Julian Alberto Patino

An economic MPC approach for a microgrid energy management system

In this paper we address the problem of managing the energy production of a microgrid while satisfying a given demand. Emerging from the Smart Grid technologies, microgrids can be considered as subsections of the main grid with the capability to operate connected or isolated from the main network. An Economic Model Predictive Control (EMPC) scheme is proposed in order to achieve the optimal economic performance in the operational costs of the microgrid. The control scheme is tested in a simulated microgrid composed of a wind turbine, a set of PV panels, an energy storage device, and the connection to the main grid.

Bargaining Game Based Distributed MPC

Despite of the efforts dedicated to design methods for distributed model predictive control (DMPC), the cooperation among subsystems still remains as an open research problem. In order to overcome this issue, game theory arises as an alternative to formulate and characterize the DMPC problem. Game theory is a branch of applied mathematics used to capture the behavior of the players (agents or subsystems) involved in strategic situations where the outcome of a player is function not only of his choices but also depends on the choices of others. In this chapter a bargaining game based DMPC scheme is proposed; roughly speaking, a bargaining game is a situation where several players jointly decide which strategy is best with respect to their mutual benefit. This allows to deal with the cooperation issues of the DMPC problem. Additionally, the bargaining game framework allows to formulate solutions where the subsystems do not have to solve more than one optimization at each time step. This also reduces the computational burden of the local optimization problems.

Min-max Economic Model Predictive Control

This paper proposes a min-max Economic Model Predictive Control approach for discrete time uncertain systems: a MPC min-max strategy where the worst-case performance with respect to uncertainties is optimized. Unfortunately, many min-max MPC formulations yield intractable optimization problems with exponential complexity, for this reason a min-max algorithm for a certain type of model uncertainty is derived in this paper. The transformation of the original problem into a second-order cone program is the most remarkable feature meaning that the min-max problem is written as a convex program. The result is an optimization problem with polynomial complexity.

Frequency and voltage control of a power system with information about grid topology

In this paper, frequency and voltage control schemes are presented for conventional power systems. Using those control loops, a model identification was performed in order to obtain relationships between frequency and power variations in active and reactive power on the IEEE 9 bus benchmark. The use of Bode plots as an analysis tool for determine system frequency sensitivity to load disturbances is illustrated through simulation. The results show that the study of system sensitivities could lead to the development of disturbance rejection strategies that enhance system operation and reliability.

Game Theory Based Distributed Model Predictive Control for a Hydro-Power Valley Control

Abstract Hydro-power valleys are large scale systems used to power energy production. The stored water is also used for navigation and agriculture purposes. Recently the control of hydro-power valleys has been formulated as a centralized optimal control problem. However, the scale of the systems make unfeasible real time implementations for centralized controllers. In this work we propose the use of the game theory to formulate a distributed model predictive control scheme to control an hydro-power valley. The proposed control scheme is tested by using a power reference tracking scenario as a test-bed.

A performance comparison for wind power integration into the grid system

The integration level of wind power into the grid system over the world has been growing at a very fast rate in the last few years and is still keeping this pace. In the future, wind power is expected to be at least comparable to the conventional power generation systems. Large-scale wind farms will need to be integrated into traditional power grid systems, creating the need to establish technical standards to make this integration feasible. This paper covers the performance comparison of a power system with and without the inclusion of a large-scale wind farm. The global system model is presented along to a case of study and the simulation results.

Model identification for control of a distillation column

This paper presents the system identification techniques as an alternative to model processes for control purposes. A methodology for obtaining adequate empirical models for distillation columns is presented. This methodology is applied to a benchmark of a distillation column at laboratory scale (Column A). In this work, experimental data fit a state space model using subspace identification techniques.

A comparison of Extended Kalman Filter and Levenberg-Marquardt methods for neural network training

This paper presents a performance comparison of both the Levenverg-Marquardt and Extended Kalman Filter methods for neural network training. As a testbed, an indoor localization problem was solved by the neural network from the RSSI data obtained through a experimental measurement. Both methods were used to train the network, and the MSE (mean squared error) was employed as the performance metric.

A comparison of Kalman-based schemes for localization and tracking in sensor systems

The challenge of target tracking is one of the most important applications of WSNs (Wireless Sensor Networks). Traditionally, Kalman filter and its derivatives are some of the most popular algorithms for solving the signal tracking problem. In a WSNs tracking application, the target motion and state update dynamics might be modelled by linear or non-linear structures depending on the specific scenario. This paper compares extended Kalman Filters with the P, PV and PVA dynamics models for object tracking in sensor networks.

Analysis of Control Sensitivity Functions for Power System Frequency Regulation

This work studies the behavior of the Control Sensitivity Functions derivated from the frequency regulation structure in power systems. Here, we explore the performance of the sensitivity functions in the presence of changes in the parameters of frequency regulation and power system components. A one-area power system is employed as the simulation benchmark. Results of frequency-domain analysis with Bode plots highlight the more significant parameters for Load Frequency Control and the different changes in sensitivity functions.