Alejandro Marquez

Robust Energy Management System Based on Interval Fuzzy Models

Energy management systems (EMSs) are used for operators to optimize, monitor, and control the performance of a power system. In microgrids, the EMS automatically coordinates the energy sources aiming to supply the demand. The coordination is carried out considering the operating costs, the available energy, and the generation and transmission capabilities of the grid. With this purpose, the available energy of the sources is predicted, and the operating costs are minimized. Thereby, an optimal operation of the microgrid is achieved. Often, the optimization procedure is executed throughout a receding horizon (model predictive control approach). Such approach provides some robustness to the microgrid operation. But, the high variability of the nonconventional energy sources makes the prediction task very complex. As a consequence, the reliable operation of the microgrid is compromised. In this paper, a scenario-based robust EMS is proposed. The scenarios are generated by means of fuzzy interval models. These models are used for solar power, wind power, and load forecasting. Since interval fuzzy models provide a range rather than a trajectory, upper and lower boundaries for these variables are obtained. Such boundaries are used to formulate the EMS as a robust optimization problem. In this sense, the solution obtained is robust against any realization of the uncertain variables inside the intervals defined by the fuzzy models. In addition, the original robust optimization problem is transformed into an equivalent second-order cone programming problem. Hence, desired mathematical properties such as the convexity of the optimization problem might be guaranteed. Therefore, efficient algorithms, based, e.g., on interior-point methods, could be applied to compute its solution. The proposed EMS is tested in the microgrid installed in Huatacondo, a settlement located at the north of Chile.

Model reduction using proper orthogonal decomposition and predictive control of distributed reactor system

This paper studies the application of proper orthogonal decomposition (POD) to reduce the order of distributed reactor models with axial and radial diffusion and the implementation of model predictive control (MPC) based on discrete-time linear time invariant (LTI) reduced-ordermodels. In this paper, the control objective is to keep the operation of the reactor at a desired operating condition in spite of the disturbances in the feed flow. This operating condition is determined by means of an optimization algorithm that provides the optimal temperature and concentration profiles for the system. Around these optimal profiles, the nonlinear partial differential equations (PDEs), that model the reactor are linearized, and afterwards the linear PDEs are discretized in space giving as a result a high-order linear model. POD and Galerkin projection are used to derive the low-order linear model that captures the dominant dynamics of the PDEs, which are subsequently used for controller design. An MPC formulation is constructed on the basis of the low-order linear model. The proposed approach is tested through simulation, and it is shown that the results are good with regard to keep the operation of the reactor.

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.

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.

Infinite Horizon MPC and model reduction applied to large scale chemical plant

This paper studies the application of Infinite Horizon Model predictive Control (MPC) and model reduction by means of Hankel norm to chemical process of interest in the field of control of large, complex and networked systems. In this paper we first describes the model of the complete process which is composed by three reactors and three distillation columns. Later, we show the main aspects about model reduction by means of Hankel norm and Infinite Horizon MPC and finally the model obtained through numerical linearization and model reduction is used to design centralized Infinite Horizon Model predictive Control (MPC).

Moving horizon estimator for measurement delay compensation in model predictive control schemes

This paper deals with the problem of the loss of performance of the pair observer-controller, when measurements have a delay due to communication over networks. Here we consider the case where the estimation of the states is carried out using a moving horizon estimator (MHE), the control actions are computed by using a centralized model predictive controller (MPC), and the delay varies randomly and is n times the sampling time (n ∈ ℕ). In order to tackle the loss of performance associated with the pair MHE-MPC, an MHE with variable structure is proposed. The resulting pair MHE-MPC was tested using the four tank process as a test bed showing an improvement on the performance.

IHMPC and POD to the control of a non-isothermal tubular reactor

Abstract This paper presents the result of applying POD (Proper Orthogonal Decomposition) and IHMPC (Infinite Horizon Model Predictive Control) to the control of a non-isothermal tubular reactor. This paper is based on a previous work of O.M. Agudelo, J.J. Espinosa, B. De Moor Control of a Tubular Chemical Reactor by means of POD and Predictive Control Techniques , in Proceedings of the European Control Conference 2007 (ECC 2007), pp. 1046–1053, Kos, Greece, 2007, where a finite horizon model predictive control and POD techniques are applied a non-isothermal tubular reactor. In this paper the control objective is to keep the operation of the reactor at a desired operating condition in spite of the disturbances in the feed flow. POD and Galerkins method are used to derive the low order linear model that captures the dominant dynamics of the PDEs, which are subsequently used for controller design. Two IHMPC formulations are constructed on the basis of the low order linear model and are demonstrated, through simulation, to achieve the control objectives.

Urban traffic control in the city of Medelín: A PID control approach

In this paper a methodology that combines a classical PID control strategy and new simulation scenarios is proposed for controlling intersections in the city of Medellín. There are several advantages of PID controllers. The simple computation of the PID control is a remarkable advantage in real time applications, which motivates its application in this work. In addition, and with the objective to simulate a real intersection, a methodology for setting up urban traffic simulation scenarios involving tools such as SUMO and Traci4Matlab is used in this paper. Finally, with the purpose to illustrate the performance of PID controllers at road intersections, the methodology proposed here is applied in a well known intersection in the city of Medellín.

A mathematical model of pedestrians in signalized intersections

In this article, a new mathematical model for the dynamic behaviour of pedestrians at a cross-pad is proposed. This model is focused on signalized intersections of traffic networks. The unknown parameters are estimated through real data obtained in a cross-walk of Medellin - Colombia. Simulation results comparing the real data and the theoretical data based on the proposed model.

Integration of economic MPC, energy load and price estimation with Holt Winters models

This work proposes a nominal economic model predictive control (MFC) that makes use of the Holt Winters model to estimate the load and spot price of the short term energy portfolio used as plant. The portfolio is composed by four energy forward agreements and the ability to buy energy in the spot market, the objective is to supply the energy required by the costumers minimizing the operation cost of the operation. The case of study is focused in the Colombian energy market.