IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i13p3568-d1696089.html
   My bibliography  Save this article

Techno-Economic Analysis of Non-Wire Alternative (NWA) Portfolios Integrating Energy Storage Systems (ESS) with Photovoltaics (PV) or Demand Response (DR) Resources Across Various Load Profiles

Author

Listed:
  • Juwon Park

    (The School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea)

  • Sung-Kwan Joo

    (The School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea)

Abstract

The Non-Wire Alternative (NWA) approach has gained attention as a strategy to replace or defer traditional grid infrastructure upgrades by leveraging integrated solutions combining Energy Storage Systems (ESSs) with Distributed Energy Resources (DERs). The overall feasibility and economics of distributed flexibility solutions can be enhanced by leveraging the synergies among various DERs for NWA deployment. This study presents the results of a techno-economic analysis of an NWA portfolio that integrates Photovoltaic (PV) generation and Demand Response (DR) resources with ESSs. Three representative load profiles are analyzed under different load growth scenarios: a balanced mix of industrial, commercial, and residential loads; residential-dominant loads; and commercial/industrial-dominant loads. The analysis shows that the combined deployment of PVs and DRs significantly reduces the required ESS capacity. Furthermore, economic analysis based on Benefit–Cost Analysis (BCA) demonstrated that combining ESSs with either PVs or DRs enhances economic efficiency compared with an NWA portfolio that relies on ESSs alone, particularly under low-capacity factor conditions. However, the effectiveness of a DR or PV varies depending on the load profile. DR is less effective when the peak load durations are prolonged, whereas PV offers limited economic benefits under residential loads with the evening peak demand. These techno-economic results highlight the importance of tailoring NWA portfolios to specific load conditions to maximize both technical performance and economic value.

Suggested Citation

  • Juwon Park & Sung-Kwan Joo, 2025. "Techno-Economic Analysis of Non-Wire Alternative (NWA) Portfolios Integrating Energy Storage Systems (ESS) with Photovoltaics (PV) or Demand Response (DR) Resources Across Various Load Profiles," Energies, MDPI, vol. 18(13), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3568-:d:1696089
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/13/3568/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/13/3568/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    2. Guk-Hyun Moon & Rakkyung Ko & Sung-Kwan Joo, 2020. "Integration of Smart Grid Resources into Generation and Transmission Planning Using an Interval-Stochastic Model," Energies, MDPI, vol. 13(7), pages 1-12, April.
    3. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    4. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    5. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    6. Mathiesen, B.V. & Lund, H. & Connolly, D. & Wenzel, H. & Østergaard, P.A. & Möller, B. & Nielsen, S. & Ridjan, I. & Karnøe, P. & Sperling, K. & Hvelplund, F.K., 2015. "Smart Energy Systems for coherent 100% renewable energy and transport solutions," Applied Energy, Elsevier, vol. 145(C), pages 139-154.
    Full references (including those not matched with items on IDEAS)

    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. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    2. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    3. Laslett, Dean & Carter, Craig & Creagh, Chris & Jennings, Philip, 2017. "A large-scale renewable electricity supply system by 2030: Solar, wind, energy efficiency, storage and inertia for the South West Interconnected System (SWIS) in Western Australia," Renewable Energy, Elsevier, vol. 113(C), pages 713-731.
    4. Afroz, Zakia & Urmee, Tania & Shafiullah, G.M. & Higgins, Gary, 2018. "Real-time prediction model for indoor temperature in a commercial building," Applied Energy, Elsevier, vol. 231(C), pages 29-53.
    5. Copp, David A. & Nguyen, Tu A. & Byrne, Raymond H. & Chalamala, Babu R., 2022. "Optimal sizing of distributed energy resources for planning 100% renewable electric power systems," Energy, Elsevier, vol. 239(PE).
    6. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    7. Yeh, Wei-Chang & He, Min-Fan & Huang, Chia-Ling & Tan, Shi-Yi & Zhang, Xianyong & Huang, Yaohong & Li, Li, 2020. "New genetic algorithm for economic dispatch of stand-alone three-modular microgrid in DongAo Island," Applied Energy, Elsevier, vol. 263(C).
    8. Dezhou Kong & Jianru Jing & Tingyue Gu & Xuanyue Wei & Xingning Sa & Yimin Yang & Zhiang Zhang, 2023. "Theoretical Analysis of Integrated Community Energy Systems (ICES) Considering Integrated Demand Response (IDR): A Review of the System Modelling and Optimization," Energies, MDPI, vol. 16(10), pages 1-22, May.
    9. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
    10. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    11. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    12. Athanasiadis, C.L. & Papadopoulos, T.A. & Kryonidis, G.C. & Doukas, D.I., 2024. "A review of distribution network applications based on smart meter data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    13. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    14. Sigurjonsson, Hafthor Ægir & Clausen, Lasse R., 2018. "Solution for the future smart energy system: A polygeneration plant based on reversible solid oxide cells and biomass gasification producing either electrofuel or power," Applied Energy, Elsevier, vol. 216(C), pages 323-337.
    15. Peecock, Anna & Huang, Jiangyi & Martinez-Felipe, Alfonso & McKenna, Russell, 2025. "Reviewing sector coupling in offshore energy system integration modelling: the North Sea context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    16. Carro, A. & Chacartegui, R. & Ortiz, C. & Carneiro, J. & Becerra, J.A., 2022. "Integration of energy storage systems based on transcritical CO2: Concept of CO2 based electrothermal energy and geological storage," Energy, Elsevier, vol. 238(PA).
    17. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    18. He, Wei & Wang, Jihong, 2018. "Optimal selection of air expansion machine in Compressed Air Energy Storage: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 87(C), pages 77-95.
    19. Yuwen You & Zhonghua Wang & Zhihao Liu & Chunmei Guo & Bin Yang, 2024. "Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network," Energies, MDPI, vol. 17(16), pages 1-19, August.
    20. Gallo, A.B. & Simões-Moreira, J.R. & Costa, H.K.M. & Santos, M.M. & Moutinho dos Santos, E., 2016. "Energy storage in the energy transition context: A technology review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 800-822.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:18:y:2025:i:13:p:3568-:d:1696089. 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.