Transmission System Expansion Planning Using a Sigmoid Function to Handle Integer Investment Variables

Oliveira, Edimar J. de; Silva, I.C. da; Pereira, J.L.R.; Carneiro, S.

doi:10.1109/tpwrs.2005.852065

Cited by 77 publications

(34 citation statements)

References 22 publications

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“…Most of the heuristic methods are based on the selection of candidate transmission lines in association with their sensitivity indices which reflect the ability to alleviate overloading problems [3], [4]. In general, it continuously selects a number of new candidate lines so that the system is brought into a safe state.…”

Section: Heuristic Methods For Tsepmentioning

confidence: 99%

Transmission system expansion planning with consideration of N-1 security constraints

Asadamongkol

Eua‐arporn

2009

2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolog

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This paper presents a heuristic method for solving transmission system expansion problems (TSEP) with consideration of N-1 security constraints. The method is divided into two phases. An initial plan is established in the first phase by a search process which is based on a modified simplex method and sensitivity indices. The second phase starts from the initial plan and performs the local search in the defined neighborhood. Additionally, reconstruction of the new lines on the existing right of ways, which is one of the interesting issues of TSEP in the urban area, is taken into account. The proposed method provides very satisfactory results compared with others. KEYWORDSheuristic method, N-1 security cons traint, simplex method, transmission system expansion planning.

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Section: Heuristic Methods For Tsepmentioning

confidence: 99%

Transmission system expansion planning with consideration of N-1 security constraints

Asadamongkol

Eua‐arporn

2009

2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolog

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“…Later works also deploy other optimization methods, such as mixed-integer linear programming (Alguacil et al (2003), de la Torre et al (2008)), Benders decomposition (Binato et al (2001)) or heuristic methods (de Oliveira et al (2005)). …”

Section: Authors Simeon Hagspiel (Ewi)mentioning

confidence: 99%

Cost-optimal power system extension under flow-based market coupling

Hagspiel

Jägemann

Lindenberger

et al. 2014

Energy

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract Electricity market models, implemented as dynamic programming problems, have been applied widely to identify possible pathways towards a cost-optimal and low carbon electricity system. However, the joint optimization of generation and transmission remains challenging, mainly due to the fact that different characteristics and rules apply to commercial and physical exchanges of electricity in meshed networks. This paper presents a methodology that allows to optimize power generation and transmission infrastructures jointly through an iterative approach based on power transfer distribution factors (PTDFs). As PTDFs are linear representations of the physical load flow equations, they can be implemented in a linear programming environment suitable for large scale problems. The algorithm iteratively updates PTDFs when grid infrastructures are modified due to cost-optimal extension and thus yields an optimal solution with a consistent representation of physical load flows. The method is first demonstrated on a simplified three-node model where it is found to be robust and convergent. It is then applied to the European power system in order to find its cost-optimal development under the prescription of strongly decreasing CO 2 emissions until 2050. Terms of use: Documents in

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“…The investment problem is solved by an evolutionary method, while the generation problem is solved by a known optimization method. In prior studies [3][4][5][6][7][8][9][10][11], researchers have solved the same problem using the evolutionary method by sensitivity analysis. In each step of the algorithm, the sensitivity index is used for determining the added circuits (lines).…”

Section: Introductionmentioning

confidence: 99%

“…In each step of the algorithm, the sensitivity index is used for determining the added circuits (lines). The sensitivity index can be generated based on the algorithm implemented in an electrical system like minimum depletion [3], load feeding [4], lowest criteria [6], a lighter version of its own mathematical model [5,7,8], or the optimal load flow [9,10]. In most models, the internal point method is used for solving the linear or non-linear planning problem in each iteration.…”

Section: Introductionmentioning

confidence: 99%

Transmission Expansion Planning Using TLBO Algorithm in the Presence of Demand Response Resources

Zakeri

Abyaneh

2017

Energies

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Transmission Expansion Planning (TEP) involves determining if and how transmission lines should be added to the power grid so that the operational and investment costs are minimized. TEP is a major issue in smart grid development, where demand response resources affect short-and long-term power system decisions, and these in turn, affect TEP. First, this paper discusses the effects of demand response programs on reducing the final costs of a system in TEP. Then, the TEP problem is solved using a Teaching Learning Based Optimization (TLBO) algorithm taking into consideration power generation costs, power loss, and line construction costs. Simulation results show the optimal effect of demand response programs on postponing the additional cost of investments for supplying peak load.

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Transmission System Expansion Planning Using a Sigmoid Function to Handle Integer Investment Variables

Cited by 77 publications

References 22 publications

Transmission system expansion planning with consideration of N-1 security constraints

Transmission system expansion planning with consideration of N-1 security constraints

Cost-optimal power system extension under flow-based market coupling

Transmission Expansion Planning Using TLBO Algorithm in the Presence of Demand Response Resources

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