Towards Energy Management Negotiation Between Distributed AC/DC Networks

The ongoing development of technology has changed the nature of electric networks in the field of electric power generation. In the same vein, the generation units and demands are getting close by the integration of distributed resources within the distribution system with the aim of bringing more advantages to the system. Considering the promising concept of hybrid networks, which comprises different distributed resources, as a supportive element for the power system, this paper mainly focuses on modeling a distributed hybrid network, through which both the AC and DC loads can be supplied within the hybrid network. This network is operated in a distributed way where no independent operator is assumed for the operation. The primal-dual method of multipliers (PDMM) as an effective distributed method handles the operation of this network. So far, this method has only been applied to the AC grid or DC grid separately, while the presented method is modified for the operation of the hybrid network. Convergence speed and preciseness are mentioned as the advantages of this method which dominates the alternating direction method of multipliers (ADMM). In real cases, there are some errors in the output power of the renewable resources. Aiming to make the presented PDMM method more applicable, the uncertainty is also modelled using the Unscented Transform (UT) approach, as a way of uncertainty modelling which has shown attractive features, especially the capability of correlation modelling; therefore, it is more preferable for uncertainty modeling in many cases compared to the other methods. The proposed work is implemented on a smart island and the authenticity of this work is proved by comparing the performance of the proposed distributed approach with the centralized method.

[1]  Ziad M. Ali,et al.  A two-stage stochastic framework for effective management of multiple energy carriers , 2020 .

[2]  Ziad M. Ali,et al.  Towards distributed based energy transaction in a clean smart island , 2020 .

[3]  Abdollah Kavousi-Fard,et al.  On the assessment of the impact of a price-maker energy storage unit on the operation of power system: The ISO point of view , 2020 .

[4]  Mahmoud-Reza Haghifam,et al.  Energy management and operation modelling of hybrid AC–DC microgrid , 2014 .

[5]  Fanghong Guo,et al.  Parallel alternating direction method of multipliers , 2020, Inf. Sci..

[6]  Amitava Ray,et al.  Optimal green energy source selection: An eclectic decision , 2020, Energy & Environment.

[7]  Mazhar Ali,et al.  Alternating direction method of multipliers for the optimal siting, sizing, and technology selection of Li-ion battery storage , 2020 .

[8]  Taher Niknam,et al.  Security-Constrained Unit Commitment Problem With Transmission Switching Reliability and Dynamic Thermal Line Rating , 2019, IEEE Systems Journal.

[9]  Ebrahim Farjah,et al.  Power Control and Management in a Hybrid AC/DC Microgrid , 2014, IEEE Transactions on Smart Grid.

[10]  Chen Qi,et al.  A Decentralized Optimal Operation of AC/DC Hybrid Distribution Grids , 2018, IEEE Transactions on Smart Grid.

[11]  Wencong Su,et al.  An effective stochastic framework for smart coordinated operation of wind park and energy storage unit , 2020 .

[12]  Mohamed A. Mohamed,et al.  Optimal Scheduling and Management of a Smart City Within the Safe Framework , 2020, IEEE Access.

[13]  Peng Wang,et al.  Stochastic management of hybrid AC/DC microgrids considering electric vehicles charging demands , 2020 .

[14]  Ghadir Radman,et al.  Effective Energy Management of Hybrid AC–DC Microgrids With Storage Devices , 2019, IEEE Transactions on Smart Grid.

[15]  Wencong Su,et al.  Multi-agent energy management of smart islands using primal-dual method of multipliers , 2020 .

[16]  Taher Niknam,et al.  A scenario-based approach for the design of Smart Energy and Water Hub , 2020 .

[17]  Richard Heusdens,et al.  On Relationship between Primal-Dual Method of Multipliers and Kalman Filter , 2017, ArXiv.

[18]  Peng LI,et al.  Multi-objective optimal operation of hybrid AC/DC microgrid considering source-network-load coordination , 2019, Journal of Modern Power Systems and Clean Energy.

[19]  Xiangfeng Wang,et al.  Multi-Agent Distributed Optimization via Inexact Consensus ADMM , 2014, IEEE Transactions on Signal Processing.

[20]  Zhenkun Li,et al.  Energy Management for Hybrid AC/DC Distribution System With Microgrid Clusters Using Non-Cooperative Game Theory and Robust Optimization , 2020, IEEE Transactions on Smart Grid.

[21]  Mohammad Hassan Khooban,et al.  Simultaneous energy management and optimal components sizing of a zero-emission ferry boat , 2020 .

[22]  Mohammad Hassan Khooban,et al.  An Efficient and Cost-Effective Power Scheduling in Zero-Emission Ferry Ships , 2020, Complex..

[23]  Hak-Man Kim,et al.  Robust Optimal Operation of AC/DC Hybrid Microgrids Under Market Price Uncertainties , 2018, IEEE Access.

[24]  Lixing Yang,et al.  Distributed optimal control for multiple high-speed train movement: An alternating direction method of multipliers , 2020, Autom..

[25]  Ameena Saad Al-Sumaiti,et al.  An Intelligent Secured Framework for Cyberattack Detection in Electric Vehicles’ CAN Bus Using Machine Learning , 2019, IEEE Access.

[26]  Gang Zhang,et al.  A Fixed-Point Based Distributed Method for Energy Flow Calculation in Multi-Energy Systems , 2020, IEEE Transactions on Sustainable Energy.

[27]  Richard Heusdens,et al.  Distributed Optimization Using the Primal-Dual Method of Multipliers , 2017, IEEE Transactions on Signal and Information Processing over Networks.

[28]  Ziad M. Ali,et al.  A Secured Energy Management Architecture for Smart Hybrid Microgrids Considering PEM-Fuel Cell and Electric Vehicles , 2020, IEEE Access.

[29]  Sylvie Thiébaux,et al.  Dynamic Optimal Power Flow in Microgrids using the Alternating Direction Method of Multipliers , 2014, ArXiv.

[30]  Mahmud Fotuhi-Firuzabad,et al.  A comprehensive review on uncertainty modeling techniques in power system studies , 2016 .