Congestion management based roulette wheel simulation for optimal capacity selection: Probabilistic transmission expansion planning

Gupta, Neeraj; Shekhar, Rajiv; Kalra, Prem Kumar

doi:10.1016/j.ijepes.2012.06.046

Cited by 28 publications

(29 citation statements)

References 20 publications

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“…Similarly, we demonstrate that the test of voltage stability of transmission line follows from the joint consideration of the real and complex power flow Eqs. (1) and (2). In this apprehension, the specific possible considerations are given subsequently.…”

Section: Test For the Validity Of The Proposed Modelmentioning

confidence: 99%

“…The goal of this research paper is to advance the state-of-art in the power system reliability [1][2][3] and voltage stability [4][5][6]. It is well known that for effective power system planning, the appropriate reactive compensation [3,[6][7][8][9][10][11] is essential with a set of proper network parameters (resistance (R) and reactance (X and C)) and associated planning.…”

Section: Introductionmentioning

confidence: 99%

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Intrinsic geometric analysis of the network reliability and voltage stability

Gupta

Tiwari

Bellucci

2013

International Journal of Electrical Power & Energy Systems

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a b s t r a c tThis paper presents the intrinsic geometric model for the solution of power system planning and its operation. This problem is large-scale and nonlinear, in general. Thus, we have developed the intrinsic geometric model for the network reliability and voltage stability, and examined it for the two and three parameter systems. The robustness of the proposed model is illustrated by introducing variations of the network parameters. Exact analytical results show the accuracy as well as the efficiency of the proposed solution technique.

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Section: Test For the Validity Of The Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intrinsic geometric analysis of the network reliability and voltage stability

Gupta

Tiwari

Bellucci

2013

International Journal of Electrical Power & Energy Systems

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“…Including the above mentioned costs into a weighted graph, the shortest distance between the starting point and the termination point of a path can be determined using shortest path algorithms. If the weights assigned to the edges of the graph are costs per km of transmission line, the shortest path algorithm delivers the minimum transmission system cost between two vertices (15).…”

Section: Optimization Of Investment Time Points and Grid Topologymentioning

confidence: 99%

“…Mathematical optimization methods include linear programming [6]- [8], quadratic programming, non-linear programming [9], integer programming and mixedinteger programming [10]- [14] and their combinations. For high dimensional non-convex problems these methods become computationally expensive and have been enhanced by heuristic methods [15]- [17]. The most popular include sensitivity analysis [18], [19], genetic algorithms [20]- [25], simulated annealing [26]- [28], tabu search [29], [30] and particle swarm optimization [31], [32].…”

Section: Introductionmentioning

confidence: 99%

Stepwise Investment Plan Optimization for Large Scale and Multi-Zonal Transmission System Expansion

Ergun

Rawn

Belmans

et al. 2016

IEEE Trans. Power Syst.

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Abstract-This paper develops a long term transmission expansion optimization methodology taking the probabilistic nature of generation and demand, spatial aspects of transmission investments and different technologies into account. The developed methodology delivers a stepwise investment plan to achieve the optimal grid expansion for additional transmission capacity between different zones. In this paper, the optimization methodology is applied to the Spanish and French transmission systems for long term optimization of investments in interconnection capacity.

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“…[1][2][3][4][5][6][7][8][9][10][11][12][13]. Using these techniques, TEP has, over the years, evolved from cost-based to value-based approaches [9][10][11][12][13][14]. In the value-based approach, Min-cut-max-flow (MCMF) algorithm and load flow based curtailment strategy have been used to calculate expected demand/ energy not served (EDNS/EENS) [1,3,8,12], as reliability measures.…”

mentioning

confidence: 99%