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Zeitschriftenartikel zum Thema „Energy arbitrage“

Autor: Grafiati

Veröffentlicht am 24. Juni 2021

Zuletzt aktualisiert am 18. Februar 2022

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1

Overturf, Michael C., and Brian Flynn. "Energy Sufficiency Arbitrage." Distributed Generation & Alternative Energy Journal 27, no. 4 (2012): 53–73. http://dx.doi.org/10.1080/21563306.2012.10554222.

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2

Flamm, Benjamin, Annika Eichler, Roy S. Smith, and John Lygeros. "Price arbitrage using variable-efficiency energy storage." Journal of Physics: Conference Series 1343 (November 2019): 012060. http://dx.doi.org/10.1088/1742-6596/1343/1/012060.

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3

Brivio, Claudio, Stefano Mandelli, and Marco Merlo. "Battery energy storage system for primary control reserve and energy arbitrage." Sustainable Energy, Grids and Networks 6 (June 2016): 152–65. http://dx.doi.org/10.1016/j.segan.2016.03.004.

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4

Bassett, Kyle, Rupp Carriveau, and David S. K. Ting. "Energy arbitrage and market opportunities for energy storage facilities in Ontario." Journal of Energy Storage 20 (December 2018): 478–84. http://dx.doi.org/10.1016/j.est.2018.10.015.

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5

Hashmi, Md Umar, Deepjyoti Deka, Ana Busic, Lucas Pereira, and Scott Backhaus. "Arbitrage With Power Factor Correction Using Energy Storage." IEEE Transactions on Power Systems 35, no. 4 (2020): 2693–703. http://dx.doi.org/10.1109/tpwrs.2020.2969978.

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6

Nakajima, Tadahiro. "Expectations for Statistical Arbitrage in Energy Futures Markets." Journal of Risk and Financial Management 12, no. 1 (2019): 14. http://dx.doi.org/10.3390/jrfm12010014.

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Energy futures have become important as alternative investment assets to minimize the volatility of portfolio return, owing to their low links with traditional financial markets. In order to make energy futures markets grow further, it is necessary to expand expectations of returns from trading in energy futures markets. Therefore, this study examines whether profits can be earned by statistical arbitrage between wholesale electricity futures and natural gas futures listed on the New York Mercantile Exchange. On the assumption that power prices and natural gas prices have a cointegration relationship, as tested and supported by previous studies, the short-term deviation from the long-term equilibrium is regarded as an arbitrage opportunity. The results of the spark-spread trading simulations using historical data from 2 January 2014 to 29 December 2017 show about 30% yield at maximum. This study shows the possibility of generating earnings in energy futures market.

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7

Ponnaganti, Pavani, Birgitte Bak-Jensen, Brian Vejrum Wæhrens, and Jesper Asmussen. "Assessment of Energy Arbitrage Using Energy Storage Systems: A Wind Park’s Perspective." Energies 14, no. 16 (2021): 4718. http://dx.doi.org/10.3390/en14164718.

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With the growing application of green energy, the importance of effectively handling the volatile nature of these energy sources is also growing in order to ensure economic and operational viability. Accordingly, the main contribution of this work is to evaluate the revenue potential for wind parks with integrated storage systems in the day-ahead electricity markets using genetic algorithm. It is achieved by the concept of flexible charging–discharging of the Energy Storage System (ESS), taking advantage of the widespread electricity prices that are predicted using a feedforward-neural-network-based forecasting algorithm. In addition, the reactive power restrictions posed by grid code that are to be followed by the wind park are also considered as one of the constraints. Moreover, the profit obtained with a Battery Energy Storage System (BESS) is compared with that of a Thermal Energy Storage System (TESS). The proposed method gave more profitable results when utilizing BESS for energy arbitrage in day-ahead electricity markets than with TESS. Moreover, the availability of ESS at wind park has reduced the wind power curtailment.

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8

Salles, Mauricio, Junling Huang, Michael Aziz, and William Hogan. "Potential Arbitrage Revenue of Energy Storage Systems in PJM." Energies 10, no. 8 (2017): 1100. http://dx.doi.org/10.3390/en10081100.

