A rolling horizon framework with social spider algorithm for solving the energy management problem of utility microgrids

This paper presents a new approach for solving the energy management problem of utility microgrids. In this approach, the rolling horizon concept is utilized to constitute a framework, in which an efficient metaheuristic technique known as the social spider algorithm is used for solving the problem. Before presenting the proposed approach, the mathematical formulation of the problem has been introduced, and the problem is formulated as a mixed-integer dynamic optimization problem. The proposed approach has been applied for solving the energy management problem of a utility microgrid with specifications obtained from the previous literature. The results obtained from using the social spider algorithm are promising.

[1]  Amin Khodaei,et al.  Provisional Microgrid Planning , 2017, IEEE Transactions on Smart Grid.

[2]  Mehdi Savaghebi,et al.  An Optimal Energy Management System for Islanded Microgrids Based on Multiperiod Artificial Bee Colony Combined With Markov Chain , 2017, IEEE Systems Journal.

[3]  Andreas Sumper,et al.  Real time experimental implementation of optimum energy management system in standalone Microgrid by using multi-layer ant colony optimization , 2016 .

[4]  W. T. Elsayed,et al.  Social spider algorithm for solving the transmission expansion planning problem , 2017 .

[5]  Ali Ahmadian,et al.  Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method , 2015 .

[6]  Victor O. K. Li,et al.  A social spider algorithm for global optimization , 2015, Appl. Soft Comput..

[7]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

[9]  Abdollah Kavousi-Fard,et al.  Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices , 2013 .

[10]  Jianhua Zhang,et al.  Optimal energy management strategy for an isolated industrial microgrid using a Modified Particle Swarm Optimization , 2016, 2016 IEEE International Conference on Power and Renewable Energy (ICPRE).

[11]  Whei-Min Lin,et al.  Energy Management Strategy for Microgrids by Using Enhanced Bee Colony Optimization , 2015 .

[12]  Ali Reza Seifi,et al.  Expert energy management of a micro-grid considering wind energy uncertainty , 2014 .

[13]  Rodrigo Palma-Behnke,et al.  A Microgrid Energy Management System Based on the Rolling Horizon Strategy , 2013, IEEE Transactions on Smart Grid.

[14]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[15]  Taher Niknam,et al.  Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel , 2011 .

[16]  Abdollah Kavousi-Fard,et al.  Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids , 2014 .

[17]  Carlos Moreira,et al.  A view of microgrids , 2019, Advances in Energy Systems.

[18]  Victor O. K. Li,et al.  A social spider algorithm for solving the non-convex economic load dispatch problem , 2015, Neurocomputing.

[19]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[20]  S. Chowdhury,et al.  Microgrids and Active Distribution Networks , 2009 .

[21]  Soodabeh Soleymani,et al.  Scenario-based stochastic operation management of MicroGrid including Wind, Photovoltaic, Micro-Turbine, Fuel Cell and Energy Storage Devices , 2014 .