Virtual power plants (VPPs) are a critical technology for distribution systems that can integrate various renewable energy resourcescontrollable loads and energy storage systems into one specific power plant through a...
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Virtual power plants (VPPs) are a critical technology for distribution systems that can integrate various renewable energy resourcescontrollable loads and energy storage systems into one specific power plant through a distributed energy management system. This paper proposes a coordinated dispatch optimization model between the main grid and VPPs aiming to minimize both the power generation cost and total system active loss. When the time of the equivalent dispatching model is not divisible due to the existence of a time coupling constraint inside the VPPs, this model can obtain the global optimal solution through iteration between the main grid and the VPPs. By employing multi-parametric quadratic programming to obtain accurate critical domains and optimal cost functions, the convergence speed and stability are significantly improved. Additionally, a reactive power and voltage optimization technique leveraging the generalized Benders decomposition is presented for the coordination of the main grid and the VPPs. Moreover, the impact of distributed energy resource (DER) clusters on the main grid was studied, from which we proved that the proposed approach can expeditiously abate energy production expenditure and system active dissipation whilst enhancing the system equilibrium.
This chapter provides new methods for the construction, storage and retrieval of the explicit MPC solution in the case with quadratic cost and linear constraints. By exploiting the geometric interpretation of the MPC ...
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This chapter provides new methods for the construction, storage and retrieval of the explicit MPC solution in the case with quadratic cost and linear constraints. By exploiting the geometric interpretation of the MPC problem, we: (i) construct the explicit solution (i.e., enumerate the critical regions and associated affine laws) in an efficient manner;(ii) store it as a partially ordered set;and (iii) provide a modified graph traversal algorithm for efficient point location (i.e., identifying the currently active critical region and its associated control law).
With the wide application of distributed energy technology in active distribution networks (ADNs), virtual power plants (VPPs) are introduced as a promising integration and management technology. To coordinate their o...
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With the wide application of distributed energy technology in active distribution networks (ADNs), virtual power plants (VPPs) are introduced as a promising integration and management technology. To coordinate their operation schedule and ensure confidentiality, we establish a coordinated optimal power flow (OPF) model and propose an efficient decentralized algorithm based on multi-parametric quadratic programming and Benders decomposition. In feasible sub-problems, the quadratic exchanging functions are built, which greatly accelerates the convergence process. In infeasible sub-problems, feasible cut sets can be calculated based on benders decomposition. Besides, an effective relaxation method is developed to address the degeneracy problem. To eliminate redundant feasible cut sets, a predetermined feasible region is constructed according to the characteristics of each VPP. The effectiveness of the proposed method is demonstrated via numerical tests using two cases of different scales. The results show that the proposed method converges much faster than some prevailing methods. Furthermore, the coordinated OPF has a better fuel economy than the conventional OPF scheme.
A line-of-sight (LOS) guidance law with an explicit model predictive control (MPC) is proposed for an underactuated autonomous underwater vehicle (AUV). The derived LOS law is based on quaternion angles thus avoids th...
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A line-of-sight (LOS) guidance law with an explicit model predictive control (MPC) is proposed for an underactuated autonomous underwater vehicle (AUV). The derived LOS law is based on quaternion angles thus avoids the problem of singularity inherited in Euler angles. The proposed guidance and control scheme is computationally inexpensive and generates a robust optimal control signal for AUV dynamics. In view of the practical implementation, the constraints on control planes and parametric uncertainties are taken into consideration while designing the proposed control law. The effectiveness of the proposed controller is verified by both simulation and experimentation using a prototype AUV developed in the laboratory. Subsequently, multiple experiments were conducted to ascertain the applicability of the proposed algorithm in a practical scenario.
This paper presents a multiple model predictive control (MPC) with a softly switching for aero-engines. Firstly, the different local working regions of the nonlinear aeroengines model are linearized. Then, a model pre...
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This paper presents a multiple model predictive control (MPC) with a softly switching for aero-engines. Firstly, the different local working regions of the nonlinear aeroengines model are linearized. Then, a model predictive controller is designed for each linear model. The traditional hard switching methods for multiple model predictive controllers may result in bad transient behavior. Therefore, a softly switching mechanism is studied in this paper, which adopts the convex combination of the MPC objective functions before and after switching to ensure the smooth transition of aero-engines when switching in adjacent regions. The multi-parametric quadratic programming (MP-QP) algorithm is then used to solve the suboptimal solution of the problem, which reduces the amount of calculation and obtains an explicit solution. Finally, the reported simulation results of the turbofan engine verify the effectiveness of the suggested algorithm. Copyright (C) 2021 The Authors.
This paper presents a multiple model predictive control (MPC) with a softly switching for aero-engines. Firstly, the different local working regions of the nonlinear aeroengines model are linearized. Then, a model pre...
