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

We build upon previous work out of UC Berkeley's energy, controls, andapplications laboratory (eCal) that developed a model for price prediction ofthe energy day-ahead market (DAM) and a stochastic load scheduling fordistributed energy resources (DER) with DAM based objective\cite{Travacca}. Insimilar fashion to the work of Travacca et al., in this project we take thestandpoint of a DER aggregator pooling a large number of electricity consumers- each of which have an electric vehicle and solar PV panels - to bid theirpooled energy resources into the electricity markets. The primary contributionof this project is the optimization of an aggregated load schedule forparticipation in the California Independent System Operator (CAISO) real time(15-minute) electricity market. The goal of the aggregator is to optimallymanage its pool of resources, particularly the flexible resources, in order tominimize its cost in the real time market. We achieves this through the use ofa model predictive control scheme. A critical difference between the prior workin \cite{Travacca} is that the structure of the optimization problem isdrastically different. Based upon our review of the current and publicliterature, no similar approaches exist. The main objective of this projectwere building methods. Nevertheless, to illustrate a simulation with 100prosumers was realized. The results should therefore be taken with a grain ofsalt. We find that the Real Time operation does not substantially decrease orincrease the total cost the aggregator faces in the RT market, but this isprobably due to parameters that need further tuning and data, that need betterprocessing.

Market based embedded Real Time Operation for Distributed Resources and Flexibility