نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشگاه سمنان.

2 استادیار، دانشگاه گیلان.

3 دانشجوی دکتری، دانشگاه سمنان.

چکیده

در این پژوهش با هدف ارزیابی کارایی نواحی تولید و انتقال نیرو در صنعت برق ایران در طی زمان و با توجه به ماهیت سیستمی مراحل تولید و انتقال برق، دو رویکرد تحلیل پوششی داده‌های شبکه‌ای و تحلیل پنجره با هم ترکیب شده است. این سیستم شبکه‌ای دارای خروجی نامطلوب و مطلوب نهایی و میانی بوده و دارای دو مرحله تولید و انتقال برق در بازه زمانی 1391 تا 1393 است. متغیرهای ورودی و خروجی مرحله اول و دوم به‌ترتیب مصرف داخلی نیروگاه‌ها، سوخت مصرفی و بازده حرارتی، راندمان، قدرت نامی نیروگاه و قدرت عملی نیروگاه، حداکثر بار تولیدی، تولید ویژه و تولید ناویژه، ظرفیت پست‌های انتقال نیرو و طول خطوط انتقال نیرو و درنهایت انرژی تحویلی و تلفات انرژی در نظر گرفته شده است. نتایج نشان می‌دهد که با توجه به مجموعه امکان ادغام‌شده طی سه دوره، در سال 1392 از نظر کارایی کل و کارایی مرحله تولید بالاترین تعداد واحدهای کارا وجود دارد؛ درحالی‌که در مقایسه با مرز کارای سه‌دوره‌ای، مرحله تولید برق بیشترین واحدهای ناکارا را دارد و افزایش تعداد واحدهای کارا در مرحله کارایی کل ناشی از کارایی بالای شرکت‌های برق منطقه‌ای است.

کلیدواژه‌ها

عنوان مقاله [English]

An Integrated Approach of DEA with a Variety of Outputs and Windows Analysis for Evaluating Efficiency of the Power Industry

نویسندگان [English]

  • Mohsen Shafiei Nikabadi 1
  • Keikhosro Yakideh 2
  • Akram Oveysi Omran 3

1 Assistant Professor, Semnan University.

2 Assistant Professor, Guilan University.

3 Ph.D. Candidate, Semnan University.

چکیده [English]

The aim of this study is to evaluate efficiency of the power industry using an integrated approach of Network DEA with a variety of outputs and Windows Analysis. This network system includes undesirable, final desirable and intermediate output in time interval of 2012 to 2014. Input and output variables of first and second stages were respectively considered as domestic consumption of power stations, fuel consumption, thermal output, efficiency, nominal power of power station and operational power of power station, maximum production, especial production and gross production, capacity of power transmission substations, and finally delivery energy and energy losses. The results indicate that due to the possibility of integration over three times, in terms of total efficiency and efficiency of production stage, there is highest number of efficient units. In 2013 However, compared to efficient frontier of three periods, electricity production stage had the greatest number of inefficient units and increasing the number of efficient units in the total efficiency stage is due to high efficiency of regional electricity companies.

کلیدواژه‌ها [English]

  • Window Analysis
  • Network Data Envelopment Analysis
  • Undesirable Output
  • Power Production
  • Power Transmission
1. Arocena, P. (2008). Cost and quality gains from diversification and vertical integration in the electricity industry: A DEA approach. Energy Economics, 30, 39–58.
2. Asmild, M., Paradi, C.V., Aggarwall, V., Schaffnit, C.  (2004). Combining DEA window analysis with the Malmquist Index approach in a study of the Canadian banking industry, Journal of Productivity Analysis, 21(1), 67–89.
3. Avkiran, A., K., (2015), an illustration of dynamic network DEA in commercial banking including robustness tests, Omega, (55), 141-150.
4. Bisschop, J. (2012), AIMMS Optimization Modeling, Paragon Decision Technology, Bellevue, WA 98004, USA.
5.Chen Y, Cook WD, Li N, Zhu J. (2009). Additive efficiency decomposition in two-stage DEA, European Journal of Operational Research, 196(3), 1170–6.
6. Chen Y, Zhu J., (2004). Measuring information technology's indirect impact on firm performance, Information Technology and Management, 5 (1), 9– 22.
7. Chen C, Yan H., (2011). Network DEA model for supply chain performance evaluation, European Journal of Operational Research; 213(1), 17– 55.
8. Chen P-C, Chang C-C, Yu M-M, & Hsu S-H. (2012). Performance measurement for incineration plants using multi-activity network data envelopment analysis: The case of Taiwan., Journal of Environmental Management, 93(1), 95– 103.
9. Cook WD, Liang L, & Zhu J. (2010), Measuring performance of two-stage network structures by DEA: a review and future perspective. Omega, 38(6), 423–30.
10. Du JA, Liang LA, Chen Y, Cook WD, Zhu J., (2011). A bargaining game model for measuring performance of two-stage network structures. European Journal of Operational Research; 210(2), 390–7.
11. Färe R., (1991). Measuring Farrell efficiency for a firm with intermediate inputs. Academia Economic, Papers 19, 329–40.
12. Färe R, and Grosskopf S., (1996), Productivity and intermediate products: A frontier approach. Econ Left 1996; 50 (1): 65–70.
13. Färe R, Grosskopf S., (2000). Network DEA. Socio-Economic Planning Sciences, 34(1). 35–49.
14. Fukuyama H, Weber WL. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs, Omega, 38(5). 398– 409.
15. Herrera-Restrepo, O., Triantis, K., Trainor, J., Murray-Tuite, P., & Edara, P. (2016). A multi-perspective dynamic network performance efficiency measurement of an evacuation: A dynamic network-DEA approach, Omega, (60), 45-59.
16. Kao C, Hwang S-N. (2010). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429.
17. Khalili- Damghani, K., Shahmir, S. (2015). Uncertain network data envelopment analysis with undesirable outputs to evaluate the efficiency of electricity power production and distribution processes, Computers & Industrial Engineering, (88), 131-150.
18. Kopsakangas, M. & Svento, R. (2008), Estimation of cost-effectiveness of the Finnish electricity distribution utilities. Energy Economics, 30, 212-29.
19. Kordrostami S, Amirteimoori A. (2005). Un-desirable factors in multi-component performance measurement. Applied Mathematics and Computation, 171(2), 721–9.
20. Liang L, Cook WD, Zhu J. (2008). DEA models for two-stage processes: game approach and efficiency decomposition. Naval Res Logistics (NRL), 55(7), 643–53.
21. Lozano S, Gutierrez E, and Moreno P, (2013). Network DEA approach to airports performance assessment considering undesirable outputs. Applied Mathematical Modelling, 37(4), 1665– 1676.
22. Maghbouli M, Amirteimoori A, Kordrostami S. (2014). Two-stage network structures with undesirable outputs: a DEA based approach. Measurement, 48(0), 109– 18.
23. Seiford LM, Zhu J. (2002). Modeling undesirable factors in efficiency evaluation, European Journal of Operational Research; 142(1), 16–20.
24. Tone K, & Tsutsui, M. (2009). Network DEA: A slack-based measure approach. European Journal of Operational Research; 197(1), 243–52.
25. Tone K. & Tsutsui, M., (2014), Dynamic DEA with network structure: A slacks-based measure approach, Omega, (42), 124-131.
26. Wang K, Huang W, Wu J, Liu Y-N. (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, 44 (0), 5–20.
27. Zhou Z, Sun L, Yang W, Liu W, Ma C. (2013). A bargaining game model for efficiency decomposition in the centralized model of two-stage systems. Computers & Industrial Engineering, 64(1), 103– 8.