Ask the expert:
Smart cities need better
We have smart phones, electric cars, intelligent appliances and smart houses—and we’re working on “Smart Cities.” Rapidly expanding technological innovations are nothing short of astounding. At the heart of it all, however, is the power grid.
The current power grid was not designed for today’s demands, which means future innovations, such as smart cities, could be compromised. What changes do we need to ensure our power output keeps pace with modern demands?
To find out more about what researchers are doing to help transform the grid, we asked Dr. Tajana Rosing for her perspective. Dr. Rosing is a professor, a holder of the Fatamico Endowed Chair, and a director of the System Energy Efficiency Lab (SEELab) at the University of California, San Diego (UCSD).
SEELab researchers are collaborative, which allows them to see problems from multiple perspectives. Together with colleagues across disciplines, they are working on smart-grid research, which includes individual load energy reduction and automation, energy storage and energy management. Their research encompasses all aspects of power consumption but, for the purposes of this article, we’ll look at their efforts to improve residential energy consumption through better usage predictions and grid management to support smart cities.
Residential energy consumption represents about 38% of the total energy consumption in the United States and is less-extensively studied by researchers. However, finding efficiencies within this sector is paramount if society wishes to fully capitalize on the quality-of-life improvements promised by the smart-cities concept.
The Internet of Things (IoT)
The Internet of Things (IoT) provides a starting point for Rosing’s work. The IoT allows various areas and objects to communicate with each other—creating a smart network that proves the adage “the whole is greater than the sum of its parts.” For smart cities, the IoT provides an opportunity to develop pinpoint accuracy regarding energy consumption data for residential use.
Rosing’s team leverages the IoT and context-aware computing to better determine load predictions and drive distributed power control. (Context-aware computing considers situational and environmental information to provide an enriched, customized user experience.) Armed with this information, providers can improve grid stability and hasten outage recoveries, in the rare cases when they cannot be simply prevented by smart-grid research efforts.
Improving Grid Efficiency
Rosing’s team focuses its research on the following elements, all aimed at improving grid efficiency:
S2Sim: Smart Grid Swarm Simulator—This simulator moves beyond what was already available. The weakness in previous simulators was they performed well under isolated circumstances but not under real-world pressures. The smart grid’s more autonomous structure, “where the loads, generators and energy storage devices have their own distributed control algorithms,” called for something revolutionary and informed the creation of S2Sim, which tests from a broader grid perspective. “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 takes these elements into account.
User activity-based residential energy estimation—Through a large-scale evaluation of a residential energy solution and the testing of hundreds of houses, Rosing’s team developed a framework that estimates residential energy demand based on the specific 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.” The results are used to create reproduceable profiles of different energy-demand characteristics.
Optimum battery control strategies—Batteries, used in conjunction with load shaping and renewable energy storing, help with grid management. Rosing and her team provided a new algorithm, ECO-DAC: Energy Control over Divide and Control, which uses a more accurate nonlinear battery model that has only a 4.5% error, as compared to older technology with a 45% error rate, which could result in an outage or simply reduce potential savings. The team’s case studies on the UCSD campus show that this more accurate measurement decreased electricity costs by 21% and the consumption variance by 92% to help grid stability.
HomeSim: Residential Energy Simulation—A residential energy simulation platform measures and manages the impact of technologies, such as renewable energy and different battery types, on the grid. “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.”