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Optimal adaptive heuristic algorithm based energy optimization with flexible loads using demand response in smart

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Summary
This summary is machine-generated.

This study introduces an optimal adaptive wind-driven optimization (OAWDO) method for smart grid demand response load scheduling. OAWDO effectively minimizes electricity costs and peak-to-average demand ratios while maintaining user satisfaction.

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Area of Science:

  • Smart Grids and Energy Optimization
  • Artificial Intelligence in Energy Management
  • Consumer Behavior in Energy Systems

Background:

  • Demand response (DR) is crucial for energy optimization in smart grids, offering benefits to utilities and consumers.
  • Implementing DR faces challenges, including managing time-varying pricing, renewable energy intermittency, and appliance demand.
  • Minimizing peak-to-average demand ratios (PADR) and electricity costs while ensuring customer satisfaction are key objectives.

Purpose of the Study:

  • To develop and implement a load scheduling controller (LSC) for household appliances using an advanced optimization technique.
  • To model and solve the complex load scheduling problem considering various grid and consumer factors.
  • To minimize electricity costs and PADR, and mitigate rebound peak effects without compromising user comfort.

Main Methods:

  • Utilized an optimal adaptive wind-driven optimization (OAWDO) algorithm, a stochastic method, for load scheduling.
  • Integrated time-varying pricing, renewable energy, appliance demand, battery storage, and grid constraints into the LSC model.
  • Combined an inclined block tariff with real-time-varying pricing to prevent rebound peaks.

Main Results:

  • The OAWDO algorithm effectively schedules home appliances to minimize costs and PADR.
  • Simulations in MATLAB demonstrated OAWDO's superiority over Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), Fire-fly Optimization Algorithm (FFOA), and Wind-Driven Optimization (WDO).
  • OAWDO achieved significant reductions in power costs, PADR, and rebound peak formation compared to other methods.

Conclusions:

  • The proposed OAWDO-based LSC is an effective solution for demand response load scheduling in smart grids.
  • This approach successfully balances economic benefits with user comfort, addressing key challenges in energy optimization.
  • OAWDO offers a robust and efficient method for managing energy demand and supply uncertainties.