Tang, Qiang and Xie, Ming-zhong and Yang, Kun and Luo, Yuan-sheng and Li, Ping (2016) Price Learning Based Load Distribution Strategies for Demand Response Management in Smart Grid. International Journal of Smart Home, 10 (11). pp. 79-94. DOI https://doi.org/10.14257/ijsh.2016.10.11.08
Tang, Qiang and Xie, Ming-zhong and Yang, Kun and Luo, Yuan-sheng and Li, Ping (2016) Price Learning Based Load Distribution Strategies for Demand Response Management in Smart Grid. International Journal of Smart Home, 10 (11). pp. 79-94. DOI https://doi.org/10.14257/ijsh.2016.10.11.08
Tang, Qiang and Xie, Ming-zhong and Yang, Kun and Luo, Yuan-sheng and Li, Ping (2016) Price Learning Based Load Distribution Strategies for Demand Response Management in Smart Grid. International Journal of Smart Home, 10 (11). pp. 79-94. DOI https://doi.org/10.14257/ijsh.2016.10.11.08
Abstract
In this paper, a Price learning based Load Distribution Strategy (PLDS) is proposed at first. In PLDS model, Smart Power Service, Utility Company and History Load Curves are included, and by considering both the average electricity consumption cost and the average electricity consumption habit, we proposed a convex optimization model to solve the model. In order to accelerate the convergence of PLDS, a price learning mechanism is proposed, which learns a price curve according to the history price data, and predicts price as a learned price for the next iteration. The optimization cycle of PLDS is one day or 24 hours, and in order to further improve the peak shaving performance, an extended version of PLDS named PLRS (Price learning based Load Redistribution Strategy) is proposed, whose optimization cycle length is 1 hour. The optimization models of PLDS and PLRS are the same, and the differences between them are the optimization cycle and the constraint conditions. In the simulation, we compared the convergence performance, peaking shaving performance and total cost among PLDS, PLRS and other strategy ODC in reference [11], and we found that the convergence performances of PLDS and PLRS are both better than that of ODC. The peak shaving performance of PLRS is better than that of ODC in the long term, and the total cost of PLRS is very close to that of ODC.
Item Type: | Article |
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Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 29 Jun 2017 14:10 |
Last Modified: | 23 Oct 2024 06:18 |
URI: | http://repository.essex.ac.uk/id/eprint/19912 |