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Electricity theft: A comparative analysis
Abstract
Electricity theft can be in the form of fraud (meter tampering), stealing (illegal connections), billing irregularities, and unpaid bills. Estimates of the extent of electricity theft in a sample of 102 countries for 1980 and 2000 are undertaken. The evidence shows that theft is increasing in most regions of the world. The financial impacts of theft are reduced income from the sale of electricity and the necessity to charge more to consumers. Electricity theft is closely related to governance indicators, with higher levels of theft in countries without effective accountability, political instability, low government effectiveness and high levels of corruption. Electricity theft can be reduced by applying technical solutions such as tamper-proof meters, managerial methods such as inspection and monitoring, and in some cases restructuring power systems ownership and regulation.
... Dishonest people either steal electricity directly by illegally tapping on distribution lines or bribe utility employees to avoid risks of being detected and/or convicted due to overdue electricity consumption (Jamil andAhmad, 2014, 2019). Due to non-technical losses, utilities lose electricity worth billions of dollars on an annual basis and to avoid such losses, the honest paying utility customers incur high tariffs and/or experience power outages (Jamil and Ahmad, 2019;Smith, 2004;Wesonga, 2020). ...
... This paper concentrates on this question for two reasons: First, there is lean literature on electricity theft and electricity security. Existing studies either concentrate on the drivers of electricity theft (Smith, 2004;Yakubu et al., 2018;Razavi and Fleury, 2019;Jamil, 2018;Saini, 2017;Depuru et al., 2011) or dominantly articulate measurement controversies regarding electricity security (Wabukala et al., 2022;Neelawela et al., 2019b;Sarhan et al., 2021;Larsen et al., 2017) without providing the associated impacts between the two constructs. Second, while deterrence and punitive measures (technological and legal) exist and have adequately been profiled in literature (Jamil and Ahmad, 2019;Smith, 2004;Sharma et al., 2016;Shokoya and Raji, 2019;Dike et al., 2015;Saini, 2017;Depuru et al., 2011) their implementation has not yielded expected targets. ...
... Existing studies either concentrate on the drivers of electricity theft (Smith, 2004;Yakubu et al., 2018;Razavi and Fleury, 2019;Jamil, 2018;Saini, 2017;Depuru et al., 2011) or dominantly articulate measurement controversies regarding electricity security (Wabukala et al., 2022;Neelawela et al., 2019b;Sarhan et al., 2021;Larsen et al., 2017) without providing the associated impacts between the two constructs. Second, while deterrence and punitive measures (technological and legal) exist and have adequately been profiled in literature (Jamil and Ahmad, 2019;Smith, 2004;Sharma et al., 2016;Shokoya and Raji, 2019;Dike et al., 2015;Saini, 2017;Depuru et al., 2011) their implementation has not yielded expected targets. Given that electricity theft has a socio-economic dimension, a strategy that targets on reducing electricity theft while providing affordable alternatives for households may improve electricity security in Uganda. ...
Renewable energy sources (RES) dominance in Uganda's electricity mix is challenged by affordability and theft. To assess electricity affordability, the study proposed a probabilistic method to quantify the households into different electricity categories for both urban and rural areas. Alternative electricity billing schemes based on Scenarios A to D for the households to enhance legal connection and consumption of electricity were proposed. The study established that the utility registers the highest electricity theft losses in rural households. The monthly utility revenue collected in urban areas was about 2.9 times that collected in rural areas because of the higher number of legally connected households with a monthly consumption of 1.5 times than that of rural households. From the monthly income spendable on electricity, rural and urban households could only afford 25.07 kWh and 38.29 kWh, respectively, which are less than the average household electricity consumption for Uganda. Also, the initial connection fee to the power grid is very high for the households to afford it in a single down payment. Of the proposed alternative billing schemes, Scenario B and Scenario D yield the least monthly utility revenue collected for the urban and rural households, respectively.
... For instance, the CFE reports that in the first semester of 2021, the company lost over US$1.750mn in electricity thefts; this number is equivalent to a 36,6GWh which accounts for 11.6 % of CFE's total sales during the first six months of 2021 (Cervantes, 2021). Theft of electricity happens through several forms, including consumer meter tampering, illegal tapping (using a foul nightline or diablitos), and billing problems (unpaid bills irregularities or with billing) (Sharma, Pandey, Punia, & Rao, 2016;Smith, 2004;Wong, Blankenship, Urpelainen, Ganesan, & Balani, 2021). The shared element that these illegal activities have in common is that they result in revenue loss for the utility companies. ...
... Research on this area suggests that corruption remains a persistent concern to prevent the theft of energy equipment or energy services (including non-technical losses in electricity supply, distribution or transmission) (Sovacool, 2021). For instance, Smith notes that one of the central reasons for electricity theft is the collaboration of customers and utility employees (Smith, 2004); this allows billing irregularities and misreporting without damaging the electricity meter. Electricity theft via illegal connections also raises safety concerns that range from electric shock and even the death of a person who operates it, wires that start sparking and could cause fire during extreme weather conditions killing persons who are inadvertently electrocuted after entangling with illegally strung throw-ups (Depuru, Wang, & Devabhaktuni, 2011). ...
... households and the focus groups) 33 openly admitted to stealing electricity, while others, although they did not explicitly mention in the interviews they stole electricity, they did not have a meter installed. Some of the main reasons for electricity theft that are reported in the literature include the following: high electricity prices, corruption, poor enforcement of the law against electricity theft, and poor quality of power supplied (Lewis, 2015;Sharma et al., 2016;Smith, 2004;Wong et al., 2021;Yakubu, 2018). However, our research goes beyond these and found that at least in the MCMA, other reasons for stealing electricity existed as Table 6 presents. ...
So-called ''slum-dwellers" living in informal settlements in Mexico City Metropolitan Area (MCMA) often confront poor health outcomes, face chronic accommodation insecurity and are frequent victims of social intolerance, discrimination and racism. In addition, they usually reside in living environments with precariously hazardous conditions that often lead to their well-being endangerment. Based on extensive original research with slum-dwellers from the MCMA including focus groups (N = 18 participants), household interviews (N = 51 participants), and site visits (N = 5), this study investigates their energy and housing needs, transport and mobility patterns and challenges to their overall quality of life and health. The MCMA is one of the largest metropolitan regions globally, and most of its inhabitants experience a ''double energy vulnerability," circumstances whereby people are at an intensified risk of energy and transport poverty simultaneously. Our investigation circles around three key themes. In exploring the subject of extreme poverty and vulnerability, we show not only the problems they confront but also illegal practices such as electricity thefts and coping strategies. In investigating the subject of perpetual peripheralization, we show troubling patterns of discrimination, racism and social intolerance. In exploring the subject of spatial justice, we suggest a set of policies that ought to help achieve it.
... During the transmission and distribution of electrical power, part of electricity is lost which commonly knew as the T&D losses (Smith, 2004). T&D losses are further divided into two parts namely technical and non-technical losses (Smith, 2004). ...
