How can you optimize simulation-based control models for different types of facilities?

1 What is simulation-based control?

Simulation-based control is a method of designing and implementing control strategies for systems that are modeled by simulations. Simulations are mathematical representations of the physical processes and behaviors of the systems, such as heat transfer, fluid flow, or electricity generation. Simulation-based control uses simulations to predict the future states and outputs of the systems, and then adjusts the control inputs accordingly to achieve the desired goals, such as minimizing energy consumption, maximizing comfort, or ensuring reliability.

2 Why use simulation-based control?

Simulation-based control has several advantages over conventional control methods, such as rule-based or feedback control. First, simulation-based control can account for the nonlinearities, uncertainties, and interactions of the systems, which are often difficult to capture by simple rules or equations. Second, simulation-based control can optimize the control inputs over a long horizon, rather than reacting to the current or past states of the systems. This can lead to more proactive and efficient control actions. Third, simulation-based control can handle multiple and conflicting objectives, such as balancing energy savings and occupant comfort, or reducing emissions and costs.

5 How to adapt the optimization method for different types of facilities?

Different types of facilities may have different characteristics, objectives, and constraints, which may require adapting the optimization method accordingly. For example, in buildings, the simulation model may include thermal, lighting, and ventilation systems with an optimization objective of minimizing energy consumption while maintaining occupant comfort and indoor air quality. The constraints may include outdoor weather conditions, building codes, and occupant preferences. The optimization method should take into account the stochasticity and variability of the demand and supply of energy, as well as the human factors and behaviors of the occupants. In industrial plants, the simulation model may include production, transportation, and storage systems with an optimization objective of maximizing productivity while minimizing costs and emissions. The constraints may include process specifications, equipment limitations, and market conditions. The optimization method should be able to handle the nonlinearities and interactions of the processes, as well as the uncertainties and disturbances of the inputs and outputs. Finally, in smart grids, the simulation model may include generation, transmission, and distribution systems with an optimization objective of balancing supply and demand while ensuring reliability and stability. The constraints may include physical laws, network topology, and operational rules. The optimization method should be able to cope with the dynamics and complexity of the power system as well as the coordination and communication of agents and devices.