Internet of Things with Applications to Smart Grid and Green Energy
Renewable energy integration, load energy management, and smart grid research involves researching across several domains, including individual load energy reduction and automation, energy storage, context-aware energy management, better prediction mechanisms, user activity modeling and smart grid pricing and load integration. The correlation among these fields builds a more comprehensive, useful picture of the distributed grid, and enables solving problems effectively.
Smart Grid, Context and the Internet of Things
The smart grid is driven by data: smart metering of plug loads, forecasting of renewable resources, battery decay curves, etc. In addition, we leverage the growing amount of context, especially user data, afforded by the pervasive sensing and ubiquitous actuation found in the Internet of Things (IoT). Our work in this area covers two implementations:
- HomeSim: a residential electrical energy simulation platform that enables investigating the impact of technologies such as renewable energy, user modeling, and different battery types.
- Context engine: a new approach for IoT application design with modular functional units, general-purpose machine learning and reusability of intermediate data. The proposed approach reduces the computational complexity of IoT applications and promotes determinism of application characteristics: scalability, latency, throughput, etc.
We apply our context engine approach to determining user activity prediction, significantly reducing application overhead and improving latency and parallelism. Our activity prediction is further used in conjunction with S2
Sim for improved load prediction and better energy pricing and demand-response.
Leveraging IoT and context-aware computing to determine application-specific context (e.g. plug load prediction) to drive distributed control and more stable grid behavior
S2Sim: Smart Grid Swarm Simulator
The smart grid is moving towards a more autonomous structure, where the loads, generators and energy storage devices have their own distributed control algorithms. Although many publications show the good performance of their control algorithms under isolated conditions, the real performance must be tested from a broader perspective of the grid. There is a need for a simulator to test multiple, possibly diverse, heterogeneous control algorithms working simultaneously to observe the effect of the algorithms on each other and also the grid itself.
S2Sim enables the co-simulation of heterogeneous distributed control algorithms, calculates the power flow in order to gain insight on the voltage stability, and enables the usage of coordinators to provide feedback to the individual control algorithms if needed. Our case studies show that, even well performing algorithms can cause instability when they come together, and a control algorithm cannot be justified without being tested within the grid. (S2Sim Code)
S2Sim Internal Structure Overview
User activity-based residential energy estimation
Residential energy constitutes a significant portion of the total US energy consumption. Many researchers have been focusing on this domain, proposing energy-aware solutions for houses due to the potential of significant energy and cost savings. However, a thorough study requires a large scale evaluation of a residential energy solution, testing the outcomes with hundreds of houses. To address this aspect, we develop a framework that estimates the residential energy demand based on the physical activities of household members. Our framework leverages population studies and surveys, considers family characteristics and demographics and plots expected energy behavior of a house based on statistical values. It does not require any historical or real-time power consumption data, hence is highly non-intrusive. It can be used to create various house profiles with different energy demand characteristics in a reproducible manner. Comparison with real data shows that our model captures the power demand differences between different family types and accurately follows the trends, especially peaks, seen in real data.
Energy consumption estimation framework: matching household member activities with appliance usage, then aggregating to obtain the total house demand
Optimum Battery Control Strategies
Batteries are energy storage devices that enable shaping the power consumption of a load that it is connected to. Shifting the time of use or flattening the consumption profile can both decrease the electricity cost and improve the grid stability. We provide a new algorithm, ECO-DAC: Energy Control over Divide and Control, that can calculate the optimum usage trajectory of a battery with only O(N2) complexity. ECO-DAC uses a more accurate nonlinear battery model that has only 4.5% error. A linear battery model causes up to 43% error, especially under high currents. Our case studies on the UCSD campus show that we can decrease the electricity cost by 21% and the consumption variance by 92% to help the grid stability.
Batteries are used in conjunction with load shaping and renewable energy storing
Renewable Energy in Data Centers
Renewable energy is an efficient solution to decrease the energy cost of data centers and the environmental effects of traditional coal-based (brown) energy. We use accurate prediction algorithms to reduce the effects of the intermittent nature of the renewable sources. Our analysis demonstrates that prediction leads to better renewable energy integration to the system and reduces the amount of energy wasted. Our solution is applicable not only to a single data center but also to a network of data centers and the backbone network connecting them. We show that the backbone network can decrease its brown energy costs significantly with dynamic, renewable energy aware routing mechanisms. We also build multiple scheduling algorithms that can both minimize the energy costs and maximize the job performance of geographically distributed data centers.
Datacenter server architecture overview with renewable (green) and grid (brown) energy integration
HomeSim: Residential Energy Simulation
Residential energy constitutes 38% of the total energy consumption in the United States. HomeSim, a residential energy simulation platform makes it possible to investigate the impact of technologies such as renewable energy, and different battery types. With HomeSim, we can simulate a number of different scenarios, including centralized vs. distributed in-home energy storage, intelligent appliance rescheduling, and outage management. Using measured residential data, HomeSim quantifies different benefits for different technologies and scenarios, including up to 50% reduction in grid energy through a combination of distributed batteries and reschedulable appliances.
HomeSim Architecture Overview
Alper Sinan Akyurek, Baris Aksanli and Tajana Rosing. "S2Sim: Smart Grid Swarm Simulator". International Green and Sustainable Computing Conference (IGSC), 2015 (Accepted).
Jagannathan Venkatesh, Peerapol Tinnakornsrisuphap, Shengbo Chen, Tajana Rosing, "Lifetime-dependent Battery Usage Optimization for Grid-Connected Residential Systems". Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES), 2015
Jagannathan Venkatesh, Christine Chan, Alper Sinan Akyurek, Tajana Rosing. "A Context Driven IoT Middleware Architecture". SRC TechCon, 2015
Baris Aksanli, Alper Sinan Akyurek, Tajana Simunic Rosing, "User Behavior Modeling for Estimating Residential Energy Consumption", EAI International Conference on Smart Grids for Smart Cities (SGSC), 2015.
Baris Aksanli, Alper Sinan Akyurek, Tajana Simunic Rosing, "Minimizing the Effects of Data Centers on Microgrid Instability", International Green and Sustainable Computing Conference (IGSC), 2015.
Baris Aksanli, Alper Sinan Akyurek, Madhur Behl, Meghan Clark, Alexandre Donze, Prabal Dutta, Patrick Lazik, Mehdi Maasoumy, Rahul Mangharam, Truong X. Nghiem, Vasumathi Raman, Anthony Rowe, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia, Tajana Simunic Rosing, and Jagannathan Venkatesh. "Distributed Control of a Swarm of Buildings Connected to a Smart Grid". 1st ACM International Conference on Embedded Systems For Energy-Efficient Buildings (BuildSys), 2014.
Baris Aksanli and Tajana Rosing. "Energy Management and Cost Analysis in Residential Houses using Batteries". SRC TechCon, 2014.
Bengu Ozge Akyurek, Alper Sinan Akyurek, Jan Kleissl and Tajana Rosing. "TESLA: Taylor Expanded Solar Analog Forecasting". International Conference on Smart Grid Communications (SmartGridComm), 2014.
Baris Aksanli and Tajana Rosing. "Providing Regulation Services and Managing Data Center Peak Power Budgets". Design, Automation and Test in Europe (DATE), 2014.
Alper Sinan Akyurek, Bill Torre and Tajana Rosing. "ECO-DAC: Energy Control over Divide and Control". International Conference on Smart Grid Communications (SmartGridComm), 2013.
Baris Aksanli and Tajana Rosing. "Optimal Battery Configuration in a Residential Home with Time-of-Use Pricing". International Conference on Smart Grid Communications (SmartGridComm), 2013.
Jagannathan Venkatesh, Baris Aksanli, Tajana Rosing, Jean-Claude Junqua, and Philippe Morin. "HomeSim: Comprehensive, Smart, Residential Energy Simulation and Scheduling". International Green Computing Conference (IGCC), 2013.
Jagannathan Venkatesh, Baris Aksanli, and Tajana Rosing. "Residential Energy Simulation and Scheduling: A Case Study Approach". International Symposium on Computers and Communications (ISCC), 2013.
Baris Aksanli, Jagannathan Venkatesh, Tajana Rosing, and Inder Monga. "A Comprehensive Approach to Reduce the Energy Cost of Network of Datacenters". International Symposium on Computers and Communications (ISCC), 2013. (Best Student Paper Award)
Baris Aksanli, Jagannathan Venkatesh, Liuyi Zhang and Tajana Rosing. Utilizing Green Energy Prediction to Schedule Mixed Batch and Service Jobs in Data Centers. International Workshop on Power Aware Computing and Systems (HotPower), 2011. (Best of HotPower)
Baris Aksanli, Jagannathan Venkatesh, and Tajana Rosing. "Datacenter Modeling and Simulation with Focus on Energy Efficiency and Green Energy Integration". IEEE Computer Special Issue on Modeling and Simulation of Smart and Green Computing Systems, 2012.
Peerapol Tinnakornsrisuphap, Qualcomm Research Center
Shengbo Chen, Qualcomm Research Center
Jean-Claude Junqua, Panasonic Silicon Valley Laboratory
Philippe Morin, Panasonic Silicon Valley Laboratory
TerraSwarm, MultiScale Systems Center (MuSyC), National Renewable Energy Laboratory (NREL), Center for Networked Systems (CNS)