Economic Model Predictive Control Method Research Articles

Innovative air supply management in fuel cells: An economic predictive control approach without pre-measured OER

Existing fuel cell air supply control methods improve the economic performance of Proton Exchange Membrane Fuel Cells (PEMFC) under steady-state conditions through oxygen excess ratio curve, while ignoring the transient process of the air supply system under varying operating conditions. Furthermore, the oxygen excess ratio curve may exhibit deviations due to fuel cell aging and measurement inaccuracies, which would further diminish the economic performance derived from the control process. Not relying on pre-measuring the oxygen excess ratio curve, an EMPC (Economic Model Predictive Control) method is proposed for fuel cell air supply systems. It incorporates an economic cost function reflecting the economic objectives of the air supply system. By dynamically optimizing the parasitic power of the air compressor, this method enhances the overall economic performance by achieving performance optimization for both transient and steady-state processes of the fuel cell system. The asymptotic stability of PEMFC is achieved through the utilization of terminal penalty functions and terminal domain constraints. Stability and convergence analysis is carried out by employing auxiliary optimization problems and the Lyapunov method. In transient processes, EMPC method can achieve a maximum 4.97 % improvement in the economic performance of PEMFC. A hardware in loop (HIL) experiment was finally conducted to show the real-time capacity of the proposed EMPC method.

Two‐layer dynamic economic nonlinear model predictive control for a Lithium‐ion battery charge process with random disturbances

AbstractThe charging process of lithium‐ion batteries is necessary for normal operation, and improper lithium‐ion battery charging strategy can cause side reactions, significant temperature rise, performance degradation, and safety concerns. This paper proposes a two‐layer dynamic economic nonlinear model predictive control economic model predictive control (EMPC) method for lithium‐ion battery charge management in which the non‐Gaussian random noise of voltage and current signal are taken into account. In the two‐layer EMPC, the upper layer finds the optimal reference trajectory, and the power in the future prediction horizon is chosen as an economic indicator to optimize the upper layer. The lower layer tracks the optimal trajectory, and model predictive control based on generalized correntropy is used. The economic cost of the system and the dynamic changes of the process are considered to greatly reduce the computational complexity and realize rapid battery charge management. Finally, the simulation results show that the tracking error of the two‐layer EMPC method based on generalized correntropy is stable around 0, while the tracking error of the two‐layer EMPC method based on MSE is stable around 0.003. The average control action times of the proposed two‐layer EMPC and single‐layer EMPC are 0.0052 and 0.0267 s, respectively. It is verified that the proposed method performs better for the lithium‐ion battery charging process with random disturbances.

Optimal Load Distribution in DG Sources Using Model Predictive Control and the State Feedback Controller for Switching Control

The distributed energy management of interconnected microgrids, which is based on model predictive control (MPC), relies on the cooperation of all the agents (i.e., microgrids). Model predictive control or MPC is widely used in industrial applications as an effective tool for dealing with multivariable limited control problems. MPC uses an explicit system model to predict the future horizon of the system and its outputs. This predictability allows calculating the optimal order of inputs to minimize output errors over a limited horizon, which is effected by the limitations of the system. This study presents a distributed economic model predictive control method using the new state feedback controller to control the switching of interface converters and compensate for the unbalanced and nonlinear loads. In this model, the islanding mode and the reconnection of the grid are considered to improve the transient behavior of the system to achieve steady-state power distribution. It has been proposed that it could obtain better results in predictive control, utilizing similarity transform in the state matrix and its modification. First, this model is simulated on distributed generation sources with power-sharing and local loads using the state feedback controller in MATLAB Simpower. Then, the performance of the proposed method is evaluated, confirming that it is more reliable than the FS-MPC and DSVM-MPC methods.

A New Control Strategy for the Aluminum Reduction Process Using Economic Model Predictive Control

Abstract In the Hall-Heroult process, the evolving design and operating conditions require an advance on the development of new process control strategies. Especially, with the technology of individual anode current measurement now available, it becomes possible to monitor and control the localized conditions in an aluminum reduction cell. In this paper, a new cell control strategy is developed based on the Economic Model Predictive Control method, aiming to improve the current efficiency of the aluminum reduction process and hence maximize its economic benefits through advanced control algorithms. In addition, a more balanced cell condition is achieved through reduced spatial and temporal variations of process variables, such as alumina concentration, anode cathode distance, anode current. The simulation results demonstrate the superiority of the new method by comparing its control performance with that of the traditional method.

Economic model predictive control for transport-reaction systems with target profiles

For transport-reaction processes with certain general cost functions indicating comprehensive profit, the existing linear–quadratic optimal control or model predictive control (MPC) methods may be conservative. Due to their spatial characteristics, dynamic evolutions and final target static spatial distributions under different control strategies may result in remarkable variation of operation performance. This article presents a novel economic model predictive control (EMPC) synthesis for systems described by hyperbolic/parabolic partial differential equations (PDEs) with pre-optimized static spatially distributed profile, from both convergence and performance points of view. Instead of deriving approximate ordinary differential equations (ODEs) and applying finite-dimensional control strategies, this work addresses the optimization and stabilization problem on the basis of infinite dimensional model itself, which is realized by Cayley–Tustin (CT) transformation without any type of spatial approximation. The accumulated economic cost, input/output constraints are explicitly considered in the finite horizon optimal control problem (FHOCP), with an additional energy-like contractive constraint accounting for closed-loop convergence toward the target optimal steady-state profile. By directly solving Lyapunov functions associated with PDEs models, the parameters in the FHOCP are suitably defined such that the composite EMPC system admits algorithm feasibility and closed-loop stability. Finally, the proposed method is applied to typical examples; numerical results indicates the effectiveness and remarkable performance improvement of the EMPC method compared to standard MPC results.

Robust Economic Model Predictive Control Based on a Zonotope and Local Feedback Controller for Energy Dispatch in Smart-Grids Considering Demand Uncertainty

Electrical smart grids are complex MIMO systems whose operation can be noticeably affected by the presence of uncertainties such as load demand uncertainty. In this paper, based on a restricted representation of the demand uncertainty, we propose a robust economic model predictive control method that guarantees an optimal energy dispatch in a smart micro-grid. Load demands are uncertain, but viewed as bounded. The proposed method first decomposes control inputs into dependent and independent components, and then tackles the effect of demand uncertainty by tightening the system constraints as the uncertainty propagates along the prediction horizon using interval arithmetic and local state feedback control law. The tightened constraints’ upper and lower limits are computed off-line. The proposed method guarantees stability through a periodic terminal state constraint. The method is faster and simpler compared to other approaches based on Closed-loop min–max techniques. The applicability of the proposed approach is demonstrated using a smart micro-grid that comprises a wind generator, some photovoltaic (PV) panels, a diesel generator, a hydroelectric generator and some storage devices linked via two DC-buses, from which load demands can be adequately satisfied.

Economic-Driven Frequency Regulation in Multi-Terminal HVDC Systems: A Cooperative Distributed Approach

In this paper, a distributed economic model predictive control (EMPC) method is proposed for economic operation and frequency regulation in isolated AC subsystems interconnected through multi-terminal HVDC (MTDC) transmission system. Economic-driven frequency regulation, including economic operation and frequency regulation, normally implemented in a separate and hierarchical way, can be integrated through the proposed EMPC. Instead of competing with neighboring subsystems, the proposed distributed EMPC encourages cooperation among the interconnected subsystems, improving system-wide control performance. A suitable Lyapunov function is utilized to prove the stability of the proposed method. Simulations are used to demonstrate the performance of the proposed EMPC method.

Application of economic MPC to the energy and demand minimization of a commercial building

This paper presents an application case study of an economic model predictive control (EMPC) method for optimizing the building demand and energy cost under the time-of-use price policy. The control strategy is comprised of an economic objective function that accounts for the combination of energy and demand costs with a time-of-use rate structure, a dynamic thermal process and power model of the building thermal mass dynamics, and a set of constraints to ensure the building is operated properly. The optimization is a min–max optimization problem and is converted to a linear program. The EMPC method is implemented in a commercial office building located in Milwaukee, Wisconsin, USA. An internet-based control architecture is developed to carry out tests with the EMPC controller at a remote location. The test results show that the EMPC strategy is capable of shifting the peak demand to off-peak hours and reducing energy costs compared to a baseline case for the building.