• Renewable energy power generation R&D Investment under incomplete information.

    May 2020 / by

    The incomplete information of the future prospects of new renewable energy power generation technology, which is contained in external random events regarding policy, technology and market, together with the technical uncertainty of R&D success will affect the investment decision of renewable electric power enterprises. The critical beliefs, which are necessary for single enterprise and duopoly renewable energy power enterprises for their investment in new power generation technologies, are obtained respectively by taking into consideration the incomplete information, technical uncertainty and competitiveness and construction of decisionmaking model based on real options, and the equilibrium type that may generate in market competition of the two symmetric enterprises and the generation conditions are further analyzed in this paper. The result indicated that the winner take- all privilege granted to the leading innovator and the technical uncertainty to innovative technology by policy will make the following enterprises react in two ways, investment delay or investment advancing. The faster signal arrives, the higher the signal quality is and the higher is the investment belief of the following investor. Moreover, the R&D competition equilibrium among enterprises can result in preemptive equilibrium and simultaneous investment equilibrium.

    Keywords: Incomplete information, renewable energy power, R&D, option game, game equilibrium.

    References

    [1] Thijssen J J J, van Damme E E C, Huisman K J M, Kort P M. Investment under Vanishing Uncertainty due to Information Arriving over Time[R]. Working Paper, No. 2001-14, Center, Tilburg University, 2001a. [2] Thijssen J J J, Huisman K J M, Kort P M. Strategic Investment under Uncertainty and Information Spillovers[R]. Working Paper, No. 2001-91, Center, Tilburg University, 2001b. [3] Dixit A K, Pindyck R S. (1994): Investment under Uncertainty [M]. Princeton, NJ: Princeton University Press. [4] Lambrecht B, Perraudin W. (2003): Realoptions and Preemption under Incomplete Information[J]. Journal of Economic Dynamics & Control, 27(4): 619-643. [5] Decamps J, Mariotti T, Villeneuve S. (2005): Investment Timing under Incomplete Information[J]. Mathematics of Operations Research, 30(2): 472–500. [6] Wu Jianzu, Xuan Huiyu. (2006): Option-game analysis on optimal R&D investment timing of enterprises with incomplete information [J]. Systems Engineering —Theory & Practice, 26(4): 50-54. [7] Cai Qiang, Zeng Yong, Deng Guangjun. (2008): Patent investment with incomplete information [J]. System Engineering, 26(9): 64-67. [8] Cai Qiang, Deng Guangjun, Zeng Yong. (2009): Influence of randomly arriving incomplete information on patent competition [J]. Systems Engineering – Theory & Practice, 29(4): 81-91. [9] 2012, 28(1): 115-120. Wang Xiaotian, Xue Huifeng. Positive analysis and policy implication on factors affecting renewable energy power generation investment decision- making behaviours [J]. Journal of Xi’an University of Technology, 28(1): 115- 120. [10]Wang Wenping, Yang Hongping. (2008): Application of real option theory on wind power generation project investment decision-making [J]. Power Grid and Clean Energy, 8:42-46. [11] Liu Jun et al. (2013): Photovoltaic power generation investment decision – making model based on real-option theory. Energy Conservation Technology, 31(1): 75-78. [12] Huang Wenjie, Huang Yi. (2010): Power generation option-game investment decision-making model based on risk preferences of investors [J]. Journal of North China Electric Power University, 37(2): 99-103. [13] Fudenberg D, Tirole J. (1985): Preemption and Rent Equalization in the Adoption of New Technology [J]. Review of Economic Studies, 52(3): 383-401.

    Contingency analysis of a 10-bus power system using power world simulator.

    May 2020 / by

    In the deregulated regime electricity market is oligopolistically competitive. It is one of the challenging tasks for the power system engineers to maintain security of the power system. The security will be maintained if the system becomes able to operate within system constraints, like limits of bus voltage magnitudes, current and power flow over the lines etc, to name a few, in the event of outage, i.e. contingency, of any component like generator or transmission line. The goal of the contingency analysis is to give the operator about the static security information. Contingency analysis of a power system is a major activity in power system planning and operation. In the present work, the authors have considered a hypothetical 10-Bus system and studied the contingency analysis of the system based on one by one outage of generators and lines using Power World simulator. The results of major violations are shown in the paper. The linear sensitivity factors are the faster techniques to estimate the post contingency values of different quantities of interest. In the present work one sensitivity factor like line outage distribution factor (LODF) has been estimated. Optimal power flow at major violation has been performed to identify the effective system adjustment.