... During the transmission and distribution of electrical power, part of electricity is lost which commonly knew as the T&D losses (Smith, 2004). T&D losses are further divided into two parts namely technical and non-technical losses (Smith, 2004). Technical losses are those which occur within the transmission and distribution network whereas non-technical losses occur due to illegal connection and hence inaccurate flow of energy (Clarke and Xu, 2004). ...
... Non-technical losses or electricity theft is a crime in the entire world (Eskel and Thiele, 1999). The power utility companies charge electricity bill based on readings which appear on the energy meter at the consumer premises (Smith, 2004). ...
Pakistan is facing an annual electricity shortfall of 5000 MW since 2005 due to lack of effective planning on harnessing indigenous energy resources and dependence on imported fuels. In this regard, a qualitative analysis is presented to address the ongoing energy crises and to suggest the development of domestic energy assets and recommends a policy framework to meet electricity goals. Status of energy in Pakistan is presented in the context of energy supply and demand and discusses the major issues such as electricity theft and circular debt. The reasons are identified for power system degradation, energy crises and their potential impacts on the economy of Pakistan is discussed. Further, domestic energy assets are evaluated for their capacity of power production, including solar, wind, geothermal, biomass, tidal, hydro, coal, and natural gas. The main variables of Pakistan’s energy status are power system restructuring, power system planning and policies, electricity theft, energy crisis and circular debt. The key challenges for policy development are also addressed using possible approaches and used these approaches as input in making policy framework by considering the domestic energy resource through incorporating the policy actors, policy criteria, and policy tools. It is found that Pakistan has a renewable potential of 3425.796 GW and a non-renewable potential of 104.883 GW to meet the future energy demand. It is suggested to exploit domestic energy assets for power production, optimize energy planning and policy to find a secure and sustainable energy option for Pakistan.
... N technical losses are caused by actions external to the power system and consist prima of electricity theft, non-payment by customers and errors in accounting and record ke ing [3]. According to Smith [10], theft can be subdivided into four further categories: The treatment and categorisation of losses differ from country to country; thus, crosscountry comparisons of electrical energy losses are far from straightforward. However, multiple literatures categorise losses as being either technical or non-technical, and total T&D losses is made up of this combination [3,[7][8][9]. ...
... Non-technical losses are caused by actions external to the power system and consist primarily of electricity theft, non-payment by customers and errors in accounting and record keeping [3]. According to Smith [10], theft can be subdivided into four further categories: ...
... For example, while non-technical losses associated with theft and metering error appear to be accounted for by most European utilities, there are differences regarding the treatment of other non-technical losses such as non-metered supply for public lighting or non-metered "internal consumption" by the electricity utility. Furthermore, it is difficult to obtain accurate data associated with non-technical losses, as electricity theft can only be estimated but not measured exactly [10] and some utilities prefer not to disclose loss data, keeping the information private to the company's sectors and employees [12]. However, it has been estimated that globally non-technical losses could amount to USD 80-100 billion per annum [13][14][15]. ...
Non-technical loss of electricity (comprising theft, fraud, non-payment and billing irregularities) is a significant issue, particularly in developing countries, and represents a large financial burden on utility companies, governments and society as a whole. This paper takes a wholistic and global view of the challenge and provides a broad perspective of the interrelated issues. Media reports and public perception of non-technical losses tend to focus on residential consumers, particularly those with limited financial resources, whereas review of more robust literature indicates that the largest proportion of non-technical losses is often due to industry, state-owned enterprises and relatively well-off residential consumers. Measures to reduce non-technical losses focusing on average residential consumers, such as pre-paid metering, therefore have limited effect on overall losses. Strengthening of legal and regulatory frameworks, particularly with regard to those larger users, and installing high security tamper-resistant metering systems for commercial consumers may have more effect. The reasons for non-technical losses, especially theft, are complex, but the customer–utility relationship is a key determinant. Improvement of this relationship through local participation in development of renewable energy schemes, such as rooftop solar photovoltaics, could bring benefit if challenges such as financing, design of the distribution system, utility company codes and standards and competence in post installation maintenance can be overcome.
... Since consumers take measures to steal electricity, studying their behavior may suggest appropriate intervention. Past studies show that electricity theft is major contributor to non-technical losses in developing countries (Smith, 2004;Depuru et al., 2011;Jamil and Ahmad, 2014;Razavi and Fleury, 2019). ...
... Electricity delivered to consumers' interface may differ from electricity generated at power plants and this difference is termed as technical and non-technical losses (Jamil and Ahmad, 2014). Smith (2004) described electricity theft as any form of fraud, pilfering (illegal abstraction), billing irregularities (corruption), and unpaid bills. Moreover, distribution companies may fail to recover their receivables from the consumers due to improper reporting by utility employees or illegal use of electricity (Jamil, 2013;Raza et al., 2022). ...
Electricity theft is a chronic issue and energy worth billions of dollars is stolen annually from electricity grids mainly in the developing countries. This study highlights socio-economic and institutional factors deteriorating the electricity theft situation using the path analysis by employing a measurement model in the analysis of a moment structures (AMOS). Preceding the path analysis, statistical package for social sciences (SPSS) is used for descriptive statistics, reliability analysis through Cronbach's alpha test, and exploratory factor analysis through KMO & Barlett's test of sphericity. The study analyses the factors that facilitate the illegal consumption of electricity in the service area of Islamabad Electric Supply Company, Pakistan, utilizing a primary dataset collected through a structured questionnaire from the twin cities of Islamabad and Rawalpindi. The analysis is based on perception of the consumers. The factors that significantly and positively affect the behavior towards theft include corruption diluting deterrence and power outages/load shedding, whereas the age of the respondent negatively affects their perception towards electricity theft. The role of electricity tariff rate, electricity consumption pattern, probability of detection, the rule of law, education of the respondent and quality of conduct of utility officials appeared insignificant in motivating consumers to steal electricity.
... Non-technical losses (NTLs), the focus of this study, are losses caused by electricity theft, metering, or billing errors or even by consumer units without metering equipment and are therefore associated with the commercial management of the distribution utilities [3][4][5][6]. ...
Non-technical losses (NTLs) are one of the main problems that electricity distribution utilities face in developing regions such as Latin America, the Caribbean, sub-Saharan Africa, and South Asia. Particularly in Brazil, based on the socioeconomic and market variables concerning all the distribution utilities, the National Electric Energy Agency (ANEEL) has formulated several specifications of econometric models for panel data with random effects, all aimed at determining an index that reflects the difficulty of combating NTLs according to the intrinsic characteristics of each distribution area. Nevertheless, given the exhaustive search for combinations of explanatory variables and the complexity inherent to defining regulatory NTL targets, this process still requires the evaluation of many models through hypothesis and goodness-of-fit tests. In this regard, this article proposes an automatic model-selection technique for panel data regressions to better assist the Agency in establishing NTL regulatory targets for the distribution of utilities in this country. The proposed technique was applied to panel data containing annual observations from 62 Brazilian electricity distribution utilities from 2007 to 2017, thus generating 1,097,789 models associated with the regression types in the panel data. The main results are three selected models that showed more adherence to the actual capacity of Brazilian distribution utilities to reduce their NTLs.
