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The Value of Investing in Domestic Energy Storage Systems

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

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Abstract

In this paper, we investigate whether investments in battery storage systems, coupled with existing PV plants, are profitable in the phasing out of incentives. In detail, we analyze the investment decision of a household, who has already invested in a PV plant and has to decide whether and when to invest in the adoption of battery storage systems (BSS). We provide a Real Option Model to determine the value of the opportunity to invest and its optimal timing. The existing PV plant gives the household the opportunity to invest in BSS adoption, and this opportunity is analogous to a call option. Our findings show that negative NPV investments may turn to be profitable if the household optimally exercises the option to defer. The greater the volatility of energy prices, the greater the option value to defer, and the greater the opportunity cost of waiting (i.e., the greater the energy prices drift), the smaller the option value to defer.

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Notes

  1. 1.

    Real estate assets, which are powered by RES and generate smaller carbon footprints, are in fact more attractive for prospective homebuyers [24, 25].

  2. 2.

    For larger capacities, BSS can only partially discharge during night and the next-day excess PV power will limit battery charge. Since nowadays energy consumption is particularly high in the evening, when there is not PV power generation, BSS are mostly used to satisfy night-time energy demand [17, 42,43,44,45]. By storing surpluses in PV production during daytime activity, BSS discharge in late afternoon, night, and early morning, when PV generation is insufficient [18].

  3. 3.

    Under the hypothesis that markets are complete, the investment present value coincides with the expected value of discounted cash flows it generates.

  4. 4.

    It can be easily demonstrated that \( \delta \equiv \hat{\mu } - \mu > 0 \) [36, 62]. In addition, \( \hat{\mu } = r + MRP, \) where MRP is market risk premium and \( r - \delta \) is the certainty equivalent rate of return.

  5. 5.

    In Italy, variable rate contracts are based on the National Single Price (PUN), i.e., the average of Zonal Prices in the Day-Ahead Market, weighted for total purchases and net of purchases for Pumped-Storage Units and of purchases by Neighboring Countries’ Zones (http://www.mercatoelettrico.org/en/). Under these contracts, prosumers are price-takers.

  6. 6.

    This installed power can satisfy the average demand of a household of four people.

    (http://www.fotovoltaiconorditalia.it/idee/impianto-fotovoltaico-3-kwdimensioni-rendimenti).

  7. 7.

    On average, in Northern Italy, a 1-KW plant produces about 1100 1500 KWh/year, whereas in the South, due to more favorable weather conditions, the average is 1500–1800 KWh/year (www.fotovoltaicoenergia.com; http://re.jrc.ec.europa.eu/pvgis/).

  8. 8.

    p0 at time t = 0 is calculated as the average of PUN yearly prices in the period January 2016-September 2019 provided by Gestore Mercati Energetici (GME), a company owned by the Ministry of Economy and Finance, which operates power, gas and environmental markets, vested with the organisation and economic management of the wholesale Power Market.

  9. 9.

    http://www.dt.tesoro.it/export/sites/sitodt/modules/documenti_it/debito_pubblico/dati_statistici/Principali_tassi_di_interesse_2015.pdf.

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Authors and Affiliations

  1. Department of Civil, Architectural and Environmental Engineering, University of Padova, Padova, Italy

    Chiara D’Alpaos & Francesca Andreolli

Authors
  1. Chiara D’Alpaos
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  2. Francesca Andreolli
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Correspondence to Chiara D’Alpaos .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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D’Alpaos, C., Andreolli, F. (2020). The Value of Investing in Domestic Energy Storage Systems. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12250. Springer, Cham. https://doi.org/10.1007/978-3-030-58802-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-58802-1_11

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