کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1732962 | 1521491 | 2013 | 17 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A new multi-objective for environmental and economic management of Volt/Var Control considering renewable energy resources A new multi-objective for environmental and economic management of Volt/Var Control considering renewable energy resources](/preview/png/1732962.png)
• Consider environmental and economical aspects in the Volt/Var Control problem.
• Present a Multiobjective θ-Smart Bacterial Foraging Algorithm.
• Consider the effect of Renewable Energy Sources on the VVC problem.
• Consider the electrical energy losses and the total electrical energy costs.
• Consider the total emissions of RESs.
Environmental and economical aspects of Volt/Var Control problem as well as technical issues of the networks, transform the classical problem into multiobjective one. In this paper, a Multiobjective θ-Smart Bacterial Foraging Algorithm (Mθ-SBFA) is proposed to solve the VVC problem in distribution networks including Renewable Energy Sources (RESs) involving the conflicting objectives; i.e. the electrical energy losses, the voltages deviations, the total electrical energy costs and the total emissions of RESs and substations. The proposed algorithm goes through the search space in the polar coordinates instead of the Cartesian one; whereby the feasible space is more compact. Also, several modifications are applied to restraint the premature convergence of the solutions. Hence, the bacteria in chemotaxis process are moved in short or long steps as well as swimming movement. Furthermore, replacement the least healthy bacteria are done by the strong dominated solutions reserved along the iterative search process. A new approach based on the variance of the objective functions is utilized to select the population; thereby lead to uniformly distributed the Pareto Optimal Front (POF) as well as reach to extreme points of the solutions. Niching mechanism, besides the smart population, leads the bacteria towards the lesser covered space of the POF. A fuzzy clustering technique controls the size of the repository when it gets filled. Finally, two test distribution feeders are used to assess the performance of the proposed modifications.
Journal: Energy - Volume 55, 15 June 2013, Pages 236–252