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A two-stage stochastic unit commitment considering demand-side provider and wind power penetration from the ISO point of view

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Abstract

The need for reserve provision in power systems is growing progressively due to the increase in wind power penetration level. This paper aimed to present a model from the ISO point of view for using demand-side resources as flexible resources that are aggregated as a demand-side provider (DSP). The DSP consists of demand response aggregator as grid backup and energy storage systems plus plug-in electric vehicles (PEVs) parking lot aggregators as fast reserve. In this regard, a two-stage stochastic programming model has been presented for energy and reserve determination in the unit commitment problem in the form of mixed integer linear programming (MILP). In this study, the incentive-based model of the capacity market program has been employed to achieve a certain level of demand-side response. Meanwhile, the Weibull probability distribution function and truncated Gaussian distribution have been applied to generate wind speed scenarios and model the uncertainties of PEVs’ behavior, respectively. The results obtained in this study demonstrate that the simultaneous scheduling of energy sources and demand-side aggregators for energy supply as well as reserve has led to the reduction of wind energy uncertainty and improved system operational conditions. Also, increase in the number of PEVs present in the parking lot, in addition to reducing the operation costs, leads to increased grid reserve level as well as considerable reduction of wind power spillage.

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Abbreviations

\({\text{es}} \in {\text{ES}}\) :

Index (set) of ESS

\(f \in \left( {F^{i} } \right)\) :

Index (set) of segments of the cost function of conventional generation units i

\(i \in I\) :

Index (set) of conventional generation units

\(j \in J\) :

Index (set) of load

\(M_{{I/j/{\text{ES}}/{\text{PL}}/{\text{WF}}}}\) :

Relation of the set of units/loads/ES/PL/WF into the set of buses.

\(n/r\) :

Indices the grid buses

\(p\) :

Number of PEVs

\({\text{pl}} \in {\text{PL}}\) :

Index (set) of parking lot

\(t \in \left( T \right)\) :

Index (set) of time duration

\({\text{wf}} \in {\text{WF}}\) :

Index (set) of wind farm

\(\psi\) :

Set of transmission lines

\(b_{i,f,t}\) :

The scheduled output power in the day-ahead market

\(B_{n,r}\) :

Susceptance between buses n and r

\(C_{i,t}^{{{\text{Su}}}}\) :

Start-up cost

\(C_{i,t}^{{{\text{RU}}/D}}\) :

The value of up/down spinning reserve

\(C_{{{\text{es,}}t}}^{{{\text{ES}}}}\) :

The energy value of ESSs in discharge mode

\(C_{{{\text{es}},t}}^{{{\text{RU}}/D}}\) :

The value of up/down reserve capacity of ESSs

\(C_{{{\text{pl,}}t}}^{{{\text{PL}}}}\) :

The energy value of the parking lot

\(C_{{{\text{pl}},t}}^{{{\text{RU}}/D}}\) :

The value of up/down reserve capacity of parking lot

\(C_{{{\text{es}},t,w}}^{U/D}\) :

The value of up/down reserve of ESSs

\(C_{i,f,t}^{d}\) :

The value of section f of proposed energy of generation units

\(C_{i,t,w}^{A}\) :

Start-up cost in the real-time market

\(C_{{{\text{pl}},t,w}}^{U/D}\) :

The value of up/down reserve capacity of parking lot

\({\text{cap}}_{{p,t_{{\text{p}}}^{{{\text{arv}}}} ,t_{{\text{p}}}^{{{\text{dep}}}} }}^{{{\text{PEV}}}}\) :

PEV capacity which arrived at the parking lot at the \(t^{{{\text{arv}}}}\) time and left the parking lot at the \(t^{{{\text{dep}}}}\) time

\({\text{Cap}}_{{{\text{pl}},t}}^{{{\text{PL}}}} /{\text{Cap}}_{{{\text{pl}},t,w}}^{{{\text{PL}}}}\) :

The total battery capacity of the parking lot in the day-ahead/real-time market

\(d_{{\text{t}}}\) :

Duration of time period

\(d_{j}^{{{\text{ini}}}}\) :

