The Smart Grid group is conducting research in the development of next generation smartgrid technology through cutting edge techniques in data driven modeling, highly accurate and rapid predictive analytics and optimizations. This work titled DEEP SOLAR is a part of the ENERGISE program with funding for research from Office of Energy Efficiency and Renewable Energy SunShot Initiative of Department of Energy (DoE).


Previously, the group successfully conducted research into informatics-driven scalable software architectures to address realtime power management in the domain of Smart Power Grids. This work was funded by the US Department of Energy as part of the five-year Los Angeles Smart Grid Demonstration project, to forecast and curtail power consumption by thousands of electricity consumers on-demand through large scale information processing and consumer pattern detection.


This cyberphysical domain provides unique challenges to many existing computer science algorithms, approaches and frameworks due to the data complexity, dynamism, scale and need for realtime response.

Areas of Interest:

data driven modeling, predictive analytics, optimizations, semantic information integration, graph analytics, stream processing, cyber physical security, and cloud computing

Research

DEEP SOLAR

The DEEP SOLAR project aims to develop the Internet of Energy grid infrastructure of the future through accurate data-driven models of the Smartgrid and its components with a focus on integrating renewable energy sources. We use the latest applied machine learning and data analytics techniques to develop accurate predictive models of various distributed energy resources (DERS) such as solar energy generators (distributed Solar PVs) and distributed storage components (EVs, batteries) and their interactions using Smartgrid IoT data from our utility partners. The expected outcome of the project is a scalable Dynamic Scenario Analysis Software Toolkit for operational planning under deep solar penetraion. More information can be found at the DEEP SOLAR website.

Internet-of-Energy (IoE) Enabled Distributed Energy System

In order to achieve high penetration of renewables, we focus on developing a co-ordinated controlled ecosystem based on AMI and intelligent devices that we label a “Live Energy Map” (LEM) of the status of all energy things in the network of prosumers. Our LEM abstraction associates a rich set of attributes that can be used to model both static as well as dynamic variables of the grid. The LEM will update in real time with changing dynamic variables.


Data Analytics

In a Smart Microgrid, both the utility and the consumer can benefit from analytics based on data for electricity consumption, generation and related variables. There are several benefits and costs associated with performing analytics at different spatial and temporal granularities.


In our research, we perform analytics for the USC campus microgrid to develop reliable electricity consumption forecasting models that would work for different spatial (building-level and campus-level) and temporal granularities (15-min and daily intervals). Our goal is to provide the facility managers more insight into the consumption patterns on the campus and enable them to plan consumption and curtailment activities for Demand Response optimization in the campus microgrid.


The USC campus microgrid is a test-bed for the DOE- sponsored Los Angeles Smart Grid Demonstration Project (SGDP) [26]. The USC campus has over 170 buildings and 45,000 student and staff population with diverse demographics. THe Facilities Management Services (FMS) on campus collects data on power usage at 15-minute intervals from 170 smart power meters on campus. The goal of the SGDP is to demonstrate DR optimization in the campus microgrid.


Optimizations for Real-time Grid Operations

Mitigating supply demand mismatch under dynamically changing load and supply curves is critical to ensure smooth grid operations. Net load balancing decisions which manipulate the synthetic reserves i.e. storage, demand curtailment, solar curtailment etc. need to be made in real time under tight grid operational constraints.


In our research, we focus on developing optimization framework that enables grid net-load smoothing through scheduling, regulation and control of synthetic reserves obtained from incentivised prosumers. The optimization framework dynamically integrates information about the current state of the distribution network and prediction from our data-driven models and computes a range of solutions to ensure supply-demand balance. We also focus on developing approximation algorithms which are computationally tractable and provide bounded error guarantees.


Security in Cyber Physical Systems

Cyber physical systems are becoming increasingly complex with the advancement of internet infrastructure, internet of things (IoT), and sensor networks. Smart grids are an emerging class of such systems. It contains an advanced metering infrastructure (AMI) at customers and sub-stations, distributed sensor networks to maintain the situational awareness, communication network to communicate these sensor readings and distributed power generation, transmission and storage units. Real time state estimation in smart grid is one key problem when it comes to smart grid security. Complex interactions happen between the interconnected cyber layer and the physical layer of these systems demands advanced analytics.


The goal of this research is to model the Cyber Physical Systems using dynamic graphs and develop fast graph theoretical algorithms to detect complex cyber attacks and maintain situational awareness. A system for real time threat detection in cyber physical systems will be developed.

Cloud Computing

Click here to read more about Cloud Computing Projects.

