Two-level Model Predictive Control Research Articles

Installation and Testing of a Two-Level Model Predictive Control Building Energy Management System

Installation and Testing of a Two-Level Model Predictive Control Building Energy Management System

A two-level MPC method for the operation of a gas pipeline system under demand variation

A gas pipeline system is in an almost continual state of transition due to changing gas demand. A control method is required to keep a gas pipeline operating in an energy-saving status and provide reliable transmission service for the customers. Model predictive control (MPC) predicts the future states of a system, making it suitable for following gas demand changes. However, to avoid computation time challenges associated with formulating the MPC problem as a large-scale MINLP, an online two-level MPC system is proposed, comprising an upper-level economic MPC (EMPC) and a lower-level tracking MPC (TMPC). EMPC adopts energy cost as the objective function and utilizes a simplified nonlinear model to address long-horizon optimization, while TMPC tracks the status optimized in EMPC, to find the optimal output pressure and the on-off status of the compressors and compressor stations. The performance of the method is demonstrated through application to case studies.

Eco-driving for Truck Platoon Velocity Planning Based on V2X Communication Considering Road Geometry

This paper proposes a two-level model predictive control scheme for a truck platoon traveling on a highway. In the upper layer, the velocity profiles of the whole trucks are designed over the receding horizon with consideration of the road geometry, speed limit, and energy efficiency In the lower layer, for the purpose of tracking reference velocity, the engine torque, the transmission gear, and the brake torque are formulated as the traction force optimization based on Pontryagin's minimum principle to simplify the mixed-integer nonlinear programming problem, in which the selection of the gear is traversed as an offline table for demand traction force and the engine/brake torque being easily determined. Simulation results show that the proposed control scheme performs better than the constant headway gap policy in fuel consumption with the same effect of driving safety.

Two-level model predictive control energy management strategy for hybrid power ships with hybrid energy storage system

Compared with the load power characteristics of ground power systems, the intermittent and random fluctuation of ship load power demand brings challenges to the energy management system of ship power systems. To achieve fuel economy and solve the problem of power fluctuation, the hybrid energy storage system (HESS) composed of the battery pack and ultra-capacitor is applied to the diesel-electric hybrid ship. A two-level model predictive control (MPC) strategy consisting of two optimization stages is proposed. The first stage aims to determine the power distribution solution of the diesel generator and battery at a large time-scale. In the second stage, the operation strategy of the ultracapacitor is determined at a small time-scale based on the power distribution solution of the first stage. The conventional MPC-based strategy and global optimization strategy based on dynamic programming (DP) are compared with the proposed strategy. The simulation results show that the proposed strategy has better fuel saving ability, and realizes the clear power frequency division between the supercapacitor and battery. In addition, the proposed strategy can also approach the performance of global optimization.

Economic Model-Predictive Control for Aircraft Forced Landing: Framework and Two-Level Implementation

This article presents an optimization-based control framework for the autonomous forced landing of a fixed-wing unmanned aircraft (UA). A two-level model-predictive control (MPC) scheme is proposed to realize this framework, where an economic model-predictive control (EMPC) in a long piecewise constant fashion is proposed at the high level, while a short fixed-horizon linear time-varying MPC at the low level responds to fast dynamics of UA and tracks the optimal path provided by the high-level controller, alleviating computational burden compared to the high-frequency single-layer MPC scheme. Compared with a single EMPC setup with high sampling frequency, this hierarchical EMPC controller can significantly reduce the computational complexity and make it feasible to be implemented in real time. In addition, it also responds to disturbances (e.g., wind) and aircraft fast dynamics in a timely manner. The recursive feasibility and stability of the high- and low-level MPC schemes are established. The performance of the proposed EMPC forced landing function is illustrated by simulation case studies on both Aerosonde and Skywalker X8, compared favorably with competing schemes.

