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Pradeep n singh_praveenkyadav

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Pradeep n singh_praveenkyadav

  1. 1. Application of Business Analytics in Big Data of Power Sector Pradeep N. Singh & Praveen K. Yadav Department of Information Systems & Power Management, University of Petroleum & Energy Studies (UPES), INDIA ABSTRACT: - This paper provides how Business Analytics can be proving to be boon for Indian power sector. It can better manage it along making the Indian power sector more financial viable thus promoting competition in the Power Market, which is one of the prime objectives of Indian Electricity Act, 2003. Its potential is not limited to distribution sector utilities but also has wide scope in Power Generating Utilities and Transmission companies. This paper discusses the role of integration of Business Analytics with Big Data of power sector, which is also an important function in power quality based services movement. It also gives insights of Software Architecture, software requirements and software system attributes. It also discusses the crucial role of energy efficiency and how business analytics can help in reducing the energy intensity for India. ARTICLE INFO: - General Terms: Energy Demand, Sustainability, Competitive edge and Analytic Reports. Keywords: AMR, MRD, CMRI, MIDAS, Energy Efficiency, Power Management, Data management systems, Energy efficiency, Technical & Commercial Losses, Energy Intensity and Energy Conservation. 1. INTRODUCTION Indian Power Sector is mainly governed by the Ministry of Power. It consists of three major segments – Generation, Transmission & Distribution. Generation is further bifurcated into three sectors namely Central, State & Private Sector. India is world’s sixth largest energy consumer, accounting for 3.4% of global energy feeding. Due to India’s economic rise, the demand for energy has grown at an average of 3.6% per annum over the past 30 years. At the end of May 2013, the installed power generation capacity of India stood at 225.133 GW, while the per capita energy consumption stood at 778 KWh (Jan 2012). Introduction to power quality based services and increase in priorities towards “retail” players In India, there is need of full-fledged retail markets in present scenario. End-users are not participating in the present markets. Integration of renewable resources such as solar power and wind power are going at one end. Upcoming policies, regulations and standards are encouraging the integration of small-scale renewables to the utility grid. At this situation, it is expected that advent of automation and Smart Grids, SCADA (Supervisory Control & Data Acquisition), MIDAS (Modular Integrated
  2. 2. Distribution Automation System), OMS (Outage Management System), and on-line metering and billing in India may enable retail markets for open up competition avenues for the entry of end-users/consumers The integration of Business Analytics with Big Data of power sector is also an important function in power quality based services movement. This requires effective metering and billing standards with effective communication protocols. Apart from these necessary requirements, there will be a need for upgrading the present power systems. In this direction, this paper has identified the need for introduction of power quality based services to few retail markets as per the consumers requirements in the respective market. The reason behind this proposal is increasing need of reactive power at the load end and increasing focus on power quality services that directly increases revenue of mini distribution franchises or Distribution Company. 1.1. PURPOSE Analytics uses an innovative approach whereby user gets a complete view of all the meters whose data is downloaded and the utility gets the complete analytics of their business processes and the knowledge about the areas where improvement in terms of technology as well as other aspects are required. The software gives a logical view of the entire meter reading data according to various parameters. All the data available from the meter can be converted into database & spreadsheet format for further analysis & billing purpose. A flexible wizard is being presented to the user for converting various information on a format using which meter data can be converted to database or spreadsheet. 1.2. Web Application The web application is the front end of the software. This web application will have following objectives: 1. Integrate the application with ISU CSS. 2. Facilitate to monitor, analysis, and control and report various parameters of the MRD (Meter Reading Data). 1.3. Server Application The server application is the back end of the software. This server application will have following objectives: An application server is a server that delivers software applications with services such as security, data services, transaction support, load balancing, and management of large distributed systems. 1.4. Scope This Software helps to analyse MRD. Billing, meter change details. It helps various departments in various ways & indirectly helps in loss reduction. 1. Enforcement to monitor theft activity of consumers. 2. ONM to monitor low voltage, unbalanced current, etc. 3. Legal court cases supporting data on basis of MRD. 4. Meter testing labs. 2. Business Analytics Methodology Business Analytics is a strategic initiative by which organizations measure, analyze, drive and evaluate the effectiveness of their competitive strategy. Business Analytics projects go through the following stages as shown in Fig. 1.
