ABSTRACT
Existing electricity networks do not have information about their endpoints due to their hierarchical structure. Internet of things technology allows two-way communication with customers. This work proposes an energy management system for optimal planning of a microgrid, considering demand response and uncertainties on the internet of things framework. The planning problem is solved using the first and the second-level Benders decomposition method. Then, the model third level is developed and optimized by genetic-fuzzy algorithm. For energy management in the internet of things platform, first the consumers are clustered based on their consumption by C-Means algorithm and then the network sensor energy consumption is optimized by genetic-fuzzy algorithm. To choose the optimal solution, a non-dominant fuzzy decision process beam is adopted. Based on the numerical results, the developed model outperforms the two-level model as well as the three-level model that uses particle swarm optimization.
Nomenclature
Disclosure statement
No potential conflict of interest was reported by the author(s).