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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The implementation of a multi-microgrid (MMG) system with multiple renewableenergy sources enables the facilitation of electricity trading. To tackle theenergy management problem of a MMG system, which consists of multiple renewableenergy microgrids belonging to different operating entities, this paperproposes a MMG collaborative optimization scheduling model based on amulti-agent centralized training distributed execution framework. To enhancethe generalization ability of dealing with various uncertainties, we alsopropose an improved multi-agent soft actor-critic (MASAC) algorithm, whichfacilitates en-ergy transactions between multi-agents in MMG, and employsautomated machine learning (AutoML) to optimize the MASAC hyperparameters tofurther improve the generalization of deep reinforcement learning (DRL). Thetest results demonstrate that the proposed method successfully achieves powercomplementarity between different entities, and reduces the MMG systemoperating cost. Additionally, the proposal significantly outperforms otherstate-of-the-art reinforcement learning algorithms with better economy andhigher calculation efficiency.

Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm