Nonlinear Powerflow Control Design presents an innovative control system design process motivated by renewable energy electric grid integration problems. The concepts developed result from the convergence of three research and development goals:
โข to create a unifying metric to compare the value of different energy sources โ coal-burning power plant, wind turbines, solar photovoltaics, etc. โ to be integrated into the electric power grid and to replace the typical metric of costs/profit;
โข to develop a new nonlinear control tool that applies power flow control, thermodynamics, and complex adaptive systems theory to the energy grid in a consistent way; and
โข to apply collective robotics theories to the creation of high-performance teams of people and key individuals in order to account for human factors in controlling and selling power into a distributed, decentralized electric power grid.
All three of these goals have important concepts in common: exergy flow, limit cycles, and balance between competing power flows. In place of the typical zero-sum, stability vs. performance, linear controller design process, the authors propose a unique set of criteria to design controllers for a class of nonlinear systems with respect to both performance and stability, and seamlessly integrating information theoretic concepts. A combination of thermodynamics with Hamiltonian systems provides the theoretical foundation which is then realized in a series of connected case studies. It allows the process of control design to be viewed as a power flow control problem, balancing the power flowing into a system against that being dissipated within it and dependent on the power being stored in it โ an interplay between kinetic and potential energies.
Highlights of several of the case studies feature current renewable energy problems such as the future of electric power grid control, wind turbine load alleviation, and novel control designs for micro-grids that incorporate wind and sunlight as renewable energy sources. The sustainability of self-organizing systems are dealt with as advanced topics. Research scientists, practicing engineers, engineering students, and others with a background in engineering will be able to develop and apply this methodology to their particular problems.