The initial demand before participate in the CAP in the day-ahead market

\(d_{j,t}\) :

The level of scheduled participation variation of loads based on CAP in the day-ahead market

\(E_{{{\text{es}},t,w}}\) :

The level of stored energy of ESSs

\(E_{{{\text{es}}}}^{\max /\min }\) :

Max/min energy range of ESSs

\(E_{{{\text{es}},t,w,{\text{ini}}}}\) :

The initial storage of ESSs

\({\text{FC}}_{i,t}\) :

Cost function of the production unit

\(f_{n,r,t}^{0} /f_{n,r,t,w}\) :

Power flow between buses n and r in the base case and under the scenario

\(f_{n,r}^{\max }\) :

Maximum capacity between buses n and r

\(g_{{{\text{es}},t}}^{{{\text{Ch}}/{\text{DCh}}}}\) :

Binary variable of charge/discharge status

\({\text{INC}}_{j,t}^{{{\text{CAP}}}}\) :

The cost of incentive payments to the consumer of load participating in the CAP

\(J_{i,t,w}\) :

The state of committing unit in the real-time market

\(L_{j,t,w}^{{{\text{shedding}}}}\) :

Load shedding costs

\(N_{{t_{{\text{p}}}^{{{\text{arv}}}} ,t_{{\text{p}}}^{{{\text{dep}}}} }}^{{{\text{PEV}}}}\) :

The total number of PEVs which arrived at the parking lot at the \(t^{{{\text{arv}}}}\) time and left the parking lot at the \(t^{{{\text{dep}}}}\) time

\(N_{{{\text{pl}}}}^{{{\text{PL}},{\text{arv}}/{\text{dep}}}}\) :

The number of PEVs arriving/departing to/from the parking lot

\(N^{{\text{PL,max}}}\) :

Maximum capacity of PEVs in the parking lot

\(N_{{{\text{pl}},t}}^{{{\text{PL}}}} /N_{{{\text{pl}},t,w}}^{{{\text{PL}}}}\) :

The number of PEVs in the parking lot in day-head/real-time market

\(P_{{{\text{es}},t}}^{{{\text{Ch}}/{\text{DCh}}}}\) :

Charge/discharge power of ESSs in the day-ahead market

\(P_{{{\text{pl,}}t}}^{{{\text{EJ}},{\text{PL2G}}/{\text{G2PL}}}}\) :

Injected power from PL2G/G2PL in the day-ahead market

\(P_{i}^{\max /\min }\) :

Maximum/minimum output power of unit i

\(P_{{_{i,t} }}\) :

Production capacity of each production unit in the day-ahead market

\(P_{{{\text{wf}},t}}^{{{\text{wp}},s}}\) :

The scheduled wind power generation in the day-ahead market

\(P_{{{\text{es}},t}}^{{{\text{Ch}}/{\text{DCh}},\max }}\) :

Maximum charge/discharge power of ESSs

\({\text{Per}}_{{{\text{cont}}}}^{{{\text{Min}}/{\text{Max}}}}\) :

Maximum/minimum probability of participation in the CAP

\(P_{r}\) :

Nominal power

\(R_{i,t}^{U/D}\) :

Up/down spinning reserve of conventional generation units in the day-ahead market

\(R_{{{\text{es}},t}}^{U/D}\) :

Up/down reserve of ESSs in the day-ahead market

\({\text{RU}}/D_{i,t}\) :

Up/down ramp rate of conventional generation units

\(R_{j,t}^{U/D,\max }\) :

The maximum up/down spinning reserves for participating in the CAP

\(R_{j,t}^{U/D}\) :

The scheduled up/down spinning reserves of load j in the day-ahead market

\(R_{{{\text{pl}},t}}^{U/D}\) :

Up/down reserve capacity of the parking lot in the day-ahead market

\(r_{i,f,t,w}^{G}\) :

The reserve of conventional generation units in the real-time market

\(r_{{{\text{es}},t,w}}^{U/D}\) :

Up/down spinning reserves of ESSs in the real-time market

\(r_{{{\text{pl,}}t,w}}^{U/D}\) :

