Optimization of Islanded Microgrids

This paper studies the optimization of operation of islanded microgrids. The objective is to minimize the fuel consumption and power losses of an islanded microgrid with distributed energy resources (DERs). The optimization algorithm identifies available output of non-dispatch able DERs, then active/reactive dispatch is carried out to minimize the objectives according to fitness functions and constraints. The optimization algorithm also considers slack bus selection as it affects both line losses and running costs. Non dominant Sorting Genetic Algorithm II (NSGA-II) is used to solve the multi-objective optimization problem. The algorithm is tested on two load profiles to simulate all possible conditions.

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

[2]  E. Riva Sanseverino,et al.  Optimal set points regulation of distributed generation units in micro-grids under islanded operation , 2010, 2010 IEEE International Symposium on Industrial Electronics.

[3]  Wei-Tzer Huang,et al.  Power flow analysis of a grid-connected high-voltage microgrid with various distributed resources , 2011, 2011 Second International Conference on Mechanic Automation and Control Engineering.

[4]  Jaime Lloret,et al.  Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks , 2013 .

[5]  C. S. Edrington,et al.  Online economic environmental optimization of a Microgrid using an Improved Fast Evolutionary programming technique , 2009, 41st North American Power Symposium.

[6]  M.T. Bina,et al.  Improvement of load bus voltages considering the optimal dispatch of active and reactive powers , 2008, 2008 43rd International Universities Power Engineering Conference.

[7]  G. B. Gharehpetian,et al.  Voltage profile improvement in a microgrid including PV units using genetic algorithm , 2012, Iranian Conference on Smart Grids.

[8]  Sanjib Kumar Panda,et al.  Optimization of economic load dispatch for a microgrid using evolutionary computation , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[9]  Kyu-Ho Kim,et al.  An efficient operation of a micro grid using heuristic optimization techniques: Harmony search algorithm, PSO, and GA , 2012, 2012 IEEE Power and Energy Society General Meeting.

[10]  Heikki N. Koivo,et al.  System modelling and online optimal management of microgrid with battery storage , 2007 .

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

[12]  Rudy Setiabudy,et al.  Review of microgrid technology , 2013, 2013 International Conference on QiR.