Abstract




 
   

IJE TRANSACTIONS C: Aspects Vol. 31, No. 6 (June 2018) 1185-1193   

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  COMBINED GENETIC ALGORITHM AND MONTE CARLO SIMULATION TO WIND POWER GENERATION PLANNING IN DISTRIBUTION NETWORKS BASED ON TIME-OF-USE RATE DEMAND RESPONSE PROGRAM UNDER LOAD AND PRICE UNCERTAINTIES
 
S. Shojaabadi, M. Jokarzadeh, V. Talavat, M. Namdari and
 
( Received: December 27, 2016 – Accepted: March 02, 2018 )
 
 

Abstract    The scope of this study is the optimal planning of wind power generation (WG) in distribution networks considering demand response programs at presence of some uncertainties. A Latin hypercube sampling (LHS) at an embedded genetic algorithm (GA)-based approach is proposed in order to solve the optimization problem modeled mathematically under a general framework. The considered uncertainties include: future load growth uncertainty, output power of wind turbine and electricity market price. In this paper a mathematical model is represented to maximize the obtained total benefit for distribution system manager (DSM) from providing upstream-grid power, reducing active power losses and improving reliability in case of technical limitations as constraints, and the sitting and sizing of WG as optimization variables. This paper attempts to study the effect of demand response programs (DRP) on total benefit function, where the time-of-use rates of demand response programs have been modeled and consequently its influence on load profile has been discussed. The proposed GA with embedded LHS is used in the 9-bus network considering several scenarios. The test results demonstrate that the voltage profile and power-supply reliability for customers can be improved and the network loss is reduced, substantially.

 

Keywords    distribution system manager, wind generation, distribution network, genetic algorithm, Mont Carlo simulation, optimal planning

 




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