Big Data Analytics: Research Needs. Ali Ghassemian

Transcription

1 Big Data Analytics: Research Needs Ali Ghassemian April 28, 2016

2 Plan DOE s Grid Modernization Initiative (GMI) represent a comprehensive effort to help shape the future of our nation s grid and solve the challenges of integrating conventional and renewable sources with energy storage and smart buildings, while ensuring that the grid is resilient and secure to withstand growing cybersecurity and climate challenges. DOE s comprehensive Grid Modernization Multi-Year Program Plan (MYPP), is the blueprint for DOE s R&D agenda and the modernized grid. The plan lays out the agenda and priorities for the Department s research, development, and demonstration efforts to enable a modernized grid, building on concepts and recommendations from DOE s Quadrennial Energy Review (QER) and Quadrennial Technology Review (QTR). The plan describes the R&D activities that DOE will focus on over the next five years, including continued opportunities for public-private partnerships and enhanced collaboration among DOE offices. Through the GMI, MYPP, QER, and QTR the Department will help frame new grid architecture design elements, develop new planning and real-time operations platforms, provide metrics and analytics to improve grid performance, and enhance government and industry capabilities for designing the infrastructure and regulatory models needed for successful grid modernization. Successful transformation requires a coordinated, whole-grid approach designed to research, develop, demonstrate, and assist in the deployment of key technologies. 2

3 Challenges Modern-day demands on the U.S. electricity grid have required the introduction of smart devices to improve resilience and reliability in the face of changing grid characteristics and dynamics such as an increase in renewables, electric vehicle integration, changing load patterns, changing flow, and faster performance speeds. As a result, today s grid produces an enormous amount of data and a significant challenge is how to enable grid operators to make sense of such a large quantity of grid state and customer data in near-real time. Today s technologies, tools, and techniques are not presently up to the challenge. 3

4 Big Data Challenges Numerous sensors of different types placed in networks provides massive amount of data Data from different sources need to be validated and integrated. Data needs to be processed into actionable information to be useful for system operators and engineers As data volumes increases, scalable solutions to handle huge amounts of data become a necessity. 4

5 What is Needed and how to Achieve it? Develop advanced computational (software) and control technologies (hardware) to improve the reliability, resilience, and efficiency of the nation s electricity systems Help prevent blackouts and improve reliability by providing wide-area real-time visibility into the conditions of the grid Track and expand the use of real-time data in grid control rooms and energy management systems Build the technology to manage and control future grid operations Improve the performance of modeling tools and computations that are the basis of grid operations Assess and mitigate near- and long-term risks to energy infrastructures and systems Track and expand the use of quantitative risk and uncertainty methods in Federal and state-level energy system decisions regarding energy infrastructure investment 5

6 Advanced Grid Modeling Program AGM Develops the Core Operational and Planning Components Supporting the Future Grid Advances the computational and mathematical methods underpinning operator tools Seeks to develop faster than real time analytical tools through work in three main areas Data management and analytics Mathematical methods and computation Models and simulations 6

7 Main Areas for Analytical Tools Data management and analytics (DM&A): These activities focus on the way data is collected, used, stored, and archived to improve applicability of large, multisource datasets for real-time operations and off-line planning studies. Mathematical Methods & Computation (MM&C): Effort addresses emerging mathematical and computational challenges arising in power systems, developing new algorithms and software libraries. Models & Simulations (M&S): Research on a new class of fast, high fidelity capabilities that underpin better grid operations and planning in a large-scale, dynamic and stochastic environment. 7

8 Projects in AGM Developing State Estimation that can run at a unprecedented 0.5 s speed. Developing Dynamic Contingency Analysis tools (DCAT) Integrating Planning Dynamics and system Protection simulations models in Computer aided protection engineering tool (CAPE) that allows performing a more accurate analysis of the behavior of protection equipment during the first cycles after a fault condition. 8

9 Projects in AGM Developing probabilistic Methods for Electric Grid Operations Refining the MMWG models by modeling governor deadband and adjusting active governor ratio and load composition in order to match up measured EI frequency responses. This will help with validating power system dynamic model that is used to perform contingency analysis. Developing Dynamic Models for Year 2030 Eastern Interconnection 9

