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

Price-based demand response (DR) of heating, ventilating, andair-conditioning (HVAC) systems is a challenging task, requiring comprehensivemodels to represent the building thermal dynamics and game theoreticinteractions among participants. This paper proposes an online learning-basedstrategy for a distribution system operator (DSO) to determine optimalelectricity prices, considering the optimal DR of HVAC systems in commercialbuildings. An artificial neural network (ANN) is trained with building energydata and represented using an explicit set of linear and nonlinear equations,without physics-based model parameters. An optimization problem for price-basedDR is then formulated using this equation set and repeatedly solved offline,producing data on optimal DR schedules for various conditions of electricityprices and building thermal environments. Another ANN is then trained online todirectly predict DR schedules for day-ahead electricity prices, which isreferred to as meta-prediction (MP). By replacing the DR optimization problemwith the MP-enabled ANN, an optimal electricity pricing strategy can beimplemented using a single-level decision-making structure, which is simplerand more practical than a bi-level one. In simulation case studies, theproposed single-level strategy is verified to successfully reflect the gametheoretic relations between the DSO and commercial building operators, so thatthey effectively exploit the operational flexibility of the HVAC systems tomake the DR application profitable, while ensuring the grid voltage stabilityand occupants thermal comfort.

Relieving the Need for Bi-Level Decision-Making for Optimal Retail Pricing via Online Meta-Prediction of Data-Driven Demand Response of HVAC Systems