Up/down parking lot reserve in the real-time market

\(r_{i,t,w}^{U/D}\) :

Up/down spinning reserve of conventional generation units in the real-time market

\({\text{SOE}}_{{{\text{pl}},t,w}}^{{{\text{PL}}}}\) :

Available energy in the parking lot due to the arrival/departure of PEVs in the real-time market

\({\text{SOE}}_{{{\text{pl,}}t,w}}^{{{\text{arv}}/{\text{dep}}}}\) :

The total amount of energy that is added/decreased due to the arrival/departure of new PEVs to/from the parking lot in the real-time market

\({\text{SOC}}_{{{\text{pl}}}}^{{\max /{\text{min}}}}\) :

Max/min state of charge of parking lot

\({\text{soc}}_{{\text{p}}}^{{{\text{PEV}},\max /\min }}\) :

Max/min truncated area for the initial state of charge of PEV p

\({\text{soc}}_{{\text{p}}}^{{{\text{PEV}},{\text{ini}}}}\) :

Initial state of charge of PEV p in the arrival time

\(t_{{\text{p}}}^{{\text{arv/dep}}}\) :

Arrival/departure time of PEV p

\(t_{{\text{p}}}^{{{\text{arv}}/{\text{dep}},\max /\min }}\) :

Min/max truncated area arrival/departure time of PEV p

\(T^{s}\) :

Response time to provide a reservation by each production units

\(u_{i,t}\) :

Binary variable of committing units in the day-ahead market

\(U_{{{\text{pl,}}t}}^{{{\text{PL2G}}/{\text{G2PL}}}}\) :

Binary variable indicating the state of discharging/charging of the parking lot

\(V_{j,t}^{{{\text{LOL}}}}\) :

Value of load shedding

\(V_{{{\text{wf}}}}^{s}\) :

Value of wind power spillage

\(V_{{{\text{wf}},t,w}}^{{{\text{wp}}}}\) :

Predicted wind power speed in scenario w

\(V_{{{\text{ci}}}}\) :

Cut-in speed

\(V_{r}\) :

Nominal speed

\(V_{{{\text{co}}}}\) :

Cut-out speed

\(WS_{{{\text{wf}},t,w}}\) :

Wind power spillage costs

\(y/z_{i,t}\) :

Binary variables of start-up/shut-down units

\(\alpha_{i} ,\beta_{i} ,\gamma\) :

Coefficients of fuel cost function of conventional generation units

\(\gamma^{{{\text{Ch}}/{\text{DCh}}}}\) :

Charging/discharging rate of PEVs

\(\Delta d_{j,t}\) :

Demand participate in the CAP

\(\delta_{{{\text{n}}/{\text{r}},t}}^{0} /\delta_{{{\text{n}}/{\text{r}},t,w}}\) :

Voltage angle of the line between buses n and r in base case and under scenario

\(\eta_{{{\text{Ch}}/{\text{DCh}}}}^{{{\text{PL}}}}\) :

Charge/discharge parking lot efficiency

\(\eta_{{{\text{Ch}}/{\text{DCh}}}}^{{{\text{ES}}}}\) :

The percentage of charge/discharge efficiency of ESSs

\(\lambda_{i,t}^{{{\text{Su}}}}\) :

Start-up offer cost of unit i in t hour

\(\mu_{{{\text{avr}}/{\text{dep}}/{\text{soc}}}}\) :

Mean value of arrival time/departure time/SOC random variables

\(\zeta_{{{\text{es}}}}^{{{\text{ini}}}}\) :

Initial percent charging of ESSs

\(\zeta_{t}\) :

Contract level of PEVs owners of suitable SOC

\(\Pi_{{\text{w}}}\) :

Probability of wind power scenarios

\(\sigma_{{{\text{arv}}/{\text{dep}}/{\text{soc}}}}\) :

Standard deviation of arrival time/departure time/SOC random variables

\(\chi\) :

Percentage of ESSs participation in the reserve allocation

CAP:

Capacity market program

DR:

Demand response

DSP:

Demand-side provider

ESSs:

Energy storage systems

GDF:

