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Energy Coordinative Optimization of Wind-Storage-Load Microgrids Based on Short-Term Prediction

Author

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  • Changbin Hu

    (College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
    Inverter Technologies Engineering Research Center of Beijing, Beijing 100144, China)

  • Shanna Luo

    (College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
    Inverter Technologies Engineering Research Center of Beijing, Beijing 100144, China)

  • Zhengxi Li

    (College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
    Inverter Technologies Engineering Research Center of Beijing, Beijing 100144, China)

  • Xin Wang

    (College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
    Inverter Technologies Engineering Research Center of Beijing, Beijing 100144, China)

  • Li Sun

    (College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100144, China)

Abstract

According to the topological structure of wind-storage-load complementation microgrids, this paper proposes a method for energy coordinative optimization which focuses on improvement of the economic benefits of microgrids in the prediction framework. First of all, the external characteristic mathematical model of distributed generation (DG) units including wind turbines and storage batteries are established according to the requirements of the actual constraints. Meanwhile, using the minimum consumption costs from the external grid as the objective function, a grey prediction model with residual modification is introduced to output the predictive wind turbine power and load at specific periods. Second, based on the basic framework of receding horizon optimization, an intelligent genetic algorithm (GA) is applied to figure out the optimum solution in the predictive horizon for the complex non-linear coordination control model of microgrids. The optimum results of the GA are compared with the receding solution of mixed integer linear programming (MILP). The obtained results show that the method is a viable approach for energy coordinative optimization of microgrid systems for energy flow and reasonable schedule. The effectiveness and feasibility of the proposed method is verified by examples.

Suggested Citation

  • Changbin Hu & Shanna Luo & Zhengxi Li & Xin Wang & Li Sun, 2015. "Energy Coordinative Optimization of Wind-Storage-Load Microgrids Based on Short-Term Prediction," Energies, MDPI, vol. 8(2), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:2:p:1505-1528:d:45992
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    References listed on IDEAS

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    1. Paiva, J.E. & Carvalho, A.S., 2013. "Controllable hybrid power system based on renewable energy sources for modern electrical grids," Renewable Energy, Elsevier, vol. 53(C), pages 271-279.
    2. Khalid, M. & Savkin, A.V., 2014. "Minimization and control of battery energy storage for wind power smoothing: Aggregated, distributed and semi-distributed storage," Renewable Energy, Elsevier, vol. 64(C), pages 105-112.
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    Cited by:

    1. Bo Hu & Nan Wang & Zaiming Yu & Yunqing Cao & Dongsheng Yang & Li Sun, 2021. "Optimal Operation of Multiple Energy System Based on Multi-Objective Theory and Grey Theory," Energies, MDPI, vol. 15(1), pages 1-21, December.
    2. Andrés Henao-Muñoz & Andrés Saavedra-Montes & Carlos Ramos-Paja, 2018. "Optimal Power Dispatch of Small-Scale Standalone Microgrid Located in Colombian Territory," Energies, MDPI, vol. 11(7), pages 1-20, July.
    3. Alberto Dolara & Francesco Grimaccia & Giulia Magistrati & Gabriele Marchegiani, 2017. "Optimization Models for Islanded Micro-Grids: A Comparative Analysis between Linear Programming and Mixed Integer Programming," Energies, MDPI, vol. 10(2), pages 1-20, February.
    4. Lintao Yang & Honggeng Yang, 2019. "Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting," Energies, MDPI, vol. 12(8), pages 1-23, April.
    5. Ha-Lim Lee & Yeong-Han Chun, 2018. "Using Piecewise Linearization Method to PCS Input/Output-Efficiency Curve for a Stand-Alone Microgrid Unit Commitment," Energies, MDPI, vol. 11(9), pages 1-13, September.

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