Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management

The employment of intelligent energy management systems likely allows reducing consumptions and thus saving money for consumers. The residential load demand must be met, and some advantages can be obtained if specific optimization policies are taken. With an efficient use of renewable sources and po...

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Veröffentlicht in:Cognitive computation Jg. 5; H. 2; S. 264 - 277
Hauptverfasser: Boaro, Matteo, Fuselli, Danilo, Angelis, Francesco De, Liu, Derong, Wei, Qinglai, Piazza, Francesco
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer-Verlag 01.06.2013
Springer Nature B.V
Schlagworte:
Adaptive algorithms
Adaptive systems
Alternative energy sources
Approximation
Artificial Intelligence
Biomedical and Life Sciences
Biomedicine
Computation by Abstract Devices
Computational Biology/Bioinformatics
Dynamic programming
Electric power demand
Electrical loads
Energy costs
Energy management systems
Heuristic
Linear programming
Neural networks
Neurosciences
Optimization
Optimization techniques
Policies
Power
Power management
Renewable energy
Renewable resources
Scheduling
Smart grid
Training
Turbines
Velocity
Energy scheduling
Approximate dynamic programming
Adaptive dynamic programming
Neural networks
Battery management
ISSN:1866-9956, 1866-9964
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Zusammenfassung:The employment of intelligent energy management systems likely allows reducing consumptions and thus saving money for consumers. The residential load demand must be met, and some advantages can be obtained if specific optimization policies are taken. With an efficient use of renewable sources and power imported from the grid, an intelligent and adaptive system which manages the battery is able to satisfy the load demand and minimize the entire energy cost related to the scenario under study. In this paper, an adaptive dynamic programming–based algorithm is presented to face dynamic situations, in which some conditions of the environment or habits of customer may vary with time, especially using renewable energy. Based on the idea of smart grid, we propose an intelligent management scheme for renewable resources combined with battery implemented with a faster and simpler scheme of dynamic programming, by considering only one critic network and some optimization policies in order to satisfy the load demand. Since this kind of problem is suitable to avoid the training of an action network, the training loop among the two neural networks is deleted and the training process is greatly simplified. Computer simulations confirm the effectiveness of this self-learning design in a typical residential scenario.
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ISSN:1866-9956
1866-9964
DOI:10.1007/s12559-012-9191-y