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9

Peñaranda, Andrés F., David Romero-Quete, and Camilo A. Cortés. "Grid-Scale Battery Energy Storage for Arbitrage Purposes: A Colombian Case." Batteries 7, no. 3 (2021): 59. http://dx.doi.org/10.3390/batteries7030059.

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This study seeks to determine a suitable arbitrage strategy that allows a battery energy storage system (BESS) owner to obtain the maximum economic benefits when participating in the Colombian electricity market. A comparison of different arbitration strategies from the literature, such as seasonal, statistical, and neural networks-based models, is performed. To determine BESS’s optimal operation, a Mixed Integer Linear Programming (MILP) optimization problem is formulated, including a battery degradation model based on an upper piecewise linear approximation method. A financial evaluation of the different arbitrage strategies is carried out, resulting, for all the analyzed cases, in a negative net present value (NPV); thus, the results show that the income obtained from BESS when only performing energy arbitrage in the Colombian market do not compensate the investment costs. Results have also shown that strategies based on statistical and prediction models have a better performance than seasonal strategies, especially in atypical circumstances such as COVID-19.

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10

Gundogdu, Burcu, Daniel Gladwin, and David Stone. "Battery energy management strategies for UK firm frequency response services and energy arbitrage." Journal of Engineering 2019, no. 17 (2019): 4152–57. http://dx.doi.org/10.1049/joe.2018.8226.

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11

Zamani-Dehkordi, Payam, Hamed Chitsaz, Logan Rakai, and Hamidreza Zareipour. "A price signal prediction method for energy arbitrage scheduling of energy storage systems." International Journal of Electrical Power & Energy Systems 122 (November 2020): 106122. http://dx.doi.org/10.1016/j.ijepes.2020.106122.

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12

Walawalkar, Rahul, Jay Apt, and Rick Mancini. "Economics of electric energy storage for energy arbitrage and regulation in New York." Energy Policy 35, no. 4 (2007): 2558–68. http://dx.doi.org/10.1016/j.enpol.2006.09.005.

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13

Wankmüller, Florian, Prakash R. Thimmapuram, Kevin G. Gallagher, and Audun Botterud. "Impact of battery degradation on energy arbitrage revenue of grid-level energy storage." Journal of Energy Storage 10 (April 2017): 56–66. http://dx.doi.org/10.1016/j.est.2016.12.004.

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14

Sadighmanesh, Abdolreza, Mehran Sabahi, and Mahdi Zavvari. "Probabilistic dispatch in hybrid-microgrid system with considering energy arbitrage." Journal of Renewable and Sustainable Energy 11, no. 2 (2019): 025904. http://dx.doi.org/10.1063/1.5081890.

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15

Chau, Frankie, Jing-Ming Kuo, and Yukun Shi. "Arbitrage opportunities and feedback trading in emissions and energy markets." Journal of International Financial Markets, Institutions and Money 36 (May 2015): 130–47. http://dx.doi.org/10.1016/j.intfin.2015.02.002.

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16

Babacan, Oytun, Elizabeth L. Ratnam, Vahid R. Disfani, and Jan Kleissl. "Distributed energy storage system scheduling considering tariff structure, energy arbitrage and solar PV penetration." Applied Energy 205 (November 2017): 1384–93. http://dx.doi.org/10.1016/j.apenergy.2017.08.025.

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17

Hain, Martin, Julian Hess, and Marliese Uhrig-Homburg. "Relative value arbitrage in European commodity markets." Energy Economics 69 (January 2018): 140–54. http://dx.doi.org/10.1016/j.eneco.2017.11.005.

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18

Krishnamurthy, Dheepak, Canan Uckun, Zhi Zhou, Prakash R. Thimmapuram, and Audun Botterud. "Energy Storage Arbitrage Under Day-Ahead and Real-Time Price Uncertainty." IEEE Transactions on Power Systems 33, no. 1 (2018): 84–93. http://dx.doi.org/10.1109/tpwrs.2017.2685347.

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19

Srithapon, Chitchai, Prasanta Ghosh, Apirat Siritaratiwat, and Rongrit Chatthaworn. "Optimization of Electric Vehicle Charging Scheduling in Urban Village Networks Considering Energy Arbitrage and Distribution Cost." Energies 13, no. 2 (2020): 349. http://dx.doi.org/10.3390/en13020349.

