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A Multi-Objective Demand Response Optimization Model for Scheduling Loads in a Home Energy Management System.

Jaclason M Veras1, Igor Rafael S Silva2, Plácido R Pinheiro3

  • 1Graduate Program in Applied Informatics, University of Fortaleza (UNIFOR), Fortaleza-CE 60811-905, Brazil. jaclason@ufpi.edu.br.

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Summary

This study introduces a home energy management system (HEMS) that optimizes appliance scheduling for cost savings and consumer comfort. The HEMS effectively reduces electricity bills while maintaining user satisfaction, ensuring a more stable power grid.

Keywords:
demand responseenergy managementload schedulingmulti-objective optimization

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Operations Research

Background:

  • Demand Response (DR) programs incentivize consumers to adjust energy use based on price or grid reliability.
  • Existing DR solutions primarily focus on cost reduction for consumers.
  • There is a need for integrated systems that balance cost savings with consumer comfort and grid stability.

Purpose of the Study:

  • To propose a novel Home Energy Management System (HEMS) for optimizing appliance scheduling.
  • To develop a multi-objective Demand Response (DR) optimization model considering real-time pricing (RTP) and consumer comfort.
  • To ensure the stability and safety of the electrical power system (EPS) through intelligent energy management.

Main Methods:

  • Formulated a multi-objective nonlinear programming problem for DR optimization.
  • Employed the Non-Dominated Sorted Genetic Algorithm II (NSGA-II) to solve the optimization problem.
  • Simulated HEMS performance across 15 Brazilian families with diverse consumption patterns.

Main Results:

  • The HEMS successfully reduced electricity costs for all simulated scenarios.
  • Consumer satisfaction/comfort levels were minimally affected by the optimized scheduling.
  • Significant cost reductions were observed, with one household saving 8.65% on their electricity bill.
  • The system demonstrated potential for creating a more uniform energy demand and a safer EPS.

Conclusions:

  • The proposed HEMS effectively balances electricity cost reduction with consumer comfort.
  • The multi-objective optimization model provides a robust framework for intelligent home energy management.
  • The HEMS contributes to a more reliable and economically efficient electrical power system.