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

Existing electricity networks do not have information about their endpoints due to their hierarchical structure. Internet of things technology allows two-way communication with customers. This work proposes an energy management system for optimal planning of a microgrid, considering demand response and uncertainties on the internet of things framework. The planning problem is solved using the first and the second-level Benders decomposition method. Then, the model third level is developed and optimized by genetic-fuzzy algorithm. For energy management in the internet of things platform, first the consumers are clustered based on their consumption by C-Means algorithm and then the network sensor energy consumption is optimized by genetic-fuzzy algorithm. To choose the optimal solution, a non-dominant fuzzy decision process beam is adopted. Based on the numerical results, the developed model outperforms the two-level model as well as the three-level model that uses particle swarm optimization.

Energy management in microgrids considering the demand response in the presence of distributed generation resources on the IoT platform