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Electric vehicles (EV) replacing the internal combustion engine vehicle may be the solution for the particulate matter (PM) 2.5 pollution issue. However, the uncontrolled charging of EVs would challenge the power system operation. Therefore, it is necessary to implement some level of control over the EV charging procedure, especially in the residential network. In this paper, an optimization of EVs charging scheduling considering energy arbitrage and the distribution network cost of an urban village environment is presented. The optimized strategy focuses on decreasing the loss of EV owners’ energy arbitrage benefit, introduced as the penalty cost. Also, peak demand, power loss, and transformer aging are included in the estimation of the cost function for the distribution network. The optimization problem is solved using the genetic algorithm. As a case study, data from the urban village in Udon Thani, Thailand, are utilized to demonstrate the applicability of the proposed method. Simulation results show a reduction in the loss of energy arbitrage benefit, transformer peak load, power loss and the transformer loss of life. Therefore, the application of the optimized EV charging can prolong transformer lifetime benefiting both the EV owner and the distribution system operator.

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20

Tan, Xiaoqi, Yuan Wu, and Danny H. K. Tsang. "Pareto Optimal Operation of Distributed Battery Energy Storage Systems for Energy Arbitrage under Dynamic Pricing." IEEE Transactions on Parallel and Distributed Systems 27, no. 7 (2016): 2103–15. http://dx.doi.org/10.1109/tpds.2015.2478785.

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21

Terlouw, Tom, Tarek AlSkaif, Christian Bauer, and Wilfried van Sark. "Multi-objective optimization of energy arbitrage in community energy storage systems using different battery technologies." Applied Energy 239 (April 2019): 356–72. http://dx.doi.org/10.1016/j.apenergy.2019.01.227.

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22

Abdullah, Wan Syakirah Wan, Miszaina Osman, Mohd Zainal Abidin Ab Kadir, and Renuga Verayiah. "Battery energy storage system (BESS) design for peak demand reduction, energy arbitrage and grid ancillary services." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 1 (2020): 398. http://dx.doi.org/10.11591/ijpeds.v11.i1.pp398-408.

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<span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Renewable Energy (RE) penetration is a new phenomenon in power systems. In the advent of high penetration of RE in the systems, several issues have to be addressed especially when it involves the stability and flexibility of the power systems. Battery Energy Storage System (BESS) has gained popularity due to its capability to store energy and to serve multiple purposes in solving various power system concerns. Additionally, several BESS can be combined to operate as Virtual Power Plant (VPP). This study will involve the design and implementation of BESS for five potential customer sites for the demonstration project and to be possibly integrated into one VPP system. The study is expected to demonstrate bill savings to the customers with BESS due to peak demand reduction and energy arbitrage savings.</span><table class="MsoNormalTable" style="width: 444.85pt; border-collapse: collapse; border: none; mso-border-alt: solid windowtext .5pt; mso-yfti-tbllook: 1184; mso-padding-alt: 0in 5.4pt 0in 5.4pt; mso-border-insideh: .5pt solid windowtext; mso-border-insidev: .5pt solid windowtext;" width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr style="mso-yfti-irow: 0; mso-yfti-firstrow: yes; mso-yfti-lastrow: yes; height: 63.4pt;"><td style="width: 290.6pt; border: none; border-top: solid windowtext 1.0pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; height: 63.4pt;" valign="top" width="387"><p class="MsoNormal" style="margin-top: 6.0pt; text-align: justify;"><span style="font-size: 9.0pt; color: black; mso-bidi-font-style: italic;">Renewable Energy (RE) penetration is a new phenomenon in power systems. In the advent of high penetration of RE in the systems, several issues have to be addressed especially when it involves the stability and flexibility of the power systems. Battery Energy Storage System (BESS) has gained popularity due to its capability to store energy and to serve multiple purposes in solving various power system concerns. Additionally, several BESS can be combined to operate as Virtual Power Plant (VPP). This study will involve the design and implementation of BESS for five potential customer sites for the demonstration project and to be possibly integrated into one VPP system. The study is expected to demonstrate bill savings to the customers with BESS due to peak demand reduction and energy arbitrage savings.</span></p></td></tr></tbody></table>

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23

Mantar Gundogdu, Burcu, Dan T. Gladwin, Shahab Nejad, and David Andrew Stone. "Scheduling of grid-tied battery energy storage system participating in frequency response services and energy arbitrage." IET Generation, Transmission & Distribution 13, no. 14 (2019): 2930–41. http://dx.doi.org/10.1049/iet-gtd.2018.6690.