... The research model was a "one type" change problem that paved the way to decrease the frequency of attacked meters based on the parity of an aggregated load to evade detection. A hybrid detection framework was proposed to check malicious activities by integrating an algorithm for grid sensor placement along with observability analysis to increase the rate of detection [24]. This work could be used to improve the network observability and detection accuracy, which was made even better by the grid-placed sensor deployment. ...
... The average level of T&D losses in Sub-Saharan Africa was around 27.5% in 2009 although the system losses substantially ranges from 14.5% in Angola to 68% in Swaziland (ESMAP, 2009). Reforms have also been unable to reduce electricity theft in most regions of the developing world considering that the quality of governance such as effective accountability, political stability, and government effectiveness and corruption control can reduce energy theft in developing countries (Smith, 2004). ...
... In 1998 the government of Pakistan took strong action and recovered Rs. 2.4 billion of unpaid electricity bills. [2]. Electricity theft happens in each locale in every area of Pakistan which is major trouble and has an awful impact on electricity. ...
Electricity theft stays a gigantic misfortune brought about by electricity appropriation organizations. This robbery emerges significantly increased when this is done by consumers. For example, meter bypassing, tempering meter, and so on. The main reason for the losses is the direct connection of distribution lines which means Kunda or hooking from electricity lines. So, an intensive method is required to stop this kind of electricity theft. This examination study offers a methodology for taking care of electricity meter bypassing and altering. The overall system is a design based on a GSM module, Arduino Mega, Arduino Nano, and three current sensors, in which one current sensor measures the incoming current from the main grid and the other two sensors measure the current from the consumption side and compare the incoming and consumption current values. When there is any value increase from the compared values, it will detect electricity theft. This method will automatically read and send the current sensor values and also detect electricity theft.
... The research model was a "one type" change problem that paved the way to decrease the frequency of attacked meters based on the parity of an aggregated load to evade detection. A hybrid detection framework was proposed to check malicious activities by integrating an algorithm for grid sensor placement along with observability analysis to increase the rate of detection [24]. This work could be used to improve the network observability and detection accuracy, which was made even better by the grid-placed sensor deployment. ...
As big data, its technologies, and application continue to advance, the Smart Grid (SG) has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology (ICT) and cloud computing. As a result of the complicated architecture of cloud computing , the distinctive working of advanced metering infrastructures (AMI), and the use of sensitive data, it has become challenging to make the SG secure. Faults of the SG are categorized into two main categories, Technical Losses (TLs) and Non-Technical Losses (NTLs). Hardware failure, communication issues, ohmic losses, and energy burnout during transmission and propagation of energy are TLs. NTL's are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft, along with tampering with AMI for bill reduction by fraudulent customers. This research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load profile. In our proposed methodology, a hybrid Genetic Algorithm and Support Vector Machine (GA-SVM) model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London, UK, for theft detection. A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%, compared to studies conducted on small and limited datasets.
... One of the significant difficulties confronting electric power suppliers overall is electricity theft that is the only act of utilizing electricity from the service company without the company's approval or assent. Electricity theft, which could occur as a result of billing discrepancies, meter altering, and unlawful association, and unpaid bills are generally done at the user end [6]. ...
The advent of the new millennium with the digital age and space technology promise favors humankind in every perspective. The technology renders us with electric power and has infinite use in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, various methods consider energy theft. Energy theft is a universal electric problem in many countries, with a loss of billions of dollars faced by electric companies. This energy contention is deep rooted, having so many root causes with rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft.
Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying to combat this problem giving better results than the previous approaches. Random Forest (RF) classifier gave overwhelming good results with high accuracy.
In our proposed solution, we use a novel Convolution Neural Network (CNN) with RUSBoost Manta Ray Foraging Optimization (RUSB-MRFO) and RUSBoost Bird Swarm Algorithm (RUSB-BSA) models, which proves to be very innovative. The Accuracy of our proposed approaches, rus-MRFO and rus-BSA, have a 91.5% and a 93.5%, respectively. The use of these techniques gives accurate results with a promising future.
... (1) To complement paper [1] Section 2.10 constructed a theory of technical and nontechnical losses. The background on loss detection algorithms is found in [2][3][4]59,60]. That work showed three main issues: (1.1) that the non-fraud or non-loss signature is farther at the "feature space" from the axes than the loss signature, as observed in Figure 10 for example, (1.2) that the energetic distribution of a sum of Gaussians is translated to spectral space to a sum of Gaussians with an inverse width frequency to the time relationship, and (1.3) that using additional features stretches the distance between loss and non-loss clusters of signatures at the feature space. ...
This paper describes an electricity technical/nontechnical loss detection method capable of loss type identification, classification, and location. Several technologies are implemented to obtain that goal: (i) an architecture of three generative cooperative AI modules and two additional non-cooperative AI modules for data knowledge sharing is proposed, (ii) new expert consumption-based knowledge of feature collaboration of the entire consumption data are embedded as features in an AI classification algorithm, and (iii) an anomaly pooling mechanism that enables one-to-one mapping of signatures to loss types is proposed. A major objective of the paper is an explanation of how an exact loss type to signature mapping is obtained simply and rapidly, (iv) the role of the reactive energy load profile for enhancing signatures for loss types is exemplified, (v) a mathematical demonstration of the quantitative relationship between the features space to algorithm performance is obtained generically for any algorithm, and (vi) a theory of “generative cooperative modules” for technical/nontechnical loss detection is located and mapped to the presented system. The system is shown to enable high-accuracy technical/nontechnical loss detection, especially differentiated from other grid anomalies that certainly exist in field conditions and are not tagged in the universal datasets. The “pooling” architecture algorithm identifies all other loss types, and a robotic process automation module obtains loss type localization. The system feeds from the entire smart metering data, not only the energy load profile. Other solutions, such as a stand-alone algorithm, have difficulty in obtaining low false positive in field conditions. The work is tested experimentally to demonstrate the matching of experiment and theory.
... The first one is the electricity's coverage of the household activities, such as the electricity indicators do not encompass outdoor activities, e.g., traveling or dining out. The second is the accuracy of the electricity data as the existence of the "electricity theft" phenomenon [38], particularly in highly informal countries or regions. That is, the poor households may seek ways of defrauding the charge for the electricity metering system. ...
Inequality is a growing public concern and economic threat. The degree of which, however, varies greatly depending on the choice of indicators for measurement. In this paper, we compare the strength and weaknesses of the existing indicators such as income and wealth and propose a new measure of inequality based on household electricity consumption. We believe that our measure has the advantage of capturing both service flows and stock values of durables, embodying both the outcome and opportunity inequality, and confronting fewer measurement issues. The new inequality measure based on electricity consumption may complement the existing ones by providing a relatively complete and well-balanced picture of the overall welfare inequality.
... In this scenario, one of the most significant development goals is related to reforms in the energy sector to make electricity accessible to the entire population (Dertinger and Hirth, 2020). However, distributors' investment capacity and network quality are compromised because not all electricity consumed is billed (Lewis, 2015;Smith, 2004). This electricity corresponds to non-technical losses (NTLs) caused by electricity theft, fraud, In 2019, NTLs in Brazil totaled 35.9 TWh, which corresponds to about US$ 1.79 billion 1 . ...
