Statistical analysis on results of optimal power sharing between linked microgrids

ABSTRACT The future distribution network will be made of interconnected distribution systems, so-called microgrids (MGs). MGs provide an effective means of utilising energy from small-scale renewable resources. The probabilistic power generation behaviour renewable generations and load forecasting errors are the most important uncertainties in the MG operation. The proposed methodology of this paper applies these uncertainties into the operation problem in order to find the practical solutions. So, in this research the economic operation of multi-MGs is formulated as a cost-based objective function which is minimised using particle swarm optimisation algorithm. As a result, the problem outputs must be defined by probability distribution functions (PDFs) in order to achieve comprehensive analysis of the literature. Another contribution of the paper, which rises the accuracy of the analysis in operation discussion, is applying suitable fitting criteria to select the best PDF for each obtained result based on Akaike's information criterion.

[1]  R. Iravani,et al.  Microgrids management , 2008, IEEE Power and Energy Magazine.

[2]  D. C. Taylor,et al.  Model selection and Akaike Information Criteria: An example from wine ratings and prices , 2014 .

[3]  Sajad Najafi Ravadanegh,et al.  A study on optimal power sharing in interconnected microgrids under uncertainty , 2016 .

[4]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[5]  Bijaya Ketan Panigrahi,et al.  Adaptive particle swarm optimization approach for static and dynamic economic load dispatch , 2008 .

[6]  Nima Nikmehr,et al.  Optimal operation of distributed generations in micro-grids under uncertainties in load and renewable power generation using heuristic algorithm , 2015 .

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

[8]  Sajad Najafi Ravadanegh,et al.  Heuristic probabilistic power flow algorithm for microgrids operation and planning , 2015 .

[9]  Su Ling.  Research on Economic Operation of Grid-Connected Microgrid , 2010 .

[10]  M. Shahidehpour,et al.  Microgrid Planning Under Uncertainty , 2015, IEEE Transactions on Power Systems.

[11]  Satish J. Ranade,et al.  Capacity Discovery in customer-driven micro-grids , 2009, 41st North American Power Symposium.

[12]  Wenxin Liu,et al.  Stable Multi-Agent-Based Load Shedding Algorithm for Power Systems , 2011, IEEE Transactions on Power Systems.

[13]  Peng Peng,et al.  An optimal purchase and sale power model considering microgrids , 2015 .

[14]  Sajad Najafi Ravadanegh,et al.  Reliability evaluation of multi-microgrids considering optimal operation of small scale energy zones under load-generation uncertainties , 2016 .

[15]  H J Laaksonen,et al.  Protection Principles for Future Microgrids , 2010, IEEE Transactions on Power Electronics.

[16]  T.C. Green,et al.  Fuel consumption minimization of a microgrid , 2005, IEEE Transactions on Industry Applications.

[17]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[18]  Ding Ming,et al.  Dynamic economic dispatch for microgrids including battery energy storage , 2010, The 2nd International Symposium on Power Electronics for Distributed Generation Systems.