    Keywords:Contingency analysis, power world simulator, LODF.

    References

    1. Wood A.J, Wollenberg B.F. (1996): Power generation, operation and control. John Wiley & Sons Inc. 2. Kothari D.P., Nagrath I.J. (2011): Modern power system analysis.TMH Publication. 3. Mishra V.J., Khardenvis M. D. (2012): Contingency analysis of power system. In: International Conference on Emerging Frontiers in Technology for Rural Area (EFITRA), pp-31-34. 4. Aliyan E., et al, (2020): Decision tree analysis to identify harmful contingencies and estimate blackout indices for predicting system vulnerability. Electric Power Systems Research 178. 5. Basu A. K., Chowdhury S., Chowdhury S. P., Paul S. (2011): Microgrids: Energy management by strategic deployment of DERs – A comprehensive survey. Renewable and Sustainable Energy Reviews, 15, pp. 4348-4356. 6. Contingency analysis – PowerWorld. available at: https://www.powerworld.com 7. Zhai C., et al, (2019): A model predictive approach to protect power systems against cascading blackouts. Electrical Power and Energy Systems 113, 310–321.

    Channel confluence dynamics in the lower Chel river system, eastern sub-Himalayan West Bengal, India.

    May 2020 / by

    In the present study channel confluence movements of two confluence points namely, Chel-Kumlai rivers’ confluence and Chel-Neora rivers’ confluence in the lower Chel-Neora river system has been analyzed using multi temporal Landsat images and topographical sheets. The study area is a part of region popularly known as ‘Dooars’ which falls in the zone of transition between the dissected outer Himalayan hill surface and the gently rolling Teesta-Brahmaputra plains, and is famous for notorious incidents of channel avulsion and river capture activities. This study tries to elucidate various fluvial processes responsible for confluence dynamics. The temporal scale of the study spans 62 years (1955-2017) and establishes the fact that the confluence points of the rivers have shifted both upstream and downstream in much variable rates during the assessment period. Avulsions, cut-offs, aggradations and river capture has been identified as controlling processes for such confluence dynamics.

    Keywords: Chel river, aggradation, confluence dynamics, junction angle.

