IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i3p210-d65963.html
   My bibliography  Save this article

Spatial and Temporal Characteristics of PV Adoption in the UK and Their Implications for the Smart Grid

Author

Listed:
  • J. Richard Snape

    (Institute of Energy and Sustainable Development, De Montfort University, The Gateway, Leicester LE1 9BH, UK)

Abstract

Distributed renewable electricity generators facilitate decarbonising the electricity network, and the smart grid allows higher renewable penetration while improving efficiency. Smart grid scenarios often emphasise localised control, balancing small renewable generation with consumer electricity demand. This research investigates the applicability of proposed decentralised smart grid scenarios utilising a mixed strategy: quantitative analysis of PV adoption data and qualitative policy analysis focusing on policy design, apparent drivers for adoption of the deviation of observed data from the feed-in tariff impact assessment predictions. Analysis reveals that areas of similar installed PV capacity are clustered, indicating a strong dependence on local conditions for PV adoption. Analysing time series of PV adoption finds that it fits neither neo-classical predictions, nor diffusion of innovation S-curves of adoption cleanly. This suggests the influence of external factors on the decision making process. It is shown that clusters of low installed PV capacity coincide with areas of high population density and vice versa , implying that while visions of locally-balanced smart grids may be viable in certain rural and suburban areas, applicability to urban centres may be limited. Taken in combination, the data analysis, policy impact and socio-psychological drivers of adoption demonstrate the need for a multi-disciplinary approach to understanding and modelling the adoption of technology necessary to enable the future smart grid.

