A model predictive control approach to the periodic implementation of the solutions of the optimal dynamic resource allocation problem

Zhang, Jiangfeng; Xia, Xiaohua

doi:10.1016/j.automatica.2010.10.049

Cited by 55 publications

(31 citation statements)

References 18 publications

(21 reference statements)

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“…The MPC finds the optimal control inputs by predicting the future based on the present state of the system. In literatures [11], [12], the MPC algorithm convergence and the robustness against disturbances in controller implementation or state measurement have been investigated and verified for a kind of constrained minimization problem over a receding finite horizon. It is noted that theoretical proof of such stability and robustness of MPC exists only in a few circumstances [13], rarely in the case of discrete variable MPC [14].…”

Section: Introductionmentioning

confidence: 99%

A Multistate-Based Control System Approach Toward Optimal Maintenance Planning

Wang

Xia

2017

IEEE Trans. Contr. Syst. Technol.

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Abstract-This paper incorporates the multi-state system into a control system approach to address the maintenance plan optimization problem. The maintenance plan focuses on the totality of a set of items. A building energy efficiency retrofitting context is employed, where the retrofitted items are categorized into several homogeneous groups. The homogeneous group population dynamics and the aggregate performance dynamics under the impacts of multistate deteriorations and maintenances are for-mulated as a control system model. Thereafter, the maintenance planning optimization is cast into an optimal control problem. An MPC based approach is employed to solve the optimal control problem with system uncertainties. A case study is conducted to illustrate the effectiveness of the present approach.Index Terms-Maintenance plan optimization, Multi-state system, Control system modelling, Model predictive control.

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Section: Introductionmentioning

confidence: 99%

A Multistate-Based Control System Approach Toward Optimal Maintenance Planning

Wang

Xia

2017

IEEE Trans. Contr. Syst. Technol.

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“…Only "the first part" of the sequence is applied to control at the next state [25,26]. MPC has been widely used in the closed-loop control for adaptively changing control variables according to external disturbances [26,27,28]. MPC is applied in this work because of its capability to explicitly handle constraints and to adjust the power flows when disturbances occur.…”

Section: Introductionmentioning

confidence: 99%

Demand side management of photovoltaic-battery hybrid system

2015

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In the electricity market, customers have many choices to reduce electricity cost if they can economically schedule their power consumption. Renewable hybrid system, which can explore solar or wind sources at low cost, is a popular choice for this purpose nowadays. In this paper optimal energy management for a grid-connected photovoltaic-battery hybrid system is proposed to sufficiently explore solar energy and to benefit customers at demand side. The management of power flow aims to minimize electricity cost subject to a number of constraints, such as power balance, solar output and battery capacity. With respect to demand side management, an optimal control method (open loop) is developed to schedule the power flow of hybrid system over 24 hours, and model predictive control is used as a closed-loop method to dispatch the power flow in real-time when uncertain disturbances occur. In these two kinds of applications, optimal energy management solutions can be obtained with great cost savings and robust control performance.

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“…The advantages of using this approach over open loop optimization approaches include reduced dimensions, resulting in easier computation. Some of the major advantages of MPC are convergence and robustness and these are well demonstrated by the application of MPC to power economic dispatching problems with a six-unit system (Kaabeche and Ibtiouen, 2014;Xia et al, 2011;Zhang and Xia, 2011).…”

Section: Introductionmentioning

confidence: 99%

Energy dispatch strategy for a photovoltaic–wind–diesel–battery hybrid power system

2014

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In this paper, an energy dispatch model that satisfies the load demand, taking into account the intermittent nature of the solar and wind energy sources and variations in demand, is presented for a solar photovoltaic-wind-diesel hybrid power supply system. Model predictive control techniques are applied in the management and control of such a power supply system. The emphasis in this work is on the co-ordinated management of energy flow from the battery, wind, photovoltaic and diesel generators when the system is subject to disturbances. The results show that the advantages of the approach become apparent in its capability to attenuate and its robustness against uncertainties and external disturbances. When compared with the open loop model, the closed-loop model is shown to be more superior owing to its ability to predict future system behavior and compute appropriate corrective control actions required to meet variations in demand and radiation. Diesel consumption is generally shown to be more in winter than in summer. This work thus presents a more practical solution to the energy dispatch problem. Keywords: energy management, disturbance, intermittent nature, hybrid energy system, optimization scheme

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A model predictive control approach to the periodic implementation of the solutions of the optimal dynamic resource allocation problem

Cited by 55 publications

References 18 publications

A Multistate-Based Control System Approach Toward Optimal Maintenance Planning

A Multistate-Based Control System Approach Toward Optimal Maintenance Planning

Demand side management of photovoltaic-battery hybrid system

Energy dispatch strategy for a photovoltaic–wind–diesel–battery hybrid power system

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