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24

Benth, Fred Espen, Lars Ekeland, Ragnar Hauge, and Bj⊘Rn Fredrik Nielsen. "A note on arbitrage‐free pricing of forward contracts in energy markets." Applied Mathematical Finance 10, no. 4 (2003): 325–36. http://dx.doi.org/10.1080/1350486032000160777.

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25

Kadri, Abdeslem, and Kaamran Raahemifar. "Jurisdiction-based optimisation of BESS operating with solar arrays for energy arbitrage." International Journal of Industrial Electronics and Drives 4, no. 4 (2018): 215. http://dx.doi.org/10.1504/ijied.2018.099616.

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26

Kadri, Abdeslem, and Kaamran Raahemifar. "Jurisdiction-based optimisation of BESS operating with solar arrays for energy arbitrage." International Journal of Industrial Electronics and Drives 4, no. 4 (2018): 215. http://dx.doi.org/10.1504/ijied.2018.10021236.

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27

Sarker, Mushfiqur R., Daniel Julius Olsen, and Miguel A. Ortega-Vazquez. "Co-Optimization of Distribution Transformer Aging and Energy Arbitrage Using Electric Vehicles." IEEE Transactions on Smart Grid 8, no. 6 (2017): 2712–22. http://dx.doi.org/10.1109/tsg.2016.2535354.

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28

Flatley, Lisa C., Robert S. MacKay, and Michael Waterson. "Optimal strategies for operating energy storage in an arbitrage or smoothing market." Journal of Dynamics and Games 3, no. 4 (2016): 371–98. http://dx.doi.org/10.3934/jdg.2016020.

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29

Latini, Luca, Marco Piccirilli, and Tiziano Vargiolu. "Mean-reverting no-arbitrage additive models for forward curves in energy markets." Energy Economics 79 (March 2019): 157–70. http://dx.doi.org/10.1016/j.eneco.2018.03.001.

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30

Zafirakis, Dimitrios, Konstantinos J. Chalvatzis, Giovanni Baiocchi, and Georgios Daskalakis. "The value of arbitrage for energy storage: Evidence from European electricity markets." Applied Energy 184 (December 2016): 971–86. http://dx.doi.org/10.1016/j.apenergy.2016.05.047.

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31

Harrold, Daniel J. B., Jun Cao, and Zhong Fan. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning." Energy 238 (January 2022): 121958. http://dx.doi.org/10.1016/j.energy.2021.121958.

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32

Telaretti, Enrico, Mariano Ippolito, and Luigi Dusonchet. "A Simple Operating Strategy of Small-Scale Battery Energy Storages for Energy Arbitrage under Dynamic Pricing Tariffs." Energies 9, no. 1 (2015): 12. http://dx.doi.org/10.3390/en9010012.

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33

Hemmati, Reza, Hasan Mehrjerdi, and Mosayeb Bornapour. "Hybrid hydrogen-battery storage to smooth solar energy volatility and energy arbitrage considering uncertain electrical-thermal loads." Renewable Energy 154 (July 2020): 1180–87. http://dx.doi.org/10.1016/j.renene.2020.03.092.

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34

Chen, Yihsu, and Makoto Tanaka. "Permit banking in emission trading: Competition, arbitrage and linkage." Energy Economics 71 (March 2018): 70–82. http://dx.doi.org/10.1016/j.eneco.2018.01.032.

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35

Marini, Abbas, Luigi Piegari, S.-Saeedallah Mortazavi, and Mohammad-S. Ghazizadeh. "Coordinated Operation of Energy Storage Systems for Distributed Harmonic Compensation in Microgrids." Energies 13, no. 3 (2020): 771. http://dx.doi.org/10.3390/en13030771.

