Real-time microgrid economic dispatch based on model predictive control strategy

Real-Time Optimization for Economic Model Predictive Control

In this paper, we develop an efficient homogeneous and self-dual interior-point method for the linear programs arising in economic model predictive control. To exploit structure in the optimization problems, the algorithm employs a highly specialized Riccati iteration procedure. Simulations show that in comparison to conventional interior-point methods, our solver is a) significantly faster per...

On real-time robust model predictive control

High-speed applications impose a hard real-time constraint on the solution of a model predictive control (MPC) problem, which generally prevents the computation of the optimal control input. As a result, in mostMPC implementations guarantees on feasibility and stability are sacrificed in order to achieve a realtime setting. In this paper we develop a real-time MPC approach for linear systems th...

Deep Neural Network-Based Method for Economic Dispatch in Microgrid

Decentralized and Real-Time Power Dispatch Control for an Islanded Microgrid Supported by Distributed Power Sources

Microgrids can deploy traditional and/or renewable power sources to support remote sites. Utilizing renewable power sources requires more complicated control strategies to achieve acceptable power quality and maintain grid stability. In this research, we assume that the grid stability problem is already solved. As a next step, we focus on how the power can be dispatched from multiple power sour...

A Real-Time Solution to Fuzzy Model Based Predictive Control

This paper addresses optimization issues in fuzzy model-based predictive control. The success of linear predictive control in controlling constrained linear processes is mainly due to the fact that the on-line optimization problem is a convex one, usually a quadratic programme. When the process model is nonlinear fuzzy one, non-convex time consuming optimization is necessary, with no guarantee ...