Q-Learning-Based Model Predictive Control for Energy Management in Residential Aggregator

This article presents a demand response scheduling model in a residential community using an energy management system aggregator. The aggregator manages a set of resources, including photovoltaic system, energy storage system, thermostatically controllable loads, and electrical vehicles. The solution aims to dynamically control the power demand and distributed energy resources to improve the matching performance between the renewable power generation and the consumption at the community level while trading electricity in both day-ahead and real-time markets to reduce the operational costs in the aggregator. The problem can be formulated as a mixed-integer linear programming problem in which the objective is to minimize the operation and the degradation costs related to the energy storage system and the electric vehicles batteries. To mitigate the uncertainties associated with system operation, a two-level model predictive control (MPC) integrating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning reinforcement learning model is designed to address different time-scale controllers. MPC algorithm allows making decisions for the day-ahead, based on predictions of uncertain parameters, whereas <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm addresses real-time decisions based on real-time data. The problem is solved for various sets of houses. Results demonstrated that houses can gain more benefits when they are operating in the aggregate mode. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners:</i> Residential buildings besides commercial and public sectors are among the building sectors responsible for high-energy consumption. Numerous measures have been considered to construct more energy-efficient buildings, such as implementing new effective insulation materials and increasing the utilization ratio of sunlight. However, there is also a need for practical solutions to reduce the greenhouse gas emissions and avoid power peak from the residential sector. Under this situation, energy management system aggregator (EMSA) offers the opportunity to exploit the flexibility potential of various houses and other available distributed energy resources, promoting their participation in ancillary services and benefiting from rewards and lower energy bills. In this article, an innovative and comprehensive model predictive control-based scheduling optimization that considers uncertainties of renewable resources and weather conditions is formulated. It can be considered as a practical solution in order to optimally control the operation of a residential community. We proposed a curtailable demand response (DR), where customers agree to participate in DR programs defined by the EMSA in response to price changes.

Generic stochastic particle filter algorithm for predictive energy optimization of a Plug-in Hybrid Electric Vehicle extended by a battery temperature control and implemented on a Hardware-in-the-Loop system

This work presents an implementation and demonstration of a generic stochastic particle filter-based algorithm (GSPFA) to solve the energy management problem of a Plug-in Hybrid Electric Vehicle (PHEV, P2). The objective is to minimize energy consumption considering the driving mission-specific state of charge and the battery system’s desired temperature range. To achieve this objective, a two-level Model Predictive Control (MPC) approach is used for trajectory planning in the distance domain. The control signals are vehicle velocity, torque-split, gear shift, and the internal combustion engine on/off state. The mixed-integer nonlinear optimal control problem is solved by the second MPC. The PHEV model is executed on the real-time system of a Hardware-in-the-Loop test bench with a battery module as a real component to measure physical responses. A reference driving cycle (CADC URM130) without temperature control leads to a temperature increase of the battery of 2.1K. By allowing a temperature increase of only 1K under equal boundary conditions, the results show that this increase could be maintained with a temperature delta of 0.4K at the end of the cycle. By targeting a maximum temperature delta with identical boundary conditions, a temperature delta of 4K was achieved.

Two-Level MPC Speed Profile Optimization of Autonomous Electric Vehicles Considering Detailed Internal and External Losses

This paper proposes a novel two-level model predictive control (MPC) speed control algorithm for autonomous vehicles as a successive convex optimization problem focused on both energy use and arrival time. Internal losses such as detailed motor/inverter efficiency and battery loss, as well as external losses, such as wind and grade, are considered. The effect of the higher accessory energy usage of autonomous vehicles on the energy-optimal speed profile is considered in the algorithm and investigated in the paper. The proposed successive convex approach produces a highly accurate optimal speed profile while also being solvable in real-time with the vehicle on-board computing resources. An electric vehicle model is created in MATLAB/Simulink and validated to real-world logged driving data. This vehicle model is used to perform a variety of simulated test cases, which show an energy savings potential of about 1% to 20% for different driving conditions, compared to a non-energy-optimal driving profile.

