| 1 |
Spillover as a cause of bias in baseline evaluation methods for demand response programs
Todd A, Cappers P, Spurlock CA, Jin L
Applied Energy, 250, 344, 2019 |
| 2 |
Simple electric utility platform: A hardware/software solution for operating emergent microgrids
Manur A, Venkataramanan G, Sehloff D
Applied Energy, 210, 748, 2018 |
| 3 |
Demand response of customers in Kitakyushu smart community project to critical peak pricing of electricity
Li YX, Gao WJ, Ruan YJ, Ushifusa Y
Energy and Buildings, 168, 251, 2018 |
| 4 |
Are vulnerable customers any different than their peers when exposed to critical peak pricing: Evidence from the US
Cappers P, Spurlock CA, Todd A, Jin L
Energy Policy, 123, 421, 2018 |
| 5 |
Optimization of Time-Varying Electricity Rates
Mays J, Klabjan D
Energy Journal, 38(5), 67, 2017 |
| 6 |
A field study using an adaptive in-house pricing model for commercial and industrial customers in Korea
Kim MJ
Energy Policy, 102, 189, 2017 |
| 7 |
Assessing fairness of dynamic grid tariffs
Neuteleers S, Mulder M, Hindriks F
Energy Policy, 108, 111, 2017 |
| 8 |
Critical peak electricity pricing for sustainable manufacturing: Modeling and case studies
Wang Y, Li L
Applied Energy, 175, 40, 2016 |
| 9 |
Variability of electricity load patterns and its effect on demand response: A critical peak pricing experiment on Korean commercial and industrial customers
Jang D, Eom J, Park MJ, Rho JJ
Energy Policy, 88, 11, 2016 |
| 10 |
Designing a critical peak pricing scheme for the profit maximization objective considering price responsiveness of customers
Park SC, Jin YG, Song HY, Yoon YT
Energy, 83, 521, 2015 |
