Detailed study, multi-objective optimization, and design of an AC-DC smart microgrid with hybrid renewable energy resources

Abstract Hybrid renewable system is a particular type of energy systems which can be used as Distributed Generation (DG) resources to reduce network losses and increase its efficiency. Overall, at design phase, there are two major constraints: first, availability, and second, the cost of equipment. In this paper, considering these constraints and using DGs as Renewable Energy Sources (RES) including wind turbines and photovoltaics, an intelligent method based on multi-objective particle swarm optimization is utilized. Besides, battery bank has been used as a backup unit and energy storage of the hybrid system to reduce the volatility of RESs. The purposes of this paper are: to provide a comprehensive analysis on new structures of AC and DC systems, and then, to determine the capacity and optimal design with hybrid RESs in a smart microgrid to increase the availability and reduce network costs. In order to demonstrate the possibility of proposed approach, an optimized method is designed and implemented in two scenarios (Basic, and Maximum Renewable). Effectiveness of the proposed approach is applied over a real study case. By comparing the proposed method with multi-objective genetic algorithm, simulation results show that the proposed method has effective performance in reducing costs and improving availability.

[1]  Saad Mekhilef,et al.  A PSO-DQ Current Control Scheme for Performance Enhancement of Z-Source Matrix Converter to Drive IM Fed by Abnormal Voltage , 2018, IEEE Transactions on Power Electronics.

[2]  Ersan Kabalci,et al.  An islanded hybrid microgrid design with decentralized DC and AC subgrid controllers , 2018, Energy.

[3]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[4]  Mohammad Ghiasi,et al.  A Detailed Study for Load Flow Analysis in Distributed Power System , 2018 .

[5]  Alvaro Luna,et al.  Multiterminal DC grids: operating analogies to AC power systems , 2017 .

[6]  Robert S. Balog,et al.  Decoupled Active and Reactive Power Predictive Control for PV Applications Using a Grid-Tied Quasi-Z-Source Inverter , 2018, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[7]  Rolando Simoes,et al.  An optimal performance based new multi-objective model for heat and power hub in large scale users , 2018 .

[8]  S. Mishra,et al.  Permanent Magnet Synchronous Generator-Based Standalone Wind Energy Supply System , 2011, IEEE Transactions on Sustainable Energy.

[9]  Kumars Rouzbehi,et al.  Power Flow Control in Multi-Terminal HVDC Grids Using a Serial-Parallel DC Power Flow Controller , 2018, IEEE Access.

[10]  Robert S. Balog,et al.  Autotuning Technique for the Cost Function Weight Factors in Model Predictive Control for Power Electronic Interfaces , 2019, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[11]  Saifur Rahman,et al.  A decision support technique for the design of hybrid solar-wind power systems , 1998 .

[12]  Rolando Simoes,et al.  A New Spinning Reserve Requirement Prediction with Hybrid Model , 2018 .

[13]  Farhad Samadi Gazijahani,et al.  Reliability constrained two-stage optimization of multiple renewable-based microgrids incorporating critical energy peak pricing demand response program using robust optimization approach , 2018, Energy.

[14]  Pedro RODRIGUEZ,et al.  Multi-terminal DC grids: challenges and prospects , 2017 .

[15]  Arindam Ghosh,et al.  DC Microgrid Technology: System Architectures, AC Grid Interfaces, Grounding Schemes, Power Quality, Communication Networks, Applications, and Standardizations Aspects , 2017, IEEE Access.

[16]  Amin Safari,et al.  Probabilistic multi-objective arbitrage of dispersed energy storage systems for optimal congestion management of active distribution networks including solar/wind/CHP hybrid energy system , 2018 .

[17]  Jeyraj Selvaraj,et al.  Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming , 2017 .

[18]  Mehmet Uzunoglu,et al.  Modeling, control and simulation of an autonomous wind turbine/photovoltaic/fuel cell/ultra-capacitor hybrid power system , 2008 .

[19]  Robert S. Balog,et al.  Multi-Objective Optimization and Design of Photovoltaic-Wind Hybrid System for Community Smart DC Microgrid , 2014, IEEE Transactions on Smart Grid.

[20]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[21]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[22]  Ahmad Amiri,et al.  Toward improved mechanical, tribological, corrosion and in-vitro bioactivity properties of mixed oxide nanotubes on Ti-6Al-7Nb implant using multi-objective PSO. , 2017, Journal of the mechanical behavior of biomedical materials.

[23]  Nasrudin Abd Rahim,et al.  Long-term electric energy consumption forecasting via artificial cooperative search algorithm , 2016 .

[24]  Mohammad Ghiasi,et al.  Extracting Appropriate Nodal Marginal Prices for All Types of Committed Reserve , 2019 .

[25]  Martin Marz,et al.  Overview of different topologies and control strategies for DC micro grids , 2015, 2015 IEEE First International Conference on DC Microgrids (ICDCM).

[26]  Mohammad Ghiasi,et al.  Optimal capacitor placement to minimizing cost and power loss in Tehran metro power distribution system using ETAP (A case study) , 2016, Complex..

[27]  Juan C. Vasquez,et al.  Advanced LVDC Electrical Power Architectures and Microgrids: A step toward a new generation of power distribution networks. , 2014, IEEE Electrification Magazine.

[28]  M. Negnevitsky,et al.  A Novel Operation and Control Strategy for a Standalone Hybrid Renewable Power System , 2013, IEEE Transactions on Sustainable Energy.

[29]  A. Emadi,et al.  A New Battery/UltraCapacitor Hybrid Energy Storage System for Electric, Hybrid, and Plug-In Hybrid Electric Vehicles , 2012, IEEE Transactions on Power Electronics.

[30]  Daryoush Nazarpour,et al.  Multi-objective scheduling of electric vehicles intelligent parking lot in the presence of hydrogen storage system under peak load management , 2018, Energy.

[31]  Zhi-Hong Mao,et al.  Ship to Grid: Medium-Voltage DC Concepts in Theory and Practice , 2012, IEEE Power and Energy Magazine.

[32]  Alireza Nouri,et al.  RETRACTED: Optimal performance of fuel cell-CHP-battery based micro-grid under real-time energy management: An epsilon constraint method and fuzzy satisfying approach , 2018, Energy.

[33]  Noradin Ghadimi,et al.  Multi-objective energy management in a micro-grid , 2018, Energy Reports.

[34]  Nasser Yousefi,et al.  A new prediction model of electricity load based on hybrid forecast engine , 2019 .

[35]  Ishwari Tank,et al.  Renewable based DC microgrid with energy management system , 2015, 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES).

[36]  Mostafa Modiri-Delshad,et al.  Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options , 2016 .

[37]  Mohammad Ghiasi,et al.  An analytical methodology for reliability assessment and failure analysis in distributed power system , 2018, SN Applied Sciences.

[38]  J. Jeslin Drusila Nesamalar,et al.  Optimizing renewable based generations in AC/DC microgrid system using hybrid Nelder-Mead – Cuckoo Search algorithm , 2018, Energy.

[39]  Adel Akbarimajd,et al.  Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting , 2018, Energy.