10 Projects in AGM Developing GridPACK which is an open source HPC library that includes functionality such as Power Flow, Dynamic Simulation, Power Flow Contingency Analysis, and Dynamic Security Assessment Power System Parallel Dynamic Simulation Framework for Real- Time Wide-Area Protection and Control Developed Management & Optimization of VARs for Future Transmission Infrastructure with High Penetration of Renewable Generation 10

11 Main areas of concentration in AGM projects applicable to the Big Data Parallel computing for big data analysis integration of multiple data sources PMU, SCADA, Weather, etc. Improved event detection and data mining algorithms Improved processed and actionable information 11

12 Data Analytics CURENT (Center for Untra-Wide-Area Resilient Electric Energy Transmission Networks) Data Processing Data compression and reconstruction; using synchro-phasor data; use load data to identify potential demand response Data Integration Overcoming challenges of integrating data from different information systems Data Mining and Statistics Correlating data from IEDs, weather, power systems to identify vulnerabilities and locate faults Cyber and Data Security - Produced a new method to detect coordinated cyberattacks in power grid information systems 12

13 GMLC DOE announced funding in January, 2016 of up to $220 million over three years for DOE s National Labs and partners. The Grid Modernization Laboratory Consortium (GMLC) funding will support critical research and development in the AGM Subprogram. The concentration is in: 1. Load Modeling 2. Protection 3. Solvers 13

14 GMLC AGM Projects Load Modeling: Contribute towards development and validation of mathematical model structure capturing emerging load dynamic behaviors. Protection: Enhance protection system (misoperation) modeling capabilities, as a platform for the study and coordination of protection devices and approaches. Solvers: Create, maintain, and enhance a scalable math solver library for grid planning and operations tools that work on a variety of computational platforms 14

15 National Academies of Science, Engineering, and Medicine DOE commissioned National Research Council (NRC) to engage in a study with the following main charge: What are the critical areas of mathematical and computational research that much be addressed for the next-generation electric transmission and distribution (grid) system? Identify future needs. In what ways, if any, do current research efforts in these areas (including non-u.s. efforts) need to be adjusted or augmented? 15

16 Findings 1. Analyze the vast amount of that data coming to control centers and represent the results in a way suitable for timely decision making. 2. Improve modeling of grid operation to increase efficiency. 3. Advances in the mathematical and computational algorithm are needed to address the challenges arise from the integration of more alternative energy sources into the system as well as to help reduce the risk of voltage collapse and enable lines to be used within the broader limits, and flexible ac transmission systems and storage technology can be used for eliminating stability-related line limits. 16

17 Findings 4. Establish a more complete models that include the dynamic interaction between the transmission and distribution systems. 5. Establish a better planning models for accurate forecasting to deal with uncertainties (such as distributed-generation technologies, climate change, shifting rainfall, frequency of intense weather events, changes in policies to reduce emissions of carbon dioxide) 6. Establish a modeling and mitigation plans for high-impact, lowfrequency events (including coordinated physical or cyberattack; pandemics; high-altitude electromagnetic pulses; and large-scale geomagnetic disturbances) by doing fundamental research in mathematics and computer science that could yield dividends for predicting the consequences of such events and limiting their damage. 17

18 NAS Recommendations The future grid will rely on integrating advanced computation and massive data to create a better understanding that supports decision making. To complement this research specifically focused on tools for the next-generation grid, a range of potentially applicable research exists. Examples include research into the general topics of: uncertainty quantification simulation and analysis of complex adaptive systems simulation and analysis of multi-time-scale systems methods for characterizing and controlling resilience and reliability. 18

19 NAS Recommendations Make data available for testing Create synthetic data for development, testing and validation Advance the ACOPF Research on Data driven approaches applied to the operations, planning and maintenance of power systems. Improve the nonlinear, nonconvex optimization algorithms DOE and NSF should sponsor the development of new-open source software for research community Promote collaboration between national labs and universities Integration of theory and computational methods should be a high-priority research area DOE should support research to extend dynamical systems theory and associated numerical methods to encompass classes of systems that include electric grids 19

20 Summary AGM program intends to enhance reliability, resiliency, security, and efficiency of the electric power and enable advanced mitigation and recovery strategies, by: Accelerating performance Developing predictive/corrective decision-support capabilities Integrating model platforms 20

21 Question? 21