customer baseline load models for residential sector in a smart-grid environment

Calculation of Consumer Baseline Load for Residential Sectors in the Context of Smart Grid

Contact Info

Product

Resources

About