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An optimisation algorithm for distributed energy resources management in micro-scale energy hubs

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  • Roldán-Blay, Carlos
  • Escrivá-Escrivá, Guillermo
  • Roldán-Porta, Carlos
  • Álvarez-Bel, Carlos

Abstract

In this paper, a new algorithm for optimal management of distributed energy resources in facilities with distributed generation, energy storage systems and specific loads – energy hubs – is shown. This method consists of an iterative algorithm that manages optimal energy flows to obtain the minimum energy cost based on availability of each resource, prices and expected demand. A simulation tool has been developed to run the algorithm under different scenarios. Eight different scenarios of an energy hub have been simulated to illustrate the operation of this method. These scenarios consist of a demand curve under different conditions related to the existence or absence of renewable energy sources and energy storage systems and different electricity tariffs for grid supply. Partial results in the iterative process of the developed algorithm are shown and the results of these simulations are analysed. Results show a good level of optimisation of energy resources by means of optimal use of renewable energy sources and optimal management of energy storage systems. Moreover, the impact of this optimised management on carbon dioxide emissions is analysed.

Suggested Citation

  • Roldán-Blay, Carlos & Escrivá-Escrivá, Guillermo & Roldán-Porta, Carlos & Álvarez-Bel, Carlos, 2017. "An optimisation algorithm for distributed energy resources management in micro-scale energy hubs," Energy, Elsevier, vol. 132(C), pages 126-135.
    • Handle: RePEc:eee:energy:v:132:y:2017:i:c:p:126-135
    DOI: 10.1016/j.energy.2017.05.038
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    2. Ahmad, Tanveer & Chen, Huanxin, 2018. "Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment," Energy, Elsevier, vol. 160(C), pages 1008-1020.

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