Grid integration of distributed renewables through coordinated demand response

Mahnoosh Alizadeh, Tsung Hui Chang, Anna Scaglione

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations


There is a growing interest in developing solutions to facilitate large scale integration of distributed renewable energy resources and, in particular, contain the adverse effects of their volatility. In this paper, we introduce a neighborhood-level demand response program that aims at coordinating the Home Energy Management Systems (HEMS) of residential customers in order to opportunistically consume spikes of locally generated renewable energy. We refer to this technique as Coordinated Home Energy Management (CoHEM). Our model predictive control technique modulates the aggregate load to follow a dynamically forecasted generation supply. Both centralized and decentralized deployments of CoHEM are considered. The decentralized version requires a more demanding communication backbone to connect individual HEMS but, it is more resilient to failures of individual computational units or communication links and, compared to the centralized model, it preserves consumers privacy. In our numerical results section, we compare the scenario where individual HEMS optimize their energy use selfishly, under a hypothetical dynamic pricing program, to the performance of the centralized and decentralized versions of our proposed CoHEM architecture. The results highlight the advantages of using the CoHEM model in absorbing the fluctuations in the generation output of distributed renewables.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Decision and Control
Number of pages6
StatePublished - 2012
Externally publishedYes
Event51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States
Duration: Dec 10 2012Dec 13 2012


Other51st IEEE Conference on Decision and Control, CDC 2012
Country/TerritoryUnited States
CityMaui, HI

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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