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Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management

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Abstract

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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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grants 60904037, 60921061, and 61034002, in part by Beijing Natural Science Foundation under Grant 4102061, and in part by China Postdoctoral Science Foundation under Grant 201104162.

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Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University Politecnica delle Marche, 60131, Ancona, Italy

    Matteo Boaro, Danilo Fuselli, Francesco De Angelis & Francesco Piazza

  2. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Derong Liu & Qinglai Wei

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  1. Matteo Boaro
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  2. Danilo Fuselli
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  3. Francesco De Angelis
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  4. Derong Liu
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  5. Qinglai Wei
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  6. Francesco Piazza
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Correspondence to Derong Liu.

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Boaro, M., Fuselli, D., Angelis, F.D. et al. Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management. Cogn Comput 5, 264–277 (2013). https://doi.org/10.1007/s12559-012-9191-y

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  • DOI: https://doi.org/10.1007/s12559-012-9191-y

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