Cost optimization of hybrid composite flywheel rotors for energy storage

High-velocity and long-lifetime operating conditions of modern high-speed energy storage flywheel rotors may create the necessary conditions for failure modes not included in current quasi-static failure analyses. In the present study, a computational algorithm based on an accepted analytical model was developed to investigate the viscoelastic behavior of carbon fiber reinforced polymer composite flywheel rotors with an aluminum hub assembled via a press-fit. The Tsai‑Wu failure criterion was applied to assess failure. Two simulation cases were developed to explore the effects of viscoelasticity on composite flywheel rotors, i.e., a worst-case operating condition and a case akin to realistic flywheel operations. The simulations indicate that viscoelastic effects are likely to reduce peak stresses in the composite rim over time. However, viscoelasticity also affects stresses in the hub and the hub-rim interface in ways that may cause rotor failure. It was further found that charge-discharge cycles of the flywheel energy storage device may create significant fatigue loading conditions. It was therefore concluded that the design of composite flywheel rotors should include viscoelastic and fatigue analyses to ensure safe operation.

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Development of high speed composite flywheel rotors for energy storage systems

Advanced Composite Materials ◽

10.1163/156855102753613273 ◽

2002 ◽

Vol 11 (1) ◽

pp. 40-49 ◽

Cited By ~ 14

Author(s):

K. Takahashi ◽

S. Kitade ◽

H. Morita

Keyword(s):

Energy Storage ◽

High Speed ◽

Storage Systems ◽

Energy Storage Systems ◽

Composite Flywheel ◽

Flywheel Rotors

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Baseload power potential from optimally-configured wind, solar and storage power plants across the United States

10.21203/rs.3.rs-86826/v1 ◽

2020 ◽

Author(s):

Joshua D Rhodes ◽

Aditya Choukulkar ◽

Brianna Cote ◽

Sarah A McKee ◽

Christopher T M Clack

Keyword(s):

United States ◽

Energy Storage ◽

Power Plants ◽

Cost Optimization ◽

The United States ◽

Solar Pv ◽

Wholesale Market ◽

Optimal Technology ◽

Current Price ◽

The Cost

Abstract In the present paper, we assessed the potential for local wind, solar PV, and energy storage to provide baseload (constant, uninterrupted) power in every county of the contiguous United States. The amount of available capacity between 2020 and 2050 was determined via a least-cost optimization model that took into account changing costs of constituent technologies and local meteorological conditions. We found that, by 2050, the potential exists for about 6.8 TW of renewable baseload power at an average cost of approximately $50 / MWh, which is competitive with current wholesale market rates for electricity. The optimal technology configurations constructed always resulted in over two hours of emergency energy reserves, with the amount increasing as the price of energy storage falls. We also found that, given current price decline trajectories, the model has a tendency to select more solar capacity than wind over time. A second part of the study performed three million simulations followed by a regression analysis to generate an online map-based tool that allows users to change input costs assumptions and compute the cost of renewable baseload electricity in every contiguous US county.

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Electricity Cost Optimization in Energy Storage Systems by Combining a Genetic Algorithm with Dynamic Programming

Mathematics ◽

10.3390/math8091526 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1526 ◽

Cited By ~ 2

Author(s):

Seung-Ju Lee ◽

Yourim Yoon

Keyword(s):

Genetic Algorithm ◽

Dynamic Programming ◽

Energy Storage ◽

Storage Systems ◽

Cost Optimization ◽

Energy Storage Systems ◽

Electricity Cost ◽

Efficient Charge ◽

Time Of Use Pricing ◽

Charge Discharge

Recently, energy storage systems (ESSs) are becoming more important as renewable and microgrid technologies advance. ESSs can act as a buffer between generation and load and enable commercial and industrial end users to reduce their electricity expenses by controlling the charge/discharge amount. In this paper, to derive efficient charge/discharge schedules of ESSs based on time-of-use pricing with renewable energy, a combination of genetic algorithm and dynamic programming is proposed. The performance of the combined method is improved by adjusting the size of the base units of dynamic programming. We show the effectiveness of the proposed method by simulating experiments with load and generation profiles of various commercial electricity consumers.

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