Parallel simulated annealing applied to long term transmission network expansion planning

Gallego, Ramón Alfonso; Alves, Alexandre; Monticelli, A.; Romero, Rubén

doi:10.1109/59.574938

Cited by 127 publications

(65 citation statements)

References 14 publications

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“…(5) and (6) respectively; and for VAr sources by (7) while voltage magnitudes are restricted by (8). Capacity limits (MVA) of the line flows are presented by (9) and (10), while capacity constraints of the newly added circuits are shown by (11). The costs of VAR sources can be defined as follows:…”

Section: Teprpp Mathematical Modelmentioning

confidence: 99%

See 1 more Smart Citation

A New Model for Transmission Network Expansion and Reactive Power Planning in a Deregulated Environment

Mahmoudabadi¹,

Rashidinejad²,

Zeinaddini-Maymand³

2012

ENG

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Transmission network expansion planning (TNEP) is a challenging issue especially in new restructured electricity markets environment. TNEP can be incorporated with reactive power planning in which the operating conditions will be satisfied. In this paper a combinatorial mathematical model has been presented to solve transmission expansion and reactive power planning problem (TEPRPP) simultaneously. The proposed model is a non-convex problem having a mixed integer nonlinear nature where the number of candidate solutions to be evaluated increases exponentially according to the system size. The objective function of TEPRPP comprises the new circuits' investment and production costs as well as load curtailment penalty payments. A real genetic algorithm (RGA) aimed to obtaining a significant quality solution to handle such a complicated problem has been employed. An interior point method (IPM) is applied to solve the proposed concurrent optimization problem in the solution steps of TEPRPP model. This paper proposes a new methodology for the best location as well as the capacity of VAr sources; it is tested on two well-known systems; the Garver and IEEE 24-bus systems. The obtained results show the capability and the viability of the proposed TEPRPP model incorporating operating conditions.

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Section: Teprpp Mathematical Modelmentioning

confidence: 99%

“…Various optimization techniques have been used to solve such a crucial problem. These methods are classified as classical optimization techniques [7,8] as well as meta-heuristics such as Simulated Annealing [9], Genetic Algorithms [10], Tabu Search [11] and Greedy Randomized Adaptive Search Procedure (GRASP) [12].…”

Section: Introductionmentioning

confidence: 99%

A New Model for Transmission Network Expansion and Reactive Power Planning in a Deregulated Environment

Mahmoudabadi¹,

Rashidinejad²,

Zeinaddini-Maymand³

2012

ENG

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show abstract

“…In the recent period, Lattore et al [13] [32][33][34], disjunctive mixed integer programming [35], branch and bound algorithm [36], implicit enumeration [37,38], Benders decomposition [12, 39,40], maximum flow [12], hierarchical decomposition [41], sensitivity analysis [42,43], genetic algorithm (GA) [44][45][46][47][48][49], object-oriented programming [50], game-theory [51][52][53][54], simulated annealing [55,56], expert systems [57,58], fuzzy set [49,59,60], greedy randomised adaptive search [61], non-convex optimisation [62], tabu search [63], ant-colony [64], data-mining [65], particle swarm optimisation (PSO) [66][67][68][69], harmony search [70,71], artificial neural network (ANN) [72], game theory [73], and robust optimisation techniques [74][75]…”

Section: Grid Developmentmentioning

confidence: 99%

Time‐horizons in the planning and operation of transmission networks: an overview

Khuntia

Tuinema

Rueda

et al. 2016

IET Generation, Transmission & Distribution

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In the planning and operation of power systems, actions are taken in different processes and time-horizons. The purpose of these actions is to secure a high reliability level. Although the three main processes (grid development, asset management, and system operation) are described in literature, there has been no explicit study on the time-horizons (long-term, mid-term, and short-term) and actual time-scale (decades, years, months, etc.) that these processes focus on. This study aims at making a review of the various activities performed by transmission system operators while reviewing the concept of each time-horizon and methodologies developed in literature. As decisions taken in different time-horizons can influence each other, the interactions and overlapping are discussed.

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“…Transmission network expansion planning (TNEP) is one of the important decisionmaking activities in electric utilities. It determines the characteristic and future electric power network [12] [13]. The planner has to estimate the most economic network which feeds the loads with the required degree of quality and to minimize construction and operational cost, while meeting technical and reliability constrains [3][4][5].TNEP should be satisfy the required adequacy of lines for delivering safe and reliable electric power to load centre However bacterial foraging can find multiple solutions [2] [14] in one single simulation run due to their population based search based approach.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Optimization for Transmission Network Expansion Planning using Modified Bacterial Foraging Technique

Jaganathan¹,

Palaniswami²,

Kumar³

2010

IJCA

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Multi-objective optimisation is a proven well known parameter tuning technique in complex power system problems. It is especially suited to solve complex transmission network expansion planning. This paper proposes a practical method for transmission network expansion planning by bacterial foraging technique. The electricity industry has always been interested in expanding investment in the transmission sector of the industry. As load demand increases and generation expands to meet the need, transmission expansion becomes important in order to increase social welfare by reducing total system operating cost, and to make the system more reliable. In this context, two objectives: investment cost and network adequacy restrictions are considered to overcome the drawbacks of conventional mathematical optimization method in arriving at local optimum and dimension disasters, we introduced the bacterial foraging technique into transmission network optimal planning for the first time, from which the optimal scheme is generated. The bacterial foraging is used as the optimization tool to obtain the Pareto approximation set solutions. The proposed algorithm is implemented on typical IEEE 6 bus systems and performance is assessed by statistical test.

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Parallel simulated annealing applied to long term transmission network expansion planning

Cited by 127 publications

References 14 publications

A New Model for Transmission Network Expansion and Reactive Power Planning in a Deregulated Environment

A New Model for Transmission Network Expansion and Reactive Power Planning in a Deregulated Environment

Time‐horizons in the planning and operation of transmission networks: an overview

Multi Objective Optimization for Transmission Network Expansion Planning using Modified Bacterial Foraging Technique

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