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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We consider the problem of smart and flexible loads providing contingencyreserves to the electric grid and provide a Distributed Gradient Projection(DGP) algorithm to minimize loads' disutility while providing contingencyservices. Each load uses locally obtained grid-frequency measurements andinter-load communication to coordinate their actions, and the privacy of eachload is preserved: only gradient information is exchanged---not disutility orconsumption information. We provide a proof of convergence of the proposed DGPalgorithm, and we compare its performance through simulations to that of a"dual algorithm" previously proposed in the literature that solved the dualoptimization problem. The DGP algorithm solves the primal problem. Its mainadvantage over the dual algorithm is that it is applicable to convex---but notnecessarily strictly convex---consumer disutility functions, such as a model ofconsumer behavior that is insensitive to small changes in consumption, whilethe dual algorithm is not. Simulations show the DGP algorithm aids in arrestinggrid-frequency deviations in response to contingency events and performs betteror similarly to the dual algorithm in cases where the two can be compared.

Distributed demand-side contingency-service provisioning while  minimizing consumer disutility through local frequency measurements and  inter-load communication