Recent Publications

  • Sanmukh R. Kuppannagari, Rajgopal Kannan, and Viktor K. Prasanna Optimal Net Load Balancing in Smartgrids with High PV Penetration, The 4th ACM International Conference on Systems for Energy-Electricitycient Built Environments (BuildSys 2017), November 2017.

  • Charith Wickramaarachchi, Rajgopal Kannan, Charalampos Chelmis, Viktor Prasanna, PReSS Towards a Secure Smart Grid: Protection Recommendations against Smart Spoofing, 2017 IEEE PES Innovative Smart Grid Technologies Conference, pp 1-5, April 2017.

  • Charith Wickramaarachchi, Charalampos Chelmis, Rajgopal Kannan, Viktor K. Prasanna, Protecting Critical Buses in Power-Grid Against Data Attacks: Adaptive Protection Schemes for Smart Cities, IEEE Future Technologies Conference (FTC '16), pp. 1047-1056, December 2016.

  • Charith Wickramaarachchi, Sanmukh R. Kuppannagari, Rajgopal Kannan, Viktor K. Prasanna, Improved Protection Scheme for Data Attack on Strategic Buses in the Smart Grid, 4th IEEE Conference on Technologies for Sustainability (SusTech), pp. 96-101, October 2016.

  • Sanmukh R. Kuppannagari, Rajgopal Kannan, Charalampos Chelmis and Viktor K. Prasanna, Implementation of Learning-Based Dynamic Demand Response on a Campus Micro-grid, 25th International Joint Conference on Artificial Intelligence, IJCAI-16 Demo Track.

  • Michail Misyrlis, Charalambos Chelmis, Kannan Rajgopal, Viktor K. Prasanna, Sparse Causal Temporal Modeling to Inform Power System Defense, to appear in Procedia Computer Science, 2016.

  • Sanmukh R. Kuppannagari, Rajgopal Kannan, Charalampos Chelmis, Arash S. Tehrani, and Viktor K. Prasanna Optimal Customer Targeting for Sustainable Demand Response in Smart Grids, International Conference on Computational Science, 2016

  • Ranjan Pal and Viktor Prasanna, The STREAM Mechanism to Improve CPS Security The Case of the Smart Grid, IEEE Transactions on Computer-Aided Design for Integrated Circuits and Systems, 2016

  • Fabian Knirsch, Dominik Engel, Cristian Neureiter, Marc Frincu and Viktor Prasanna, Privacy Assessment of Data Flow Graphs for an Advanced Recommender System in the Smart Grid, Communications in Computer and Information Science, Springer, Vol 576, 2016

  • Ranjan Pal, Charalampos Chelmis, Marc Frincu and Viktor Prasanna, MATCH for the Prosumer Smart Grid The Algorithmics of Real-Time Power Balance, IEEE Transactions on Parallel and Distributed Systems (TPDS), 2016

  • Saima Aman,Charalampos Chelmis and Viktor Prasanna, Learning to REDUCE: A Reduced Electricity Consumption Prediction Ensemble, AAAI Workshop on Artificial Intelligence for Smart Grids and Smart Buildings (AAAI '16), February 2016

  • Charith Wickramaarachchi, Alok Kumbhare, Charalampos Chelmis and Viktor Prasanna, Hatrick: A System for Real-time Threat Detection in Cyber Physical Systems, Annual Computer Security Applications Conference (ACSAC '15), December 2015

  • Sanmukh R. Kuppannagari, Rajgopal Kannan and Viktor K. Prasanna, An ILP Based Algorithm for Optimal Customer Selection for Demand Response in Smartgrids, International Conference on Computational Science and Computational Intelligence (CSCI '15), December 2015

  • Saima Aman, Marc Frincu, Charalampos Chelmis, Mohammad Noor and Viktor Prasanna, Prediction Models for Dynamic Demand Response: Requirements, Challenges, and Insights, IEEE Conference on Smart Grid Communications (SmartGridComm '15), November 2015

  • Yogesh Simmhan, Neel Choudhury, Charith Wickramaarachchi, Alok Kumbhare, Marc Frincu, Cauligi Raghavendra and Viktor Prasanna, Distributed Programming over Time-series Graphs, IEEE International Parallel and Distributed Processing Symposium (IPDPS), May 2015.

  • Charalampos Chelmis, Jahanvi Kolte and Viktor K. Prasanna, Big Data Analytics for Demand Response: Clustering Over Space and Time, IEEE International Conference on Big Data Workshops, Big Data 2015

  • Ranjan Pal, Charalampos Chelmis, Chandra Tadepalli, Marc Frincu, Saima Aman and Viktor Prasanna, Online Time Series Clustering for Demand Response: A Theory to Break the 'Curse of Dimensionality', ACM e-Energy, July 2015 (challenge paper).

