Energy efficiency of cold storage systems has remained a challenge to industries over the years. Despite several attempts made by experts to surmount the challenge, there remains a huge potential for improvement. Ther...
详细信息
ISBN:
(纸本)9781665479783
Energy efficiency of cold storage systems has remained a challenge to industries over the years. Despite several attempts made by experts to surmount the challenge, there remains a huge potential for improvement. Therefore, a smell agent optimization (SAO) algorithm is proposed to tune a supervisory model predictive control (MPC) scheme to achieve reduction in high cooling load caused by high ambient temperatures. By this reduction, the compressor is saved from operating at saturated state, a state that is highly inefficient and detrimental to product quality. Simulation results show that the SAO-based supervisory model predictive controller achieved better performance than the traditional regulatory modelpredictivecontroller by reducing the cooling load by 55.5%. The SAO-based supervisory MPC also performed better in keeping the product temperature below 5 degrees C while the regulatory MPC response settled at 6.4 degrees C, a temperature that is detrimental to food safety and quality. The results imply that SAO-based supervisory MPC achieved a significant improvement in energy efficiency while preserving the product within the safe temperature range recommended by the food authority. All simulations are performed using MATLAB 2020a.
This paper presents the design and evaluation of a dynamic simulator for an ISCC (integrated solar combined cycle) plant. The design of the simulator is based on the phenomenological equations for both a combined cycl...
详细信息
This paper presents the design and evaluation of a dynamic simulator for an ISCC (integrated solar combined cycle) plant. The design of the simulator is based on the phenomenological equations for both a combined cycle plant and a solar plant. The simulator incorporates a regulatory control strategy based on PI (proportional-integral) controllers and was developed in the MATLAB/Simulink (R) environment. A MPC (modelpredictivecontrol) strategy established at a supervisory level is presented. The intent of the strategy is to regulate the steam pressure of the superheater of the ISCC plant. The combined use of the simulator and the supervisorycontrol strategy allows for the quantification of the reduction in fuel consumption that can be achieved when integrated solar collectors are used in a combined cycle plant. The ISCC plant simulator is suitable for designing, evaluating and testing control strategies and for planning the integration of solar and combined cycle plants. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
In this work, we design a distributed supervisory model predictive control (MPC) system for optimal management and operation of distributed wind and solar energy generation systems integrated into the electrical grid ...
详细信息
In this work, we design a distributed supervisory model predictive control (MPC) system for optimal management and operation of distributed wind and solar energy generation systems integrated into the electrical grid to facilitate the development of the so-called "smart electrical grid". We consider a topology in which two spatially distributed energy generation systems, a wind subsystem and a solar subsystem, are integrated in a DC power grid, providing electrical power to a local area, and each subsystem is coupled with an energy storage device. A supervisory MPC optimization problem is first formulated to take into account optimality considerations on system operation and battery maintenance;then a sequential and an iterative distributed supervisory MPC architectures are developed to coordinate the actions of the subsystems accordingly. Simulations of 24-hour system operation are carried out under the different control architectures to demonstrate the applicability and effectiveness of the distributed supervisorypredictivecontrol design.
In this paper, sequential nonlinear Distributed modelpredictivecontrol (DMPC) algorithms for large-scale systems that can handle constraints are proposed. The proposed algorithms are based on nonlinear MPC strategy,...
详细信息
ISBN:
(纸本)9781479906529
In this paper, sequential nonlinear Distributed modelpredictivecontrol (DMPC) algorithms for large-scale systems that can handle constraints are proposed. The proposed algorithms are based on nonlinear MPC strategy, which uses a state-dependent nonlinear model to avoid the complexity of the nonlinear programming (NLP) problem. In this distributed framework, local MPCs solve convex optimization problem and exchange information via one directional communication channel at each sampling time to achieve the global control objectives of the system. Numerical simulation results show that the performance of the proposed DMPC algorithms is close to the centralized NMPC but computationally more efficient compared to the centralized one.
暂无评论