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Energy storage systems (ESSs) bring various opportunities for a more reliable and flexible operation of microgrids (MGs). Among them, energy arbitrage and ancillary services are the most investigated application of ESSs. Furthermore, it has been shown that some other services could also be provided by ESSs such as power quality (PQ) improvements. This issue could be more challenging in MGs with widespread nonlinear loads injecting harmonic currents to the MG. In this paper, the feasibility of ESSs to act as coordinated active harmonic filters (AHF) for distributed compensation was investigated. An optimization model was proposed for the coordination of the harmonic compensation activities of ESSs. The model takes into account the various technical and systematic constraints to economically determine the required reference currents of various AHFs. Simulation cases showed the performance of the proposed model for enhancing the harmonic filtering capability of the MG, reduction in the compensation cost, and more flexibility of the distributed harmonic compensation schemes. It was also shown that ESS activities in harmonic compensation do not have much of an effect on the ESSs revenue from energy arbitrage. Hence, it could make ESSs more justifiable for use in MGs.

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36

Mancarella, Pierluigi, Gianfranco Chicco, and Tomislav Capuder. "Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services." Energy 161 (October 2018): 381–95. http://dx.doi.org/10.1016/j.energy.2018.07.111.

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37

Connolly, D., H. Lund, P. Finn, B. V. Mathiesen, and M. Leahy. "Practical operation strategies for pumped hydroelectric energy storage (PHES) utilising electricity price arbitrage." Energy Policy 39, no. 7 (2011): 4189–96. http://dx.doi.org/10.1016/j.enpol.2011.04.032.

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38

White, Chris, Ben Thompson, and Lukas G. Swan. "Comparative performance study of electric vehicle batteries repurposed for electricity grid energy arbitrage." Applied Energy 288 (April 2021): 116637. http://dx.doi.org/10.1016/j.apenergy.2021.116637.

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39

Hesse, Holger, Volkan Kumtepeli, Michael Schimpe, et al. "Ageing and Efficiency Aware Battery Dispatch for Arbitrage Markets Using Mixed Integer Linear Programming." Energies 12, no. 6 (2019): 999. http://dx.doi.org/10.3390/en12060999.

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To achieve maximum profit by dispatching a battery storage system in an arbitrage operation, multiple factors must be considered. While revenue from the application is determined by the time variability of the electricity cost, the profit will be lowered by costs resulting from energy efficiency losses, as well as by battery degradation. In this paper, an optimal dispatch strategy is proposed for storage systems trading on energy arbitrage markets. The dispatch is based on a computationally-efficient implementation of a mixed-integer linear programming method, with a cost function that includes variable-energy conversion losses and a cycle-induced battery capacity fade. The parametrisation of these non-linear functions is backed by in-house laboratory tests. A detailed analysis of the proposed methods is given through case studies of different cost-inclusion scenarios, as well as battery investment-cost scenarios. An evaluation with a sample intraday market data set, collected throughout 2017 in Germany, offers a potential monthly revenue of up to 8762 EUR/MWh cap installed capacity, without accounting for the costs attributed to energy losses and battery degradation. While this is slightly above the revenue attainable in a reference application—namely, primary frequency regulation for the same sample month (7716 EUR/MWh cap installed capacity)—the situation changes if costs are considered: The optimisation reveals that losses in battery ageing and efficiency reduce the attainable profit by up to 36% for the most profitable arbitrage use case considered herein. The findings underline the significance of considering both ageing and efficiency in battery system dispatch optimisation.

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40

Abramova, Ekaterina, and Derek Bunn. "Optimal Daily Trading of Battery Operations Using Arbitrage Spreads." Energies 14, no. 16 (2021): 4931. http://dx.doi.org/10.3390/en14164931.

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An important revenue stream for electric battery operators is often arbitraging the hourly price spreads in the day-ahead auction. The optimal approach to this is challenging if risk is a consideration as this requires the estimation of density functions. Since the hourly prices are not normal and not independent, creating spread densities from the difference of separately estimated price densities is generally intractable. Thus, forecasts of all intraday hourly spreads were directly specified as an upper triangular matrix containing densities. The model was a flexible four-parameter distribution used to produce dynamic parameter estimates conditional upon exogenous factors, most importantly wind, solar and the day-ahead demand forecasts. These forecasts supported the optimal daily scheduling of a storage facility, operating on single and multiple cycles per day. The optimization is innovative in its use of spread trades rather than hourly prices, which this paper argues, is more attractive in reducing risk. In contrast to the conventional approach of trading the daily peak and trough, multiple trades are found to be profitable and opportunistic depending upon the weather forecasts.