  3. 3. Fig 1: Life cycle of BA System 2.1. Analysis Every Business Analytics project should clearly justify the cost and benefits of solving a business problem. Requirement analysis is performed including a predefined set of critical path factor and key performance indicators. The end users require kPIs. The analysis phase provides a high-level design of the various components of the solution. Because of dynamic nature of Business Analytics projects, modifications in objective, estimate, technology, people, sponsors and users can influence the success of the project. 2.2. Designing Based on the requirements and the complexity of the solution, appropriate Business Analytics technologies are selected. Prototyping is best method for analysis of the functional deliverables. The access requirements of the business must match with database design schema In most case, writing extensions to the tool capabilities and preprocessing the data are frequently required. 2.3. Development The life cycle of Business Analytics system repeats with the operating methodology at a new level of focus. The full process of flow of data or information across the organization consisting analysis, modification, re-evaluation, optimization and tuning. 2.4. Deployment Once all components of the Business Analytics application are tested, the application is deployed to the user ends. The success of Business Analytics project primarily lies on the quality of end user training and support. This stage includes the development of predefined reports and analyses for business users, and laying the groundwork for advanced high-level analytics in the future. 2.5. Evolution Measuring the success of application, extending the Business Analytics application across the enterprise or organization and increasing cross- functional information sharing are the goals of evolution. 3. Business Analytics in Power Sector Engineering analysis may require a full year/Half year of available data for analysis. 3.1. Categorization of Knowledge Area This knowledge area can be briefly characterized into following major categories: Analysis Desiging DevelopmentDeployment Evolution
  4. 4. Fig 2: Business Analytics Knowledge Area 1. Operational/Analytical Data: These operational applications are the real-time or near real-time applications like available customer meter data, Grid connected DG tracking information transfer capability (ATC) margins, spot bidding. 2. Front-end analytics: These analytics functions help the business to operate beyond real-time management of the grid. Examples include forecasting methods and models that support generation planning and development, demand management programs or spot market power purchases. These data uses are typically same-hour, same-day applications, but there is time limitation to scrub the data and try again to get data or information from the field. 3. Back-end analytics: These analytics functions are the non-real-time application that provides rate analysis (settlement mechanisms) and decision making, based on the processing of data from the KCC, MRD from AMR, and CMRI to XML through API, SCADA. The analytics transform data into actionable or decision-making information. This is where the planners, accountants, engineers and standards engineers will find the information they need to do their jobs. 3.2 Business Analytics application in Power Sector Utilities are using analytics to: 1. Understanding and developing customer profiles such as length of time in business, the type of business, number of employees, program results and so on. 2. Understand which are the areas of high Transmission & Distribution Losses and analyze their causes. 3. Improve the revenue realization by overcoming by reducing commercial losses. 4. Analyzing generating station efficiencies and T&D Efficiencies. 5. Useful for analyzing the consumer energy consumption pattern and load flow studies. 6. Useful for load forecasting and energy balancing studies. 7. Useful for generating Management Information system reports. 8. Can be very useful in analyzing the demand and supply miss-match and to bridge the gap between them. 9. Understanding the segments, objectives and prioritize customers for specific energy efficiency. 10. Generation of useful information from the huge amount of data coming in the utilities from diverse applications. 11. Analyzing the past data and records. 12. Useful for faster decision making based on real time data and factual data and to review the progress of the implemented decisions. 13. Enhancing and empowering business processes to become more profitable. 14. Report generation as per Organizational structure. These are the some of the applications specified above and much more applications of it are developing and will be developed in future for Indian Power Sector utilities. Operational Back-EndFront-End
  5. 5. 3.3 Necessity of Business Analytics for Indian Power Sector Complex Environment, political, economic and societal pressures are placing intense demands on power sector to make smarter decisions, deliver results and demonstrate accountability for meeting the energy shortage issue prevailing in the Indian Power Sector. An unprecedented “information explosion” both facilitates and complicates the ability of Power utilities to achieve and influence the desirable outcomes. A incredible opportunity exists to use the large amount of data to make better, fact- based decisions. Yet, the amount of data and its increasingly diverse and interactive nature can also paralyze power utilities as they try to separate the noteworthy from the not-worthy. Analytics goes beyond reporting and providing the mechanism to sort through this turmoil of information and the utilities respond with better decisions. Today, however, most power utilities are spending more time collecting and organizing data than to analyze it. Analyzing talent also tends to be more concentrated within organizations, rather than persistent across them. This can make it more difficult to discover useful insights which can be obtained by looking at information across multiple agencies and databases. To utilize its potential power in the public sector, analytics will have to become a core management competency. Building competency will require utilities to focus on four strategies – 1. Focus on the desired outcomes to move beyond the issues. 2. Direct the management of information around its use. 3. Use analytics-enabled insights to meet specific objectives. 4. Model and embed analytics discipline in management practices. To improve the cash flows to the power distribution companies and thus to the transmission and generating companies, the potential of Business Analytics can be effectively utilized and it will also help in making this sector financially viable and better management will be accomplished. 4. Business Analytics in Energy Efficiency India currently is facing major shortage of electricity generation capacity, even though it is the world’s fourth leading energy consumer after United States, China and Russia. For providing simple access of electricity to its consumers, it will require large capital investments. The increasing demand for demand for power has increased usage of fossil fuels which has led to the greater imports of them due to shortage of our production resources and depleting fossil fuel reserves. In this context, energy efficiency and conservation both play a dominant role. It has been estimated that nearly 25000 MW can be saved by implementing end use energy efficiency and demand side management techniques throughout India. The demand for energy can be reduced by decreasing the energy intensity and improving the energy efficiency. 4.1. Energy Intensity It is the ratio of energy consumption to the Gross Domestic Product (GDP). It is calculated as units of energy per unit of GDP. It is a degree of energy efficiency of a nation’s economy.
