Reprint

Optimization and Control in Energy Management: Mathematical Modeling and Simulation

Edited by
April 2024
322 pages
  • ISBN978-3-7258-0775-8 (Hardback)
  • ISBN978-3-7258-0776-5 (PDF)

This book is a reprint of the Special Issue Optimization and Control in Energy Management: Mathematical Modeling and Simulation that was published in

Computer Science & Mathematics
Engineering
Physical Sciences
Public Health & Healthcare
Summary

The present reprint contains 15 articles published in the Special Issue “Optimization and Control in Energy Management: Mathematical Modeling and Simulation” of the journal Mathematics, which covers a wide range of topics connected to the energy management systems, i.e., EMS optimization and control techniques for managing the power flow in response to supply, demand, power quality, and storage conditions. These topics include demand side management and demand response, artificial intelligence optimization methods for renewable energy systems and energy management, frequency regulation using virtual power plants, modeling and simulation for energy management, EMS in smart electrical grids, energy-efficient systems, energy conservation techniques, EMS in mini- and microgrids, algorithms, numerical simulations, mathematical modeling, analytical understanding, control theory, load and renewable energy generation prediction techniques, energy management for smart cities and homes, energy management for improving power quality, various optimization techniques and methods in EMS, and various control methods and strategies in EMS. It is hoped that this reprint will be interesting and useful for those working in the area of EMS, power systems’ optimal operation, and renewable energy systems, as well as for those having the proper mathematical background and the willingness to be familiar with the recent advances in the mathematical modeling and simulation of energy systems, which are essential tools for the strategic planning and optimal operation of hybrid energy systems in the form of islanded or grid-connected mini or microgrids.

Format
  • Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
multi-level charging stations; power grid network; multi-agent system; stability; variable-speed wind turbine (VSWT); frequency support; frequency regulation; systematic literature review (SLR); wind energy conversion system (WECS); renewable energy; crow search algorithm; discrete–continuous codification; master–slave strategy; location and sizing of photovoltaic generation units; reduction in total annual operating costs; alternating current networks; motor drivers; power converters; full-bridge Buck inverter; DC motor; differential flatness; flatness-based control; trajectory tracking task; DC networks; discrete–continuous metaheuristic; parallel processing tool; photovoltaic generation; variable power demand; variable renewable generation; PV module; mathematical model; MPP reference generator; maximum power point trackers; residential energy management; reinforcement learning; Q-learning; smart grid; blockchain technology; smart contracts; energy infrastructure; grid connected network; optimization algorithm; master-slave strategy; parallel processing; photovoltaic generation; battery systems; energy loss; environmental emissions; linear programming; economic dispatch; hydrothermal scheduling; HVDC link; Lagrange multipliers; optimal power flow; Q-learning; electric vehicles; artificial neural network; plug-in hybrid electric vehicles; time series forecasting; strategy planning; electricity production; integer programming; renewable energy; WECS; DFIG; BESS; PI controller; FOPID-controller; RSC; behavioral economics; cost optimization; energy community; energy conservation; energy economics; energy policy; local electricity market; renewable energy; social nudge; electric vehicles; charging stations; renewable energy; red kite optimization algorithm; battery; ultracapacitor; hybrid; energy; management; fuzzy; control