Distributed renewable generation and storage system sizing based on smart dispatch of microgrids

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Abstract

This paper considers the contribution of independent owners (IOs) operating within microgrids (MGs) toward green power generation in deregulated energy markets. An optimization scheme is introduced for sizing distributed renewable generation (DRG) and a distributed energy storage system (DESS) based on a novel energy management system (EMS) that accounts for demand response (DR), DESS dispatch and performance degradation, dynamic pricing environments, power distribution loss and irregular renewable generation. The proposed EMS utilizes an iterative Newton-Raphson linear programming algorithm that schedules resources in order to minimize the objective function, to deal with the complicated nonlinear nature of the problem and to enable efficient long-term assessments. The EMS is used to evaluate candidate solutions that are generated by a genetic algorithm (GA) to determine the optimal combination of DRG and DESS. A case study for IEEE 34-bus distribution MG in Okinawa, Japan, is used for testing the algorithm and analyzing the potential for IO/MG investments and their strategies.

Figures

  • Figure 1. Conceptual image of a smart microgrid (MG) with distributed renewable generation (DRG) and distributed energy storage systems (DESSs).
  • Figure 2. Flowchart of the proposed optimization framework.
  • Figure 3. One year profile of hourly wind speed data in Okinawa.
  • Figure 4. Energy unit price versus purchased power.
  • Table 1. Technical and economic data used in the optimization.
  • Figure 5. Profit maximization through the iterative Newton-Raphson linear programming (NRLP) dispatch procedure without a demand response (DR) program.
  • Figure 6. f1 ini ization through the iterative NRLP dispatch procedure assuming 5% controllable load.
  • Table 2. System optimization results without controllable load.

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CITATION STYLE

APA

Atia, R., & Yamada, N. (2016). Distributed renewable generation and storage system sizing based on smart dispatch of microgrids. Energies, 9(3). https://doi.org/10.3390/en9030176

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