Paul Mc Namara

Weight optimisation for iterative distributed model predictive control applied to power networks

This paper presents a weight tuning technique for iterative distributed Model Predictive Control (MPC). Particle Swarm Optimisation (PSO) is used to optimise both the weights associated with disturbance rejection and those associated with achieving consensus between control agents. Unlike centralised MPC, where tuning focuses solely on disturbance rejection performance, iterative distributed MPC practitioners must concern themselves with the trade off between disturbance rejection and the overall communication overhead when tuning weights. This is particularly the case in large scale systems, such as power networks, where typically there will be a large communication overhead associated with control. In this paper a method for simultaneously optimising both the closed loop performance and minimising the communications overhead of iterative distributed MPC systems is proposed. Simulation experiments illustrate the potential of the proposed approach in two different power system scenarios.

Distributed MPC for frequency regulation in multi-terminal HVDC grids

Abstract Multi-Terminal high voltage Direct Current (MTDC) transmission lines enable radial or meshed DC grid configurations to be used in electrical power networks, and in turn allow for significant flexibility in the development of future DC power networks. In this paper distributed MPC is proposed for providing Automatic Generation Control (AGC) in Alternating Current (AC) areas connected to MTDC grids. Additionally, a novel modal analysis technique is derived for the distributed MPC algorithm, which in turn can be used to determine the convergence and stability properties of the closed-loop system.

Distributed MPC for Frequency Regulation in Multi-Terminal HVDC Grids

Abstract Multi-Terminal high voltage Direct Current (MTDC) transmission lines enable radial or meshed DC grid configurations to be used in electrical power networks, and in turn allow for significant flexibility in the development of future DC power networks. In this paper distributed MPC is proposed for providing Automatic Generation Control (AGC) in Alternating Current (AC) areas connected to MTDC grids. Additionally, a novel modal analysis technique is derived for the distributed MPC algorithm, which in turn can be used to determine the convergence and stability properties of the closed-loop system.

Model Predictive Control based AGC for multi-terminal DC grids

With increasing DC grid connections between non-synchronous AC systems it is desirable that DC connections would take a role in frequency regulation for connected AC grids. A number of primary and secondary P and PI based controllers have been designed previously for this purpose. Here Model Predictive Control is proposed for including DC power controllers in the provision of Automatic Generation Control.

Coordination of a multiple link HVDC system using local communications based Distributed Model Predictive Control

Abstract As the complexity of power networks increases, the installation of devices such as High Voltage Direct Current links (HVDC) and Flexible AC Transmission Systems (FACTS), and the use of advanced control techniques, can be used to improve network stability. Model Predictive Control (MPC) is an example of such an advanced control technique. However, it is often impractical to implement this technique in a centralised manner, as often the problem can be too computationally complex or several independent controllers may be responsible for different subsystems. Distributed approaches use communication between a number of controllers to approximate control of a centralised system. In this paper it is proposed to use distributed MPC for controlling a multiple link HVDC system using local communications only.

Design of MPC-based controller for a Generalized Energy Storage system model

This paper presents a control strategy based on Model Predictive Control for Energy Storage Systems. The mathematical formulation of this controller is outlined, and the procedure for applying this controller to a Generalized Energy Storage model is then documented. The dynamic performance of the control strategy presented is compared with that of a PI-based control technique. A comprehensive case study based on the New England 39-bus 10-machine test system with the inclusion of Energy Storage Systems is presented and discussed.

Hierarchical Demand Response for Peak Minimisation using Dantzig-Wolfe Decomposition

Summary form only given: Demand Response (DR) algorithms manipulate the energy consumption schedules of controllable loads so as to satisfy grid objectives. Implementation of DR algorithms using a centralised agent can be problematic for scalability reasons, and there are issues related to the privacy of data and robustness to communication failures. Thus it is desirable to use a scalable decentralised algorithm for the implementation of DR. In this paper, a hierarchical DR scheme is proposed for Peak Minimisation (PM) based on Dantzig-Wolfe Decomposition (DWD). In addition, a Time Weighted Maximisation option is included in the cost function which improves the Quality of Service for devices seeking to receive their desired energy sooner rather than later. The paper also demonstrates how the DWD algorithm can be implemented more efficiently through the calculation of the upper and lower cost bounds after each DWD iteration.

Feasibility assessment of Plug and Play Model Predictive Control for use in DC grids

Distributed control techniques can allow Transmission System Operators (TSOs) to coordinate their responses via TSO-TSO communication, providing a level of control that lies between that of centralised control and communication free decentralised control of interconnected power systems. Recently the Plug and Play Model Predictive Control (PnPMPC) toolbox has been developed in order to allow practitioners to design distributed controllers based on tube-MPC techniques. In this paper, some initial results using the PnPMPC toolbox for the design of distributed controllers to enhance AGC in AC areas connected to Multi-Terminal HVDC (MTDC) grids, are illustrated, in order to evaluate the feasibility of applying PnPMPC for this purpose.

Improving Distributed Model Predictive Control Performance Via Weight Optimization using PSO

Abstract Abstract In recent years there has been much research performed in developing Distributed Model Predictive Control (DMPC) techniques which allow a Model Predictive Control (MPC) scheme to be distributed amongst a number of agents. By optimizing the weights in an MPC system, performance can be improved. In this paper, a PSO based weight optimization method for a DMPC system is developed and it is shown how DMPC performance can be optimized whilst constraining the number of iterations of the optimization algorithm.