Multi-objective optimal operation in a micro-grid considering economic and environmental goals

The optimal operation of micro-grids has attracted the attention of many developed societies considering different objectives, such as the operational cost, pollution rate, and the increasingly extensive use of renewable energy resources in this field. The aggregation of these mostly contradictory goals in an optimization problem can provide an appropriate response for the users of these systems. In this study, in order to manage energy in systems with various micro-grids that differ in quality, we aim to apply the multi-objective particle swarm optimization method to obtain the optimal distribution of energy resources in a sample micro-grid, while simultaneously satisfying economic and pollution related operational objectives.

[1]  Eleonora Riva Sanseverino,et al.  A Generalized Framework for Optimal Sizing of Distributed Energy Resources in Micro-Grids Using an Indicator-Based Swarm Approach , 2014, IEEE Transactions on Industrial Informatics.

[2]  Yan Li,et al.  Microgrid stability: Classification and a review , 2016 .

[3]  Li Xuebin,et al.  Study of Multi-objective Optimization and Multi-attribute Decision-making for Dynamic Economic Emission Dispatch , 2009 .

[4]  Sajad Najafi Ravadanegh,et al.  Optimal Power Dispatch of Multi-Microgrids at Future Smart Distribution Grids , 2015, IEEE Transactions on Smart Grid.

[5]  Claudia-Adina Dragos,et al.  Online identification of evolving Takagi-Sugeno-Kang fuzzy models for crane systems , 2014, Appl. Soft Comput..

[6]  Andreas Sumper,et al.  Experimental Validation of a Real-Time Energy Management System Using Multi-Period Gravitational Search Algorithm for Microgrids in Islanded Mode , 2014 .

[7]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[8]  Taher Niknam,et al.  Multi-operation management of a typical micro-grids using Particle Swarm Optimization: A comparative study , 2012 .

[9]  T.C. Green,et al.  Energy Management in Autonomous Microgrid Using Stability-Constrained Droop Control of Inverters , 2008, IEEE Transactions on Power Electronics.

[10]  Xiao Hong Hao,et al.  Multi-Objective Operation Optimization of a Micro-Grid Using Modified Honey Bee Mating Optimization Algorithm , 2014 .

[11]  Deqiang Gan,et al.  Environmental-economic unit commitment using mixed-integer linear programming , 2011 .

[12]  Taher Niknam,et al.  A modified shuffle frog leaping algorithm for multi-objective optimal power flow , 2011 .

[13]  Bahman Taheri,et al.  Economic dispatch in a power system considering environmental pollution using a multi-objective particle swarm optimization algorithm based on the Pareto criterion and fuzzy logic , 2017 .

[14]  Edwin Lughofer,et al.  On-line assurance of interpretability criteria in evolving fuzzy systems - Achievements, new concepts and open issues , 2013, Inf. Sci..

[15]  Mohsen Eskandari,et al.  Operational Strategy Optimization in an Optimal Sized Smart Microgrid , 2015, IEEE Transactions on Smart Grid.

[16]  Mariesa L. Crow,et al.  Multi-Objective Dynamic Economic Dispatch with Demand Side Management of Residential Loads and Electric Vehicles , 2017 .

[17]  Ken Nagasaka,et al.  Multiobjective Intelligent Energy Management for a Microgrid , 2013, IEEE Transactions on Industrial Electronics.

[18]  Zhao An,et al.  Multi-objective optimization of a low specific speed centrifugal pump using an evolutionary algorithm , 2016 .

[19]  Amjad Anvari-Moghaddam,et al.  Optimal energy management of a micro-grid with renewable energy resources and demand response , 2013 .

[20]  Bangyin Liu,et al.  Smart energy management system for optimal microgrid economic operation , 2011 .

[21]  Dejan Dovzan,et al.  Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process , 2015, IEEE Transactions on Fuzzy Systems.

[22]  Kannan Subramanian,et al.  Environmental and economic power dispatch of thermal generators using modified NSGA-II algorithm , 2015 .