Gaussian distribution function

H&N:

Here-and now

ISO:

Independent system operator

MILP:

Mixed integer linear programming

PEVs:

Plug-in electric vehicles

PL2G/G2PL:

Parking lot/grid to grid/parking lot

SOC:

State of charge

UC:

Unit commitment

W&S:

Wait and see

WPDF:

Weibull probability distribution function

References

  1. Asensio Ł-M, Catalão J, Conteras J (2015) Smart and sustainable power systems: operations, planning, and economics of insular electricity grids, 1st edn. CRC Press, Cambridge

    Google Scholar 

  2. Ren G, Liu J, Wan J, Guo Y, Yu D (2017) Overview of wind power intermittency: Impacts, measurements, and mitigation solutions. Appl Energy 204:47–65. https://doi.org/10.1016/j.apenergy.2017.06.098

    Article  ADS  Google Scholar 

  3. Siano P (2014) Demand response and smart grids—a Aalami survey. Renew Sustain Energy Rev 30:461–478. https://doi.org/10.1016/j.rser.2013.10.022

    Article  Google Scholar 

  4. Alami H, Moghaddam MP, Yousefi G (2010) Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl Energy 87:243–250. https://doi.org/10.1016/j.apenergy.2009.05.041

    Article  ADS  Google Scholar 

  5. Mansouri SA, Ahmarinejad A, Sheidaei F, Javadi MS, Rezaee Jordehi A, Esmaeel Nezhad A, Catalão JPS (2022) A multi-stage joint planning and operation model for energy hubs considering integrated demand response programs. Int J Electr Power Energy Syst 140:108103. https://doi.org/10.1016/j.ijepes.2022.108103

    Article  Google Scholar 

  6. Mansouri SA, Nematbakhsh E, Ahmarinejad A, Rezaee Jordehi A, Javadi MS, Alavi Matin SA (2022) A Multi-objective dynamic framework for design of energy hub by considering energy storage system, power-to-gas technology and integrated demand response program. J Energy Storage 50:104206. https://doi.org/10.1016/j.est.2022.104206

    Article  Google Scholar 

  7. Mansouri SA, Ahmarinejad A, Nematbakhsh E, Javadi MS, Rezaee Jordehi A, Catalao JP (2021) Energy management in microgrids including smart homes: a multi-objective approach. Sustain Cities Soc 69:102852. https://doi.org/10.1016/j.scs.2021.102852

    Article  Google Scholar 

  8. Mansouri SA, Ahmarinejad A, Javadi MS, Esmaeel Nezhad A, Shafie-Khah M, Catalão JPS (2021) Demand response role for enhancing the flexibility of local energy systems. In: Distributed energy resources in local integrated energy systems, pp 279–313. https://doi.org/10.1016/B978-0-12-823899-8.00011-X

  9. ‏[9] Salkuti SR, (2019) Multi-objective based optimal scheduling of microgrid considering uncertainties. In: International conference on cutting-edge technologies in engineering (ICon-CuTE), pp 50–56. https://doi.org/10.1109/IConCuTE47290.2019.899159

  10. Salkuti SR (2023) Advanced technologies for energy storage and electric vehicles. Energies 16:2312. https://doi.org/10.3390/en16052312

    Article  Google Scholar 

  11. Hetzer J, David CY, Bhattarai K (2008) An economic dispatch model incorporating wind power. IEEE Trans Energy Convers 23:603–611. https://doi.org/10.1109/TEC.2007.914171

    Article  ADS  Google Scholar 

  12. Salkuti SR (2022) Emerging and advanced green energy technologies for sustainable and resilient future grid. Energies 15:6667. https://doi.org/10.3390/en15186667

    Article  Google Scholar 

  13. Mansouri SA, Nematbakhsh E, Ahmarinejad A, Rezaee Jordehi A, Javadi MS, Marzband M (2022) A hierarchical scheduling framework for resilience enhancement of decentralized renewable-based microgrids considering proactive actions and mobile units. Renew Sustain Energy Rev 168:112854. https://doi.org/10.1016/j.rser.2022.112854

