A Holistic Comparative Analysis of Different Storage Systems using Levelized Cost of Storage and Life Cycle Indicators

Jülch, Verena; Telsnig, Thomas; Schulz, Maximilian; Hartmann, Niklas; Thomsen, Jessica; Eltrop, Ludger; Schlegl, Thomas

doi:10.1016/j.egypro.2015.07.553

Cited by 36 publications

(8 citation statements)

References 6 publications

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“…In Table A.8 we summarize the yearly power production LCOE (the levelized cost of electricity produced from each power plant without considering any future load curtailment or other losses) and average selling price for each generation technology and LCOS (levelized cost of storage; cf. Jülch et al, 2015) and the average selling price of each storage technology for weather-year 2006. For all power production technologies, the average market selling price is higher than the LCOE, the difference being very low for the VRE resources (offshore wind ~€0.10/ ℎ , onshore wind ~€0.15/ ℎ and solar PV ~€0.40/ ℎ ) while for the other technologies this difference is much greater, especially for biogas (€44.40/ ℎ ).…”

Section: Resultsmentioning

confidence: 99%

How Sensitive are Optimal Fully Renewable Power Systems to Technology Cost Uncertainty?

Shirizadeh¹,

Perrier²,

Quirion³

2022

The Energy Journal

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Many studies have demonstrated the feasibility of fully renewable power systems. Yet the future costs of key technologies are highly uncertain, and little is known about the robustness of a renewable power system to these uncertainties. To analyze it, we build 315 cost scenarios by varying the costs of key technologies and we model the optimal renewable power system for France, simultaneously optimizing investment and dispatch. We add to the literature by studying a consecutive 18-years weather period; by testing all combinations of technology costs rather than changing them one-at-a-time; and by calculating the regret from optimizing the energy mix on the basis of cost assumptions that do not materialize.Our results indicate that the cost of a 100% system is not that sensitive to uncertainty. Admittedly, the optimal energy mix is highly sensitive to cost assumptions: across our scenarios, the installed capacity in PV, onshore wind and power-togas varies by a factor of 5, batteries and offshore wind even more. However, in every scenario the total production and storage cost is similar to, or lower than the current cost. This indicates that renewable technologies will become by and large substitutable. Moreover, even if the energy mix is optimized based on cost assumptions which turn out to be wrong, the extra cost is low: 4% in average and less than 9% in 95% of the scenarios.

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Section: Resultsmentioning

confidence: 99%

How Sensitive are Optimal Fully Renewable Power Systems to Technology Cost Uncertainty?

Shirizadeh¹,

Perrier²,

Quirion³

2022

The Energy Journal

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“…LCOE is not directly applicable in assessing storage cost since energy storage does not generate its energy. Adapting the basic idea behind LCOE, Jülch et al [29,39] defined Levelized Cost of Storage (LCOS) as the sum of all the annual expenses (such as operational cost and charging cost) and capital cost divided by the sum of delivered energy over the lifetime of the storage. The approach is relatively similar to LCOE, but instead of generating the energy, LCOS purchases electricity and the delivered energy incorporates the cycle lost (charge and discharge).…”

Section: Economic Assessmentmentioning

confidence: 99%

Dynamic Cost-Optimal Assessment of Complementary Diurnal Electricity Storage Capacity in High PV Penetration Grid

Dumlao

Ishihara

2021

Energies

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Solar Photovoltaics (PV) is seen as one of the renewable energy technologies that could help reduce the world’s dependence on fossil fuels. However, since it is dependent on the sun, it can only generate electricity in the daytime, and this restriction is exacerbated in electricity grids with high PV penetration, where solar energy must be curtailed due to the mismatch between supply and demand. This study conducts a techno-economic analysis to present the cost-optimal storage growth trajectory that could support the dynamic integration of solar PV within a planning horizon. A methodology for cost-optimal assessment that incorporates hourly simulation, Monte Carlo random sampling, and a proposed financial assessment is presented. This approach was tested in Japan’s southernmost region since it is continuously increasing its solar capacity and is at the precipice of high PV curtailment scenario. The results show the existence of a cost-optimal storage capacity growth trajectory that balances the cost penalty from curtailment and the additional investment cost from storage. This optimal trajectory reduces the impact of curtailment on the energy generation cost to manageable levels and utilizes more solar energy potential that further reduces CO2 emissions. The results also show that the solar capacity growth rate and storage cost significantly impact the optimal trajectory. The incorporation of the Monte Carlo method significantly reduced the computational requirement of the analysis enabling the exploration of several growth trajectories, and the proposed financial assessment enabled the time-bound optimization of these trajectories. The approach could be used to calculate the optimal growth trajectories in other nations or regions, provided that historical hourly temperature, irradiance, and demand data are available.

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“…The ESS LCOE, also called Levelized Cost of Storage (LCOS), represents the cost of the electricity delivered by an ESS according to its lifetime estimation. The criteria can be evaluate by including different parameters as in [18], [36], where the authors propose to include in the LCOE the cost of the energy needed to charge the ESS. Moreover, in [38]the authors show that the LCOE clearly depends on the ESS applications.…”

Section: B Lcoementioning

confidence: 99%

“…The time horizon chosen for this study is 25 years, considering that corresponds to an average value of SC calendar lifetime, according to data given in [5], [13], [20]. The discount rate can vary from 3.5% to 8% regarding the values proposed in the literature, we selected the worst value of 3.5% as proposed by [18].…”

Section: B Lcoementioning

confidence: 99%

Impacts of supercapacitors on battery lifetime in hybrid energy storage system in building integrated photovoltaic DC micro-grid

Gaetani-Liseo

Alonso

Jammes

2018

2018 7th International Conference on Renewable Energy Research and Applications (ICRERA)

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This paper focuses on a study of an hybrid energy storage system constituted by lead acid batteries and supercapacitors and designed to work in a low voltage DC microgrid supplying by photovoltaic sources and dedicated to building application. We proposed a simulation methodology in order to evaluate supercapacitors performances and behaviors for long term analysis. We quantify their impacts on battery lifetime and on the levelized cost of electricity of our storage system. This analysis has been validated on the micro-grid developed in LAAS-CNRS in ADREAM building integrated photovoltaic. The power profiles used for our work correspond to 1 years production and consumption data from 100m 2 floor of the building. Results of comparative studies with different load profiles, SC costs and methods to estimated battery lifetime are presented and discussed.

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A Holistic Comparative Analysis of Different Storage Systems using Levelized Cost of Storage and Life Cycle Indicators

Cited by 36 publications

References 6 publications

How Sensitive are Optimal Fully Renewable Power Systems to Technology Cost Uncertainty?

How Sensitive are Optimal Fully Renewable Power Systems to Technology Cost Uncertainty?

Dynamic Cost-Optimal Assessment of Complementary Diurnal Electricity Storage Capacity in High PV Penetration Grid

Impacts of supercapacitors on battery lifetime in hybrid energy storage system in building integrated photovoltaic DC micro-grid

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