    References

    1. Brookfield, M.E., (1998): The evolution of the great river systems of southern Asia during the Cenozoic India–Asia collision: rivers draining southwards. Gemorphology 22, 285-312. 2. Chakraborty S, Datta K (2013): Causes and Consequences of Channel Changes – A Spatio-Temporal Analysis using Remote Sensing and Gis— Jaldhaka-Diana River System (Lower Course), Jalpaiguri (Dooars), West Bengal, India. J Geogr Nat Disast 3: 107. doi:10.4172/2167- 0587.1000107 3. Chakraborty S, Mukhopadhyay S. (2014): A comparative study on the nature of channel confluence dynamics in the lower jaldhaka river system, west bengal, india. International Journal of Geology, Earth & Environmental Sciences. 2014 Vol.4 (2) May-August, pp. 87-97. 4. Friend, P.F., Sinha, R., (1993): Braiding and meandering parameters. In: Best, J.L., Bristow, C.S. (Eds.), Braided Rivers, pp. 105–111. 5. Gibling, M.R., Tandon, S.K., Sinha, R., Jain, M., (2005): Discontinuity bounded alluvial sequences of the sounthern Gangetic plains, India; aggradation and degradation in response to monsoonal strength. Journal of Sedimentary Research 75 (3), 369–385. 6. Horton, R.E., (1970): Erosional development of streams: quantitative physiographic factors. In: Dury, G.H. (Ed.), River and River Terraces. Macmillan, London, pp. 117–165. 7. Howard, A.D., (1971):. Optimal angles of stream junction: geometric stability to capture and minimum power criteria. Water Resources Research 7, 863–873. 8. Jones, L.S., Schumm, S.A., (1999): Causes of avulsion: an overview. In: Smith, N.D., Rogers, J. (Eds.), Fluvial Sedimentology VI. Special Publication of International Association of Sedimentologists, vol. 28, pp. 171–178. 9. Leier, A.L., DeCelles, P.G., Pelletier, J.D., (2005): Mountains, monsoons and megafans. Geology 33 (4), 289–292. 10. Mather, A.E., (2000): Adjustment of a drainage network to capture induced base-level change: an example from the Sorbas Basin, SE Spain. Gemorphology 34, 271–289. 11. Mather, A.E., Harvey, A.M., Stokes, M., (2000): Quantifying long-term catchment changes of alluvial fan systems. Geological Society America Bulletin 112 (12), 1825–1833. 12. Mitra, D., Tangri, A.K., Singh, I.B., (2005): Channel avulsion of the Sarda River system, Ganga plain. International Journal of Remote Sensing, 26 (5), 929–936. 13. Roy N, Sinha R (2007): Understanding Confluence Dynamics in the Alluvial Ganga-Ramganga Valley, India: An Integrated Approach Using Geomorphology and Hydrology. Geomorphology 92: 182-197. 14. Singh, I.B., (1987): Sedimentological history of Quaternary deposits in Gangetic Plain. Indian Journal of Earth Sciences, 14 (3–4), 272–282. 15. Starkel L, Sarkar S, Soja R and Prokop P (2008): Present-day evolution of the Sikkimese-Bhutanese Himalayan Piedmont. Warszawa. Polska Akademia Naukinstytut Geografii I Przestrzennego Zagospodarowania. Prace Geograficzne NR 219 16. Stevaux JC, Franco AA, Etchebehere de Carlos ML and Fujita RH (2009): Flow structure and dynamics in large tropical river confluence: example of the Ivaí and Paraná Rivers, Southern Brazil. Geociências 28(1) 5-13 17. Tangri, A.K., (1986):. Understanding the dynamics of Ghaghra river system in Uttar Pradesh, India, using satellite remote sensing. Proceedings of the Seventh Asian Conference on Remote Sensing. Korean Society of Remote Sensing. Asian Association on Remote Sensing, Seoul, Korea, pp. 1-6. 18. Williams, M.A.J., Clarke, M.F., (1984): Late Quaternary environments in north-central India. Nature 308, 633-635. Williams, M.A.J., Clarke, M.F., 1995. Quaternary geology and prehistoric environments in the Son and Belan valleys, north central India. Memoir Geological Society of India 32, 282-307.

    Research on the decision of state maintenance for transmission line based on set pair analysis

    May 2020 / by

    Overhead transmission lines are important parts of a power system; their operation state directly affects the reliability level of the entire power system. With the in-depth development of state maintenance work for power grids, correctly evaluating the reliability of overhead transmission lines is the key to successful maintenance. A maintenance decision model for transmission lines is established in this study based on set pair analysis to achieve human financial control and low maintenance efficiency. Full consideration is provided to the influence of environmental factors, and a theoretical basis for transmission line maintenance decision is established.

    Keywords: Overhead transmission line, state maintenance, set pair analysis, reliability.

    References:

    1. Han D. J., White A. (1995): Transformer Design for High Reliability [J]. The reliability of transmission and distribution equipment, (406): 29-31. 2. Van Schijndel A., Wetzer J. M. (2006): Forecasting Transformer Reliability [J]. IEEE conference on electrical insulation and dielectric phenomena, 577-582. 3. Md. Mafijul Islam Bhuiyan, Petr Musilek, et al. (2010): Evaluating Thermal Aging Characteristics of Electric Power Transmission Lines [J]. Electrical and computer engineering (CCECE), 2010 23rd Canadian Conference, 1-4. 4. Aggarwal R. K., Johns A. T., Jayasinghe J.A.S.B. (2000): An Overview of The condition Monitoring of Overhead Line [J]. Electric power system research, 6:22. 5. Harly W., Sokolov J. (2000): Contribution of Panel on Modern Maintenance Techniques for Enhancing the Reliability of Insulation of Power Transmission Systems. CIGRE-Report Pi-06 : 87-91. 6. Wang H., Phara H. (1999): Some Maintenance Models and Availability with Imperfect Maintenance in Production Systems [J]. Annals of operations research, 91(3): 306. 7. GuoWeiyue. (2006): Preventing Measures of Bird Interference with Transmission Lines and Substation Equipment in U. S [J]. Electric Power, 39(8): 82-84.

     

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