영문내용
(English Abstract) |
Incentive-based demand response (DR) program refers to reducing energy demand on the customer side is of great significance for the smart grid, which can guarantee a stable and reliable power supply. In this kind of DR program, the customers are paid incentives according to the load reduction amount during the period of DR. However, inaccurate customer baseline load (CBL) estimation will reduce the rebate of customers, which causes the hardness of attracting the customers to participate in the DR program. Consequently, in this work, CBL estimation is studied for incentive-based demand response, where the docker container technique is leveraged to collect the energy load data from each individual customer through a one-to-one communication mode. In particular, the Max (4/5) method and the Mid (6/10) method are adopted in the docker container application layer to estimate the CBL. Additionally, Symmetric Additive Adjustment (SAA) is utilized to adjust the evaluated CBL since it can increase or decrease the unadjusted CBL, where the objective is to find out the months that are most beneficial to the customers such that to help customers gain more rebate by making decisions of choosing the best months to adopt SAA. The experiment results show the SAA-based Max (4/5) method outperforms the SAA-based Mid (6/10) method. Besides, the optimal months to adopt SAA are January, March, April, June, August, September, and October via the SAA-based Max (4/5) method for a case study, compared to the Max (4/5) method. |