A Two-Level MPC for Energy Management Including Velocity Control of Hybrid Electric Vehicles

Todays cruise control systems try to keep a constant speed given by the driver without regarding the energy consumption. There is, however, possibilities to save energy by choosing the optimal velocity is neglected. Solving the underlying control problem for long distances with an algorithm that runs on a vehicle control unit is not straightforward, especially when it comes to hybrid electric vehicles (HEV). In order to overcome the computational burden, this paper presents a two-level model predictive control approach. It uses a sequential quadratic program (SQP) that comprises the total travel distance computing a target set for a lower layer that uses discrete state-space dynamic programming and Pontryagin's maximum principle to compute the optimal control for a short horizon. The computational efficiency is shown by a case study for an HEV passenger car with parallel powertrain, which reveals a cost advantage of up to 39% compared to a benchmark solution that exactly follows the velocity of the SQP while both approaches yield the same travel time.

Real-Time Control Algorithms for a Hybrid Electric Race Car Using a Two-Level Model Predictive Control Scheme

Since 2014, the Formula 1 power unit has consisted of an internal combustion engine equipped with an electrified turbocharger and a powerful electric motor with regenerative braking and boosting capabilities. This hybrid electric powertrain is controlled by a dedicated energy management system, whose decisions significantly influence the achievable lap time, as well as the fuel and electric energy consumption. Therefore, feedback control algorithms must be designed in order to follow lap time optimal strategies and properly react to disturbances. In this paper, we propose a real-time control algorithm based on a two-level model predictive control (MPC) scheme. A high-level controller, based on a convex model of the system, is used to periodically update the optimal strategies, whereas a zone MPC scheme is designed as a linear program to follow these trajectories in an optimal way. Each iteration of the low-level MPC can be calculated within few milliseconds, thus allowing for a suitable update frequency. The optimality of the presented controller is verified using a benchmark simulator, and its performance is finally tested on a third-party high-fidelity nonlinear simulator of the race car.

Domestic appliances energy optimization with model predictive control

A vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24h period of a summer day was simulated a three-level tariff scheme was used. The new contribution of the proposal is a modulation scheme of a two-level Model Predictive Control’s control signal as an interface block between the Model Predictive Control output and the domestic appliance that functions as a two-state power switch, thus reducing the Model Predictive Control implementation costs in home appliances with thermal regulation requirements.

Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

Energy storage devices are expected to be more frequently implemented in wind farms in near future. In this paper, both pumped hydro and fly wheel storage systems are used to assist a wind farm to smooth the power fluctuations. Due to the significant difference in the response speeds of the two storages types, the wind farm coordination with two types of energy storage is a problem. This paper presents two methods for the coordination problem: a two-level hierarchical model predictive control (MPC) method and a single-level MPC method. In the single-level MPC method, only one MPC controller coordinates the wind farm and the two storage systems to follow the grid scheduling. Alternatively, in the two-level MPC method, two MPC controllers are used to coordinate the wind farm and the two storage systems. The structure of two level MPC consists of outer level and inner level MPC. They run alternatively to perform real-time scheduling and then stop, thus obtaining long-term scheduling results and sending some results to the inner level as input. The single-level MPC method performs both long- and short-term scheduling tasks in each interval. The simulation results show that the methods proposed can improve the utilization of wind power and reduce wind power spillage. In addition, the single-level MPC and the two-level MPC are not interchangeable. The single-level MPC has the advantage of following the grid schedule while the two-level MPC can reduce the optimization time by 60%.

PRICE-DRIVEN COORDINATION FOR SOLVING PLANT-WIDE MPC PROBLEMS

Two-level model predictive control (MPC) is the dominant multi-variable control technology in the process industries. In large-scale MPC applications, such as plant-wide control, two common approaches are centralized and decentralized MPC schemes, which represent the two extremes in the “trade-off” among the desired characteristics of an implemented MPC system. Alternatively the coordination of decentralized MPC systems may offer the best attributes of each of the extremes. The price-driven coordination scheme requires the existence of “equilibrium prices” and has extensive large-scale applications in economic planning. On-line solutions to large-scale optimization problems require an efficient price-adjustment method. As the coordination problem for decentralized MPC falls into the category of limited resource case, this work develops an efficient price-adjustment algorithm by using Newton's method, in which sensitivity analysis and active set change identification techniques are employed. The proposed price-adjustment strategy is incorporated into a coordinated, decentralized MPC scheme that shows a high degree of accuracy, while retaining the reliability of original decentralized scheme at a reasonable computational load.