Model predictive control algorithm of flexible load in consideration of both electricity cost and comfort
This paper proposes a novel model predictive control algorithm based on intelligent terminal, which considers both the economic benefit and the customer comfort. In this algorithm, novel models for air-conditioner and water-heater are created, which obviously simplify the algorithm. In the new models, the multivariable thermodynamic process is separated into several independent univariate sub-processes, which can be solved easily. In addition, the algorithm obtains the model parameters by parametric regression from history data. By this way, the optimal results are independent of the outdoor temperature and other confounding factors. Thus, the complexity of the algorithm is significantly reduced. More, considering both electricity cost and customer satisfaction, a dual-objective optimization algorithm is implemented to get the control strategy, which is solved by particle swarm optimization (PSO) and linear programming. At last, a simulation result on MATLAB shows the economic benefits and load shifting effect.