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Modeling and Optimization of Time-Of-Use Electricity Pricing Systems.

Ying-Chao Hung1, George Michailidis2

  • 1Department of Statistics, National Chengchi University, Taipei, 11605 Taiwan.

IEEE Transactions on Smart Grid
|September 1, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a stochastic model for consumer electricity demand to optimize Time-of-Use (TOU) pricing. The goal is to minimize customer electricity costs by selecting optimal TOU contract features.

Keywords:
Monte Carlo simulationTime-of-Use pricingcontract capacityfractional Brownian motionmixed-integer programmingself-similarity

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Area of Science:

  • Electrical Engineering
  • Applied Mathematics
  • Energy Economics

Background:

  • Time-of-Use (TOU) pricing is crucial for grid efficiency and consumer cost management.
  • Understanding consumer power demand is key to designing effective TOU strategies.
  • Existing models may not fully capture the complexities of power demand fluctuations.

Purpose of the Study:

  • To develop a general stochastic modeling framework for consumer power demand.
  • To determine optimal Time-of-Use (TOU) contract characteristics for minimizing customer electricity costs.
  • To leverage real-world power demand data for model validation.

Main Methods:

  • Modeling peak period power demand as a constant level with fractional Brownian motion fluctuations.
  • Analyzing exceedance processes over pre-specified thresholds.
  • Formulating and solving an optimization problem using Monte Carlo simulation and numerical search.

Main Results:

  • A framework for selecting TOU contract features to minimize mean customer electricity price.
  • Demonstration of the methodology using real grid data for two pricing schemes.
  • Identification of key TOU contract parameters through stochastic modeling.

Conclusions:

  • The proposed stochastic modeling framework provides a robust method for optimizing TOU pricing.
  • The approach effectively balances grid management needs with consumer cost reduction.
  • Further research can explore extensions to non-homogeneous demand periods and more complex demand patterns.