Decentralized Control Strategy for PV Based DC Microgrid with Hybrid Storage

Improving the operational efficiency of chillers and science-based planning the cooling load distribution between the chillers and ice tank are core issues to achieve low-cost and energy-saving operations of ice storage air-conditioning systems. In view of the problems existing in centralized control architecture applied in heating, ventilation, and air conditioning, a distributed multi-objective particle swarm optimization improved by differential evolution algorithm based on a decentralized control structure was proposed. The energy consumption, operating cost, and energy loss were taken as the objectives to solve the chiller’s hourly partial load ratio and the cooling ratio of ice tank. A large-scale shopping mall in Xi’an was used as a case study. The results show that the proposed algorithm was efficient and provided significantly higher energy-savings than the traditional control strategy and particle swarm optimization algorithm, which has the advantages of good convergence, high stability, strong robustness, and high accuracy. Practical application: The end equipment of the electromechanical system is the basic component through the building operation. Based on this characteristic, taken electromechanical equipment as the computing unit, this paper proposes a distributed multi-objective optimization control strategy. In order to fully explore the economic and energy-saving effect of ice storage system, the optimization algorithm solves the chillers operation status and the load distribution. The improved optimization algorithm ensures the diversity of particles, gains fast optimization speed and higher accuracy, and also provides a better economic and energy-saving operation strategy for ice storage air-conditioning projects.

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Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems

Electronics ◽

10.3390/electronics10111261 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1261

Author(s):

Dina Emara ◽

Mohamed Ezzat ◽

Almoataz Y. Abdelaziz ◽

Karar Mahmoud ◽

Matti Lehtonen ◽

...

Keyword(s):

Energy Storage ◽

Control Strategy ◽

Voltage Stability ◽

Storage Systems ◽

Voltage Source ◽

Stable Operation ◽

Energy Storage Systems ◽

Dc Microgrid ◽

Photovoltaic Generation ◽

Dc Voltage

Recently, the penetration of energy storage systems and photovoltaics has been significantly expanded worldwide. In this regard, this paper presents the enhanced operation and control of DC microgrid systems, which are based on photovoltaic modules, battery storage systems, and DC load. DC–DC and DC–AC converters are coordinated and controlled to achieve DC voltage stability in the microgrid. To achieve such an ambitious target, the system is widely operated in two different modes: stand-alone and grid-connected modes. The novel control strategy enables maximum power generation from the photovoltaic system across different techniques for operating the microgrid. Six different cases are simulated and analyzed using the MATLAB/Simulink platform while varying irradiance levels and consequently varying photovoltaic generation. The proposed system achieves voltage and power stability at different load demands. It is illustrated that the grid-tied mode of operation regulated by voltage source converter control offers more stability than the islanded mode. In general, the proposed battery converter control introduces a stable operation and regulated DC voltage but with few voltage spikes. The merit of the integrated DC microgrid with batteries is to attain further flexibility and reliability through balancing power demand and generation. The simulation results also show the system can operate properly in normal or abnormal cases, thanks to the proposed control strategy, which can regulate the voltage stability of the DC bus in the microgrid with energy storage systems and photovoltaics.

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Decentralized Control Strategy to Improve Fairness in Active Power Curtailment of PV Inverters in Low-Voltage Distribution Networks

IEEE Transactions on Sustainable Energy ◽

10.1109/tste.2021.3088873 ◽

2021 ◽

pp. 1-1

Author(s):

Yasin Zabihinia Gerdroodbari ◽

Reza Razzaghi ◽

Farhad Shahnia

Keyword(s):

Decentralized Control ◽

Control Strategy ◽

Low Voltage ◽

Distribution Networks ◽

Active Power ◽

Voltage Distribution

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Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211024048 ◽

2021 ◽

pp. 095440702110240

Author(s):

Ziyu Zhang ◽

Chunyan Wang ◽

Wanzhong Zhao ◽

Jian Feng

Keyword(s):

Real Time ◽

Collision Avoidance ◽

Decentralized Control ◽

Control Strategy ◽

Centralized Control ◽

Lateral Control ◽

Time Performance ◽

Collision Avoidance Control ◽

Avoidance Control ◽

Control Accuracy

In order to solve the problems of longitudinal and lateral control coupling, low accuracy and poor real-time of existing control strategy in the process of active collision avoidance, a longitudinal and lateral collision avoidance control strategy of intelligent vehicle based on model predictive control is proposed in this paper. Firstly, the vehicle nonlinear coupling dynamics model is established. Secondly, considering the accuracy and real-time requirements of intelligent vehicle motion control in pedestrian crossing scene, and combining the advantages of centralized control and decentralized control, an integrated unidirectional decoupling compensation motion control strategy is proposed. The proposed strategy uses two pairs of unidirectional decoupling compensation controllers to realize the mutual integration and decoupling in both longitudinal and lateral directions. Compared with centralized control, it simplifies the design of controller, retains the advantages of centralized control, and improves the real-time performance of control. Compared with the decentralized control, it considers the influence of longitudinal and lateral control, retains the advantages of decentralized control, and improves the control accuracy. Finally, the proposed control strategy is simulated and analyzed in six working conditions, and compared with the existing control strategy. The results show that the proposed control strategy is obviously better than the existing control strategy in terms of control accuracy and real-time performance, and can effectively improve vehicle safety and stability.

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