A Study on Design of Microgrid(MG) Optimal Operation Algorithm for Development of Semi-Wheel System

  • Authors

    • Seo-Hyun Yeo
    • Jin-Wook Park
    • Jeong-Sueng Na
    • Seong-Mi Park
    • Sung-Jun Park
    https://doi.org/10.14419/ijet.v7i3.24.22679
  • semi-wheel system, microgrid, pre-validation, BESS, diesel Generator, virtual analysis
  • Background/Objectives: Recently, due to the change of power paradigm, interest in MG(Microgrid) is increasing. However, the realization of MG requires very sophisticated algorithms. Therefore, before implementation, we want to develop a virtual MG operation system that can verify economical & efficient feasibility.

    Methods/Statistical analysis: In this paper, we design MG optimal operation algorithm as the first step for the development of Semi-Wheel-System. The design method is to analyze the MG components in the Geocha island. Especially, the fuel consumption per diesel generator output and characteristics of BESS battery are analyzed. Matlab, SPSS is used to apply the curve fitting technique and trend prediction.

    Findings: As a result of designing the optimal operation algorithm that MG can operate most efficiently, the diesel generator is controlled to the optimum power ratio of 60 ~ 80%. And the BESS SOC range is controlled by setting the average value of 42.5%by using the analysis result. The simulation scenarios are designed based on the comparison of renewable energy power supply and power demand. Also, the application of the diesel generator efficiency and BESS SOC setting characteristics are also reflected. Thus, a total of 12 scenarios are constructed. As a result of simulation, it is verified that the fuel consumption of the diesel generator is minimized and the maximum energy of the BESS is obtained after the algorithm proposed in this paper is applied on it.

    Improvements/Applications: In future research, we will construct a virtual MG operation system, which can reflect various grid situations, characteristics of MG elements, and control and measure variables.

     

     



  • References

    1. [1] Sung Ho, Lee. (2018). Optimization Study of Microgrid for Energy Self-sufficient Islands(Doctorate dissertation). Sejong University, Seoul, Korea.

      [2] Sun-Il, Kim. (2014). The Study on Optimal Unit Commitment of Diesel-Engine Generator Considering Forecast of New Renewable Energy and Load in A Stand-Alone Microgrid(Masters dissertation). Hongik University, Seoul, Korea.

      [3] Byung Ha, Lee. (2016).A Study on Methodology of Optimal Operation of BESS and Diesel generators in a Microgrid Considering Efficiency Characteristics According to the Power Ratios of Diesel Generators, The Transactions of the Korean Institute of Electrical Engineers Vol. 65, No. 4, pp.539~546.

      [4] [4] Kyung Kyu, Lee. (2016). Hybrid Energy System Control Strategy Considering Properties of Diesel Generator and ESS(Masters dissertation).Chungbuk National University, Cheongju, Korea.

      [5] Ji Hoon, Kim. (2014). A study on optimal operation of a microgrid network considering characteristics of renewable energy source and certified emission reductions (Masters dissertation). Incheon National Univerisity, Incheon, Korea.

      [6] Kintner-Meyer, M., Balducci, P., Elizondo, M., Jin, C., Nguyen, T., Viswanathan, V., Guo, X., Tuffner, F. (2012). Energy Storage for Power Systems Applications : A Regional Assessment for the Northwest Power Pool (NWPP) (PNNL-19300). Pacific Northwest National Laboratory.

      [7] Kintner-Meyer, M., Balducci, P., Colella, W., Elizondo, M., Jin, C., Nguyen, T., Viswanathan, V., Zhang, Y. (2012). National Assessment of Energy Storage for Grid Balancing and Arbitrage : Phase 1, WECC (PNNL-21388). Pacific Northwest National Laboratory.

      [8] Kintner-Meyer, M., Balducci, P., Colella, W., Elizondo, M., Jin, C., Nguyen, T., Viswanathan, V., Zhang, Y. (2013). National Assessment of Energy Storage for Grid Balancing and Arbitrage : Phase Ⅱ : WECC, ERCOT, EIC – Volume 1 : Technical Analysis (PNNL-21388-PHASEⅡ /Vo1.1). Pacific Northwest National Laboratory.

      [9] Viswanathan, V., Kintner-Meyer, M., Balducci, P., Jin, C. (2013). National Assessment of Energy Storage for Grid Balancing and Arbitrage : Phase â…¡- Volume 2 : Cost and Performance Characterization (PNNL-21388-PHASE â…¡ /Vo1.2). Pacific Northwest National Laboratory.

      [10] Carnegie. R., Gotham. D., Nderitu. D., Preckel. P. V. (2013). Utility Scale Energy Storage Systems – Benefits, Applications, and Technologies. State Utility Forecasting Group.

      [11] Abbas A. Akhil, Georgianne Huff, Aileen B. Currier, Benjamin C. Kaun, Dan M. Rastler, Stella Bingqing Chen, Andrew L. Cotter, Dale T. Bradshaw, and William D. Gauntlett. (2013). DOE/EPRI 2013 Electricity Storage Handbook in Collaboration with NRECA (SAND2013-5131). Sandia National Laboratories.

      [12] YevgenBarsukov&Jinrong Qian. (2013) Battery Power Management for Portable Devices. Massachusetts, Artech House.

      [13] Sang-Il, Seo. (2014). Optimization of performance and lifetime management for generation facility of Self-generated island, Journal of Electrical World Monthly Magazine Vol. 448, No. 4, pp.36~40.Retrieved from http://www.kea.kr/elec_journal/2014_4/5.pdf

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  • How to Cite

    Yeo, S.-H., Park, J.-W., Na, J.-S., Park, S.-M., & Park, S.-J. (2018). A Study on Design of Microgrid(MG) Optimal Operation Algorithm for Development of Semi-Wheel System. International Journal of Engineering & Technology, 7(3.24), 326-332. https://doi.org/10.14419/ijet.v7i3.24.22679