  6. 6. Higher the energy intensity higher will be the price of converting energy into GDP and vice versa. Below a certain level of progress, growth results in increase in energy intensity. With further growth in economy, it starts declining. The energy intensity augmented from 0.128 KWh in 1970-71 to 0.165 KWh in 1985-86, but is has again come down to 0.148KWh in 2011-12. Fig 3: Trend in Energy Intensity per rupee (1970-71 to 2011-12) 4.2. Importance of Energy Efficiency & Conservation Importance of Efficient use of energy and its conservation lies in the fact that one unit of energy saved at the consumption level reduces the need for fresh capacity creation by 2 times to 2.5 times. Further, such saving over efficient use of energy can be achieved at less than one-fifth the cost of fresh capacity creation. Energy efficiency will definitely supplement our efforts to meet power need, apart from reducing fossil fuel feeding. 4.3. Shift from Energy Conservation to Energy Efficiency The policy concepts and goals will have to be shifted from “energy conservation” to “energy efficiency”, and from “energy inputs” to the “effectiveness of energy use” and “energy service area”. Formation of new power generation volume is costly and demands long gestation period whereas energy efficiency activities can make available additional power at reasonably low investments within a short period of time. 4.4. Drivers of Energy Efficiency growth in India The main drivers are – Energy Demand Competitive Advantage Regulatory Sustainability Climate Change Over the last 20 years, primary energy consumption in India has increased from 1.02x108 MTOE per year to 6.15x108 MTOE per year. Information used from US Energy Information Administration. 4.5. Role of Analytics in Energy Efficiency Power Utilities energy efficiency program development considers the following aspects: 1. Potential business and customer value 2. Funding Mechanisms 3. Regulatory requirements and expectations 4. Customer Incentives 5. Integrated and multi-channel marketing and sales plans 6. Identification of trade allies and contractor partners 7. Comprehensive operative plans, including plans for customer support 8. Measurement and verification 9. Accounting and financial management Data analytics touches and provides valuable input into all above factors. Feedback gained from analytics around existing programs offers useful insight. It is playing a crucial role in
  7. 7. reaching the participation and consumption reduction targets. 5. High Level Framework for BA in Big Data of Power Sector The requirements for what type of Big Data is capture and store must be documented in Big Data architecture. In addition, the requirements for delivering Big Data to the users has to be analyzed and role based. If a Big Data warehouse is purchased, database has to be extended with features that are required by Business Analytics applications. If a Big Data warehouse is built, the database will have to be designed based on the Big Data architecture developed during the previous step. At the highest and most abstract level, the logical framework view of Simulator application can be considered a set of cooperating services grouped into the following layers, as shown in figure below: Fig 4: High-level architecture view of Analytics Software in Power Sector 6. System Requirement for Running Business Analytics Application 6.1. User Classes and Characteristics There are three types of use in the application viz. Master, Admin and User View. These user types are defined below: Master: A user who can manage the user accounts, their permissions and type. Admin: A user who can edit and verify device data, manage validation and approximation rules. User View: A user who can view data, process and generate reports Basic application functionalities are accessible to all users in Super Admin, Admin and User View. Along with this, some features are only for Master and some are for Admin. 6.2. User Interfaces This web application can be access through a latest web browser. The interface will be viewed best on 1024 x 768 pixels resolution setting. The application will be fully compatible with following browsers and operating systems:  Operating System This application is independent of operating system.  Browser IE 8.0 or above Google Chrome 12.0 or above Firefox 4.0 or above Latest web browser on Android 2.2 or above User must login to access any part of the application. Not all the modules will be accessible to every user. Each module is visible Data Base XML AMR CMRI Device User Interface Web Application MRD
