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A heuristic approach for optimal sizing of ESS coupled with intermittent renewable sources systems

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  • Bridier, Laurent
  • Hernández-Torres, David
  • David, Mathieu
  • Lauret, Phillipe

Abstract

In this paper a techno-economic comparison of an energy storage system (ESS) sizing for three intermittent renewables, wind, wave and PV power, with regard to two electricity grid services is presented. The first service consists of output hourly smoothing, based on day-ahead power forecasts (S1). The second service supplies year-round guaranteed power (S2). This leads to an annual default time rate (DTR) for which the actual power supplied to the grid does not match the day-ahead power bid within a given tolerance. A heuristic optimization based on an Adaptive Storage Operation (ASO) scheduling is developed in this study. ASO enables the minimal 5%-DTR ESS capacity, power, energy and feed-in-tariffs to be inferred from the operating conditions, depending on tolerance. The simulations assess and compare the techno-economic viability and efficiency of every renewable sources coupled with ESS. PV power is more efficient with daylight hours restricted services and higher power levels can be guaranteed for S1. Wind and wave power are more suitable than PV for services dedicated to full-day power delivery, as in the case of S2. For hourly smoothing the forecast accuracy influence is studied and yields a high impact on techno-economic sizing.

Suggested Citation

  • Bridier, Laurent & Hernández-Torres, David & David, Mathieu & Lauret, Phillipe, 2016. "A heuristic approach for optimal sizing of ESS coupled with intermittent renewable sources systems," Renewable Energy, Elsevier, vol. 91(C), pages 155-165.
    • Handle: RePEc:eee:renene:v:91:y:2016:i:c:p:155-165
    DOI: 10.1016/j.renene.2016.01.021
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    2. Zaibi, Malek & Champenois, Gérard & Roboam, Xavier & Belhadj, Jamel & Sareni, Bruno, 2018. "Smart power management of a hybrid photovoltaic/wind stand-alone system coupling battery storage and hydraulic network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 146(C), pages 210-228.
    3. Jun-Mo Kim & Jeong Lee & Jin-Wook Kim & Junsin Yi & Chung-Yuen Won, 2021. "Power Conversion System Operation to Reduce the Electricity Purchasing Cost of Energy Storage Systems," Energies, MDPI, vol. 14(16), pages 1-20, August.
    4. Dong Gu Choi & Daiki Min & Jong-hyun Ryu, 2018. "Economic Value Assessment and Optimal Sizing of an Energy Storage System in a Grid-Connected Wind Farm," Energies, MDPI, vol. 11(3), pages 1-23, March.
    5. Stojiljković, Mirko M., 2017. "Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics," Energy, Elsevier, vol. 137(C), pages 1231-1251.
    6. Kutaiba Sabah Nimma & Monaaf D. A. Al-Falahi & Hung Duc Nguyen & S. D. G. Jayasinghe & Thair S. Mahmoud & Michael Negnevitsky, 2018. "Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids," Energies, MDPI, vol. 11(4), pages 1-27, April.
    7. Lappalainen, Kari & Valkealahti, Seppo, 2022. "Sizing of energy storage systems for ramp rate control of photovoltaic strings," Renewable Energy, Elsevier, vol. 196(C), pages 1366-1375.
    8. Roy, Anthony & Auger, François & Dupriez-Robin, Florian & Bourguet, Salvy & Tran, Quoc Tuan, 2020. "A multi-level Demand-Side Management algorithm for offgrid multi-source systems," Energy, Elsevier, vol. 191(C).
    9. Yajing Gao & Jing Zhu & Huaxin Cheng & Fushen Xue & Qing Xie & Peng Li, 2016. "Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories," Energies, MDPI, vol. 9(7), pages 1-15, July.

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