A Generalized Framework for Optimal Sizing of Distributed Energy Resources in Micro-Grids Using an Indicator-Based Swarm Approach

In this paper, a generalized double-shell framework for the optimal design of systems managed optimally according to different criteria is developed. Optimal design is traditionally carried out by means of minimum capital and management cost formulations and does not typically consider optimized operation. In this paper, the optimized multiobjective management is explicitly considered into the design formulation. The quality of each design solution is indeed defined by the evaluation of operational costs and capital costs. Besides, the assessment of the operational costs term is deduced by means of the solution of a multiobjective optimization problem. Each design solution is evaluated using the outcomes of a multiobjective optimization run: a Pareto hyper-surface in the n-dimensional space of the operational objectives. In the literature, commonly the evaluation of each design solution is carried out based on an approximate evaluation of the operational costs, not considering the real multiobjective optimized management. In this paper, such assessment is carried out using a suitable convergence indicator typically used for multiobjective optimization algorithms. The application is devoted to the problem of optimal sizing of distributed energy resources in medium voltage or low voltage microgrids. For this problem, the identification of the multiple operational impacts comes along with the solution of the optimal unit commitment of distributed generators. After the introductory section, the problem formulation is presented and an interesting application of the considered approach to the design of distributed energy sources in a microgrid is shown.

[1]  Chandrasekhar Yammani,et al.  Optimal placement and sizing of DER's with load models using BAT algorithm , 2013, 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT).

[2]  Ratnesh K. Sharma,et al.  Optimal energy management of a rural microgrid system using multi-objective optimization , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[3]  Graham Ault,et al.  Multi-objective planning framework for stochastic and controllable distributed energy resources , 2009 .

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

[5]  Roger C. Dugan,et al.  Planning for distributed generation , 2001 .

[6]  Federico Silvestro,et al.  Management of low voltage grids with high penetration of distributed generation: concepts, implementations and experiments , 2006 .

[7]  Arindam Ghosh,et al.  Intelligent distribution planning and control incorporating microgrids , 2011, 2011 IEEE Power and Energy Society General Meeting.

[8]  Kankar Bhattacharya,et al.  Optimal planning and design of a renewable energy based supply system for microgrids , 2012 .

[9]  Debasish Ghose,et al.  Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions , 2009, Swarm Intelligence.

[10]  Arturo D. Alarcon-Rodriguez,et al.  Multi-objective planning of Distributed Energy Resources with probabilistic constraints , 2010, IEEE PES General Meeting.

[11]  N.P. Padhy,et al.  Unit commitment-a bibliographical survey , 2004, IEEE Transactions on Power Systems.

[12]  Gaetano Zizzo,et al.  A Double-Shell Design Approach for Multiobjective Optimal Design of Microgrids , 2010 .

[13]  Graham Ault,et al.  Multi-objective planning of distributed energy resources: A review of the state-of-the-art , 2010 .

[14]  Francisco Herrera,et al.  Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis , 1998, Artificial Intelligence Review.

[15]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[16]  S. Chowdhury,et al.  Operational management of CHP-based microgrid , 2010, 2010 International Conference on Power System Technology.

[17]  F. Silvestro,et al.  An Energy Resource Scheduler Implemented in the Automatic Management System of a Microgrid Test Facility , 2007, 2007 International Conference on Clean Electrical Power.

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

[19]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[20]  Abdullah Bin Humayd Distribution System Planning with Distributed Generation: Optimal versus Heuristic Approach , 2011 .

[21]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[22]  E.F. El-Saadany,et al.  Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization , 2010, IEEE Transactions on Power Systems.

[23]  Roberto Gallea,et al.  Pareto-optimal Glowworm Swarms Optimization for Smart Grids Management , 2013, EvoApplications.

[24]  Ehab F. El-Saadany,et al.  DG allocation for benefit maximization in distribution networks , 2013, IEEE Transactions on Power Systems.

[25]  Pierluigi Siano,et al.  Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow , 2012, IEEE Transactions on Industrial Informatics.

[26]  F. Pilo,et al.  A multiobjective evolutionary algorithm for the sizing and siting of distributed generation , 2005, IEEE Transactions on Power Systems.

[27]  Ashoke Kumar Basu Microgrids: Planning of fuel energy management by strategic deployment of CHP-based DERs – An evolutionary algorithm approach , 2013 .

[28]  Mehdi Ehsan,et al.  A distribution network expansion planning model considering distributed generation options and techo-economical issues , 2010 .

[29]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[30]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..