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Customized time-of-use pricing for small-scale consumers using multi-objective particle swarm optimization

Customized time-of-use pricing for small-scale consumers using multi-objective particle swarm... Contemporary electricity markets necessitate balancing supply and demand in real time, as well as the active involvement of all participating entities in the price-setting procedure. To this end, Demand Response pricing schemes are often introduced, encouraging customers to contribute to power system stability by reducing their load during peak periods. In recent years, various Time-of-Use strategies have been designed and applied in real life scenarios without yielding the expected results. Analysis of several unsuccessful test cases revealed that customers are not willing to alter their consumption habits and reduce their comfort by responding to price signals, unless they are presented with tangible benefits (cost reduction, better energy services, etc.). In this context, current work introduces an optimization methodology for individualized ToU pricing policies, building upon authors' previous work on small-scale consumer activity and response modelling. Using a multi-objective particle swarm optimization mechanism, the appropriate rates for each implemented pricing policy are identified, leading to consumers' cost and peak load reduction, in addition to higher acceptance rates. Experiments indicate the capabilities and effectiveness of the proposed approach on simulated datasets extracted from Power TAC competition platform, as well as real-life measurements acquired from a multi-residential building in Sweden. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Building Energy Research Taylor & Francis

Customized time-of-use pricing for small-scale consumers using multi-objective particle swarm optimization

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Customized time-of-use pricing for small-scale consumers using multi-objective particle swarm optimization

Abstract

Contemporary electricity markets necessitate balancing supply and demand in real time, as well as the active involvement of all participating entities in the price-setting procedure. To this end, Demand Response pricing schemes are often introduced, encouraging customers to contribute to power system stability by reducing their load during peak periods. In recent years, various Time-of-Use strategies have been designed and applied in real life scenarios without yielding the expected results....
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Publisher
Taylor & Francis
Copyright
© 2017 Informa UK Limited, trading as Taylor & Francis Group
ISSN
1756-2201
eISSN
1751-2549
DOI
10.1080/17512549.2017.1325397
Publisher site
See Article on Publisher Site

Abstract

Contemporary electricity markets necessitate balancing supply and demand in real time, as well as the active involvement of all participating entities in the price-setting procedure. To this end, Demand Response pricing schemes are often introduced, encouraging customers to contribute to power system stability by reducing their load during peak periods. In recent years, various Time-of-Use strategies have been designed and applied in real life scenarios without yielding the expected results. Analysis of several unsuccessful test cases revealed that customers are not willing to alter their consumption habits and reduce their comfort by responding to price signals, unless they are presented with tangible benefits (cost reduction, better energy services, etc.). In this context, current work introduces an optimization methodology for individualized ToU pricing policies, building upon authors' previous work on small-scale consumer activity and response modelling. Using a multi-objective particle swarm optimization mechanism, the appropriate rates for each implemented pricing policy are identified, leading to consumers' cost and peak load reduction, in addition to higher acceptance rates. Experiments indicate the capabilities and effectiveness of the proposed approach on simulated datasets extracted from Power TAC competition platform, as well as real-life measurements acquired from a multi-residential building in Sweden.

Journal

Advances in Building Energy Research – Taylor & Francis

Published: Jan 2, 2018

Keywords: Demand response; time-of-use pricing; particle swarm optimization

References

  • A summary of demand response in electricity markets
  • Fcm: The fuzzy c-means clustering algorithm
  • Micro-MOPSO: A multi-objective particle swarm optimizer that uses a very small population size
  • Bottom-up modeling of small-scale energy consumers for effective demand response applications
  • Response modeling of small-scale energy consumers for effective demand response applications
  • A fast and elitist multiobjective genetic algorithm: Nsga-II
  • A stochastic-based decision-making framework for an electricity retailer: Time-of-use pricing and electricity portfolio optimization
  • Power tac: A competitive economic simulation of the smart grid
  • An annual framework for clustering-based pricing for an electricity retailer
  • Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings
  • Utility demand response operation considering day-of-use tariff and optimal operation of thermal energy storage system for an industrial building based on particle swarm optimization algorithm
  • Demand side management: Demand response, intelligent energy systems, and smart loads
  • Home, habits, and energy: Examining domestic interactions and energy consumption
  • Optimal real time pricing in an agent-based retail market using a comprehensive demand response model