A comparative study of sensible energy storage and hydrogen energy storage apropos to a concentrated solar thermal power plant

Mukherjee, Shubha Sankar; Meshram, Himani Anand; Rakshit, Dibakar; Saha, Bidyut Baran

doi:10.1016/j.est.2023.106629

Cited by 26 publications

(10 citation statements)

References 21 publications

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“…To do this, the subinterval T ′ is first selected where the system load is high, and in these intervals, the value of LOLP is determined. Then, weighted coefficients are calculated for all study hours according to Equation (7) [ 28 ] :

w_{\text{t}} = \frac{p_{\text{t}}}{\left(\sum\right)_{\tau \in T^{&amp;#x00026;amp;amp;amp;amp;aposx;}} p_{\tau}}

…”

Section: Various Methods Of Power Plant Capacity Valuementioning

confidence: 99%

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Enhancing Photovoltaic Farm Capacity Estimation: A Comprehensive Analysis with a Novel Approach

Hosseini Dolatabadi,

Soleimani,

Maghanaki

et al. 2024

Energy Tech

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This research paper addresses the inaccuracies in the current methods for estimating the capacity value of photovoltaic (PV) plants, which rely heavily on large‐scale data and fail to represent the actual capacity value pattern accurately. The research conducts case studies in Belgium, Texas, and California to analyze the impact of different factors on capacity value. It proposes a new metric called the Marginal Moving‐Average Limited‐Hours (MMALH) Equivalent Load‐Carring Capability (ELCC) ‐ Based capacity value. The proposed metric reduces the dependence on hourly data and better represents capacity value. The results from real case studies validate the effectiveness of the new metric, highlighting its novelty and contribution to the assessment of capacity value in PV power systems. The study emphasizes the importance of accurately assessing the capacity value of PV compared to conventional units, considering environmental factors and system parameters. The study exposes the shortcomings in current metrics and advocates for the MMALH ELCC methodology as a more precise evaluation approach. The research suggests optimizing design, employing advanced tracking systems, enhancing maintenance practices, and ensuring effective grid integration to boost solar plant efficiency. Consistent monitoring and analysis of the utilization factor are vital for pinpointing improvement areas and augmenting productivity.

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w_{\text{t}} = \frac{p_{\text{t}}}{\left(\sum\right)_{\tau \in T^{&amp;#x00026;amp;amp;amp;amp;aposx;}} p_{\tau}}

…”

Section: Various Methods Of Power Plant Capacity Valuementioning

confidence: 99%

“…To do this, the subinterval T 0 is first selected where the system load is high, and in these intervals, the value of LOLP is determined. Then, weighted coefficients are calculated for all study hours according to Equation (7) [28] :…”

Section: Cf Approximationmentioning

confidence: 99%

Enhancing Photovoltaic Farm Capacity Estimation: A Comprehensive Analysis with a Novel Approach

Hosseini Dolatabadi,

Soleimani,

Maghanaki

et al. 2024

Energy Tech

View full text Add to dashboard Cite

show abstract

“…An additional PCM mandrel of 2 mm diameter is placed at the center of each battery. Similarly, eight batteries have been arranged in series with variable contact areas of HCEs as discussed by Shrinet and Kumar [8]. The relationship has been given as,…”

Section: Problem Descriptionmentioning

confidence: 99%

“…This study introduces a novel PCM-coupled liquid cooling BTMS designed to maintain thermal uniformity in Li-ion battery packs. Shrinet et al established a unique correlation for the variable contact area of the heat conducting element (HCE) to ensure module uniformity, demonstrating a notable enhancement in terms of a 74% reduction in ∆T max [8]. However, PCM-integrated variable contact area for liquid cooling has yet to be explored.…”

Section: Introductionmentioning

confidence: 99%

Enhancing thermal management in cylindrical Li-ion battery through PCM integration with variable contact area