    Article  Google Scholar 

  14. Mansouri SA, Rezaee Jordehi A, Marzband M, Tostado-Véliz M, Jurado F, Aguado JA (2023) An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster. Appl Energy 333:120560. https://doi.org/10.1016/j.apenergy.2022.120560

    Article  Google Scholar 

  15. Mansouri SA, Nematbakhsh E, Rezaee Jordehi A, Tostado-Véliz M, Jurado F, Leonowicz Z (2022) A risk-based bi-level bidding system to manage day-ahead electricity market and scheduling of interconnected microgrids in the presence of smart homes. In: 2022 IEEE international conference on environment and electrical engineering and 2022 IEEE industrial and commercial power systems europe (EEEIC/I&CPS Europe), pp 1–6. https://doi.org/10.1109/EEEIC/ICPSEurope54979.2022.9854685

  16. Mansouri SA, Ahmarinejad A, Nematbakhsh E, Javadi M-S, Esmaeel Nezhad A, Catalão JPS (2022) A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources. Energy 245:123228. https://doi.org/10.1016/j.energy.2022.123228

    Article  Google Scholar 

  17. Salkuti SR (2018) Congestion management based on optimal rescheduling of generators and load demands using swarm intelligent techniques. Adv Electr Electron Eng 15:713–723. https://doi.org/10.15598/aeee.v15i5.2258

    Article  Google Scholar 

  18. Salkuti SR (2020) Multi-objective-based optimal transmission switching and demand response for managing congestion in hybrid power systems. Int J Green Energy 17:457–466. https://doi.org/10.1080/15435075.2020.1761811

    Article  Google Scholar 

  19. Ortega-Vazquez MA, Kirschen DS (2009) Estimating the spinning reserve requirements in systems with significant wind power generation penetration. IEEE Trans Power Syst 24:114–124. https://doi.org/10.1109/TPWRS.2008.2004745

    Article  ADS  Google Scholar 

  20. Morales JM, Conejo AJ, Pérez-Ruiz J (2009) Economic valuation of reserves in power systems with high penetration of wind power. IEEE Trans Power Syst 24:900–910. https://doi.org/10.1109/TPWRS.2009.2016598

    Article  ADS  Google Scholar 

  21. Cobos NG, Arroyo JM, Alguacil N, Street A (2019) Robust energy and reserve scheduling under wind uncertainty considering fast-acting generators. IEEE Trans Sustain Energy 10:2142–2151. https://doi.org/10.1109/TSTE.2018.2880919

    Article  ADS  Google Scholar 

  22. Mohammad Gholiha M, Afshar K, Bigdeli N (2020) Optimal reserve determination considering demand response in the presence of high wind penetration and energy storage systems. Iran J Sci Technol Trans Electr Eng 44:1403–1428. https://doi.org/10.1007/s40998-020-00328-2

    Article  Google Scholar 

  23. Habibi M, Vahidinasab V, Shfie-khah M, Catalao JPS (2021) Coordinated scheduling of energy storage systems as a fast reserve provider. Int J Electr Power Energy Syst 130:106941. https://doi.org/10.1016/j.ijepes.2021.106941

    Article  Google Scholar 

  24. Egbue O, Uko C, Aldubaisi A, Santi E (2022) A unit commitment model for optimal vehicle- to- grid operation in a power system. Int J Electr Power Energy Syst 141:108094. https://doi.org/10.1016/j.ijepes.2022.108094

    Article  Google Scholar 

  25. Aalami H, Moghaddam MP, Yousefi G (2010) Modeling and prioritizing demand response programs in power markets. Electr Power Syst Res 80:426–435. https://doi.org/10.1016/j.epsr.2009.10.007

    Article  Google Scholar 

  26. Paterakis NG, Erdinc O, Bakirtzis AG, Catalao JPS (2015) Load-following reserves procurement considering flexible demand-side resources under high wind power penetration. IEEE Trans Power Syst 30:1337–1350. https://doi.org/10.1109/TPWRS.2014.2347242

