Abstract
Distributed energy generation will dramatically grow and renewable energies together with information and communication technologies (ICT) will promote Smart Homes, Buildings and Industries. These systems will not only generate electricity to face both residential and industrial consumption on site but they will also use information obtained through smart sensors in order to manage energy flows and provide a reliable energy management.
This chapter will provide a method based on the self-consumption and self-sufficiency curves to illustrate the role that self-consumption photovoltaic systems, also called PV rooftops, may play in Smart Energy Systems. The aforementioned method is based on a matching analysis between load and photovoltaic generation profiles.
Moreover, as this analysis is based on monitored data provided by smart sensors, the influence of the recording interval when estimating the self-consumed energy will be shown, providing a proper performance analysis. Although the analysis focuses on the residential sector, the performance of a PV rooftop installed in an industrial refrigeration company will be also illustrated. In the residential sector, direct photovoltaic self-consumption (i.e., without storage systems) may cover more than 40% of the electricity load consumption. This percentage may be considerably increased with the use of different strategies such as demand side management and batteries, which may be a path toward nearly Zero Energy Buildings.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
F. Almonacid et al., Estimation of the energy of a PV generator using artificial neural network. Renew. Energy 34(12) (2009). https://doi.org/10.1016/j.renene.2009.05.020
F. Almonacid et al., Calculation of the energy provided by a PV generator. Comparative study: conventional methods vs. artificial neural networks. Energy 36(1) (2011). https://doi.org/10.1016/j.energy.2010.10.028
A.Q. Al-Shetwi et al., Grid-connected renewable energy sources: review of the recent integration requirements and control methods. J. Clean. Prod. 253, 119831 (2020). https://doi.org/10.1016/j.jclepro.2019.119831
A.J. Martínez-Calahorro et al., Photovoltaic self-consumption in industrial cooling and refrigeration, Electronics (Switzerland), 9(12), 1–21 (2020) https://doi.org/10.3390/electronics9122204
A. Ayala-Gilardón, M. Sidrach-De-Cardona, L. Mora-López, Influence of time resolution in the estimation of self-consumption and self-sufficiency of photovoltaic facilities. Appl. Energy (2018). https://doi.org/10.1016/j.apenergy.2018.08.072
T. Beck et al., Assessing the influence of the temporal resolution of electrical load and PV generation profiles on self-consumption and sizing of PV-battery systems. Appl. Energy (2016). https://doi.org/10.1016/j.apenergy.2016.04.050
BP p.l.c, Statistical Review of World Energy 2021, 70th edn. (BP, 2021)
C. Bucher, J. Betcke, G. Andersson, Effects of variation of temporal resolution on domestic power and solar irradiance measurements, in 2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013, (April) (2013). https://doi.org/10.1109/PTC.2013.6652217
Q. Cetina, R.A.J. Roscoe, P.S. Wright, Challenges for smart electricity meters due to dynamic power quality conditions of the grid: a review, in AMPS 2017 – IEEE International Workshop on Applied Measurements for Power Systems, Proceedings, pp. 7–12 (2017). https://doi.org/10.1109/AMPS.2017.8078345.