  8. 8. to only selected users that are mentioned in the user type association. 6.3. Hardware Interfaces The requirement of server and client side hardware interfaces and network hard disk are given below:  Windows Server Side Hardware Processor: Intel / AMD Server Processor RAM: 4GB or above Hard Disk: 10 GB  Database Server Side Hardware Processor: Intel / AMD Server Processor RAM: 8GB or above  Client Side Hardware Processor: Pentium III or above RAM: 256MB or above Hard Disk: 10 GB (for OS and Browser installation) 6.4. Software Interfaces The requirement of server and client side software interfaces are given below:  Windows Server Side Software Operating System: Windows Server 2003 Environment: .NET Framework 4.0 or above IIS 6.0 with latest updates  Database Server Side Software Database: Microsoft SQL Server 2008 r2  Client Side Software Operating System: OS independent Screen Resolution: 1024x768 or above Web Browser: Latest web browser 7. Business Analytics Software System Attributes Fig 5: BA Software System Attributes 7.1. Security: The application is password protected .User will have to enter the correct id and password in order to access the application. 7.2. Maintainability: The application is designed in sustainable manner. It will be easy to integrate new requirements in the individual modules. 7.3 Portability: The application will be easily manageable on any windows-based system that has any internet browser. 8. Organizations wishing to implement Business Analytics face the following challenges: 1. Providing controlled access to extensive resources with limited capacity to devices. 2. Benchmarks and performance targets Create a new information infrastructure and model to support the development and deployment of multiple applications. 3. Managing and connecting with multiple networks and Integrating to existing enterprise and legacy systems. Creating and managing the solutions that performs in and out of network coverage. Security Maintainability Portability
  9. 9. 4. Enforcing security levels and role-based access to the data warehouse. 9. The following Best Practices are used for evaluation of the adequacy and completeness of BA infrastructure: 1. Effective data integration process to create required Business Analytics that help in generating report on a daily basis. 2. Continuous monitoring processes to allow alerts and caution to be communicated intensely. 3. Automated information delivery and communication process. 4. Fully automated data warehouse administration infrastructure. 5. Availability and integrity of information on standardized dimension such as consumers, product and geography. 6. Delivery of answers to all key performance business questions. 7. Integrated enterprise portal infrastructure to deliver business process report. 8. Clear help desk and training policies, higher end user acceptance having a consistent look and touch across different applications. 10. Conclusion This paper discussed the role of Business Analytics in managing and developing the Indian power Sector through its diverse applications. It is a useful asset for the power utilities for reducing the transmission and commercial losses, for improving the cash flows, managing their operations effectively, and better plan out and implements their projects; reduce energy intensity in contrast with increasing energy efficiency and so on. It helps in all round effective management of the power sector utilities through the enhanced utilization of business analytics in managing and analyzing the big data of these power utilities. 11. Acknowledgements The authors would like to thank Dr. D. K. Punia and Prof. Anil Kumar for their valuable comments that improved the quality of this paper and BSES Yamuna Power Ltd. and University of Petroleum & Energy Studies (UPES), for their valuable guidance. 12. References [1] [Online]. Available: Indian Energy Exchange Website, www.iexindia.com, June. 2013. [2] [Online]. Available: Ministry of Power - Government of India Website, http://powermin.nic.in, June. 2013. [3] [Online]. Available: Central Electricity Authority of India Website. Available: http://www.cea.nic.in, June. 2013. [4] [Online]. Available: The Climate Group, http://www.theclimategroup.org, June 2013. [5] [Online]. Available: http://www.encyclopedia.com/doc/1G1- 196830172.html, June. 2013. [6] [Online]. Available: http://www.sei.cmu.edu /library/assets/sgmm.pdf, June. 2013. [7] [Online]. Available: North Delhi Power Limited (NDPL) website. http://www.cea.nic.in, June. 2013. [8] [Online]. Available: Bangalore Electricity Supply Company (BESCOM) website. http://www.bescom.org/, June. 2009. [9] [Online]. Available: www.powersector.com [10] [Online]. Available: http://en.wikipedia.org/wiki/Electricity_sector_in_ India

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