Elsevier

Energy

Volume 35, Issue 10, October 2010, Pages 4053-4063
Energy

A micro level study of an Indian electric utility for efficiency enhancement

https://doi.org/10.1016/j.energy.2010.06.011Get rights and content

Abstract

In this paper DEA (Data envelopment analysis), a non-parametric approach to frontier analysis, is applied to evaluate the relative performance of 29 EDDs (Electricity Distribution Divisions) of an Indian hilly state, namely Uttarakhand. Input oriented DEA is applied to evaluate the relative overall efficiency, technical efficiency and scale efficiency of EDDs. The results indicate that numerous divisions have scope for improvement in overall efficiency. Most of the utilities are inefficient due to their scale inefficiency rather than technical inefficiency. Further Slack analysis is carried out to formulate improvement directions for relatively inefficient divisions. Particular areas are identified, which are to be improved for overall efficiency enhancement through sensitivity analysis. Different alternatives for reorganization of the EDDs are explored in order to obtain most favorable and balanced scale. The result is envisaged to be instrumental to policy makers and managers to increase the operational efficiency of the EDDs leading to higher profitability of the state electricity board.

Introduction

Electrical energy is a world wide accepted significant parameter for measuring the economic and social prosperity of any nation. About 1.5–2 billion people in developing countries do not have access to the electricity and 450 million of them are in India alone [1]. Even in this scenario the AT & C (Aggregate Technical and Commercial) losses in India gnaws about 35% of energy produced [2]. To bridge over this lacunae, India instigated reform process of its power sector in 1991 with the prime aim of meeting the ever-widening gap between the demand and availability of electricity, improving the technical performance of the SEBs (State electric boards), enabling the central and state government to finance and mobilize resources for generation capacity expansion projects making third party investment in power sector imperative. Comprehensive reforms of legislation including Electricity Regulatory Commission Act 1998, Electricity Bill (2000) and Electricity Act 2003 also followed.

All the 29 states in India have resorted to restructuring process of their respective SOEUs (State Owned Electric Utilities) and these are at various stages of implementation [3]. But even about two decades after restructuring was initiated, all these states are still facing both energy and peak demand shortage. In India with the increasing population and rapid development, energy shortage and peaking shortage are increasing with time and have elevated to 11.1% and 11.9% respectively in 2008–09 from the level of 8.1% and 11.3% in 1997–98 [2]. The growth of power sector could not keep pace with the economic growth of the country. Though reforms have been implemented by most of the states, power sector continued to render unsatisfactory performance as the attention was focused on generation expansion programs mainly. Whereas the reform in distribution sector should have also been given an equal or more importance as efficiency improvement measures, keeping in view that the Indian power utilities feed a very large number of consumers, located over wide area of the subcontinent [4].

In India the distribution segment as a whole has lagged, in terms of both operation efficiency and financial performance. The financial performance of Indian Power utilities is severely hampered by low RoI (Return on Investments) and poor collection recovery from the consumers. This situation is further aggravated by poor operational efficiency. Realizing the need to accelerate the reforms in the distribution sector, the central government introduced APDRP (Accelerated Power Development & Reforms Program) for urban areas with the objective to improve the financial viability of state utilities, reducing AT & C losses, improving customer satisfaction, and increasing the reliability and quality of the power supply. The reform linked investment component also motivated restructuring and initiation of regulatory reforms in various states [5]. In this scenario of deregulation and restructuring, it is being viewed with substantial importance to evaluate the performance of the distribution utilities and recognize the scope for improvement in efficiency of various states, carrying out an intra-state analysis.

DEA (Data Envelopment Analysis) is probably the most widely used mathematical approach for benchmarking of organizational units. DEA was proposed by Charnes et al. [6]; Banker et al. [7], and built on the idea of Farrell [8] as a tool to quantify relative efficiency of different types of DMUs (Decision Making Units) viz. schools, hospitals, and power plants etc. DEA has emerged as an effective tool for management and planning, as it identifies inefficiencies of DMUs and provides targets for improvement for inefficient DMUs, and hence has applicability to diverse range of real world problems [9], [10]. In this paper relative efficiency of EDDs (Electricity Distribution Divisions) of Uttarakhand – a northern state of India, have been calculated and distinction between the efficient and inefficient EDDs have been drawn using DEA. The use of DEA for evaluating the relative efficiencies of EDDs gives a good understanding of the micro level issues in the context of resource utilization. Based on the efficiency analysis, different alternatives for reorganization of EDDs have been investigated, and a suitable solution is proposed to improve the overall efficiency of UPCL (Uttarakhand Power Corporation Ltd). This paper is organized as follows. After the introductory section, literature review is given in Section 2. Performance of Uttarakhand power sector is presented in Section 3. Section 4 discusses the selection of input and output and methodology for the DEA analysis has been discussed in Section 5. Section 6 includes results and discussion. Policy implication of the analysis is discussed in Section 7. Results of sensitivity analysis are presented in Section 8. Section 9 discusses the reorganization of EDDs. Section 10 concludes with the findings of this paper.

