Improved Model Predictive Control with Prescribed Performance for Aggregated Thermostatically Controlled Loads

Abstract

Aggregate thermostatically controlled loads (AT-CLs) are a suitable candidate for power imbalance on demand side to smooth the power fluctuation of renewable energy. A new control scheme based on an improved bilinear aggregate model of ATCLs is investigated to suppress power imbalance. Firstly, the original bilinear aggregate model of ATCLs is extended by the second-order equivalent thermal parameter model to optimize accumulative error over a long time scale. Then, to ensure the control performance of tracking error, an improved model predictive control algorithm is proposed by integrating the Lyapunov function with the error transformation, and theoretical stability of the proposed control algorithm is proven. Finally, the simulation results demonstrate that the accuracy of the improved bilinear aggregate model is enhanced; the proposed control algorithm has faster convergence speed and better tracking accuracy in contrast with the Lyapunov function-based model predictive control without the prescribed performance.

The Role of AI in Mitigating Climate Change: Predictive Modelling for Renewable Energy Deployment

“…First, we'll collect high-quality [24] datasets that include things like weather records, energy usage numbers, greenhouse gas emission totals, and so on, that affect climate change and the spread of renewable energy sources [25,26]. Next, we'll preprocess these files to remove any information that isn't needed.…”

Allocating Ex-post Deviation Cost of Virtual Power Plants in Distribution Networks

“…W ITH rapid development of distributed energy resources (DERs) and demand response, energy prosumers including electrical vehicles [1], renewable generation [2], and controllable load [3], [4] are emerging in active distribution networks. Compared with conventional inelastic consumers, the power exchange between prosumers and power grid is bidirectional and more versatile.…”