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From the grid to the smart grid, topologically

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  • Pagani, Giuliano Andrea
  • Aiello, Marco

Abstract

In its more visionary acceptation, the smart grid is a model of energy management in which the users are engaged in producing energy as well as consuming it, while having information systems fully aware of the energy demand-response of the network and of dynamically varying prices. A natural question is then: to make the smart grid a reality will the distribution grid have to be upgraded? We assume a positive answer to the question and we consider the lower layers of medium and low voltage to be the most affected by the change. In our previous work, we analyzed samples of the Dutch distribution grid (Pagani and Aiello, 2011) and we considered possible evolutions of these using synthetic topologies modeled after studies of complex systems in other technological domains (Pagani and Aiello, 2014). In this paper, we take an extra important step by defining a methodology for evolving any existing physical power grid to a good smart grid model, thus laying the foundations for a decision support system for utilities and governmental organizations. In doing so, we consider several possible evolution strategies and apply them to the Dutch distribution grid. We show how increasing connectivity is beneficial in realizing more efficient and reliable networks. Our proposal is topological in nature, enhanced with economic considerations of the costs of such evolutions in terms of cabling expenses and economic benefits of evolving the grid.

Suggested Citation

  • Pagani, Giuliano Andrea & Aiello, Marco, 2016. "From the grid to the smart grid, topologically," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 160-175.
    • Handle: RePEc:eee:phsmap:v:449:y:2016:i:c:p:160-175
    DOI: 10.1016/j.physa.2015.12.080
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    References listed on IDEAS

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    1. Pagani, Giuliano Andrea & Aiello, Marco, 2014. "Power grid complex network evolutions for the smart grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 248-266.
    2. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    3. Pagani, Giuliano Andrea & Aiello, Marco, 2013. "The Power Grid as a complex network: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(11), pages 2688-2700.
    4. Cossent, Rafael & Gómez, Tomás & Frías, Pablo, 2009. "Towards a future with large penetration of distributed generation: Is the current regulation of electricity distribution ready? Regulatory recommendations under a European perspective," Energy Policy, Elsevier, vol. 37(3), pages 1145-1155, March.
    5. Wissner, Matthias, 2011. "The Smart Grid - A saucerful of secrets?," Applied Energy, Elsevier, vol. 88(7), pages 2509-2518, July.
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    Cited by:

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    3. Lucas Cuadra & Miguel Del Pino & José Carlos Nieto-Borge & Sancho Salcedo-Sanz, 2017. "Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms," Energies, MDPI, vol. 10(8), pages 1-31, July.
    4. Bakar, Nur Najihah Abu & Hassan, Mohammad Yusri & Sulaima, Mohamad Fani & Mohd Nasir, Mohamad Na’im & Khamis, Aziah, 2017. "Microgrid and load shedding scheme during islanded mode: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 161-169.
    5. Zou, Yanli & Wang, Ruirui & Gao, Zheng, 2020. "Improve synchronizability of a power grid through power allocation and topology adjustment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    6. Tio, Adonis E. & Hill, David J. & Ma, Jin, 2020. "Can graph properties determine future grid adequacy for power injection diversity?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    7. Dongxiao Niu & Si Li & Shuyu Dai, 2018. "Comprehensive Evaluation for Operating Efficiency of Electricity Retail Companies Based on the Improved TOPSIS Method and LSSVM Optimized by Modified Ant Colony Algorithm from the View of Sustainable ," Sustainability, MDPI, vol. 10(3), pages 1-26, March.
    8. Ibrahim Abaker Targio Hashem & Raja Sher Afgun Usmani & Mubarak S. Almutairi & Ashraf Osman Ibrahim & Abubakar Zakari & Faiz Alotaibi & Saadat Mehmood Alhashmi & Haruna Chiroma, 2023. "Urban Computing for Sustainable Smart Cities: Recent Advances, Taxonomy, and Open Research Challenges," Sustainability, MDPI, vol. 15(5), pages 1-32, February.

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