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Updated: Nov 23, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
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Demand Management for Optimized Energy Usage and Consumer Comfort Using Sequential Optimization.

Mikhak Samadi1, Javad Fattahi1, Henry Schriemer1

  • 1School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada.

Sensors (Basel, Switzerland)
|December 31, 2020
PubMed
Summary

This study introduces a Clustered Sequential Management (CSM) approach for smart grids, optimizing energy efficiency and customer comfort by scheduling appliance usage. The CSM model reduces costs by 13% and peak-to-average ratios by 45%.

Keywords:
demand managementdevice schedulingsequential optimizationsmart grid

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Area of Science:

  • Smart Grid Technology
  • Energy Management Systems
  • Consumer Behavior in Energy Consumption

Background:

  • Increasing adoption of demand management technologies highlights the need for energy efficiency and improved customer experience.
  • Optimizing residential electrical load on the smart grid requires integrating customer comfort parameters like thermal comfort and appliance usage preferences.
  • Existing methods often overlook the interplay between consumer comfort and energy consumption optimization.

Purpose of the Study:

  • To propose a multi-layer architecture with a multi-objective optimization model for smart grid energy consumption.
  • To develop and evaluate a Clustered Sequential Management (CSM) approach that enhances consumer comfort through appliance scheduling.
  • To integrate thermal comfort metrics and non-thermal loads into an energy consumption scheduling model.

Main Methods:

  • Utilized thermodynamic solutions for Heating Ventilation and Air Conditioner (HVAC) systems to quantify thermal comfort.
  • Developed a hierarchical algorithm classifying appliances by load profile and consumer prioritization.
  • Applied Mixed Integer Linear Programming (MILP) and Linear Programming (LP) for consumption scheduling within a Time of Use (ToU) pricing model.

Main Results:

  • Achieved significant cost minimization, nearing 13%, through optimized energy consumption scheduling.
  • Reduced Peak-to-Average Ratios (PAR) by approximately 45%, indicating improved grid load balancing.
  • Successfully integrated consumer comfort preferences, including thermal comfort (ASHRAE standard) and electric vehicle (EV) charging, with cost minimization.

Conclusions:

  • The proposed Clustered Sequential Management (CSM) approach effectively balances energy efficiency with consumer comfort in smart grids.
  • The multi-objective optimization model provides a robust framework for scheduling diverse electrical loads, enhancing user satisfaction and grid stability.
  • This research demonstrates a practical method for optimizing smart grid operations by considering user-centric parameters alongside economic and technical objectives.