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Feasibility and economical analysis of energy storage systems as enabler of higher renewable energy sources penetration in an existing grid

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  • Aragón, Gustavo
  • Pandian, Vinoth
  • Krauß, Veronika
  • Werner-Kytölä, Otilia
  • Thybo, Gitte
  • Pautasso, Elisa

Abstract

Grid stability becomes an issue when incorporating renewable distribution generation into an electrical grid due to voltage fluctuations. This work presents an innovative solution which assists grid planners in carrying out technical and economic analysis of future grids and in taking decisions based on it. A set of tools allows the determination of the renewable energy sources and energy storage systems impact to a given grid concerning technical and economic indicators. Using these tools, a study was conducted comparing model predictive control with photovoltaics-curtailment, volt-watt and volt-var methods for the control of photovoltaics and energy storage power in an existing grid. Some highlights of the analysis are: (i) the given grid supports maximal photovoltaics penetration level of 120% without exceeding the ±10% voltage level limits; (ii) the model predictive control method aiming at the minimization of power exchange in a grid with 60% storage penetration allowed significant increase of photovoltaics penetration to 190% and reduced the maximum voltage level to 1.089pu; (iii) a user-centered design and development of the interface for grid planners resulted in a system usability scale score of 63.9. The tools enable the grid planner to take decisions when planning the future grid.

Suggested Citation

  • Aragón, Gustavo & Pandian, Vinoth & Krauß, Veronika & Werner-Kytölä, Otilia & Thybo, Gitte & Pautasso, Elisa, 2022. "Feasibility and economical analysis of energy storage systems as enabler of higher renewable energy sources penetration in an existing grid," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222007927
    DOI: 10.1016/j.energy.2022.123889
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    References listed on IDEAS

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    1. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).

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