Non-technical losses (NTLs) directly affect the electricity distribution system's quality and create significant economic problems in developing countries. There has been an advance in Brazil's regulations to combat this kind of loss in the last 15 years. However, the electricity consumed and not billed is still high, impacting the electricity tariff and distributors' investment capacity and creating difficulties in developing public policies to mitigate the problem. Thus, this article seeks to present the panorama of NTLs in Brazil and propose legislative, regulatory, business, and academic directions. For this, 28 semi-structured interviews were carried out with specialists, resulting in identifying the main challenges for identifying and mitigating NTLs in Brazil and the factors that help overcome this problem. The results demonstrate that coordinated strategic actions among all stakeholders need to be developed to combat NTLs. The cultural change in acceptance of electricity theft needs to be one of the country's main focuses. The main contribution is to disseminate information to regulatory and legislative authorities, government, concessionaires, and researchers to develop practical actions for mitigating NTLs in Brazil.
... A remote detecting technique was proposed in [29], for illegal electricity usage considering smart metering, while in [30], genetic support vector machines were used in abnormalities detection and electricity theft. A mathematical approach for energy theft identification and tampering of meters based on central observer meter, was proposed in Reference [31], while a comparative analysis of electricity theft [32], and the overviews, issues and preventions, using smart meter based approach for theft control was presented in [33]. These techniques were further extended to the security improvement of the AMI system. ...
Advanced Metering Infrastructures (AMI) help utility providers and customers to better control the use and production of electrical energy. Recently, AMI development was carried out for better energy efficiency and smart grid operations in Oman. Some benefits and functions of AMI were analyzed in this paper, considering the expected challenges that might be faced during its implementation in the power distribution grid of Oman. In addition, three design topologies of employing AMI in the power grid of Oman were investigated and compared, based on their economic benefits. Recommendations for the best practices in expanding the use of AMI, in the Oman power sector were given.
... Especially, NTL caused by fraudulent behavior can impose a direct economic loss on the power utilities and legal user. The results have been reported in various countries including Turkey [2], Jamaica [3] and India [4]. A wide range of approaches has been taken to mitigate the NTL problem. ...
One of the problems of the electricity grid system is electricity loss due to energy theft, which is known as non-technical loss (NTL). The sustainability and stability of the grid system are threatened by the unexpected electricity losses. Energy theft detection based on data analysis is one of the solutions to alleviate the drawbacks of NTL. The main problem of data-based NTL detection is that collected electricity usage dataset is imbalanced. In this paper, we approach the NTL detection problem using deep reinforcement learning (DRL) to solve the data imbalanced problem of NTL. The advantage of the proposed method is that the classification method is adopted to use the partial input features without pre-processing method for input feature selection. Moreover, extra pre-processing steps to balance the dataset are unnecessary to detect NTL compared to the conventional NTL detection algorithms. From the simulation results, the proposed method provides better performances compared to the conventional algorithms under various simulation environments.
... Special devices, such as transmission transformers and wireless sensors, use the state-based recognition concept [12]. These techniques can detect energy theft, but they necessitate the procurement of real-time system topology and additional physical measurements, which can be challenging to obtain. ...
One of the major concerns for the utilities in the Smart Grid (SG) is electricity theft. With the implementation of smart meters, the frequency of energy usage and data collection from smart homes has increased, which makes it possible for advanced data analysis that was not previously possible. For this purpose, we have taken historical data of energy thieves and normal users. To avoid imbalance observation, biased estimates, we applied the interpolation method. Furthermore, the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing. By proposing an improved version of Zeiler and Fergus Net (ZFNet) as a feature extraction approach, we had able to reduce the model's time complexity. To minimize the overfitting issues, increase the training accuracy and reduce the training loss, we have proposed an enhanced method by merging Adaptive Boosting (AdaBoost) classifier with Coronavirus Herd Immunity Optimizer (CHIO) and Forensic based Investigation Optimizer (FBIO). In terms of low computational complexity, minimized over-fitting problems on a large quantity of data, reduced training time and training loss and increased training accuracy, our model outperforms the benchmark scheme. Our proposed algorithms Ada-CHIO and Ada-FBIO, have the low Mean Average Percentage Error (MAPE) value of error, i.e., 6.8% and 9.5%, respectively. Furthermore, due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93% and 90%. Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms, which also depicts the superiority of our proposed techniques.
... Detection of theft and enhance system security Electrical grid system faces Two types losses i.e., technical losses non-technical losses (NTL) [20,21]. NTLs account for roughly 10% to 50% of total generation capacity, especially in developing nations [22], [23], [24]. Power theft is a substantial (80%) component of NTL [25]. ...
The smart grid is the future of power industry where smart meters are the most fundamental component. These are the next-generation power measuring devices for easy monitoring and control. In addition to communication networks and sophisticated computing with smart meter, significant improvement in efficiency and reliability of grid system can be achieved. Simultaneously the renewable and distributed energy sources could be incorporated leading to changes in the scenario of energy exchange by switching its direction as and when required. The positive environmental consequences as a result of significant CO 2 reduction is possible by implementing demand side integration. This paper demonstrates how smart meters can overcome different issues of conventional grids, a variety of functions and uses, along with the benefits and costs associated with them.
... Data theft of electricity in Indonesia is quite scattered, such as Balikpapan, East Kalimantan (William, 2020), West Sulawesi (Ismail, 2018), Riau, Kampar Regency (Vandiwinata, 2018), and Malang City, East Java (Arsenal, 2021). Meanwhile, on a global scale, electricity theft has increased in 102 countries from 1980 and 2000 (Smith, 2004). ...
em>The behavior of theft of electricity is a form of violation that harms many parties. This research aims to know the violation class and category of violations by electricity customers in 3 years. The research approach used was qualitative research. The data sources in this study involved 1 management party, customers in the 1<sup>st</sup> year were 1,200 people, the 2<sup>nd</sup> year were 1,200 people, and the 3<sup>rd</sup> year were 700 people. Data collection techniques used were observation, documentation, literature study, and interviews. All data were analyzed inductively including data reduction, data presentation, and verification (arranging conclusion). The results of the research showed that the total number of violations for 3 years reached 7.1% which was distributed to 2.2% for class violations and 4.9% for categories of violations. The category of violations that occurred on the customer's side showed that it was greater than class violations. The number of class 3 violations reached 0.9% and violations of this class were higher than other classes. Meanwhile, the kWh violation category reached 2.6% and this violation category was higher than other categories. Based on the rate of the number of violations each year, there was an increase in violations by 0.2% – 0.9%.</em
... Power utilities all over the globe incur significant revenue loss as a result of power theft. In the United States alone, this loss ranges from 0.5 percent to 3.5 percent of their annual income [11]. The case is even worse in underdeveloped nations where the revenue loss from this type of NTL becomes a significant portion of their gross domestic product [12,13]. ...
