ACADEMIA

You can teach an old dog new tricks.

This seems to be true for the research group led by Mengchu Zhou, a Distinguished Professor of electrical and computer engineering at the New Jersey Institute of Technology. 

In a recent study published in the IEEE/CAA Journal of Automatica Sinica, Zhou uses the Petri net to analyze and model a microgrid. 

The Petri net, named after its inventor Carl Adam Petri, is a mathematical modeling language that has been in existence for several decades. Originally invented to illustrate chemical processes, the Petri net has not only survived the test of time, but also continues to expand as scientists devise ways to use it to solve new problems. 

Modeling a power grid is a new problem, specifically a microgrid that incorporates renewable energy sources. According to the U.S. Department of Energy, microgrids are more efficient and are often more environmentally friendly. More importantly, a microgrid can disconnect from the main power grid and function autonomously, rendering it less susceptible to power grid breakdowns and more reliable during emergencies. 

In Zhou's study, he analyzes a microgrid consisting of a wind turbine, photovoltaic cell, battery, and diesel generator. He uses the Hybrid Petri net (HPN) to model this microgrid to account for both discrete and continuous events. Examples of discrete events include instances where the photovoltaic cell or battery is either turned on or off. Continuous events include the amounts of energy stored in the cell or battery, which are real values that can change continuously over time. 

Zhou's modeling illustrates how the microgrid behaves under different conditions, such as strong wind or weak sunlight. Such conditions affect the ability of the wind turbine and photovoltaic cell to support the microgrid's energy demands, and determine whether the generator should come on or if the battery needs to discharge its energy. This analysis yields data that present a clear picture of various schemes that the microgrid can operate within, as well as their respective outcomes. This information also helps engineers estimate the time and cost required for each grid component to switch its operating state.

Zhou's team is hopeful that HPN modeling can help identify the most efficient operational schemes for different microgrids and thus improve microgrid design. He wants to further develop this modeling method to analyze more complex microgrids that can meet our increasing energy demands.