Section snippets

Literature review

Studies carried out so far using DEA to investigate the relative efficiency of the power industry are described in the subsequent paragraphs of this section. Weyman-Jones [11] applied DEA to the regulated electricity distribution industry in England and Wales. He found that only five of the twelve electricity boards were technically efficient, and there was wide divergence in performance amongst them. Targets for improvement of inefficient electricity boards were suggested and implications for

Performance of Uttarakhand power sector

Electricity demand in India has increased significantly over the past two decades concomitant to the rapid economic growth in post liberalization era. High energy shortage and peak deficit are by and large a common phenomenon in all the states. This can be attributed to operational inefficiency, non-rationality at all stages of trading of energy viz. tariff setting, metering, billing, revenue etc. and inadequate resources for capacity addition [4]. In 2007–08, Uttarakhand power sector faced

Selection of inputs and outputs

Selection of input and output is the most important step in the process of performance evaluation while using DEA. In this paper the selection of inputs and outputs are made based on the insight given by related case study of Miliotis [13]. The model which is closely related to operating efficiency from four different cases for input-output selection proposed by the author is chosen in the present work. Following inputs and outputs parameters related to EDDs are selected for analysis in the

DEA

DEA, basically developed by Charnes et al. [6], is a non-parametric approach for generating the efficiency frontier for the DMUs. It is a LP (linear programming) method that deals with the multiple inputs and multiple outputs without pre-assigned weights and without imposing any functional form on the relationships between variables. This section describes the two different models employed in the present analysis.

Results of CCR model

The input oriented CCR model with CRS is applied to determine the efficiency frontier for 29 EDDs. In this study the dual LP formulation of the CCR and BCC models were run 29 times by using DEAP Version 2.1, a computer program by Coelli [38].

Overall efficiency scores of all EDDs are given in Table 4. It is clear from the CCR result that only eight EDDs are identified as efficient with efficiency score equal to 1. And rests of the division are relatively inefficient having efficiency scores

Policy implication

Above DEA analysis reveals that most of the divisions are relatively inefficient and slack analysis identifies the scope for possible reduction in O&M cost and number of employees. Table 4 demonstrate the need for induction of cost efficiency among the divisions in the power supply services owned by UPCL; as per the result, it is possible to reduce Rs 5.909 million yearly, invoking higher efficiency in the current practices of UPCL. This may yield cash surplus sufficient enough to improve

Sensitivity analysis

Sensitivity analysis is carried out to determine the effect of input & output factors on efficiency. In particular, it was analyzed the differences of the relative efficiencies of the divisions by eliminating input and output variable one at a time from DEA model and the result is summarized in Table 6. The values within the brackets represent the differences between the original efficiency and the result of changing input and output factors. Result of sensitivity analysis shows that

Reorganization of EDDs

In this study, it was investigated different reorganization alternatives for the divisions that are not operating at optimal scale. Fig. 3 is a map of Uttarakhand which represents all the 29 EDDs. For combining two adjacent divisions the following factors are also given due weightage: geographical limitations and connectivity. The procedure adopted for the reorganization of divisions is to combine inefficient divisions that represent increasing return to scale with the adjacent efficient one

Conclusion

Using DEA, the paper explores the relative performance of 29 EDDs of Uttarakhand – an Indian state. The use of DEA for evaluating the relative efficiencies of EDDs provided an insight of the micro level issues in the context of resource utilization. CCR and BCC model were applied to evaluate the overall efficiency, technical efficiency and scale efficiency of divisions. Results reveal that only 8 divisions are identified as overall efficient and EDDs located in plain region outperform those

Acknowledgements

The authors would like to thank the generous assistance provided by the Zonal headquarters (Dehradun and Haldwani) and State headquarters /Urja Bhawan (Dehradun) of Uttarakhand Power Corporation Limited. We are grateful to reviewers for their constructive comment and invaluable suggestion.

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