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Modeling and planning of smart buildings energy in power system considering demand response

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  • Dadashi-Rad, Mohammad Hosein
  • Ghasemi-Marzbali, Ali
  • Ahangar, Roya Ahmadi

Abstract

The development of industry and the increasing of the energy demand in the today’s power system make it possible to maximize the potential of existing and renewable energy resources. On the other hand, using of these resources required efficient management and planning model, because without the adequate energy management, the power system cannot reach a high-performance model with maximum efficiency. Therefore, this paper first addresses the modeling of energy management in smart buildings having responsive/non-responsive devices and renewable photovoltaic resources. To manage the solar system employment, the KNX protocol is used. Also, the batteries are used in a way that they are charged at low power consumption and it will be as a generating unit during the peak-load time, therefore, the objective function is minimizing the power system loss and the related cost. Since the proposed model is nonlinear and has some complexity, the particle swarm algorithm (PSO) is used. To achieve the minimum losses, the best candidate buses are selected based on the proposed sensitivity analysis to manage the connected buildings. As a result, the function of the overall cost is based on the amount of energy produced and sold. Finally, the presented model is examined and evaluated on modified IEEE 30-bus test system based on the statistical analysis in different scenarios. Moreover, it is conclude from the planning that the operating cost significantly controls by the charge and discharge mechanism of the battery and the photovoltaic units.

Suggested Citation

  • Dadashi-Rad, Mohammad Hosein & Ghasemi-Marzbali, Ali & Ahangar, Roya Ahmadi, 2020. "Modeling and planning of smart buildings energy in power system considering demand response," Energy, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220318776
    DOI: 10.1016/j.energy.2020.118770
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    References listed on IDEAS

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