ITC571 â€“PROJECT PROPOSAL AND PLAN Full Name Student ID Subject ITC571 â€“ Emerging Technology and Innovations Assignment No Assessment 2 – Project Proposal and Plan Due Date 18-08-2017 Lecturerâ€™s Name I. INTRODUCTION Electricity is the basic need to the society and world economy. But most of the world depends on electricity systems designed around fifty years ago. These can be inefficient and may not offer a solutionto global challenges. Smart grids will play a crucial role in the current and future developments (Asad, 2011). What is a Smart Grid? A smart grid is an intellectual and ICT based energy distributing electricity in an efficient way from source to usage. This is obtained by integrating information, telecommunication, and other electric technologies with the existing electricity system (Krenicki, 2013). Big Data – Energy and Utilities: Smart meters and Smart grids are transforming the energy industry. Companies can now intelligently monitor the distribution of energy using the data being sent back from sensors. In the past, the lack of data to energy has led to inefficiencies; enough electricity was being lost annually to power India, Germany and Canada for an entire year(Kwan, 2010). Smart grid is needed to interconnect with much more energy resources than before because renewables are very distributed. It is also about connecting the end consumers into the grid for the grid operators to make sound decisions on the energy dispatch. As a consequence we are having much more data flow back into the energy management software to be able to make that real time decisions. The quantity of data which is to be dealt and aggregated there is exploding. Security is core in the development of future smart grid systems because energy systems are critical to cities and countries; we have to take into account specifically the risks of cyber-attack into the design of future of smart grid systems. So it is needed to embed solutions and best practices from other sectors and adopt it to the specific needs of this energy environment and big data(IBM White Paper, 2014). Big data is the non-traditional strategy and technology that gathers, organizes, processes insights form big datasets. The problems regarding data exceeding the data storage of a single computer creates great delay of works. This kind of problems has massively expanded in recent time. In this research we are going to discuss the technological challenges faced by Big Data and how these problems can be rectified in future. Along with the idea, concept and use of Big data in Smart Energy Grid. 1. Background of the Study Big Data is a data set that tends to be in such a large amount that traditional data processing software application lack behind to handle. Big Data has emerged within the past years as an ideal provider of data and opportunities to enable research and decision-support application (Chen& Zhang2014). There are several complexities which includes challenges like analysis, storage. File transfers, sharing files, querying, visualization, privacy and updating information. Big Data is a massive volume of both structured and unstructured data that is so large that it becomes difficult to store and process with traditional database techniques. Smart grid is a real solution concerning the new use and prevention of electricity which balances the consumption and production in the changing energy landscape. This also concerns on introduction of information and communication technology (ICT) to electricity grid and hence integrating the actions of all the producers and consumers for safe, sustainable and cost-effective supply of electricity. 2. Research Aim and Objective The need of this research is to analyze and evaluate Big Data and the security concerns related with the safety of an organizationâ€™s or individualâ€™s privacy. Since, now-a-day data breaching has the potentiality to seriously damage the reputation and legal ethics of someone it is crucial to solve the problem and take valuable steps for the same (Dubey& Srivastava2016). In the research we are going to discuss about the security concerns of Big Data, lack of maintenance and thus the measures taken to solve them. The aim of the research is to determine the security issues and recommend respective solution for the improvement practice and the effectiveness of Smart Grid in technologies. Big Data for smart grid represents various opportunities as well as infrastructure. 3. Motivation for the Research Data breach and hacking has increased with the increase of dependency on computers and internet, making business and individuals. Big Data poses several new security challenges for traditional data encryption standards, methodologies and algorithms. Data security policies works with the structured data stores in conventional DBMS, which are not effective in handling unstructured data (Erl, Khattak& Buhler2016). The existing problems require great attention and hence must be solved within higher priority since mostly these problems concerns about the safety of people or organization. 4. Research Significance The problems and challenges related to Big Data in Smart Energy Grid are one of the biggest concerns of society since the growing companies use the technology to analyze the petabytes of data which includes various contents and storage concern for gaining insights about the business and customers. Thus, the information classification becomes much critical and the ownership must be addressed to promote reasonable classification. 