  • Marc Frincu, Charalampos Chelmis, Saima Aman, Muhammad Rizwan Saeed, Vasilis Zois, Viktor Prasanna, Carol Fern and Aras Akbari, Enabling Automated Dynamic Demand Response: From Theory to Practice, ACM e-Energy, July 2015 (poster).

  • Charalampos Chelmis, Muhammad Rizwan Saeed, Marc Frincu and Viktor Prasanna, Curtailment Estimation Methods for Demand Response: Lessons Learned by Comparing Apples to Oranges, ACM e-Energy, July 2015 (poster).

  • S. Aman, Y. Simmhan and V. K. Prasanna, Holistic Measures for Evaluating Prediction Models in Smart Grids, IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 27, No. 2, pp: 475-488, 2015.

  • Fabian Knirsch, Dominik Engel, Marc Frincu, Viktor Prasanna, Model-based Assessment for Balancing Privacy Requirements and Operational Capabilities in the Smart Grid, IEEE Conference on Innovative Smart Grid Technologies (iSGT), February 2015.

  • Fabian Knirsch, Dominik Engel, Christian Neureiter, Marc Frincu, Viktor Prasanna, Model-driven Privacy Assessment in the Smart Grid, International Conference on Information Systems Security and Privacy (ICISSP)(Best Paper Award), February 2015.

  • S. Aman, C. Chelmis and V. K. Prasanna, Influence-driven Model for Time Series Prediction from Partial Observations, AAAI Conference on Artificial Intelligence, Austin, TX, 2015.

  • C. Chelmis, S. Aman, M. Saeed, M. Frincu and V. K. Prasanna, Estimating Reduced Consumption for Dynamic Demand Response, AAAI Workshop on Computational Sustainability, Austin, Texas, 2015.

  • S. Aman, C. Chelmis, and V. K. Prasanna Addressing data veracity in big data applications, IEEE International Conference on Big Data, 2014.

  • V. Zois, M. Frincu, C. Chelmis, M. Saeed and V. K. Prasanna, Efficient Customer Selection for Sustainable Demand Response in Smart Grids, International Green Computing Conference, Dallas, Texas, 2014.

  • V. Zois, M. Frincu and V. K. Prasanna, Integrated Platform for Automated Sustainable Demand Response in Smart Grids, International Workshop on Intelligent Energy Systems, San Diego, California, 2014.

  • Charith Wickramaarachchi, Marc Frincu, Viktor Prasanna, Enabling Real-time Pro-active Analytics on Streaming Graphs Technical Report, USC, 2014.

  • Qunzhi Zhou, Yogesh Simmhan and Viktor Prasanna Towards Hybrid Online On-Demand Querying of Realtime Data with Stateful Complex Event Processing, IEEE International Conference on Big Data (BigData), 2013.

  • Charith Wickramaarachchi and Yogesh Simmhan, Continuous Dataflow Update Strategies for Mission-Critical Applications IEEE Internatrional Conference on eScience (eScience), 2013.

  • Yogesh Simmhan and Muhammad Usman Noor, Scalable Prediction of Energy Consumption using Incremental Time Series Clustering, Workshop on Big Data and Smarter Cities,2013.

  • Yogesh Simmhan, Saima Aman, Alok Kumbhare, Rongyang Liu, Sam Stevens, Qunzhi Zhou and Viktor Prasanna, Cloud-Based Software Platform for Big Data Analytics in Smart Grids, Computing in Science and Engineering , 15(4), pp. 38-47, July-Aug. 2013, IEEE and AIP ([IF 1.422, CORE C]).

  • Saima Aman, Yogesh Simmhan and Viktor K. Prasanna, Energy Management Systems: State of the Art and Emerging Trends, IEEE Communications Magazine , 51 (1) , 2013 , pp. 114 - 119 , IEEE. ([IF 3.785]).

  • Alok Kumbhare, Yogesh Simmhan and Viktor Prasanna, Cryptonite: A Secure and Performant Data Repository on Public Clouds, International Cloud Computing Conference (CLOUD), 2012.

  • Yogesh Simmhan, Vaibhav Agarwal, Saima Aman, Alok Kumbhare, Sreedhar Natarajan, Nikhil Rajguru, Ian Robinson, Samuel Stevens, Wei Yin, Qunzhi Zhou and Viktor Prasanna, Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids, IEEE International Scalable Computing Challenge (SCALE), 2012 (First Prize).

  • Wei Yin, Yogesh Simmhan and Viktor Prasanna, Scalable Regression Tree Learning on Hadoop using OpenPlanet, International Workshop on MapReduce and its Applications (MAPREDUCE), 2012.

