Open AccessDistributed generation allocation considering uncertainties
Author(s) -
Saric Mirza,
Hivziefendic Jasna,
Konjic Tatjana,
Ktena Aphrodite
Publication year - 2018
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2585
Subject(s) - multi objective optimization , mathematical optimization , distributed generation , voltage , pareto principle , computer science , power (physics) , genetic algorithm , electric power system , optimization problem , ac power , control theory (sociology) , engineering , mathematics , electrical engineering , control (management) , quantum mechanics , artificial intelligence , physics
Summary This paper presents a method for distributed generation (DG) allocation planning and investigates the extent to which system load and generation output uncertainties influence the final optimisation results. The problem is presented as a multiobjective constrained optimization problem in which the objective functions are power loss reduction and voltage profile improvements, while the constraints are the voltage, current, and short‐circuit power limits. The optimization is performed by using a multiobjective genetic algorithm. The best trade‐off among candidate solutions from Pareto front is achieved by using Bellman‐Zadeh method. The system load and DG power output uncertainties are addressed by using the possibilistic, α‐cut method. The proposed method is applied to a real 35 kV distribution system. It was demonstrated that appropriately planned DG allocation has potential to positively influence network losses and voltage profile. Further, it was determined that load and generation uncertainty, inherently present in the DG allocation problem, have significant impact on optimization results. This paper makes a contribution to the existing knowledge by applying, to a realistic test power system, a DG allocation method and by determining the extent to which load and DG output uncertainties influence the final optimization results.