Research on Operation–Planning Double-Layer Optimization Design Method for Multi-Energy Microgrid Considering Reliability

A multi-energy microgrid has multiple terminal resources and multiple distributed components for energy production, conversion, and storage. By using this grid, an interconnected network with optimized multiple energy sources can be formed. This type of grid can minimize energy waste while laying the critical foundation for an energy Internet. The multi-energy microgrid must be formed properly to ensure multi-energy coupling and complement. However, critical technologies (e.g., reliability assessment) and configuration planning methods now need further research. In this study, a novel method for the reliability evaluation of a multi-energy supply is proposed, and an operation–planning double-layer optimization design method is investigated that considers reliability. On that basis, the effects of different configuration schemes on economy and reliability are quantitatively analyzed. First, the coupling relationship between multi-energy carriers in a typical multi-energy microgrid is analyzed; subsequently, the energy efficiency and economical models of the key equipment in the grid system are determined. Monte Carlo simulation and the Failure Mode and Effect Analysis (FMEA) method are applied to evaluate the reliability with sorted indicators. A double-layer optimization model is built for a multi-energy microgrid with the optimal configuration. The impact of configuration on the reliability and economical performance of the microgrid system is quantitatively analyzed based on actual calculations. The results obtained here are relative to the capacity, configuration, operation, and energy supply reliability of the multi-energy microgrid, and may serve as the feasible guidelines for future integrated energy systems.

[1]  G. Andersson,et al.  Energy hubs for the future , 2007, IEEE Power and Energy Magazine.

[2]  Bin Liu,et al.  Multi-Agent Based Hierarchical Hybrid Control for Smart Microgrid , 2013, IEEE Transactions on Smart Grid.

[3]  Abdullah Abusorrah,et al.  Optimal Interconnection Planning of Community Microgrids With Renewable Energy Sources , 2017, IEEE Transactions on Smart Grid.

[4]  S. Conti,et al.  Generalized Systematic Approach to Assess Distribution System Reliability With Renewable Distributed Generators and Microgrids , 2012, IEEE Transactions on Power Delivery.

[5]  M. Prodanović,et al.  A survey of reliability assessment techniques for modern distribution networks , 2018, Renewable and Sustainable Energy Reviews.

[6]  Yuan Wu,et al.  Energy management of cooperative microgrids: A distributed optimization approach , 2018 .

[7]  Hongbin Sun,et al.  Integrated energy systems , 2016 .

[8]  Wei Xiang,et al.  Smart grid state estimation and stabilisation , 2018, International Journal of Electrical Power & Energy Systems.

[9]  Goran Andersson,et al.  THE INFLUENCE OF COMBINED POWER, GAS, AND THERMAL NETWORKS ON THE RELIABILITY OF SUPPLY , 2006 .

[10]  Iakovos Michailidis,et al.  Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage , 2016 .

[11]  Jianzhong Wu,et al.  A sequential Monte Carlo model of the combined GB gas and electricity network , 2013 .

[12]  Felix A. Farret,et al.  Integration of alternative sources of energy , 2006 .

[13]  Vasiliki Vita,et al.  Techno-Economic Assessment of Hybrid Energy Off-Grid System - A Case Study for Masirah Island in Oman , 2017 .

[14]  Siba Sankar Mahapatra,et al.  An intelligent approach to optimize the EDM process parameters using utility concept and QPSO algorithm , 2017 .

[15]  Bo Zhao,et al.  A two-stage multi-objective scheduling method for integrated community energy system , 2018 .

[16]  Chao Yang,et al.  Auction Mechanisms for Energy Trading in Multi-Energy Systems , 2018, IEEE Transactions on Industrial Informatics.

[17]  Goran Andersson,et al.  Reliability modeling of multi-carrier energy systems , 2009 .

[18]  Ozan Erdinc,et al.  Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households , 2014 .

[19]  Zhaohong Bie,et al.  Reliability evaluation of integrated energy systems based on smart agent communication , 2016 .

[20]  Mahmud Fotuhi-Firuzabad,et al.  Generalized Analytical Approach to Assess Reliability of Renewable-Based Energy Hubs , 2017, IEEE Transactions on Power Systems.

[21]  Hongjie Jia,et al.  A reliability assessment approach for the urban energy system and its application in energy hub planning , 2015, 2015 IEEE Power & Energy Society General Meeting.

[22]  Abdullah Abusorrah,et al.  Optimal Expansion Planning of Energy Hub With Multiple Energy Infrastructures , 2015, IEEE Transactions on Smart Grid.

[23]  Han Li,et al.  Optimal energy management for industrial microgrids with high-penetration renewables , 2017 .

[24]  Ramesh C. Bansal,et al.  Reliability and economic assessment of a microgrid power system with the integration of renewable energy resources , 2017 .

[25]  Peng Wang,et al.  Teaching distribution system reliability evaluation using Monte Carlo simulation , 1999 .

[26]  Boming Zhang,et al.  An Analytical Adequacy Evaluation Method for Distribution Networks Considering Protection Strategies and Distributed Generators , 2015, IEEE Transactions on Power Delivery.

[27]  Tingting Wang,et al.  An Evaluation Strategy for Microgrid Reliability Considering the Effects of Protection System , 2016, IEEE Transactions on Power Delivery.

[28]  M. Moradi-Dalvand,et al.  Optimal Design of Multicarrier Energy Systems Considering Reliability Constraints , 2015, IEEE Transactions on Power Delivery.

[29]  Scott Samuelsen,et al.  A generic microgrid controller: Concept, testing, and insights , 2018, Applied Energy.