Shrinet,

Kumar

2024

J. Phys.: Conf. Ser.

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Cylindrical Li-ion batteries are widely embraced in various sectors, notably electric vehicles and renewable energy storage systems. An effective thermal management system is vital for their safe and dependable operation, enhancing both the performance and reliability of the system. This study employs a distinctive hybrid cooling strategy consisting of a phase change material (PCM) at the centre of the battery and liquid cooling at the surface of the cylindrical batteries. The contact area between the coolant and the battery’s surfaces varies to make sure same heat transfers from each battery. This approach is instrumental in maintaining thermal uniformity and regulating the maximum temperature. In the numerical analysis, we employed ANSYS Fluent 2022 R1 to create the computational model encompassing the Li-ion battery, PCM, and liquid cooling system. This arrangement utilized eight 18650Li-ion batteries, with each battery housing a 2 mm radius PCM rod at the centre of the batteries. A heat-conducting element (HCE) was introduced to facilitate contact between the battery and the coolant channel. Water was selected as the coolant, and the coolant channel cross-sectional area is 65 × 2 mm2. The relationship governing the variable contact area is determined by fixing the velocity and the first battery contact area. The variable contact geometry maintains thermal uniformity throughout the battery module, resulting in a 74% enhancement in thermal uniformity. Nevertheless, the integration of PCM inside the battery effectively prevents individual batteries from surpassing specified temperature limits. It yields a remarkable 36.64% enhancement in thermal uniformity compared to situations in which PCM is not present, albeit with a 3.75% capacity loss. Furthermore, the study investigates the effects of coolant flow rates and performs an extensive analysis of temperature variation and melting fraction.

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“…Currently, various thermal management techniques are in practice, namely, air cooling, liquid cooling, hybrid cooling, phase change material based heat sinks, heat pipes, spray, and immersion cooling to deal with the temperature excursions of the cell beyond the maximum permissible limit (Set Point Temperature, T SP T ) [3,4]. The thermal performance of these cooling techniques is quantified using a term called heat loss coefficient (h).…”

Section: Introductionmentioning

confidence: 99%

Prediction of the minimum heat loss coefficient for safe operation of a Li-ion cell: A machine learning approach

Akula,

Kumar

2024

J. Phys.: Conf. Ser.

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The operating conditions, either C-rate or DoD, of all the Li-ion cells in the battery pack of EVs are subject to continuous change. In addition, the cells’ ambient temperature (Tamb ) is also not constant due to geographical or day/night conditions. As the generation and transfer of heat from the cells are vital functions of C-rate, DoD, and Tamb , choosing an appropriate heat loss coefficient for the given conditions is imperative to maintain the operating temperature of the cell below a specified Set Point Temperature (SPT). The selected heat loss coefficient must be the minimum possible such that overcooling of the cells can also be eliminated. The present study employed a machine learning based surrogate model called Gaussian Process Regression (GPR) to achieve this objective for an AMP20M1HD - A0 Li-ion pouch cell. The training and validation of the surrogate model are conducted with the samples generated using Latin Hypercube Sampling and simulated using the NTGK model available in the Ansys Fluent. The model’s accuracy is further tested for three new combinations of the operating conditions, which are not used for training or validation. Using the present model, the predicted minimum heat loss coefficient successfully regulates the cell’s maximum temperature below a user-specified SPT for the same user-given operating conditions. The developed model immensely helps in designing a cost-effective battery thermal management system with optimum cooling capacity by predicting the nature of heat loss coefficients for all plausible combinations of the operating conditions.

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A comparative study of sensible energy storage and hydrogen energy storage apropos to a concentrated solar thermal power plant

Cited by 26 publications

References 21 publications

Enhancing Photovoltaic Farm Capacity Estimation: A Comprehensive Analysis with a Novel Approach

Enhancing Photovoltaic Farm Capacity Estimation: A Comprehensive Analysis with a Novel Approach

Enhancing thermal management in cylindrical Li-ion battery through PCM integration with variable contact area

Prediction of the minimum heat loss coefficient for safe operation of a Li-ion cell: A machine learning approach

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