Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming
| Autor: | Xinliang Liu, Qinglai Wei, Yancai Xu, Derong Liu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Job shop scheduling Computer science business.industry Energy management 02 engineering and technology Solar energy Grid Renewable energy Scheduling (computing) 020901 industrial engineering & automation Smart grid Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Programming paradigm 020201 artificial intelligence & image processing business Information Systems |
| Zdroj: | IEEE/CAA Journal of Automatica Sinica. 5:36-46 |
| ISSN: | 2329-9274 2329-9266 |
| Popis: | 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 U+0028 ADHDP U+0029 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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