L.W. Chong et al., An optimal control strategy for standalone PV system with battery-supercapacitor hybrid energy storage system. J. Power Sources 331, 553–565 (2016). https://doi.org/10.1016/j.jpowsour.2016.09.061
A. Ciocia et al., Self-consumption and self-sufficiency in photovoltaic systems: effect of grid limitation and storage installation. Energies 14(6) (2021). https://doi.org/10.3390/en14061591
DNV GL, Energy Transition Outlook 2020. Power Supply and Use. A global and regional forecast to 2050, p. 84 (2020)
R. Dufo-López, J.M. Lujano-Rojas, J.L. Bernal-Agustín, Comparison of different lead-acid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems. Appl. Energy 115, 242–253 (2014). https://doi.org/10.1016/j.apenergy.2013.11.021
JRC, 2030 climate & energy framework, https://ec.europa.eu/clima/eu-action/climate-strategies-targets/2030-climate-energy-framework_en#tab-0-0. Available at: https://ec.europa.eu/clima/eu-action/climate-strategies-targets/2030-climate-energy-framework_en#tab-0-0 (Accessed: 15 June 2021). (2021)
M. Farhadi, O.A. Mohammed, Performance enhancement of actively controlled hybrid DC microgrid incorporating pulsed load. IEEE Trans. Ind. Appl. 51(5), 3570–3578 (2015). https://doi.org/10.1109/TIA.2015.2420630
A.M. Gee, F.V.P. Robinson, R.W. Dunn, Analysis of battery lifetime extension in a small-scale wind-energy system using supercapacitors. IEEE Trans. Energy Convers. 28(1), 24–33 (2013). https://doi.org/10.1109/TEC.2012.2228195
M. Gustafsson, Challenges for decision makers when feed-in tariffs or net metering schemes change to incentives dependent on a high share of self-consumed electricity, in 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC), pp. 2025–2030 (2017). https://doi.org/10.1109/PVSC.2017.8366092
N. Haegel, S. Kurtz, Global progress toward renewable electricity: tracking the role of solar. IEEE J. Photovoltaics, 1–8 (2021). https://doi.org/10.1109/jphotov.2021.3104149
J.C. Hernández et al., Primary frequency control and dynamic grid support for vehicle-to-grid in transmission systems. Int. J. Electr. Power Energy Syst. 100, 152–166 (2018). https://doi.org/10.1016/j.ijepes.2018.02.019. (July 2017)
J.C. Hernández, F. Sanchez-Sutil, F.J. Muñoz-Rodríguez, Design criteria for the optimal sizing of a hybrid energy storage system in PV household-prosumers to maximize self-consumption and self-sufficiency. Energy 186 (2019). https://doi.org/10.1016/j.energy.2019.07.157
L. Hontoria et al., An improved method for obtaining solar irradiation data at temporal high-resolution. Sustainability (Switzerland) 11(19) (2019). https://doi.org/10.3390/su11195233
J. Hu, A. Lanzon, Distributed finite-time consensus control for heterogeneous battery energy storage systems in droop-controlled microgrids. IEEE Trans. Smart Grid 10(5), 4751–4761 (2018). https://doi.org/10.1109/TSG.2018.2868112
IEA, Global Energy Review 2021. IEA Publications. Available at: https://iea.blob.core.windows.net/assets/d0031107-401d-4a2f-a48b-9eed19457335/GlobalEnergyReview2021.pdf (2021)
IIRENA, Renewable capacity statistics 2021. Abu Dhabi (2021a)
IRENA et al., Tracking SDG. The energy progress report 2021. Washington. Available at: http://www.worldbank.org (2021)
IRENA, JRC, Benchmarking. Scenario Comparisons. Key indicators for the Clean Energy Transition. Brussels (2021)
RENA, World energy transitions outlook:k: 1.5°C Pathway, International Renewable Energy Agency. Abu Dhabi. Available at: https://irena.org/publications/2021/March/World-Energy-Transitions-Outlook (2021b)
G. Jimenez-Castillo, et al., Smart meters for the evaluation of self-consumption in zero energy buildings, in 2019 10th International Renewable Energy Congress, IREC 2019, (Irec), pp. 0–5 (2019). https://doi.org/10.1109/IREC.2019.8754609
G. Jiménez-Castillo, F.J. Muñoz-Rodríguez, A.J. Martinez-Calahorro, G. Tina, C. Rus-Casas, Impacts of array orientation and tilt angles for photovoltaic self-sufficiency and self-consumption. Electronics (Switzerland) (2020a)
G. Jiménez-Castillo et al., A new approach based on economic profitability to sizing the photovoltaic generator in self-consumption systems without storage. Renew. Energy 148, 1017–1033 (2020b). https://doi.org/10.1016/J.RENENE.2019.10.086
G. Jiménez-Castillo et al., Effects of smart meter time resolution when analyzing photovoltaic self-consumption system on a daily and annual basis. Renew. Energy 164, 889–896 (2021). https://doi.org/10.1016/j.renene.2020.09.096
M. Jiménez-Torres et al., The importance of accurate solar data for designing solar photovoltaic systems-case studies in Spain. Sustainability (2017). https://doi.org/10.3390/su9020247