Abstract This paper presents a novel, sequentially executed supervised machine learning‐based electric theft detection framework using a Jaya‐optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the XGBoost algorithm to estimate the missing values in the acquired dataset during the data pre‐processing phase. An oversampling algorithm based on the Robust‐SMOTE technique is utilized to avoid the unbalanced data class distribution issue. Afterward, with the aid of few very significant statistical, temporal, and spectral features extracted from the acquired kWh dataset, the complex underlying data patterns are comprehended to enhance the accuracy and detection rate of the classifier. For effectively classifying the consumers into “Honest” and “Fraudster,” the ensemble machine learning‐based classifier KTBoost, with Jaya algorithm optimized hyperparameters, is utilized. Finally, the developed model is re‐trained using a reduced set of highly important features to minimize the computational resources without compromising the performance of the developed model. The outcome of this study reveals that the proposed theft detection method achieves the highest accuracy (93.38%), precision (95%), and recall (93.18%) among all the studied methods, thus signifying its importance in the studied area of research.
... Electricity stealing is distinct as a deceitful or prohibited employ of electricity utensils or service through the objective to evade billing charge [5]. It co mprises anonymous tapping of power fro m the wires, meter by-passing, intentional malfunctioning of meter and numerous corporeal processes by escaping fro m billing [6,7]. ...
... However, due to the increase in electricity consumption and the rise in electricity prices, the electricity charge brought considerable economic burden to the populace and enterprises [2]. Thereby, the problem of electricity stealing [3] became more severe which may lead to a great economic loss for one country. To solve this issue, amounts of research on effective detection methods has been studied and, among them, two types of ideas are popular, i.e., to monitor the physical characteristics of meters, or to analyze features of electricity data. ...
With the increasing number of electricity stealing users, the interests of countries are jeopardized and it brings economic burden to the government. However, due to the small-scale stealing and its random time coherence, it is difficult to find electricity stealing users. To solve this issue, we first generate the hybrid dataset composed of real electricity data and specific electricity stealing data. Then, we put forward the timing shift based bi-residual network (TS-BiResNet) model. It learns the features of electricity consumption data on two aspects, i.e., shallow features and deep features, and meanwhile takes time factor into consideration. The simulation results show that TS-BiResNet model can detect electricity stealing behaviors that are small-scaled and randomly coherent with time. Besides, its detection accuracy is superior to benchmark schemes, i.e., long short-term memory (LSTM) model and Bi-ResNet model.
Electricity theft is a costly problem. This paper will be focused on Pakistan and the problem of electricity theft. We will discuss its impacts and how best to fix them through the use of technology. For this purpose, we developed a smart meter, focusing on grid modernization through economic smart meter development. This paper focuses on a study carried out with the help of PESCO. It is one of the most inefficient distribution providers. The study has evaluated commercial, industrial, rural, and urban areas, covering a total area of 15 km2. The area includes several power sinks. Previous research has been used to compare the results of this case study; this included studies of other Third World countries, such as Pakistan and South Africa. The design of, clever, innovative, intelligent meters used in this study was better than the basic digital meters and had many features compatible with the E.U., and U.S.A.’s western power market and energy infrastructure. The study also discusses the potential use of neural network-trained models and IoT (internet of things) integration with cloud computing. This can provide an alternate means of data analysis, accurate prediction, and greater user accessibility. The case study is the first ever done using smart meters on such a large scale, and the compiled data has provided insight into energy consumers and their usage. The statistics can be used to isolate the most probable cause of theft and the area or location of occurrence.
In a moment of electrified literary worldmaking, the unnamed protagonist of Ralph Ellison's Invisible Man steals electricity to power the 1,369 lightbulbs and radio phonography in his subterranean refuge. Narratively and materially, electricity theft diagnoses the uneven access to and impacts of electrification, while also (temporarily) creating alternative infrastructural relationships that refuse exclusion. Centered on Ellison's Invisible Man and its representation of electricity theft, this essay analyzes a constellation of US electrifictions focused on the relationship between Blackness and electricity. Going beyond a traditional literary close reading, this essay triangulates a reading of Invisible Man with a history of General Electric's “electric Slave” advertisements from the interwar period and concludes with an analysis of a 2010 WXYZ-TV Detroit news segment focused on electricity theft. GE's advertisements necessitate a reexamination of US cultural constructions of electrification as inherently progressive and instead highlight the ideologies of unfreedom and exploitation that undergird electrified modernity. While the 2010 news segment may seem of a different place and time than Ellison's novel, both are focused on moments where racialized individuals come into contact with the large-scale system of the electricity grid and the structures of power the grid both metaphorizes and materializes. Like in Invisible Man, the electricity thieves in the news segment do more than diagnose the racist impacts and exclusions of electrified modernity, they also, through their illicit acts of siphoning that redistribute electric current, materially intervene in and reimagine the larger infrastructural system.
Power theft is big issues in the field of electricity because it harms transmission lines and results in financial losses. So, it is important to detect electricity theft effectively. A unique method is developed for detecting electricity theft that is based on the bidirectional long short-term memory (Bi-LSTM), which is compared with the exiting techniques like logistic regression (LR), support vector machine (SVM), and convolution neural network and long short-term memory (CNN-LSTM). Main objective of the proposed Bi-LSTM model is to reduce the complexity of electricity theft detection systems and also identify power theft in different scenarios effectively. A real-time dataset has been used in the proposed network. The performance of suggested Bi-LSTM model is evaluated in the MATLAB environment. The efficiency of Bi-LSTM model is assessed and contrasted with LR, SVM, and CNN-LSTM in terms of accuracy, precision, recall, and F-score and achieved better results in every parameter.
Deliberate non-enforcement of the law has been analyzed as a policy tool to redistribute income. I show that it also responds to political incentives for the provision of insurance, resembling two well-known dimensions of social policy design. I analyze data from a large informal program of social insurance in the world: informal access to electricity service. Transmission and distribution losses (TDL) in the electricity sector are counter-cyclical because non-compliance and theft increase during economic crises. By exploiting variation in political institutions, I capture political motivations for the provision of informal insurance. Using a panel of 110 developing countries (1970–2014) and instrumental variables for business cycles and regime type, I show that unlike highly entrenched autocrats, democracies tolerate increases in electricity losses during negative income shocks. This paper expands the literature on “forbearance” showing how the provision of informal insurance varies across the developing world.
