Analysis of effect factors-based stochastic network planning model

Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependence among activity durations when more than one activity is possibly affected by the same indeterminate factors. On this basis of analysis of indeterminate effect factors of durations, the effect factors-based stochastic network planning （EFBSNP） model is proposed, which emphasizes on the effects of not only logistic and organizational relationships, but also the dependent relationships, due to indeterminate factors among activity durations on the project period. By virtue of indeterminate factor analysis the model extracts and describes the quantitatively indeterminate effect factors, and then takes into account the indeterminate factors effect schedule by using the Monte Carlo simulation technique. The method is flexible enough to deal with effect factors and is coincident with practice. A software has been developed to simplify the model-based calculation, in VisualStudio.NET language. Finally, a case study is included to demonstrate the applicability of the proposed model and comparison is made with some advantages over the existing models.

Stochastic Transmission Expansion Planning Incorporating Reliability Solved Using SFLA Meta-heuristic Optimization Technique

This paper addresses stochastic transmission expansion planning(TEP)under uncertain load conditions when reliability is taken into consideration.The main objective of the proposed TEP is to minimize the total planning cost by denoting the place,number,and type of new transmission lines subject to safe operation criteria.In this paper,the objective function consists of two terms,namely,investment cost(IC)of new lines and reliability cost.The reliability cost is incorporated as the loss of load cost(LOLC).Network uncertainties in the form of loads are molded as Gaussian probability distribution function(PDF).Monte-Carlo simulation is applied to tackle the uncertainties.The proposed stochastic TEP is expressed as constrained optimization planning and solved using shuffled frog leaping algorithm(SFLA)SFLA is compared to other optimization techniques such as particle swarm optimization(PSO)and genetic algorithms(GA).Finally,stochastic planning(planning including uncertainty)and deterministic planning(planning excluding uncertainty)are compared to demonstrate impacts of uncertainty on the results.Simulation results in different cases and scenarios verify the effectiveness and viability of the proposed stochastic TEP,including uncertainty and reliability.