English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

To effectively deal with the challenge of optimal dispatch caused by uncertainties such as renewable energy in active distribution network, a day-ahead optimal dispatch strategy for active distribution network based on improved deep reinforcement learning is proposed. First, the day-ahead optimal dispatch problem for the active distribution network is modeled as a multistage stochastic programming model. Multistage models attempt to capture the dynamics of unfolding uncertainties over time, so that the interaction between decision making and uncertainty unfolding is represented more accurately and realistically, and the decision can be adjusted dynamically and adaptively. Second, with the help of deep neural network to approximate the power flow model of the active distribution network in a data-driven manner, an improved deep reinforcement learning algorithm is proposed to solve the corresponding multistage stochastic programming problem. The proposed improved deep reinforcement learning algorithm effectively solves the problem that trial-and-error information acquisition methods are difficult to apply to high-cost power systems. Finally, the effectiveness of the proposed day-ahead optimization dispatch strategy for active distribution network based on improved deep reinforcement learning is verified by a modified IEEE33 case.

Day-Ahead Optimal Dispatch Strategy for Active Distribution Network Based on Improved Deep Reinforcement Learning