Losses are involved from generation to transmission and distribution (T&D) of electricity. The score of these losses is rising in many countries severely. The main issue in electricity usage is the theft of electricity, which is dangerous for power suppliers and has caused budget losses. Detection and control of electricity theft is a challenge that involves a variety of factors such as economic, social, regional, administrative, political, infrastructure, level of education, etc. Electricity theft detection is very much important to make the power system reliability. Fictitious use of electricity lowers the quality of supply, increases a load of production, causes certain consumers to pay an extra amount of bills, and affects the overall rollback. Non-technical losses occur due to improper and illegal measurements of energy consumed by energy meters. Non-technical losses are a big problem for the occurrence of security risks and immeasurable financial losses. Sometimes it is difficult to locate the tampered meters, damaged/broken meter terminals, and/or unlawful applications that cannot be traced out at the stage of checking's done by the flying squad. IoT base smarts meters are used nowadays to overcome this problem, as they involve two-way communications. This meter is also intelligent enough to detect excessive energy use and warn the consumer to reduce consumption or stop the supply process automatically. This review contains comparisons and research among the various ways to detect theft. Accuracy is compared and analyzed with complete literature reviews. In this paper, we have studied various machine learning algorithms used to detect the theft of electricity.KeywordsLossesElectricity theftTempered metersMachine learningIoT
Electricity theft has become a matter of trouble for both customers and electricity distribution companies. Due to electricity theft, distribution companies are facing lots of T&D losses every year. Despite several technical measures adopted, there is no significant reduction in losses due to electricity theft. This fact diverts the attention towards non-technical measures which include formulation and enforcement of stringent rules and regulations. As the policies need to be enforced on electricity customers, policies should be designed in accordance with the customers’ attitude towards electricity theft. Therefore, this work has analyzed their attitude towards the role of certain socio-economic factors which strongly impact the practice of electricity theft. This study has surveyed the electricity customers of two distribution companies, UHBVN and DHBVN in State Haryana. The findings of this work may help the electricity distribution companies to chalk out better and more effective plans to put check upon the practice of electricity theft in the society.
Non-technical loss (NTL) is one of the problems in distribution utilities and electric cooperatives, comprising electricity theft, malfunction or broken meters, and arranging false meter readings. This paper assessed the level of electricity fraud activity in the Isabela Electric II Cooperative (ISELCO II) system and helped identify and detect non-technical loss activities and abnormalities. Findings of the study revealed that the forms and natures of practice of non-technical loss (NTL) in ISELCO II are jumpering, direct tapping, tampered meter, broken glass, defective meter, tilting of meter, and separate grounding. The study utilized mixed-method research, and an interview method was conducted to support the quantitative data gathered from the cooperative. The findings further revealed that the two primary sources of NTL are the kWhr meter and the billing procedure. The Pearson Product Moment Correlation was used to test the relationship between the cooperative's reported non-technical losses (NTL) and the measurement of the monthly power rate per kilowatt hour (kWh) consumed by the customers. The results showed that as non-technical losses increase, the monthly power rate per kWh increases. Hence, there is a direct effect of NTL on the monthly bill the consumer pays off. Likewise, the t-test revealed that the non-technical loss and technical loss have no significant relationship with each other despite their nature. The increasing number of people involved in electricity theft has a direct impact on consumers and the system of ISELCO II.
When technological advances were reached during the XIX century, energy started to be transformed into electricity through the use of different energy resources, technologies and processes. The importance of electricity supply in terms of energy security emerged from the fact that it is a basic service which has to meet demand requirements in real time and therefore must be guaranteed through controlling the factors that may affect the stability in supplying the service, in addition electricity industry by its own nature is connected to the energy resource system. Beside the electricity industry value chain, we found different processes or systems such as electricity generation, transmission and distribution. Although there is an extensive literature regarding security of energy supply, still there is no prior study that undertakes energy security for the electricity industry through considering the electricity industry's value chain and the core indicators that can influence negatively the continuity of electricity supply. There is a need for accomplishing new research methodologies in different energy security fields. Based on these conditions and after researching and analyzing various textbooks, papers and journal articles, our research objective is to define and develop a proper approach for evaluating energy security in the electricity industry value chain through considering ten different Latin American countries: Mexico,
The global demand for energy is booming day by day and yet the energy is required to be clean due to the strict environmental regulations. The current carbon-based economy primarily relies on energy extracted from fossil fuels. However, burning fossil fuels results in the emission of greenhouse gases and other pollutants that are deadly to the environment. The hydrogen economy is proposed as an alternative to fossil fuels, considering the high energy density by weight of hydrogen as well as its environmentally friendly nature. This modern economy depends on green hydrogen as commercial fuel and it is considered the vital energy conversion and storage strategy to fully exploit the benefits of renewable and sustainable energy resources, for example, solar and wind energies. Hydrogen energy-related technologies (production, storage, conversion, etc.) present new research frontiers. Moreover, hydrogen combined with fuel cells provides essential energy solutions for the 21st century. Fuel cells utilize hydrogen gaseous fuel to generate electricity via an electrochemical process that provides much higher efficiencies and zero pollutant output than the conventional energy conversion technologies, for example, an internal combustion engine. In addition, the reversible fuel cells utilizing renewable energies provide the most efficient water electrolysis and they are being rapidly developed for green hydrogen production. Thus, hydrogen and fuel cells present promising potential for replacing conventional energy conversion systems with clean energy systems. This chapter briefly reviews the current research status of the hydrogen and fuel cell technologies for a viable supply and storage of clean and economical energy. The various challenges hampering the massive commercialization of hydrogen and fuel cell technologies are also identified and discussed. In addition, the market and policy trends regarding hydrogen and fuel cells are discussed.
Consumer-deviant behavior costs global utility firms USD 96 billion yearly, attributable to Non-Technical Losses (NTLs). NTLs affect the operations of power systems by overloading lines and transformers, resulting in voltage imbalances and, thereby, impacting services. They also impact the electricity price paid by the honest customers. Traditional meters constitute 98 % of the total electricity meters in India. This paper argues that while traditional meters have their limitation in checking consumer-deviant behavior, this issue can be resolved with ML-based algorithms. These algorithms can predict suspected cases of theft with reasonable certainty, thereby enabling distribution companies to save money and provide consistent and dependable services to honest customers at reasonable costs. The key learning from this paper is that even if data is noisy, it is possible to create a Machine Learning Model to detect NTL with 80 percentage plus accuracy.
With the increasing number of electricity stealing users, the interests of countries are jeopardized and it brings economic burden to the government. However, due to the small-scale stealing and its random time coherence, it is difficult to find electricity stealing users. To solve this issue, we first generate the hybrid dataset composed of real electricity data and specific electricity stealing data. Then, we put forward the timing shift-based bi-residual network (TS-BiResNet) model. It learns the features of electricity consumption data on two aspects, i.e., shallow features and deep features, and meanwhile takes time factor into consideration. The simulation results show that TS-BiResNet model can detect electricity stealing behaviors that are small scaled and randomly coherent with time. Besides, its detection accuracy is superior to the benchmark schemes, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), combined convolutional neural network and LSTM (CNN-LSTM) and Bi-ResNet.
Nontechnical losses in electricity distribution networks are often associated with a countries’ socioeconomic situation. Although the amount of global losses is usually known, the separation between technical and commercial (nontechnical) losses will remain one of the main challenges for DSO until smart grids become fully implemented and operational. The most common origins of commercial losses are energy theft and deliberate or accidental failures of energy measuring equipment. In any case, the consequences can be regarded as consumption anomalies. The work described in this paper aims to answer a request from a DSO, for the development of tools to detect consumption anomalies at end-customer facilities (HV, MV and LV), invoking two types of assessment. The first consists of the identification of typical patterns in the set of consumption profiles of a given group or zone and the detection of atypical consumers (outliers) within it. The second assessment involves the exploration of the load diagram evolution of each specific consumer to detect changes in the consumption pattern that could represent situations of probable irregularities. After a representative period, typically 12 months, these assessments are repeated, and the results are compared to the initial ones. The eventual changes in the typical classes or consumption scales are used to build a classifier indicating the risk of anomaly.
