Yuzhang Lin's five-year study will help improve electric power supply's reliability and resiliency
With the integration of renewable energy and customers’ participation in the electricity markets, today’s electric power distribution systems are experiencing highly uncertain and volatile power flow patterns. To maintain the reliability and efficiency of power supply to homes and businesses, innovative monitoring and control technologies of power distribution systems are necessary.
Research by Asst. Prof. Yuzhang Lin of the Department of Electrical and Computer Engineering may provide the answer. Lin was recently awarded a five-year, $500,000 faculty early-career development (CAREER) grant by the National Science Foundation to conduct a study that will help better predict and visualize power distribution capacity and consumers’ power demand in real time.
Lin’s grant is a prestigious, highly competitive annual program that selects the nation’s best young university scholars who, according to the NSF, “most effectively integrate research and education within the context of the mission of their organization.”
Lin, who obtained his Ph.D. in electrical engineering at Northeastern University in Boston in 2018, joined UMass Lowell later that same year. He will use the CAREER grant to support his research project, “Transforming Distribution System Situational Awareness via Continuous-Time Adaptive Data Fusion.”
Electric power distribution represents the “last-mile” stage in the delivery of electric power, carrying electricity from substations to individual consumers. This power distribution grid design essentially has not changed since it was introduced in the 1880s.
Today’s distribution systems are facing numerous challenges, from severe storms, hurricanes and tornadoes to hackers and aging infrastructure – all of which can lead to power outages. System engineers and utility operators continue to address the ever-growing demand for electricity, which often occurs at random and is not easily controlled.
Electricity generated by wind turbines and solar panels provides clean, renewable energy, but it is also intermittent and difficult to control for grid operators: The wind doesn’t blow and the sun doesn’t shine consistently when customers need electricity.
Lin and his student research team aim to conceptualize and develop a model for tracking the operating conditions of power distribution systems by integrating highly diverse data from sensor networks.
According to Lin, this is a challenging task since today’s distribution systems are equipped with various types of sensors, each of which is dedicated to different applications and is not enough for making the whole grid visible. Furthermore, these sensors have diverse sampling rates, unsynchronized sampling cycles, various accuracy levels and limited communication bandwidths.
“As of now, there has been no comprehensive solution to integrating information from these sensors and providing a full picture of the distribution system’s operating conditions,” notes Lin. “Our proposed technology will provide system operators with full visibility to the grid and enable agile control algorithms for hosting renewable energy generation, as well as improving power supply reliability and resiliency.”
Lin says the project will serve as a foundation for intelligent decision-making and control of the systems without affecting the reliability and quality of the power supply.
“With complete and reliable access to the real-time operating conditions of the grid, system operators can leverage available resources such as energy storage, flexible loads and smart inverters to control power flows and stabilize the grid against volatile renewable energy generation and hazardous events,” Lin explains. “This will help the country achieve its goal of 100% renewable energy in the decades to come.”
Lin’s team will validate its technologies through collaboration with two utility companies, Commonwealth Edison and National Grid. The companies will share their data with the researchers, including distribution system models, locations and types of sensors, as well as the actual sensor measurement data.
“We will use the data to evaluate how accurately our method can capture the operating conditions,” says Lin. “We will test our algorithms under harsh environments, such as sensor failures, communications delay and intermittency, or even cyberattacks.”
Lin will recruit two Ph.D. students to work on the project, as well as at least two undergraduate students to intern during summers.
“This will create great opportunities for undergrads to get exposed to cutting-edge research, improve their modeling, simulation and programming skills, and understand the challenges of integrating renewable energy,” he says.