Virtual power plant (VPP) can integrate distributed renewable energy to participate in power grid dispatching and explore the value of distributed renewable energy. With the rapid development of distributed renewable ...
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Virtual power plant (VPP) can integrate distributed renewable energy to participate in power grid dispatching and explore the value of distributed renewable energy. With the rapid development of distributed renewable energy, the key to ensure the safe operation of VPP is to accurately calculate its dispatching boundary, which needs to consider the access capacity and uncertainty of renewable energy. Therefore, this paper proposes a calculation method of scheduling boundary probability distribution of VPP considering the maximum access capacity of renewable energy. Firstly, the similarity measurement method is used to analyze the relationship between renewable energy characteristics and access capacity, the characteristic similarity index between renewable energy equipment and load is proposed, and a two-stage access capacity evaluation model aiming at the optimal similarity and the capacity of renewable energy utilization is established. Based on the maximum access capacity evaluation of renewable energy and the theory of multi- parametricprogramming, the piecewise affine mapping between VPP access point power and renewable energy power is studied to calculate the cumulative distribution function of the VPP scheduling boundary. Furthermore, a key random variable identification method based on line shift distribution factor is proposed to realize the rapid calculation of VPP dispatching boundary, which is applied to the collaborative optimal scheduling of virtual power plant and power grid. Finally, an example is given to verify the effectiveness and accuracy of the proposed method, which can provide guidance for collaborative optimal scheduling of virtual power plant and power grid under the background of carbon peaking and carbon neutrality.
Model Predictive Control (MPC) has been applied across a wide range of engineering applications including process industries. MPC requires complete knowledge of states at the current instant which can either be measur...
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Model Predictive Control (MPC) has been applied across a wide range of engineering applications including process industries. MPC requires complete knowledge of states at the current instant which can either be measured directly or estimated using a state estimator. Of late, Moving Horizon Estimation (MHE) has been widely used as a state estimator owing to its ability to handle constraints. Both MPC and MHE involve solving an optimization problem at each sampling instant which can prove computationally burdensome for fast systems. multi-parametric programming based explicit approaches have been proposed in literature as a possible approach for solving the online optimization problems in a computationally efficient manner. In the current work, feasibility of the explicit approaches simultaneously for both MPC and MHE is investigated using simulation as well as experimental studies on a quadruple tank setup. The computational efforts required for this simultaneous implementation of the explicit approaches for MPC and MHE are compared with the conventional optimization approach. Results indicate feasibility of multi-parametric implementation for lower horizon lengths.
A propeller with a rudder can produce a thrust vector within a range of directions and magnitudes in the horizontal plane for low-speed maneuvering and dynamic positioning. The set of attainable thrust vectors is non-...
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A propeller with a rudder can produce a thrust vector within a range of directions and magnitudes in the horizontal plane for low-speed maneuvering and dynamic positioning. The set of attainable thrust vectors is non-convex because significant lift can be produced by the rudder only with forward thrust. We suggest to decompose the attainable thrust region into a finite union of convex polyhedral sets and derive a mixed-integer-like convex quadratic programming formulation of the optimal control allocation problem for marine vessels with any number of rudders as well as thrusters and other propulsion devices. Actuator rate and position constraints are explicitly taken into account. Using multi-parametric quadratic programming software, an explicit piecewise linear representation of the least-squares optimal control allocation law is pre-computed. It can be implemented with low computational complexity and high software reliability without the use of real-time optimization. The method is illustrated using a scale model ship in a basin.
Based upon the multi-parametric programming for the linear constrained optimization program, the explicit model predictive control systems for linear time-invariable and time-variable constrained system are establishe...
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ISBN:
(纸本)9781424417339
Based upon the multi-parametric programming for the linear constrained optimization program, the explicit model predictive control systems for linear time-invariable and time-variable constrained system are established respectively. The dynamic model for the mechanical vibration of elevator is developed, and schemes for vibration control of elevator and the placement of the actuator are discussed. With the established explicit model predictive control systems the active vibration control of the mechanical system of elevator is studied. Simulations are made for different working conditions of the elevator, and results show that the proposed method in this paper is effective for active vibration control of mechanical system of elevator.
A multi-input multi-output piecewise affine model identification method based on data-driven and a real-time predictive control method based on multi-parametric programming are proposed for a class of nonlinear and mu...
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ISBN:
(纸本)9789881563811
A multi-input multi-output piecewise affine model identification method based on data-driven and a real-time predictive control method based on multi-parametric programming are proposed for a class of nonlinear and multivariable processes represented by quadruple tank system. A clustering-based algorithm is designed to solve a MIMO piecewise affine autoregressive exogenous (PWARX) model identification problem. With the use of HYSDEL modeling language and its compiler tools, MIMO-PWARX model is transformed into piecewise affine (PWA) model. A multi-parametric programming method is adopted to design a predictive controller based on PWA model. The experiment with quadruple tank shows the capability of obtaining PWARX model information directly through analysis of process data based on data-driven and the effect of real-time nonlinear predictive control based on the model in this scheme.
In this paper, model predictive control and sliding mode control for quadratic boost converter is studied for its applications in grid connected solar photo voltaic systems. In model predictive control, the control pr...
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ISBN:
(纸本)9781728104201
In this paper, model predictive control and sliding mode control for quadratic boost converter is studied for its applications in grid connected solar photo voltaic systems. In model predictive control, the control problem considered is a constrained optimization problem. The objective function is formulated for reference tracking and steady-state performance of quadratic boost converter. The optimal control law is a piecewise affine function obtained by satisfying the constraints of the quadratic boost converter. In sliding mode control, a fixed frequency assisted sliding mode control law is designed which comprises of voltage and current loop. The current loop is controlled by sliding mode control, whereas the voltage loop is controlled by the traditional proportional integral controller. Simulation is carried out in MATLAB for a 90 W quadratic boost converter. The steady-state and reference tracking performance of the converter is analysed and the results are presented.
We consider the class of piecewise affine optimal state feedback control laws applied to discrete-time piecewise affine systems, motivated by recent work on the computation of closed-form MPC controllers. The storage ...
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ISBN:
(纸本)9781424414970;1424414970
We consider the class of piecewise affine optimal state feedback control laws applied to discrete-time piecewise affine systems, motivated by recent work on the computation of closed-form MPC controllers. The storage demand and complexity of these optimal closed-form solutions limit their applicability in most real-life situations. In this paper we present a novel algorithm to a posteriori reduce the storage demand and complexity of the closed-form controller without losing closed-loop stability or all time feasibility while guaranteeing a bounded performance decay compared to the optimal solution. The algorithm combines simple polyhedral manipulations with (multi-parametric) linear programming and the effectiveness of the algorithm is demonstrated on a large numerical example.
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