    Article  ADS  Google Scholar 

  27. Paterakis NG, Sánchez de la Nieta AA, Bakirtzis AG, Contreras J, Catalao JPS (2017) Effect of risk aversion on reserve procurement with flexible demand side resources from the ISO point of view. IEEE Trans Sustain Energy 8:1040–1050. https://doi.org/10.1109/TSTE.2016.2635099

    Article  ADS  Google Scholar 

  28. Zhang M, Ai X, Fang J, Yao W, Zuo W, Chen Z, Wen J (2018) A systematic approach for the joint dispatch of energy and reserve incorporating demand response. Appl Energy 230:1279–1291. https://doi.org/10.1016/j.apenergy.2018.09.044

    Article  ADS  Google Scholar 

  29. Hajibandeh N, Shafie-khah M, Talari S, Dehghan SH, Amjady N, Mariano SJPS, Catalão JPS (2019) Demand response-based operation model in electricity markets with high wind power penetration. IEEE Trans Sustain Energy 10:918–930. https://doi.org/10.1109/TSTE.2018.2854868

    Article  ADS  Google Scholar 

  30. Shafie-khah M, Heydarian-Forushani E, Osorio GJ, Gil FA, Aghaei J, Barani M et al (2015) Optimal behavior of electric vehicle parking lots as demand response aggregation agents. IEEE Trans Smart Grid 7:2654–2665. https://doi.org/10.1109/TSG.2015.2496796

    Article  Google Scholar 

  31. Heydarian- Forushania E, Golshan MEH, Siano P (2017) Evaluating the benefits of coordinated emerging flexible resources in electricity markets. Appl Energy 199:142–154. https://doi.org/10.1016/j.apenergy.2017.04.062

    Article  ADS  Google Scholar 

  32. Ahrabi M, Abedi M, Nafisi H, Mirzaei MA, Mohammadi-Ivatloo B, Marzband M (2021) Evaluating the effect of electric vehicle parking lots in transmission-constrained AC unit commitment under a hybrid IGDT-stochastic approach. Int J Electr Power Energy Syst 125:106546. https://doi.org/10.1016/j.ijepes.2020.106546

    Article  Google Scholar 

  33. Wang X, Zhang H, Zhang Sh, Wu L (2021) Impacts of joint operation of wind power with electric vehicles and demand response in electricity market. Electric Power Syst Res 201:107513. https://doi.org/10.1016/j.epsr.2021.107513

    Article  Google Scholar 

  34. Abbaspour M, Satkin M, Mohammadi-Ivatloo B, Lotfi F-H, Noorollahi Y (2013) Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES). Renew Energy 51:53–59. https://doi.org/10.1016/j.renene.2012.09.007

    Article  Google Scholar 

  35. Xu Y, Yang Dong Z, Zhang R, Hill DJ (2017) Multi-timescale coordinated voltage/var control of high renewable-penetrated distribution systems. IEEE Trans Power Syst 32:4398–4408. https://doi.org/10.1109/TPWRS.2017.2669343

    Article  ADS  Google Scholar 

  36. Grigg C, Wong P, Albrecht P et al (1999) The IEEE reliability test system-1996. A report prepared by the reliability test system task force of the application of probability methods subcommittee. IEEE Trans Power Syst 14:1010–1020. https://doi.org/10.1109/59.780914

    Article  ADS  Google Scholar 

  37. Soroudi A (2017) Power system optimization modeling in GAMS. Springer, Cham

    Book  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran

    Mahrou Pouladkhay, Maziar Mirhosseini Moghaddam & Alireza Sahab

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Contributions

MP contributed to data curation, writing—original draft preparation, software, resources, and formal analysis. MMM contributed to definition, methodology, supervision, visualization, investigation, writing—reviewing and editing, conceptualization, and validation. AS contributed to methodology, supervision, and editing.

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Correspondence to Maziar Mirhosseini Moghaddam.

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Pouladkhay, M., Mirhosseini Moghaddam, M. & Sahab, A. A two-stage stochastic unit commitment considering demand-side provider and wind power penetration from the ISO point of view. Electr Eng 106, 295–314 (2024). https://doi.org/10.1007/s00202-023-01961-w

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