This paper presents the design of a Model Predictive Control (MPC) scheme to optimally manage the thermal and electrical subsystems of a small-size building ("smart house"), with the objective of minimizing the expense for buying energy from the grid, while keeping the room temperature within given time-varying bounds. The system, for which an experimental prototype has been built, includes PV panels, solar collectors, a battery pack, an electrical heater in a thermal storage tank, and two pumps on the solar collector and radiator hydraulic circuits. The presence of binary control inputs together with continuous ones naturally leads to using a hybrid dynamical model, and the MPC controller solves a mixed-integer linear program at each sampling instant, relying on weather forecast data for ambient temperature and solar irradiance. The procedure for controller design is reported with focus on the specific application, and the proposed method is successfully tested on the experimental site.
Renewable energy test site installed at NazarbayevUniversity in Kazakhstan is considered as a controlled plant for modeling, design and simulation of proposed model predictive control (MPC) technique for the power system with nominal power up to 10 kW. The mathematical and simulation model of the control plant assumes that disturbance from consumer grid side is generated by pseudorandom numbers. The control strategy relies on switching between several controllers LCL-filter and is shortly discussed. The MISO control system is designed using the switching between two MPC controllers. The results of simulation show significant reduction of THD value which proves the effectiveness of the designed control.Keywordscomputer modeling and simulation, renewable energy, model predictive control, LCL-filter.I.
The paper focuses on a synthetic methodology of a neuro-fuzzy controller adjusted by genetic algorithm for a dynamic control object. An algorithm for controller synthesis and a genetic algorithm for adjusting the controller's parameters have been developed. The methodology has been tested on the classical problem of stabilizing a vertical pendulum on a mobile trolley. The results obtained confirm the efficiency of the methodology and allow for the conclusion that the neuro-fuzzy controller when appropriately adjusted ensures high quality of the stabilization system, even if there are random disturbances on the dynamic object
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