India uses electromechanical electricity meters, but considers the drawbacks of electricity theft, meter reading errors, billing, and consumers hesitating to pay utility bills on time. Therefore, such systems are being replaced by more sophisticated and accurate digital and electronic gauges. This paper proposes a new methodology for implementing a control-based global system for mobile communication networks (GSMs) to integrate prepaid metering system setup and remote load control. This provides a secure smart card solution for new types of prepaid power systems. It aims to reduce the number of utility bill obstacles using smart card technology. The LCD display is used to show the amount of energy consumed. Users can replenish the counter as needed by sending an SMS to the server. Users must make the first replenishment to address the issue of unpaid invoices and human error in invoices. This ultimately guarantees a legitimate return.
Now a day's efficient energy utilization is the utmost priority in many industries so as to minimize the energy cost. This paper aims to deliver the techniques involved in the control of load in industry during heavy traffic with the help of Programmable logic controller (PLC). It also explains the usage of to monitor all the load parameters of the motor on personal computer. In the considered paper we have implemented the multifunction meter (MFM) to PLC communication with Modbus Remote Terminal Unit (RTU) communication; direct values from MFM are taken as feedback to the PLC. Initially constant current values to a single load is taken into the consideration, and any higher load is being applied on the current load, the variations in amps rating is taken as reference and extra load gets tripped at the same time power is managed. With the help of energy meter voltage and amperes rating is constantly observed, if variations of loads are observed rather than any pre defined load, alarms occurs in Supervisory Control And Data Acquisition (SCADA) and power and energy gets managed.
Electric theft is the major issue faced by utility companies in different countries as it causes significant revenue losses and affects the power grid reliability. This paper presents a novel electric theft detection framework based on an unsupervised machine learning technique employing matrix profile and K-means clustering algorithm. The proposed framework is based on three stages to identify the fraudster consumers in a conventional electric consumption meter dataset acquired from Pakistan's power distribution company. Initially, the missing and inconsistent observations are filtered out from the acquired dataset. After that, the matrix profile from each consumer’s consumption profile is computed to identify the irregular and sudden changes present in them. Later, the K-means clustering algorithm is used on the datasets divided based on their computed matrix profile values in order to label each consumer into “Healthy” and Theft.” The developed framework is compared against the latest state of art machine learning algorithms and statistical-based outlier detection methods. The proposed technique achieved an accuracy of 93% and a detection rate of 91%, which is greater than all compared models.KeywordsElectric power distributionElectric theftMatrix profileK-means clustering algorithm
The governance structure of the energy sector is unequivocally titled in favour of the conventional sources. Energy access policies appear to be dictated and dominated by the conventional energy sector. Policies negotiating access do not lay due emphasis on a seamless socio-technical transition, recognizing the end-user as the single most important element. Thus, the policies appear to be bereft of adopting an ecosystem approach towards access to energy. The energy transition path is littered with various obstacles arising out of current policy design, institutional and regulatory framework, that exert powerful influence on the current deployment model of energy systems. Decentralized, rural energy systems remain an elusive dream as there are formidable ‘barriers’ to be transcended for a seamlessly favourable energy system transition that is coherent with larger constitutional (ideological) mandate. The ‘barriers’ can be broadly categorized into such categories as: (a) Promotion policy for renewable energy literacy, (b) Policy for technological acculturation, (c) Policy for self-generation by end-users, (d) Policy defining role of district, block, and village-level energy committees and lastly, (e) Policy for rural electrification to be nested in the notion of doable and achievable decentralized energy systems (DES). The ‘barriers’ to transition are region and situation-specific, as the context varies spatially in terms of demand both quantitative and qualitative. The institutional actors of the state need to look upon distributed applications as an alternative approach to energy deployment within human ecosystems. Textually strong policy proposals and concessions to large and small private actors may not be the only way to emancipate rural India from endemic energy poverty. The policy proposals cast statutory obligations on state/region-specific public actors (state-owned utilities) could further promote energy access disparity, aggravate energy poverty and may defeat the overall objective of providing just, fair, and equitable energy access across rural ecosystems dispersed across different geo-climatic zones and physiographic divisions.
Developing countries suffer from shortages of electricity, posing a severe problem for industrial firms and giving them considerable incentives to access electricity resources through bribery. We investigate how firms’ revenues, sales and productivity are affected by payments of bribes to obtain electricity connections. We use ordinary least squares (OLS), quantile and instrumental variable (IV) quantile regression analysis. OLS estimates reveal a negative effect on sales and productivity and a positive impact on profitability, while quantile regression suggests a negative effect on sales across all quantiles and a positive effect on productivity at the lower quantile. Since this research shows that bribery may negatively influence productivity, it informs shareholders that bribery may be detrimental to their long-term benefits.
For accommodating rapidly increasing power demands, power systems are transitioning from analog systems to systems with increasing digital control and communications. Although this modernization brings many far-reaching benefits, the hardware and software newly incorporated into the power systems also incur many vulnerabilities. By taking advantage of these vulnerabilities, adversaries can launch various cyber/physical attacks to tamper with electricity meter readings, i.e., to steal electricity. It is reported that total worldwide annual economic losses caused by electricity theft reached up to almost one hundred billion dollars in recent years. With methods to tamper with meter readings becoming more versatile, secret, and flexible, electricity theft tends to get even more serious in modernized power systems. For preventing adversaries from stealing electricity, researchers have done a lot of works. Although some related surveys on these works exist, they are not updated or just discuss electricity theft in a specific region. This survey aims to gain a comprehensive and in-depth understanding of the electricity theft issue. After investigating how adversaries tamper with meter readings, we systematically survey all existing detection methods up to date, which is classified into machine learning- and measurement mismatch-based methods. Adverse effects and political and socioeconomic factors of electricity theft are also provided. This survey can help relevant researchers to shape future research directions, especially in the area of developing new effective electricity theft detection methods.
Under the assumptions that the main goal of a regulatory agency is the optimization of the socioeconomic welfare added to society by the electricity sector, developing a regulatory, economic market model of such market is fundamental. Then, it becomes possible to: evaluate the economic flows among the market agents; calculate the optimal capital investments, minimal costs, and optimal tariffs yet preserving the capital yield to investors; simulate actions using agent-based models to reduce energy and financial losses due to energy theft; identify the most effective public policies to reduce energy poverty and inequalities and their impacts on the Gini index, and more. A stochastic regulatory economic model of the smart electricity market can predict returns and risks to a more fare, free, and sustainable society. All these functions are, therefore, related to different philosophic dimensions or normative aspects previously discussed, such as social aspect: flourishing of the community, fight against inequalities; economic aspect: dealing with scarcity of energy and integration of new sources; justice or juridical aspect: accessibility and fight against energy poverty; moral aspect: care for society and care for the natural environment, sustainability.
