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Energy Management in Prosumer Communities: A Coordinated Approach

Author

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  • Rodrigo Verschae

    (Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan)

  • Takekazu Kato

    (Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan)

  • Takashi Matsuyama

    (Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan)

Abstract

The introduction of uncontrollable renewable energy is having a positive impact on our health, the climate, and the economy, but it is also pushing the limits of the power system. The main reason for this is that, in any power system, the generation and consumption must match each other at all times. Thus, if we want to further introduce uncontrollable generation, we need a large ability to manage the demand. However, the ability to control the power consumption of existing demand management approaches is limited, and most of these approaches cannot contribute to the introduction of reneweables, because they do not consider distributed uncontrolled consumption and generation in the control. Furthermore, these methods do not allow users to exchange or jointly manage their power generation and consumption. In this context, we propose an augmented energy management model for prosumers (i.e., producer and consumer). This model considers controlled and uncontrolled generation and consumption, as well as the prosumer’s ability (i) to plan the intended power consumption; and (ii) to manage real-time deviations from the intended consumption. We apply this model to the energy management of prosumer communities, by allowing the prosumers to coordinate their power consumption plan, to manage the deviations from the intended consumption, and to help each other by compensating deviations. The proposed approach seeks to enhance the power system, and to enable a prosumer society that takes account social and environmental issues, as well as each prosumer’s quality of life.

Suggested Citation

  • Rodrigo Verschae & Takekazu Kato & Takashi Matsuyama, 2016. "Energy Management in Prosumer Communities: A Coordinated Approach," Energies, MDPI, vol. 9(7), pages 1-27, July.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:7:p:562-:d:74303
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    References listed on IDEAS

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    1. Goto, Mika & Inoue, Tomohiro & Sueyoshi, Toshiyuki, 2013. "Structural reform of Japanese electric power industry: Separation between generation and transmission & distribution," Energy Policy, Elsevier, vol. 56(C), pages 186-200.
    2. Nesheiwat, Julia & Cross, Jeffrey S., 2013. "Japan's post-Fukushima reconstruction: A case study for implementation of sustainable energy technologies," Energy Policy, Elsevier, vol. 60(C), pages 509-519.
    3. Stern, David I., 2012. "Modeling international trends in energy efficiency," Energy Economics, Elsevier, vol. 34(6), pages 2200-2208.
    4. Rastegar, Mohammad & Fotuhi-Firuzabad, Mahmud & Aminifar, Farrokh, 2012. "Load commitment in a smart home," Applied Energy, Elsevier, vol. 96(C), pages 45-54.
    5. Patrick L. Combettes & Jean-Christophe Pesquet, 2011. "Proximal Splitting Methods in Signal Processing," Springer Optimization and Its Applications, in: Heinz H. Bauschke & Regina S. Burachik & Patrick L. Combettes & Veit Elser & D. Russell Luke & Henry (ed.), Fixed-Point Algorithms for Inverse Problems in Science and Engineering, chapter 0, pages 185-212, Springer.
    6. Antimo Barbato & Antonio Capone, 2014. "Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey," Energies, MDPI, vol. 7(9), pages 1-38, September.
    7. Verschae, Rodrigo & Kawashima, Hiroaki & Kato, Takekazu & Matsuyama, Takashi, 2016. "Coordinated energy management for inter-community imbalance minimization," Renewable Energy, Elsevier, vol. 87(P2), pages 922-935.
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