A field study using an adaptive in-house pricing model for commercial and industrial customers in Korea
- Authors
- Kim, Min-Jeong
- Issue Date
- Mar-2017
- Publisher
- ELSEVIER SCI LTD
- Keywords
- Demand response (DR) program; Price-based program; Adaptive in-house pricing model; Peak-to-off-peak usage ratio (POR); Critical-peak pricing (CPP); Commercial and industrial tariff
- Citation
- ENERGY POLICY, v.102, pp 189 - 198
- Pages
- 10
- Journal Title
- ENERGY POLICY
- Volume
- 102
- Start Page
- 189
- End Page
- 198
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/8643
- DOI
- 10.1016/j.enpol.2016.12.024
- ISSN
- 0301-4215
1873-6777
- Abstract
- Demand response programs provide customers with economic incentives for load reductions at times of high market prices and system reliability constraints. One type of demand response programs, price-based program, induces customers to respond to changes in product rates. However, some large-scale customers find it difficult to change their electricity consumption patterns, even with rate changes, because their electricity demands are commercial and industrial. This study proposes an adaptive in-house pricing model for large-scale customers, particularly those with multiple business facilities, for self-regulating price-based program. The adaptive in-house pricing model charges higher rates to customers with lower load factors by employing a peak-to-off-peak usage ratio in order to reduce usage at times of high prices at each facility. This study analyzes the daily electricity consumption patterns of large-scale customers through a field trial of the proposed pricing model at a telecom company with 447 offices and worksites for one month. The results show that the pricing model corresponds to average reductions of 3.54-28.69% during peak-demand times for four different types of workplaces. However, reductions in electricity consumption during off-peak periods did not show a significant difference. The results of this study prove that this proposed pricing model can be successfully applied to large-scale operations.
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