5. Objective of the Research The main objective of the research is to: â€¢ To Identify the usability and Utility of Big Data in Smart Energy Grid â€¢ To evaluate the security concern of Big Data in smart energy grid â€¢ To explain the benefits and importance of big data in smart energy grid The aim of this research is to review, analyze and evaluate the issues and solution techniques highlighted by various researchers and provide a concrete analysis on the existing research till date. II. MATERIALS AND METHODS Data will be collected from the different types of previous research papers or journal articles written in the field of the research study. Figure1: Smart grid communication technologies (Source: Qiu&Antonik2014) The following table is the format approach that will be followed to achieve the aim of this research project. No Author Consideration Issue Solution / Observation / Technique 1 Yin et al. 2013 â€¢ Scalability â€¢ Real time â€¢ High Reliability â€¢ Security â€¢ Low Cost â€¢ Requirements of scalability and real time are challenging. â€¢ OSIsoft has issues with limited queries, inefficiency and unpredictability. â€¢ Energy grid data is very structured. â€¢ Measurements are not random. â€¢ Lossless compression achieves data reduction without compromising information. â€¢ Data storing in temporary location allows fast retrieval. 2 Khaouat et al. 2016 â€¢ Signal analytics â€¢ Events analytics â€¢ State analytics â€¢ Operational analytics â€¢ Customer analytics â€¢ Challenge to manage and collect large amounts of data. â€¢ Demand prediction in smart grid applications. â€¢ Real time processing. â€¢ Analytics processing â€¢ AMR: Automatic collection of usage, diagnostic and status data from smart meters. â€¢ Usage of Sensors to retrieve weather, temperatures, etc. â€¢ SCADA monitoring â€¢ Communication support for data transportation in smart grid. â€¢ Cleaning, storage, management and analysis. â€¢ Usage prediction, monitoring and production forecasting. 3 Sagiroglu et al. 2016 â€¢ Big Data issues in Smart energy grids â€¢ IT Infrastructure â€¢ Data Collection â€¢ Governance â€¢ Data integration and sharing â€¢ Data processing and Analysis â€¢ Security and Smart Energy Managements â€¢ Big data analytics is a new research field. â€¢ Due to lack of advanced technologies in analytics, support is needed. â€¢ Data type is explained in smart grids. Ex: Stream, sta
tic or xml, json, etc. â€¢ Data tools comparison like Hadoop vs HPCC in smart grid. â€¢ ETL â€“ Extract, Transform, Load processes in smart grids. 4 5 IV. RESULTS S.No. Author No. of Records Application Data Size Data Nature Real time/Offline Analytic Purpose Issues Solutions 1 Khaouat et al. 2016 2 Yin et al. 2013 3 Sagiroglu et al. 2016 Expected Outcome 1. The project will give detailed study about the analysis and evaluation of data obtained from journals related to the issues and challenges / solution techniques in big data in smart energy grid. 2. This research is also aiming at listing out all the possible big data techniques to process the data in smart energy meters and grids. 3. This study focuses on the parameters to be considered in the collection of big data related to smart energy grids. 4. Detailed graphical and figure representation of research results combined with tables and charts. V. TIMELINE Week Task 1 Obtained some knowledge about the research, e.g. how to read papers, how to evaluate papers. 2 Filtered the list of research topics and selected my area of interest. Conducted basic research on selected topic. Skimmed few journal papers related to Big Data and Smart Energy Grids. 3 Started collecting Data from various journal papers and obtained suggestions / approval from professor. 4 Studied Existing Literature review and previous published papers for reference. 5 Developing the research plan 6 Selecting research methodology to be utilized 7 Conducting research on atleast 30 journal papers 8 Data analysis 9 Data evaluation and results in graphical representation 10 Conclude the research 11 Final documentation 12 Final submission REFERENCES J. Yin, P. Sharma, I. Gorton and B. Akyoli, “Large-Scale Data Challenges in Future Power Grids,” 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering, Redwood City, 2013, pp. 324-328. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6525540&isnumber=6525477 J. Kwac and R. Rajagopal, “Data-Driven Targeting of Customers for Demand Response,” in IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2199-2207, Sept. 2016. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7313032&isnumber=7548088 Z. Asad and M. A. Rehman Chaudhry, “A Two-Way Street: Green Big Data Processing for a Greener Smart Grid,” in IEEE Systems Journal, vol. 11, no. 2, pp. 784-795, June 2017. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7399696&isnumber=7959216 A. El Khaouat and L. Benhlima, “Big data based management for smart grids,” 2016 International Renewable and Sustainable Energy Conference (IRSEC), Marrakech, 2016, pp. 1044-1047. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7983902&isnumber=7983856 S. Sagiroglu, R. Terzi, Y. Canbay and I. Colak, “Big data issues in smart grid systems,” 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, 2016, pp. 1007-1012. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7884486&isnumber=7884346 Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347. Erl, T., Khattak, W., & Buhler, P. (2016). Big data fundamentals: concepts, drivers & techniques. Prentice Hall Press. Kim, G. H., Trimi, S., & Chung, J. H. (2014).Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85. Lafuente, G. (2015). The big data security challenge. Network security, 2015(1), 12-14. Lafuente, G. (2015). The big data security challenge. Network security, 2015(1), 12-14. Marz, N., & Warren, J. (2015). Big Data: Principles and best practices of scalable realtime data systems. Manning Publications Co.. Swan, M. (2013). The quantified self: Fundamental disruption in big data science and biological discovery. Big Data, 1(2), 85-99.