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Behavioral Economics Optimized Renewable Power Grid: A Case Study of Household Energy Storage

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  • Shengyu Tao

    (Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200438, China
    Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China
    Institute for Six-Sector Economy, Fudan University, Shanghai 200433, China)

  • Yiqiang Zhang

    (Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200438, China
    Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China)

  • Meng Yuan

    (Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200438, China
    Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China)

  • Ruixiang Zhang

    (Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200438, China
    Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China)

  • Zhongyan Xu

    (Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200438, China
    Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China)

  • Yaojie Sun

    (Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200438, China
    Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China
    Institute for Six-Sector Economy, Fudan University, Shanghai 200433, China)

Abstract

Power systems optimization is generally subject to the compromise between performance and cost. The 2021 Texas grid outage illustrates the worldwide dangers for the regional-centralized power grid, with comparable advantages to safety and flexibility for the distributed energy system. The storage of household batteries helps balance grid load and increase system stability and flexibility. However, household storage battery is still not widely used today because of its high costs. Currently, research on increasing household battery storage applicability is focused largely on optimizing economic strategies, such as configuration, dispatching and subsidy policies, which rely substantially more on technologies and financial perspectives. Consumers are not ‘rational’ individuals, and non-economic incentives can affect their decisions without raising prices. This paper consequently proposes to encourage users to acquire household battery storage to increase efficiency of power dispatching and economic advantages based on behavioral economics. In this paper, an empirical research builds upon the utility model of behavioral economics incentives and purchase willingness. Moreover, the multi-objective genetic algorithm is utilized to optimize the dispatching of household battery storage by using grid variance and user revenues as optimizing goals. The results of this paper show that the behavioral economics incentive improves intention to buy the household battery energy storage by 10.7% without raising subsidies. By improving the energy dispatching strategy, peak-load shifting performance and user revenues are improved by 4.2% and 10.6%, respectively.

Suggested Citation

  • Shengyu Tao & Yiqiang Zhang & Meng Yuan & Ruixiang Zhang & Zhongyan Xu & Yaojie Sun, 2021. "Behavioral Economics Optimized Renewable Power Grid: A Case Study of Household Energy Storage," Energies, MDPI, vol. 14(14), pages 1-17, July.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4154-:d:591612
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    References listed on IDEAS

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    1. Hunt Allcott & Dmitry Taubinsky, 2015. "Evaluating Behaviorally Motivated Policy: Experimental Evidence from the Lightbulb Market," American Economic Review, American Economic Association, vol. 105(8), pages 2501-2538, August.
    2. Ibrahim Alsaidan & Abdulaziz Alanazi & Wenzhong Gao & Hongyu Wu & Amin Khodaei, 2017. "State-Of-The-Art in Microgrid-Integrated Distributed Energy Storage Sizing," Energies, MDPI, vol. 10(9), pages 1-14, September.
    3. Patrick Moriarty & Damon Honnery, 2020. "Feasibility of a 100% Global Renewable Energy System," Energies, MDPI, vol. 13(21), pages 1-16, October.
    4. Zhang, Yijie & Ma, Tao & Elia Campana, Pietro & Yamaguchi, Yohei & Dai, Yanjun, 2020. "A techno-economic sizing method for grid-connected household photovoltaic battery systems," Applied Energy, Elsevier, vol. 269(C).
    5. Dietrich, Andreas & Weber, Christoph, 2018. "What drives profitability of grid-connected residential PV storage systems? A closer look with focus on Germany," Energy Economics, Elsevier, vol. 74(C), pages 399-416.
    6. Nicholas Barberis, 2018. "Richard Thaler and the Rise of Behavioral Economics," Scandinavian Journal of Economics, Wiley Blackwell, vol. 120(3), pages 661-684, July.
    7. Holger C. Hesse & Michael Schimpe & Daniel Kucevic & Andreas Jossen, 2017. "Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids," Energies, MDPI, vol. 10(12), pages 1-42, December.
    8. Paulo Rotella Junior & Luiz Célio Souza Rocha & Sandra Naomi Morioka & Ivan Bolis & Gianfranco Chicco & Andrea Mazza & Karel Janda, 2021. "Economic Analysis of the Investments in Battery Energy Storage Systems: Review and Current Perspectives," Energies, MDPI, vol. 14(9), pages 1-29, April.
    9. Corentin Jankowiak & Aggelos Zacharopoulos & Caterina Brandoni & Patrick Keatley & Paul MacArtain & Neil Hewitt, 2019. "The Role of Domestic Integrated Battery Energy Storage Systems for Electricity Network Performance Enhancement," Energies, MDPI, vol. 12(20), pages 1-27, October.
    10. Bryan Bollinger & Kenneth Gillingham, 2012. "Peer Effects in the Diffusion of Solar Photovoltaic Panels," Marketing Science, INFORMS, vol. 31(6), pages 900-912, November.
    11. Thomas Leonard, 2008. "Richard H. Thaler, Cass R. Sunstein, Nudge: Improving decisions about health, wealth, and happiness," Constitutional Political Economy, Springer, vol. 19(4), pages 356-360, December.
    12. Schopfer, S. & Tiefenbeck, V. & Staake, T., 2018. "Economic assessment of photovoltaic battery systems based on household load profiles," Applied Energy, Elsevier, vol. 223(C), pages 229-248.
    13. Korcaj, Liridon & Hahnel, Ulf J.J. & Spada, Hans, 2015. "Intentions to adopt photovoltaic systems depend on homeowners' expected personal gains and behavior of peers," Renewable Energy, Elsevier, vol. 75(C), pages 407-415.
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    Cited by:

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    2. Ismail Marouani & Tawfik Guesmi & Badr M. Alshammari & Khalid Alqunun & Ahmed S. Alshammari & Saleh Albadran & Hsan Hadj Abdallah & Salem Rahmani, 2023. "Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods," Sustainability, MDPI, vol. 15(12), pages 1-58, June.
    3. Imaduddin Ahmed & Priti Parikh & Parfait Munezero & Graham Sianjase & D’Maris Coffman, 2023. "The impact of power outages on households in Zambia," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 40(3), pages 835-867, October.

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