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41

Fares, Robert L., and Michael E. Webber. "What are the tradeoffs between battery energy storage cycle life and calendar life in the energy arbitrage application?" Journal of Energy Storage 16 (April 2018): 37–45. http://dx.doi.org/10.1016/j.est.2018.01.002.

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42

Li, Tao, and Mohammad Shahidehpour. "Risk-Constrained Generation Asset Arbitrage in Power Systems." IEEE Transactions on Power Systems 22, no. 3 (2007): 1330–39. http://dx.doi.org/10.1109/tpwrs.2007.901753.

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43

Wu, Maoguo, and Yue Yu. "The Impact of Russia’s Oil-Dominated Energy Economic Changes on the Exchange Rate of Russian Ruble – Chinese Renminbi." European Scientific Journal, ESJ 13, no. 22 (2017): 173. http://dx.doi.org/10.19044/esj.2017.v13n22p173.

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Russia’s economic development has a close relation with China, due to geographical and historical reasons. This paper investigates whether the ruble – renminbi exchange rate changes accordingly when the pillar industry of Russia is drastically changing, and how the exchange rate changes and how it affects Russia’s economic development. In this paper, data of 7 variables spanning 122 months are selected based on related literature and availability of data. Regression analysis and empirical tests are carried out consequently. The results show that the energy price index represented by oil prices is negatively correlated with the exchange rate, and the explanatory power is as high as 41.1%. Following basic arbitrage methods and strategies, this paper verifies the feasibility of using arbitrage by comparing actual exchange rates with forecasted exchange rates. According to empirical results, problems witnessed in the process of ruble internationalization provides policy implications for China. China’s economy is utilized as an example to discuss the shortcomings of Russia’s economy. Related solutions are proposed.

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44

Babacan, Oytun, Elizabeth L. Ratnam, Vahid R. Disfani, and Jan Kleissl. "Erratum to “Distributed energy storage system scheduling considering tariff structure, energy arbitrage and solar PV penetration” [Appl. Energy 205 (2017) 1384–1393]." Applied Energy 219 (June 2018): 427. http://dx.doi.org/10.1016/j.apenergy.2017.12.079.

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45

Andresen, Gorm B., Rolando A. Rodriguez, Sarah Becker, and Martin Greiner. "The potential for arbitrage of wind and solar surplus power in Denmark." Energy 76 (November 2014): 49–58. http://dx.doi.org/10.1016/j.energy.2014.03.033.

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46

Cao, Jun, Dan Harrold, Zhong Fan, Thomas Morstyn, David Healey, and Kang Li. "Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model." IEEE Transactions on Smart Grid 11, no. 5 (2020): 4513–21. http://dx.doi.org/10.1109/tsg.2020.2986333.

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47

Gandhi, Oktoviano, Wenjie Zhang, Carlos D. Rodriguez-Gallegos, Monika Bieri, Thomas Reindl, and Dipti Srinivasan. "Analytical Approach to Reactive Power Dispatch and Energy Arbitrage in Distribution Systems With DERs." IEEE Transactions on Power Systems 33, no. 6 (2018): 6522–33. http://dx.doi.org/10.1109/tpwrs.2018.2829527.

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48

Nezamabadi, Hossein, and Mehrdad Setayesh Nazar. "Arbitrage strategy of virtual power plants in energy, spinning reserve and reactive power markets." IET Generation, Transmission & Distribution 10, no. 3 (2016): 750–63. http://dx.doi.org/10.1049/iet-gtd.2015.0402.

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Baviera, Roberto, and Tommaso Santagostino Baldi. "Stop-loss and leverage in optimal statistical arbitrage with an application to energy market." Energy Economics 79 (March 2019): 130–43. http://dx.doi.org/10.1016/j.eneco.2018.03.024.

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Mustafa, Motasem Bani, Patrick Keatley, Ye Huang, Osaru Agbonaye, Oluwasola O. Ademulegun, and Neil Hewitt. "Evaluation of a battery energy storage system in hospitals for arbitrage and ancillary services." Journal of Energy Storage 43 (November 2021): 103183. http://dx.doi.org/10.1016/j.est.2021.103183.

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