The authors construct aggregate governance indicators for six dimensions of governance, covering 175 countries in 2000-01. They apply the methodology developed in Kaufmann, Kraay, and Zoido-Lobaton ("Aggregating Governance Indicators", Policy Research Working Paper 2195, and"Governance Matters", Policy Research Working Paper 2196, October 1999) to newly available data at governance indicators comparable with those constructed for 1997-98. The data is presented I the appendix, and accessible through an interactive Web-interface at http://www.worldbank.org/wbi/governance/govdata2001.htm.
In 1996, among 196 million Indonesian people, only 47.6% had access to electricity; 45.4% in rural areas and 51.5% in urban. 64% of population lived in 61,975 villages, and electric power was supplied to 69% of villages. The government has set a goal to achieve nearly universal services by the year 2014. The government can use existing installed capacity of PLN's system more efficiently. Theft of electricity, which currently constitutes a large share of PLN's losses, should be reduced. The potential of the private sector including captive power and local communities, to participate in electric power generation can also be utilized. The large interest that has been shown by the private sector might be followed by making electric power provision profitable and therefore attractive for the private sector. PLN profits, that in 1996 were only 5.22% instead of the 8% recommended by the World Bank as the best practice for Indonesia, have to be increased by improving their performance levels. The government should also seek solutions for the extremely poor households who will never be able to afford both connection charges and a monthly bill. In 1996 the extremely poor households included 5,251,788 households, constituting 12.1% of the total Indonesian households. Only 1.2% of these households had access to electricity. The objective of this study is to seek the policies that can be implemented in Indonesia that will make it possible to generate and deliver electricity profitably, and reduce theft while providing nearly universal services. For this purpose, the options that are proposed in this study are reducing theft of electricity; something like the CAMPFIRE that has successfully reduced poaching of elephants in Africa: that is, consumer-owned systems, both partially (distribution facilities) and completely (generation and distribution facilities); performance-based regulation (PBR); and solutions for supplying the extremely poor based on the minimum subsidies from the government. Compared to the current government policies, benefit-costs analysis shows that the proposed policies result in a better situation. The potential benefits of the proposed options are greater, while the costs are much lower than the existing policies.
India has suffered acute electric power shortages over decades and indications are that the problem will become worse. The history of energy planning and performance in India has been commendable in many aspects, such as the expansion of generating capacity, self-sufficiency in nuclear technology, and the manufacture of generating equipment. Making and implementing policy that would bring about organizational and technical change to match India's electricity supply with its requirements requires costly, difficult, and unpopular decisions. But unless the clearly evident problems are resolved soon, the future of India's electricity supply will be more bleak than in the past and the impact upon the country's development more serious. -from Author
Electric power development in Asia until recently has been a monopoly of the state, with the power sector's planning, finance, construction and management being a part of government activity. The surge in demand for power, as well as external pressures, induced Asian governments to allow private sector participation in electric power. The Malaysian and Thailand cases represent different patterns of policy-making regarding privatisation. In Malaysia, the government divested Tenaga Nacional Berhad in 1992 and awarded independent power producers (IPPs) licences to build and sell electricity to Tenaga for transmission and distribution. The IPPs were awarded without tender to friends of the government and the system has enabled the IPPs to make large profits at Tenaga's expense. In the Thai case, privatisation has been a very slow process as successive governments since 1989 have not had the power to initiate extensive divestment of IPP contracting. Privatisation in Thailand is a very contentious political issue and the employees union of the Electricity Generating Authority of Thailand (Egat) is very powerful. Thus, while Malaysia has had extensive privatisation of the power sector, the system eliminates competition in power supply resulting in a higher price of electricity for consumers. Copyright © 2003 John Wiley & Sons, Ltd.
The electric power industry has entered a period of rapid change — with profound implications for the health of the global economy and natural environment. Many of today's vertically integrated utilities are likely to be restructured or broken up in the coming decade, yielding a commodity market in power generation and transmission, and a competitive services market at the local distribution level. The transformation of the industry is being driven both by broad trends toward privatization and deregulation, and by rapid advances in energy producing and consuming technologies. Devices such as fuel cells, photovoltaics, and flywheels will open the way to a more decentralized power industry in which electricity generation and storage facilities are increasingly located in customers' own facilities — integrated and controlled by new digital communications systems.
The tiger has teeth: vigilance and automation slash energy theft in India
- Co Ltd
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KPTC vigilance officer suspended. The Asian Age
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Anuradha, G., 2000. KPTC vigilance officer suspended. The Asian Age, 16 March.
Accurate metering raises profits—a holistic revenue protection program
- L F Arruda
Arruda, L.F., 2000. Accurate metering raises profits—a holistic revenue protection program. Metering International 2.
Regulation in the WB-Orissa model: cure worse than disease
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Non-technical losses: how much are you losing? Paper presented at the Fourth Annual South Africa Revenue Protection Association Conference
- A H Gower
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Michael Lipsky and street-level bureaucracy: a neglected perspective The Policy Process: A Reader
- B Hudson
Hudson, B., 1993. Michael Lipsky and street-level bureaucracy: a neglected perspective. In: Hill, M. (Ed.), The Policy Process: A Reader. London, Harvester-Wheatsheaf, pp. 386–398.
Intelligent Metering Required
- A G Iyer
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The costs of corruption for the poor—the energy sector. Energy Services for the World's Poor
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The bottom line: getting customers to pay. Energy Customer Management
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Electricity as a birthright and the problem of non-payment. Paper presented at the Third Annual South Africa Revenue Protection Conference
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Thieves lurk-the sizeable problem of stolen electricity
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Pakistan gets switched on VSO Orbit: Development Magazine on Global Issues, Number 71. Smith, T.B., 1993. India's electricity crisis: why do the lights go out?
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Power pirates leave Mexico City in the dark
- K Sullivan
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AMR-the cure for energy theft? Metering International 3
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Lucknow steals power worth Rs 100 crore! The Economic Times
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Accurate metering raises profits-a holistic revenue protection program. Metering International 2
- L F Arruda
Factors that Result in a Culture of Non-Payment. Paper presented to the Fourth Annual South Africa Revenue Protection Conference
- J Barnes
Power theft accused absconding: one held
- Indian Express
Revenue protection: the good, the bad and the ugly. Paper presented at the Third Annual South Africa Revenue Protection Conference
- K J Campbell
Michael Lipsky and street-level bureaucracy
- Hudson
Tenaga out to short-circuit electricity thefts
- Malay Mail
Research study quantifies energy theft losses. Metering International Issue 1
- J J Culwell
Regulation in the WB-Orissa model
- Dixit
Military Owes Rp. 23 billion to PLN
- Jakarta Post
The costs of corruption for the poor-the energy sector
- L Lovei
- A Mckechnie
Transforming Electricity: the Coming Generation of Change. Royal Institute of International Affairs
- W Patterson
Moving beyond AMR. Metering International 2
- A V Rajan
Pakistan gets switched on. VSO Orbit: Development Magazine on Global Issues
- M Rizvi
India's electricity crisis
- Smith