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Volume 5 Issue 1
Jan.  2018

IEEE/CAA Journal of Automatica Sinica

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    CiteScore: 11.2, Top 5% (Q1)
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Article Contents
Derong Liu, Yancai Xu, Qinglai Wei and Xinliang Liu, "Residential Energy Scheduling for Variable Weather Solar Energy Based on Adaptive Dynamic Programming," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 36-46, Jan. 2018. doi: 10.1109/JAS.2017.7510739
Citation: Derong Liu, Yancai Xu, Qinglai Wei and Xinliang Liu, "Residential Energy Scheduling for Variable Weather Solar Energy Based on Adaptive Dynamic Programming," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 36-46, Jan. 2018. doi: 10.1109/JAS.2017.7510739

Residential Energy Scheduling for Variable Weather Solar Energy Based on Adaptive Dynamic Programming

doi: 10.1109/JAS.2017.7510739
Funds:

the National Natural Science Foundation of China 61533017

the National Natural Science Foundation of China U1501251

the National Natural Science Foundation of China 61374105

the National Natural Science Foundation of China 61722312

More Information
  • The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable energy resources, are combined together as a nonlinear, time-varying, indefinite and complex system, which is difficult to manage or optimize. Many nations have already applied the residential real-time pricing to balance the burden on their grid. In order to enhance electricity efficiency of the residential micro grid, this paper presents an action dependent heuristic dynamic programming (ADHDP) method to solve the residential energy scheduling problem. The highlights of this paper are listed below. First, the weather-type classification is adopted to establish three types of programming models based on the features of the solar energy. In addition, the priorities of different energy resources are set to reduce the loss of electrical energy transmissions. Second, three ADHDP-based neural networks, which can update themselves during applications, are designed to manage the flows of electricity. Third, simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process. The comparison with the particle swarm optimization algorithm further proves that the present method has a promising effect on energy management to save cost.

     

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