Open AccessUtilising reliability‐constrained optimisation approach to model microgrid operator and private investor participation in a planning horizon
Author(s) -
Hosseinnia Hamed,
Nazarpour Daryoush,
Talavat Vahid
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.5930
Subject(s) - mathematical optimization , microgrid , reliability (semiconductor) , heuristic , fuzzy logic , pareto principle , genetic algorithm , production (economics) , computer science , time horizon , operator (biology) , mathematics , economics , microeconomics , control (management) , power (physics) , gene , biochemistry , physics , chemistry , repressor , quantum mechanics , artificial intelligence , transcription factor
A huge motivation has recently made on microgrid (MG) financial issues, aimed to investigate the contribution of MG operator (MGO) and private investor to reach an optimal operational strategy. Motivating the private investors to contribute in an energy production, is a considering benefit sharing factor by MGO to satisfy both of MGO and private investor. In this study, a reliability‐constrained optimisation approach is presented to calculate the number and size of MG system components. To this aim, planning problem is solved in two cases; full available state and state with considering random outage of units. Furthermore, all uncertainties of generation units are considered in the problem formulation. Non‐sequential Monte Carlo method is used to generate all scenarios. The proposed model simultaneously optimises two objectives, namely the benefits of MGO. The two‐stage heuristic method is used to solve the objective function. In the first stage, by utilising genetic algorithm, the solution to form the Pareto optimal front is found. In the second stage, to select the trade‐off solution among obtained Pareto solutions, the fuzzy satisfying method has been used. Simulations are carried out in two cases, with and without considering the share of a private investor of MGO's benefit, i.e. β .