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Flexible investment under uncertainty in smart distribution networks with demand side response: Assessment framework and practical implementation

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  • Schachter, Jonathan A.
  • Mancarella, Pierluigi
  • Moriarty, John
  • Shaw, Rita

Abstract

Classical deterministic models applied to investment valuation in distribution networks may not be adequate for a range of real-world decision-making scenarios as they effectively ignore the uncertainty found in the most important variables driving network planning (e.g., load growth). As greater uncertainty is expected from growing distributed energy resources in distribution networks, there is an increasing risk of investing in too much or too little network capacity and hence causing the stranding and inefficient use of network assets; these costs are then passed on to the end-user. An alternative emerging solution in the context of smart grid development is to release untapped network capacity through Demand-Side Response (DSR). However, to date there is no approach able to quantify the value of ‘smart’ DSR solutions against ‘conventional’ asset-heavy investments. On these premises, this paper presents a general real options framework and a novel probabilistic tool for the economic assessment of DSR for smart distribution network planning under uncertainty, which allows the modeling and comparison of multiple investment strategies, including DSR and capacity reinforcements, based on different cost and risk metrics.

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  • Schachter, Jonathan A. & Mancarella, Pierluigi & Moriarty, John & Shaw, Rita, 2016. "Flexible investment under uncertainty in smart distribution networks with demand side response: Assessment framework and practical implementation," Energy Policy, Elsevier, vol. 97(C), pages 439-449.
  • Handle: RePEc:eee:enepol:v:97:y:2016:i:c:p:439-449
    DOI: 10.1016/j.enpol.2016.07.038
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    5. Marie-Cécile Alvarez-Herault & Jean-Pierre Dib & Oana Ionescu & Bertrand Raison, 2022. "Long-Term Planning of Electrical Distribution Grids: How Load Uncertainty and Flexibility Affect the Investment Timing," Energies, MDPI, vol. 15(16), pages 1-27, August.
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    10. Heinrich, Carsten & Ziras, Charalampos & Syrri, Angeliki L.A. & Bindner, Henrik W., 2020. "EcoGrid 2.0: A large-scale field trial of a local flexibility market," Applied Energy, Elsevier, vol. 261(C).
    11. Fernando Salinas-Herrera & Ali Moeini & Innocent Kamwa, 2022. "Survey of Simulation Tools to Assess Techno-Economic Benefits of Smart Grid Technology in Integrated T&D Systems," Sustainability, MDPI, vol. 14(13), pages 1-36, July.
    12. Lasantha Meegahapola & Pierluigi Mancarella & Damian Flynn & Rodrigo Moreno, 2021. "Power system stability in the transition to a low carbon grid: A techno‐economic perspective on challenges and opportunities," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    13. Alirezazadeh, Atefeh & Rashidinejad, Masoud & Abdollahi, Amir & Afzali, Peyman & Bakhshai, Alireza, 2020. "A new flexible model for generation scheduling in a smart grid," Energy, Elsevier, vol. 191(C).
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