Open AccessIncorporating the effects of service quality regulation in decision‐making framework of distribution companies
Author(s) -
Jooshaki Mohammad,
Abbaspour Ali,
FotuhiFiruzabad Mahmud,
MoeiniAghtaie Moein,
Lehtonen Matti
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.2018.6141
Subject(s) - incentive , reliability (semiconductor) , revenue , computer science , heuristic , service (business) , set (abstract data type) , operations research , mathematical optimization , anticipation (artificial intelligence) , term (time) , dynamic programming , reliability engineering , engineering , business , economics , microeconomics , mathematics , algorithm , artificial intelligence , power (physics) , physics , accounting , quantum mechanics , programming language , marketing
Incentive regulations of reliability have made a link between distribution companies' revenue and their service reliability. The companies have to decide how much to spend on various projects to provide an acceptable level of reliability while anticipation of load growth. Planners and decision makers require a comprehensive framework to optimally allocate available budgets to different plans with the highest benefits considering implementation of incentive regulation. This paper proposes a decision framework for the optimum share of expansion and reliability oriented plans in presence of reward–penalty mechanisms. A two‐layer optimization model is introduced, where in the outer layer, an iterative algorithm is applied to determine the optimal set of long‐term projects including Distributed Generations (DGs) installation. A heuristic optimization algorithm is employed in this layer. Considering long‐term plans, in inner optimization layer, the optimal set of mid‐term plans including feeder reinforcement, and preventive maintenance actions are determined using algorithms such as Branch‐and‐Cut and dynamic programming techniques. The model is further implemented on a test distribution network and the results are investigated through various case studies. Obtained results show the strong influence of incentive regulation on reliability indices.