  • Qunzhi Zhou, Yogesh Simmhan and Viktor Prasanna, Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids, International Semantic Web Conference (ISWC), 2012.

  • Qunzhi Zhou, Sreedhar Natarajan, Yogesh Simmhany and Viktor Prasanna, Semantic Information Modeling for Emerging Applications in Smart Grid, International Conference on Information Technology : New Generations (ITNG), 2012.

  • Qunzhi Zhou, Yogesh Simmhan and Viktor Prasanna, Semantic Complex Event Processing over End-to-End Data Flows, 2012 , Computer Science Department, University of Southern California.

  • Yogesh Simmhan, Baohua Cao, Michail Giakkoupis and Viktor K. Prasanna, Adaptive rate stream processing for smart grid applications on clouds, International Workshop on Scientific Cloud Computing (ScienceCloud) , 2011 , pp. 33-38 , ACM.

  • Saima Aman, Yogesh Simmhan and Viktor K. Prasanna, Improving Energy Use Forecast for Campus Micro-grids using Indirect Indicators, International Workshop on Domain Driven Data Mining (DDDM) , 2011.

  • Saima Aman, Yogesh Simmhan and Viktor K. Prasanna, Smart Communication of Energy Use and Prediction in a Smart Grid Software Architecture, IEEE Coastal Los Angeles Section. (Poster).

  • Saima Aman, Wei Yin, Yogesh Simmhan and Viktor Prasanna, Machine Learning for Demand Forecasting in Smart Grid, Southern California Smart Grid Research Symposium (SoCalSGS) , 2011. (Poster).

  • Yogesh Simmhan, Alok Kumbhare, Baohua Cao and Viktor K. Prasanna, An Analysis of Security and Privacy Issues in Smart Grid Software Architectures on Clouds International Cloud Computing Conference (CLOUD), 2011, IEEE.

  • Alok Kumbhare, Yogesh Simmhan and Viktor Prasanna, Designing a Secure Storage Repository for Sharing Scientific Datasets using Public Clouds, International Workshop on Data Intensive Computing in the Clouds (DataCloud-SC11), 2011.

  • Yogesh Simmhan, Saima Aman, Baohua Cao, Michail Giakkoupis, Alok Kumbhare, Qunzhi Zhou, Karthik Gomadam and Viktor K. Prasanna, Scalable, Secure Energy Information Management for DR Analysis, Southern California Smart Grid Research Symposium (SoCalSGS), 2010 , University of Southern California. (Poster).

  • Yogesh Simmhan, Saima Aman, Baohua Cao, Mike Giakkoupis, Alok Kumbhare, Qunzhi Zhou, Donald Paul, Carol Fern, Aditya Sharma and Viktor K. Prasanna, An Informatics Approach to Demand Response Optimization in Smart Grids, 2011, University of Southern California.

  • Yogesh Simmhan, Viktor Prasanna, Saima Aman, Sreedhar Natarajan, Wei Yin and Qunzhi Zhou, Towards Data-driven Demand-Response Optimization in a Campus Microgrid, Workshop On Embedded Sensing Systems For Energy-Efficiency In Buildings (BuildSys) , 2011 , ACM. (Demo).

  • Yogesh Simmhan, Qunzhi Zhou and Viktor K. Prasanna, Chapter: Semantic Information Integration for Smart Grid Applications, 2011 , pp. 361-380 , Springer Berlin Heidelberg.

  • Qunzhi Zhou, Sreedhar Natarajan, Yogesh Simmhan and Viktor Prasanna, Semantic Information Integration and Processing for Demand Response Optimization, Southern California Smart Grid Research Symposium (SoCalSGS), 2011 (Poster).

  • Qunzhi Zhou, Yogesh Simmhan and Viktor K. Prasanna, Towards an inexact semantic complex event processing framework, International Conference on Distributed Event-Based System (DEBS), 2011 , pp. 401-402 , ACM. (Poster).

  • Yogesh Simmhan, Michail Giakkoupis, Baohua Cao and Viktor K. Prasanna, On Using Cloud Platforms in a Software Architecture for Smart Energy Grids, International Conference on Cloud Computing Technology and Science (CloudCom), 2010 , IEEE. (Poster).

  • Qunzhi Zhou, Yogesh Simmhan and Viktor K. Prasanna, Semantic Complex Event Processing for Smart Grid Information Integration and Management, Energy Forum: A System Approach Toward Green Energy Production and Adaptive Power Distribution, 2010 , IEEE Coastal Los Angeles Section. (Poster).