R. Luthander et al., Graphical analysis of photovoltaic generation and load matching in buildings: a novel way of studying self-consumption and self-sufficiency. Appl. Energy 250, 748–759 (2019a). https://doi.org/10.1016/j.apenergy.2019.05.058
R. Luthander et al., Graphical analysis of photovoltaic generation and load matching in buildings: a novel way of studying self-consumption and self-sufficiency. Appl. Energy 250, 748–759 (2019b) Disponible en: https://www.sciencedirect.com/science/article/pii/S0306261919309110. Accedido: 11 de abril de 2020
A. Mahmoudzadeh Andwari et al., A review of Battery Electric Vehicle technology and readiness levels. Renew. Sust. Energ. Rev. 78, 414–430 (2017). https://doi.org/10.1016/j.rser.2017.03.138. (October 2015)
G. Masson, J.I. Briano, M.J. Baez, Review and Analysis of PV Self-Consumption Policies, report no. T1-28:2016 (2016). Disponible en: http://iea-pvps.org/index.php?id=353
F.J. Muñoz-Rodríguez et al., A new tool to analysing photovoltaic self-consumption systems with batteries. Renew. Energy 168, 1327–1343 (2021). https://doi.org/10.1016/j.renene.2020.12.060
C.R. Osterwald, Translation of device performance measurements to reference conditions. Solar Cells 18(3–4), 269–279 (1986)
E. Proedrou, A comprehensive review of residential electricity load profile models. IEEE Access 9, 12114–12133 (2021). https://doi.org/10.1109/ACCESS.2021.3050074
I. Richardson et al., Domestic electricity use: a high-resolution energy demand model. Energ. Buildings 42(10), 1878–1887 (2010). https://doi.org/10.1016/j.enbuild.2010.05.023
P. Rodrigo et al., A new method for estimating angular, spectral and low irradiance losses in photovoltaic systems using an artificial neural network model in combination with the Osterwald model. Sol. Energy Mater. Sol. Cells (2011). https://doi.org/10.1016/j.solmat.2011.09.054
C. Rus-Casas et al., Classification of methods for annual energy harvesting calculations of photovoltaic generators. Energy Convers. Manag. 78 (2014). https://doi.org/10.1016/j.enconman.2013.11.006
M. Shabani et al., Techno-economic impacts of battery performance models and control strategies on optimal design of a grid-connected PV system. Energy Convers. Manag. 245, 114617 (2021). https://doi.org/10.1016/j.enconman.2021.114617
SolarPower Europe Global Market Outlook for Solar Power 2021–2025 (2021)
SolarPower Europe, European Market Outlook For Residential Battery Storage, European Market. Available at: https://www.ofgem.gov.uk/electricity/wholesale-market/european-market. (2020)
B. Stephen et al., Enhanced load profiling for residential network customers. IEEE Trans. Power Delivery 29(1), 88–96 (2014). https://doi.org/10.1109/TPWRD.2013.2287032
S. Steuwer, J. Volt, V. Dorizas, Q. Jossen, J. Pestiaux, P. M. Sonvilla, E. K. Velten, M. Davis, M. Hirschnitz-Garbers, K. Umpfenbach, J. Tröltzsch, Lessons learned to inform integrated approaches for the renovation and modernisation of the built environment. Final report (2020)
D.L. Talavera et al., A new approach to sizing the photovoltaic generator in self-consumption systems based on cost–competitiveness, maximizing direct self-consumption. Renew. Energy 130, 1021–1035 (2019). https://doi.org/10.1016/j.renene.2018.06.088
Y. Wang et al., Clustering of electricity consumption behavior dynamics toward big data applications. IEEE Trans. Smart Grid 7(5), 2437–2447 (2016a). https://doi.org/10.1109/TSG.2016.2548565
Y. Wang et al., Clustering of electricity consumption behavior dynamics toward big data applications. IEEE Trans. Smart Grid 7(5), 2437–2447 (2016b). https://doi.org/10.1109/TSG.2016.2548565
J. Widén, E. Wäckelgård, P.D. Lund, Options for improving the load matching capability of distributed photovoltaics: methodology and application to high-latitude data. Sol. Energy 83(11), 1953–1966 (2009). https://doi.org/10.1016/j.solener.2009.07.007
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this entry
Cite this entry
Muñoz-Rodríguez, F.J., Jiménez-Castillo, G., Rus-Casas, C. (2022). Photovoltaic Rooftops in Smart Energy Systems. In: Fathi, M., Zio, E., Pardalos, P.M. (eds) Handbook of Smart Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-72322-4_87-1
Download citation
DOI: https://doi.org/10.1007/978-3-030-72322-4_87-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72322-4
Online ISBN: 978-3-030-72322-4
eBook Packages: Springer Reference Economics and FinanceReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences