High-level Model Predictive Control Research Articles

Hierarchical model predictive control for islanded and grid-connected microgrids with wind generation and hydrogen energy storage systems

This paper presents a novel energy management strategy (EMS) to control a wind-hydrogen microgrid which includes a wind turbine paired with a hydrogen-based energy storage system (HESS), i.e., hydrogen production, storage and re-electrification facilities, and a local load. This complies with the mini-grid use case as per the IEA-HIA Task 24 Final Report, where three different use cases and configurations of wind farms paired with HESS are proposed in order to promote the integration of wind energy into the grid. Hydrogen production surpluses by wind generation are stored and used to provide a demand-side management solution for energy supply to the local and contractual loads, both in the grid-islanded and connected modes, with corresponding different control objectives. The EMS is based on a hierarchical model predictive control (MPC) in which long-term and short-term operations are addressed. The long-term operations are managed by a high-level MPC, in which power production by wind generation and load demand forecasts are considered in combination with day-ahead market participation. Accordingly, the hydrogen production and re-electrification are scheduled so as to jointly track the load demand, maximize the revenue through electricity market participation and minimize the HESS operating costs. Instead, the management of the short-term operations is entrusted to a low-level MPC, which compensates for any deviations of the actual conditions from the forecasts and refines the power production so as to address the real-time market participation and the short time-scale equipment dynamics and constraints. Both levels also take into account operation requirements and devices’ operating ranges through appropriate constraints. The mathematical modeling relies on the mixed-logic dynamic (MLD) framework so that the various logic states and corresponding continuous dynamics of the HESS are considered. This results in a mixed-integer linear program which is solved numerically. The effectiveness of the controller is analyzed by simulations which are carried out using wind forecasts and spot prices of a wind farm in center-south of Italy.

A testbed for studying the interactions between human operators and advanced control systems

Industrial chemical processes are operated under closed-loop control while being supervised by human operators. The low-level regulatory controllers are often supplemented by advanced multivariate control such as Model Predictive Control (MPC). Since MPC is complex, its control actions can be non-intuitive to the operator. The operator may turn off any controller if they suspect the control actions triggered by it. Hence, it is imperative to understand the operator's interaction with automation. In this work, we develop a testbed with the potential to understand the operator's confidence in MPC through a human factors approach. The testbed consists of a multi-unit chemical process instrumented with various sensors, multiple low-level SISO control loops, and a high-level MPC. The testbed is instrumented with a mechanism to record keystrokes, mouse movements, and mouse clicks to observe the operators’ interactions with the process (during various fault scenarios) through the provided Human Machine Interface.

Model Predictive Control-Based Hierarchical Control of a Multi-Zone Commercial HVAC System

Abstract This paper presents a novel architecture for model predictive control (MPC)-based indoor climate control of multi-zone buildings to provide energy efficiency. Unlike prior works, we do not assume the availability of a high-resolution multi-zone building model, which is challenging to obtain. Instead, the architecture uses a low-resolution model of the building that is divided into a small number of “meta-zones” that can be easily identified using existing data-driven modeling techniques. The proposed architecture is hierarchical. At the higher level, an MPC controller uses the low-resolution model to make decisions for the air handling unit (AHU) and the meta-zones. Since the meta-zones are fictitious, a lower level controller converts the high-level MPC decisions into commands for the individual zones by solving a projection problem that strikes a trade-off between two potentially conflicting goals: the AHU-level decisions made by the MPC are respected while the climate of the individual zones is maintained within the comfort bounds. The performance of the proposed controller is assessed via simulations in a high-fidelity simulation testbed and compared to that of a rule-based controller that is used in practice. Simulations in multiple weather conditions show the effectiveness of the proposed controller in terms of energy savings, climate control, and computational tractability.

A new combined control algorithm for PV-CHP hybrid systems

Due to the 2012 change in the renewable energy act the feed-in tariffs, and therefore the number of newly installed photovoltaic systems decreased dramatically in Germany. Particularly in the residential sector as the biggest market new business ideas for photovoltaic systems were developed. Hence a photovoltaic and combined heat-and-power system, which provides not only electricity but also heat. This complex system requires flexible control strategies. A new combined control algorithm is proposed that in contrast to the standard strategies can operate even under incorrect weather and load forecasts without creating discomfort. Furthermore is it applicable for cloud-based solutions. In this paper a high-level model predictive control based on a mixed integer linear programming problem is combined with an additional low-level, rule-based controller. The low-level control compares the set-points of the optimization with the actual values and corrects the set-points according to each system component until the next optimization takes place. The results show that a hybrid system can be successfully controlled by a combined control approach. In case of a cloud-based application the need for an optimization can be reduced by a factor of four without diminishing comfort or facing much higher operational costs. It has also been shown that the combined control algorithm can be used as an energy management of microgrids.

MPC with Sliding Mode Control for the Energy Management System of Microgrids

This paper presents the design of a high level Model Predictive Control (MPC) for efficient management and consumption of energy resources in microgrids locally controlled via Sliding Mode Control (SMC). The proposed MPC relies on a new model of the so-called Energy Management System (EMS), which takes into account the involved powers as new inputs. In the paper, the microgrid includes a Distributed Generation unit (DGu), based on a renewable energy source, and two Energy Storage units (ESus), working both in grid-connected and islanded operation mode and controlled by SMC. The MPC module generates the references of power for the energy storage systems, taking into account input and state constraints. The proposed approach is theoretically analyzed and the asymptotical stability of the controlled systems is proved. Simulation assessment confirms the efficacy of the proposal.

Modular integrated longitudinal and lateral vehicle stability control for electric vehicles

In this paper, the problem of integrated longitudinal and lateral vehicle stability control is addressed using a modular optimal control structure. The optimization process of the high level model predictive control (MPC) controller determines required longitudinal force and yaw moment adjustments to minimize the error between vehicle longitudinal and lateral vehicle stability dynamic states with respect to the target courses. The low level controller is designed to optimally regulate torque at each wheel based on the control inputs of the high level controller, and distribute required torque between the wheels via actuation system. The actuation system that is utilized to implement the proposed control structure functions based on all-wheel drive technology that can provide active control of both traction and yaw moment control with differential torque. The multi-layered structure of the control system allows modularity in design. The performance of the control structure is investigated by conducting experimental tests. The experimental tests have been performed on an electric Chevrolet Equinox vehicle equipped with four independent motors. The results show that the integration of the vehicle longitudinal and lateral dynamics preserves vehicle stability in a planar motion and improves the vehicle dynamic response, especially in challenging driving maneuvers.

Hierarchical Model Predictive/Sliding Mode Control of Nonlinear Constrained Uncertain Systems

This paper presents an overview of some hierarchical control schemes composed by a high level Model Predictive Control (MPC) and a low level Sliding Mode Control (SMC). The latter is realized by using the so-called Integral Sliding Mode (ISM) control approach and is meant to reject the matched disturbances affecting the plant, thus providing a system with reduced uncertainty for the MPC design. Both continuous and discrete-time solutions are discussed in the paper. Moreover, assuming the presence of a network in the control loop, a networked version of the control scheme is presented. It is a model-based event-triggered MPC/ISM control scheme aimed at minimizing the packets transmission. The input-to-state (practical) stability properties of the proposed approaches are also addressed in the paper.

Piecewise constant model predictive control for autonomous helicopters

This paper introduces an optimisation based control framework for autonomous helicopters. The framework contains a high-level model predictive control (MPC) and a low-level linear controller. The proposed MPC works in a piecewise constant fashion to reduce the computation burden and to increase the time available for performing online optimisation. The linear feedback controller responds to fast dynamics of the helicopter and compensates the low bandwidth of the high-level controller. This configuration allows the computationally intensive algorithm applied on systems with fast dynamics. The stability issues of the high-level MPC and the overall control scheme are discussed. Simulations and flight tests on a small-scale helicopter are carried out to verify the proposed control scheme.

Hierarchical Control for Smart Grids

AbstractThis paper deals with hierarchical model predictive control (MPC) of smart grid systems. The design consists of a high level MPC controller, a second level of so-called aggregators, which reduces the computational and communication-related load on the high-level control, and a lower level of autonomous consumers.The control system is tasked with balancing electric power production and consumption within the smart grid, and makes active use of the flexibility of a large number of power producing and/or power consuming units. The objective is to accommodate the load variation on the grid, arising on one hand from varying consumption, and on the other hand by natural variations in power production e.g. from wind turbines.The high-level MPC problem is solved using quadratic optimisation, while the aggregator level can either involve quadratic optimisation or simple sorting-based min-max solutions.In this paper we compare the performance and computational complexity of these two solutions and find that the performance of the two algorithms are very similar, whereas the sorting-based algorithm is much faster than the quadratic optimisation-based algorithm, thus allowing to handle vastly larger numbers of consumers.