Suggested Citation

  • J. Richard Snape, 2016. "Spatial and Temporal Characteristics of PV Adoption in the UK and Their Implications for the Smart Grid," Energies, MDPI, vol. 9(3), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:210-:d:65963
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/3/210/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/3/210/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John A. Norton & Frank M. Bass, 1987. "A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products," Management Science, INFORMS, vol. 33(9), pages 1069-1086, September.
    2. Vijay Mahajan & Eitan Muller & Frank M. Bass, 1995. "Diffusion of New Products: Empirical Generalizations and Managerial Uses," Marketing Science, INFORMS, vol. 14(3_supplem), pages 79-88.
    3. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    4. Foxon, Timothy J., 2013. "Transition pathways for a UK low carbon electricity future," Energy Policy, Elsevier, vol. 52(C), pages 10-24.
    5. Jager, Wander, 2006. "Stimulating the diffusion of photovoltaic systems: A behavioural perspective," Energy Policy, Elsevier, vol. 34(14), pages 1935-1943, September.
    6. Warren Thorngate & Bruce Edmonds, 2013. "Measuring Simulation-Observation Fit: An Introduction to Ordinal Pattern Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(2), pages 1-4.
    7. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    8. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    9. Balcombe, Paul & Rigby, Dan & Azapagic, Adisa, 2015. "Environmental impacts of microgeneration: Integrating solar PV, Stirling engine CHP and battery storage," Applied Energy, Elsevier, vol. 139(C), pages 245-259.
    10. Balta-Ozkan, Nazmiye & Yildirim, Julide & Connor, Peter M., 2015. "Regional distribution of photovoltaic deployment in the UK and its determinants: A spatial econometric approach," Energy Economics, Elsevier, vol. 51(C), pages 417-429.
    11. Balta-Ozkan, Nazmiye & Watson, Tom & Mocca, Elisabetta, 2015. "Spatially uneven development and low carbon transitions: Insights from urban and regional planning," Energy Policy, Elsevier, vol. 85(C), pages 500-510.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Baigali Erdenebat & Davaanyam Buyankhishig & Sergelen Byambaa & Naomitsu Urasaki, 2023. "A Study of Grid-Connected Residential PV-Battery Systems in Mongolia," Energies, MDPI, vol. 16(10), pages 1-14, May.
    2. Yang, Ying & Campana, Pietro Elia & Stridh, Bengt & Yan, Jinyue, 2020. "Potential analysis of roof-mounted solar photovoltaics in Sweden," Applied Energy, Elsevier, vol. 279(C).
    3. Lobaccaro, G. & Croce, S. & Lindkvist, C. & Munari Probst, M.C. & Scognamiglio, A. & Dahlberg, J. & Lundgren, M. & Wall, M., 2019. "A cross-country perspective on solar energy in urban planning: Lessons learned from international case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 209-237.
    4. Müller, Jonas & Trutnevyte, Evelina, 2020. "Spatial projections of solar PV installations at subnational level: Accuracy testing of regression models," Applied Energy, Elsevier, vol. 265(C).
    5. Bing Wang & Hua-Nan Li & Xiao-Chen Yuan & Zhen-Ming Sun, 2017. "Energy Poverty in China: A Dynamic Analysis Based on a Hybrid Panel Data Decision Model," Energies, MDPI, vol. 10(12), pages 1-14, November.
    6. Kurdi, Yumna & Alkhatatbeh, Baraa J. & Asadi, Somayeh & Jebelli, Houtan, 2022. "A decision-making design framework for the integration of PV systems in the urban energy planning process," Renewable Energy, Elsevier, vol. 197(C), pages 288-304.
    7. Moon-Hyun Kim & Tae-Hyoung Tommy Gim, 2021. "Spatial Characteristics of the Diffusion of Residential Solar Photovoltaics in Urban Areas: A Case of Seoul, South Korea," IJERPH, MDPI, vol. 18(2), pages 1-16, January.
    8. Takanobu Kosugi & Yoshiyuki Shimoda & Takayuki Tashiro, 2019. "Neighborhood influences on the diffusion of residential photovoltaic systems in Kyoto City, Japan," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 21(4), pages 477-505, October.
    9. Candelise, Chiara & Westacott, Paul, 2017. "Can integration of PV within UK electricity network be improved? A GIS based assessment of storage," Energy Policy, Elsevier, vol. 109(C), pages 694-703.
    10. Noemi Munkacsi & Krushna Mahapatra, 2019. "Communication and Household Adoption of Heating Products in Hungary," Energies, MDPI, vol. 12(2), pages 1-22, January.
    11. Alin Lin & Ming Lu & Pingjun Sun, 2018. "The Influence of Local Environmental, Economic and Social Variables on the Spatial Distribution of Photovoltaic Applications across China’s Urban Areas," Energies, MDPI, vol. 11(8), pages 1-14, July.
    12. Balta-Ozkan, Nazmiye & Yildirim, Julide & Connor, Peter M. & Truckell, Ian & Hart, Phil, 2021. "Energy transition at local level: Analyzing the role of peer effects and socio-economic factors on UK solar photovoltaic deployment," Energy Policy, Elsevier, vol. 148(PB).
    13. Anita M. Bunea & Mariangela Guidolin & Piero Manfredi & Pompeo Della Posta, 2022. "Diffusion of Solar PV Energy in the UK: A Comparison of Sectoral Patterns," Forecasting, MDPI, vol. 4(2), pages 1-21, April.
    14. Hattam, Laura & Greetham, Danica Vukadinović, 2018. "An innovation diffusion model of a local electricity network that is influenced by internal and external factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 353-365.
    15. Stewart, Fraser, 2022. "Friends with benefits: How income and peer diffusion combine to create an inequality “trap” in the uptake of low-carbon technologies," Energy Policy, Elsevier, vol. 163(C).
    16. Collier, Samuel H.C. & House, Jo I. & Connor, Peter M. & Harris, Richard, 2023. "Distributed local energy: Assessing the determinants of domestic-scale solar photovoltaic uptake at the local level across England and Wales," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
    2. Herbert Dawid & Reinhold Decker & Thomas Hermann & Hermann Jahnke & Wilhelm Klat & Rolf König & Christian Stummer, 2017. "Management science in the era of smart consumer products: challenges and research perspectives," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(1), pages 203-230, March.
    3. Mihir Dash & Anuloma Tripathy & D. Shobha & Greeshma Ramesh & Abhiyank Verma & K. Sriharsha, 2016. "Comparison of Diffusion in Telecommunications in the BRICS Economies," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 5(4), pages 229-235, November.
    4. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
    5. Barnes, Belinda & Southwell, Darren & Bruce, Sarah & Woodhams, Felicity, 2014. "Additionality, common practice and incentive schemes for the uptake of innovations," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 43-61.
    6. Shi, Xiaohui & Chumnumpan, Pattarin, 2019. "Modelling market dynamics of multi-brand and multi-generational products," European Journal of Operational Research, Elsevier, vol. 279(1), pages 199-210.
    7. Constanza Fosco, 2012. "Spatial Difusion and Commuting Flows," Documentos de Trabajo en Economia y Ciencia Regional 30, Universidad Catolica del Norte, Chile, Department of Economics, revised Sep 2012.
    8. Toka, Agorasti & Iakovou, Eleftherios & Vlachos, Dimitrios & Tsolakis, Naoum & Grigoriadou, Anastasia-Loukia, 2014. "Managing the diffusion of biomass in the residential energy sector: An illustrative real-world case study," Applied Energy, Elsevier, vol. 129(C), pages 56-69.
    9. Krishnan, Trichy V. & Feng, Shanfei & Jain, Dipak C., 2023. "Peak sales time prediction in new product sales: Can a product manager rely on it?," Journal of Business Research, Elsevier, vol. 165(C).
    10. Brito, Thiago Luis Felipe & Islam, Towhidul & Stettler, Marc & Mouette, Dominique & Meade, Nigel & Moutinho dos Santos, Edmilson, 2019. "Transitions between technological generations of alternative fuel vehicles in Brazil," Energy Policy, Elsevier, vol. 134(C).
    11. Kivi, Antero & Smura, Timo & Töyli, Juuso, 2012. "Technology product evolution and the diffusion of new product features," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 107-126.
    12. Kurdgelashvili, Lado & Shih, Cheng-Hao & Yang, Fan & Garg, Mehul, 2019. "An empirical analysis of county-level residential PV adoption in California," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 321-333.
    13. Orbach Yair & Fruchter Gila E., 2010. "A Utility-Based Diffusion Model Applied to the Digital Camera Case," Review of Marketing Science, De Gruyter, vol. 8(1), pages 1-28, June.
    14. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    15. Franses, Ph.H.B.F., 2009. "Forecasting Sales," Econometric Institute Research Papers EI 2009-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    16. Michelle M.H. Şeref & Janice E. Carrillo & Arda Yenipazarli, 2016. "Multi-generation pricing and timing decisions in new product development," International Journal of Production Research, Taylor & Francis Journals, vol. 54(7), pages 1919-1937, April.
    17. Rixen, Martin & Weigand, Jürgen, 2014. "Agent-based simulation of policy induced diffusion of smart meters," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 153-167.
    18. Hongmin Li, 2020. "Optimal Pricing Under Diffusion-Choice Models," Operations Research, INFORMS, vol. 68(1), pages 115-133, January.
    19. Sung Yong Chun & Minhi Hahn, 2008. "A diffusion model for products with indirect network externalities," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 357-370.
    20. Collier, Samuel H.C. & House, Jo I. & Connor, Peter M. & Harris, Richard, 2023. "Distributed local energy: Assessing the determinants of domestic-scale solar photovoltaic uptake at the local level across England and Wales," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:210-:d:65963. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.