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A Firefly Algorithm-Based Approach for Identifying Coalitions of Energy Providers that Best Satisfy the Energy Demand

  • Cristina Bianca PopEmail author
  • Viorica Rozina Chifu
  • Eric Dumea
  • Ioan Salomie
Chapter
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)

Abstract

Smart grids aim to ensure the network reliability by providing electricity when it is demanded, while lowering the impact they have on the surrounding environment. Due to the great advances of renewable technologies, the current trend is to encourage the integration of renewable energy providers that could participate in satisfying the requested demand. The grid reliability can be achieved by identifying in advance the coalitions of heterogeneous electricity providers that could satisfy the forecast demand. This problem can be seen as an optimization problem aiming to identify the coalition that best satisfies the electricity demand curve over a particular period of time, by taking into account the energy supply forecast for each provider. In this context, this chapter proposes an optimization methodology for identifying the coalition of providers that satisfies the electricity demand curve over a time interval using an adapted version of the firefly algorithm. In our approach, each firefly has a solution with fixed length equal to the number of available energy providers. A solution element represents a tuple consisting of a provider and a flag which indicates whether the provider has been selected to provide energy or not. The objective function used to evaluate the quality of a solution is defined as the difference between the energy supplied by the solution’s providers and the demanded energy for the considered hour. Additionally, we have introduced in the fitness function, two penalty components that penalize a solution’s adherence to the requested provider heterogeneity and maximum desired price. To apply the firefly algorithm in this context, we have redefined the firefly movement strategy using the genetic crossover and mutation operators. The proposed optimization methodology has been evaluated on an in-house developed data set consisting of forecast energy values for energy supply and demand.

Keywords

Coalitions of heterogeneous energy providers Firefly algorithm Forecast energy production and consumption Optimization problem Energy aggregator 

Notes

Acknowledgements

This work has been conducted within the eDREAM project Grant number 774478, co-funded by the European Commission as part of the H2020 Framework Programme (H2020-LCE-2017-SGS).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Cristina Bianca Pop
    • 1
    Email author
  • Viorica Rozina Chifu
    • 1
  • Eric Dumea
    • 1
  • Ioan Salomie
    • 1
  1. 1.Department of Computer ScienceTechnical University of Cluj-NapocaCluj-NapocaRomania

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