Open AccessInstalled capacity selection of hybrid energy generation system via improved particle‐swarm‐optimisation
Author(s) -
Wai RongJong,
Cheng Shan,
Lin YeouFu,
Chen YiChang
Publication year - 2014
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.2013.0092
Subject(s) - particle swarm optimization , mathematical optimization , wind power , power (physics) , computer science , backup , electricity generation , payback period , position (finance) , penalty method , automotive engineering , reliability engineering , engineering , mathematics , production (economics) , electrical engineering , physics , finance , quantum mechanics , database , economics , macroeconomics
In this study, an improved particle‐swarm‐optimisation (IPSO) method with dynamically changing inertia weight and acceleration coefficients is employed in determining the installed capacity selection of a hybrid energy generation system (HEGS). The studied HEGS, which includes wind power, photovoltaic (PV) and fuel cells, is used to suppress the penalty bill caused by exceeding the contract power capacity with the power company and to supply the backup power when needed. The objective is to achieve the optimal ratio of the installed capacity of the HEGS, so that each energy source can make the best contribution in the system, satisfy the load demand at a minimal installation cost and shorten the payback period. To realise this objective, the payback period is selected as the optimisation objective function by considering the installation cost and cost recovery. In the IPSO, the penalty technique is designed to solve the optimisation problem with equality and inequality constraints for updating the particle's position and its global best position. The proposed IPSO algorithm has been examined, tested and compared with other methods on the optimisation problem and proven to be more efficient in searching the global solution through numerical simulations of a real case.