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Residential Demand Response Strategy Based on Deep Deterministic Policy Gradient
| Content Provider | MDPI |
|---|---|
| Author | Deng, Chunyu Wu, Kehe |
| Copyright Year | 2021 |
| Description | With the continuous improvement of the power system and the deepening of electricity market reform, the trend of users’ active participation in power distribution is more and more significant. Demand response has become the promising focus of smart grid research. Providing reasonable incentive strategies for power grid companies and demand response strategies for customers plays a crucial role in maximizing the benefits of different participants. To meet different expectations of multiple agents in the same environment, deep reinforcement learning was adopted. The generative model of residential demand response strategy under different incentive policies can be trained iteratively through real-time interactions with the environmental conditions. In this paper, a novel optimization model of residential demand response strategy, based on a deep deterministic policy gradient (DDPG) algorithm, was proposed. The proposed work was validated with the actual electricity consumption data of a certain area in China. The results showed that the DDPG model could optimize residential demand response strategy under certain incentive policies. In addition, the overall goal of peak load-cutting and valley filling can be achieved, which reflects promising prospects of the electricity market. |
| Starting Page | 660 |
| e-ISSN | 22279717 |
| DOI | 10.3390/pr9040660 |
| Journal | Processes |
| Issue Number | 4 |
| Volume Number | 9 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-04-09 |
| Access Restriction | Open |
| Subject Keyword | Processes Industrial Engineering Demand Response Deep Reinforcement Learning Deep Deterministic Policy Gradient Power Consumption Strategy Optimization |
| Content Type | Text |
| Resource Type | Article |