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This paper presents a multiple model predictive control (MPC) with a softly switching for aero-engines. Firstly, the different local working regions of the nonlinear aeroengines model are linearized. Then, a model predictive controller is designed for each linear model. The traditional hard switching methods for multiple model predictive controllers may result in bad transient behavior. Therefore, a softly switching mechanism is studied in this paper, which adopts the convex combination of the MPC objective functions before and after switching to ensure the smooth transition of aero-engines when switching in adjacent regions. The multi-parametric quadratic programming (MP-QP) algorithm is then used to solve the sub-optimal solution of the problem, which reduces the amount of calculation and obtains an explicit solution. Finally, the reported simulation results of the turbofan engine verify the effectiveness of the suggested algorithm.
In this paper, we analyze and verify explicit model predictive control (MPC) through the automated double-lane-change (DLC) maneuver by employing MATLAB/Simulink and CarSim. MPC has become a useful optimization method...
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ISBN:
(纸本)9781538637050
In this paper, we analyze and verify explicit model predictive control (MPC) through the automated double-lane-change (DLC) maneuver by employing MATLAB/Simulink and CarSim. MPC has become a useful optimization method because it can anticipate future events of a system and fulfill constraints on states and inputs. However, the formidable computational complexities of online optimization are considered as a major drawback. Therefore, explicit MPC has been proposed and demonstrated to reduce the computational burden by treating the state vector as a parameter vector. This technique is referred to as multi-parametric quadratic programming (mp-QP). A DLC test of CarSim has been conducted to prove the efficacy of the proposed controller in an autonomous driving situation. In addition, a DLC test conducted using a driver model in CarSim, which is designed based on a MPC scheme, is presented for comparison with the proposed controller. The range of the prediction horizon is varied to explain the relation between the prediction horizon and the performance of the proposed controller;also, a variation of vehicle speed is implemented to prove the robustness of the proposed controller. The main contribution of this paper is to demonstrate the reduction of the computational loads of explicit MPC so that the MPC-based scheme can be designed using not only a 32-bit microcontrollers but also 16-bit, 8 bit- microcontrollers, or a field programmable gate array (FPGA), featuring relatively lower development costs and longer battery consistence.
In recent literature, explicit model predictive control (e-MPC) has been proposed to facilitate implementation of the popular model predictive control (MPC) approach to fast dynamical systems. e-MPC is based on multi-...
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In recent literature, explicit model predictive control (e-MPC) has been proposed to facilitate implementation of the popular model predictive control (MPC) approach to fast dynamical systems. e-MPC is based on multi-parametricprogramming. The key idea in e-MPC is to replace the online optimization problem in MPC by a point location problem. After locating the current point, the control law is simply computed as an appropriate linear function of the states. A variety of approaches have been proposed in literature for the point location problem. In this work, we present a novel approach based on linear machines for solving this problem. Linear machines are widely used in multi-category pattern classification literature for developing linear classifiers given representative data from various classes. The idea in linear machines is to associate a linear discriminant function with each class. A given point is then assigned to the class with the largest discriminant function value. In this work, we develop an approach for identifying such discriminant functions from the hyperplanes characterizing the given regions as in multi-parametricprogramming. Apart from being an elegant solution to the point location problem as required in e-MPC, the proposed approach also links two apparently diverse fields namely e-MPC and multi-category pattern classification. To illustrate the utility of the approach, it is implemented on a hypothetical example as well as on a quadruple tank benchmark system taken from literature.
This paper considers the computational efficiency of using generalised function parameterisations for multi-parametric quadratic programming (mp-QP) solutions to MPC. Earlier work demonstrated the potential of Laguerr...
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This paper considers the computational efficiency of using generalised function parameterisations for multi-parametric quadratic programming (mp-QP) solutions to MPC. Earlier work demonstrated the potential of Laguerre parameterisations for improving computational efficiency in that the parametric solutions either required fewer regions and/or gave larger volumes. This paper considers the potential of extending this concept to more general function parameterisations. Specifically the aim is to consider to what extent different function parameterisations affect the parametric solution complexity and feasible volumes. Extensive simulation results which suggest there are indeed benefits from using more general parameterisations than Laguerre.
In order to estimate the packet dropout in Networked Control Systems (NCS), this paper presents an explicit model predictive control. We firstly transform the model for networked control systems with data packet dropo...
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ISBN:
(纸本)9781457720727
In order to estimate the packet dropout in Networked Control Systems (NCS), this paper presents an explicit model predictive control. We firstly transform the model for networked control systems with data packet dropout into piecewise affine form. Then we solve a constrained optimal control problem with PWA model. By using the method of explicit predictive control, which is based on multi-parametric quadratic programming method, we get a state feedback control law which is explicit to the states. Based on this control law, we show that the control law is piece-wise linear and continuous for the finite horizon problem. Thus, the on-line control computation reduces to the simple evaluation of a piecewise affine model. Since the algorithm does not require repeated line optimization, it improves the on-line calculation speed of the controller, making the system better real-time. The algorithm deals with the data packet dropout well. Simulation results show the effectiveness of the method.
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