Researchers Develop New Top-Down Modeling Capability for Intelligent Power Management

Achievement date: 

Researchers at the NSF-funded Future Renewable Electric Energy Delivery and Management (FREEDM) Systems Engineering Research Center (ERC), headquartered at North Carolina State University, have developed a new modeling capability that includes an end-to-end comprehensive mathematical model of the FREEDM system, better derivations of parameters for intelligent power management (IPM) controllers and intelligent energy management (IEM) commands, and algorithms by which multiple solid-state transformers (SSTs) can share load demands.


This advance in modeling substantially enhances ability to achieve the FREEDM system's transformation of our nation's electric power grid into an efficient network that integrates alternative energy generation and novel storage methods with existing power sources. The new top-down modeling capability provides tools for analyzing system stability and precisely specifying roles of multiple system control loops. This FREEDM system state variable model (SVM) is a unifying controls framework, to ensure that controls at different levels maintain system stability (in terms of frequency, voltage, etc.) and also respond to system state changes. Storage-element design requirements—to ensure maximum feasibility of operation across an entire interconnection of energy cells—are evolving from this analysis. Posing the designs as optimization and optimal control problems in both continuous-time and discrete-time modes enables natural analysis-to-synthesis movement.


The research is focused on feasibility analysis and subsequently global controller design based on the FREEDM system comprehensive SVM. Analyses of feasibility and equilibria enabled identification of the design space for the FREEDM system parameters. Feasibility analysis yields the maximum net-power capability that the system can handle. Once the feasibility bounds are known, the system parameters can be designed accordingly to provide the required power-flow and energy-exchange flexibility. Equilibria analysis helped derive an analytical equation relating the feasible range of operation of the system with the system parameters. This relationship enabled extending the feasibility study for the full SST model and then using the results to design a better controller that handles feasibility and stability together.

The model incorporates both three- and single-phase systems. Analysis revealed that among the three stages of the SST (i.e., rectifier, dual active bridge (DAB), and inverter), the rectifier stage has the most dominating role on the feasibility range when circuit parameters of DAB and inverter stages are chosen based on some given constraints.

Detailed accomplishments for this project are (1) explicit derivation of the design requirements for IPM controllers in the form of Linear Matrix Inequalities (LMIs) based on the idea of feasibility circles (see figure); (2) derivation of the bounds for IEM commands that ensure the system operation is feasible and stable; and (3) design of constructive algorithms by which multiple SSTs can share load demands requested by the IEM over any given pre-existing operating condition.