Harmony search algorithm for transmission network expansion planning

Verma, Abhishek; Panigrahi, B. K.; Bijwe, P.R.

doi:10.1049/iet-gtd.2009.0611

Cited by 114 publications

(82 citation statements)

References 22 publications

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“…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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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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“…The idea of HS is based on mimicking the improvisation process of music players that improvise the pitches of their instruments to obtain better harmony [18]. The harmony in music is analogous to the optimization solution, and the musician's improvisations are similar to local and global search schemes in optimization methods [19].…”

Section: The Proposed Stlf Strategymentioning

confidence: 99%

“…Thus, HS has a good potential to be used as an optimization technique. Since its inception, HS has successfully been applied to several practical optimization problems [18][19][20]. However, to the best of our knowledge, no research work has so far formulated application of HS (or any modified version of it) for NN training.…”

Section: The Proposed Stlf Strategymentioning

confidence: 99%

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A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems

Amjady

Keynia

2011

Energies

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Short-term load forecast (STLF) is an important operational function in both regulated power systems and deregulated open electricity markets. However, STLF is not easy to handle due to the nonlinear and random-like behaviors of system loads, weather conditions, and social and economic environment variations. Despite the research work performed in the area, more accurate and robust STLF methods are still needed due to the importance and complexity of STLF. In this paper, a new neural network approach for STLF is proposed. The proposed neural network has a novel learning algorithm based on a new modified harmony search technique. This learning algorithm can widely search the solution space in various directions, and it can also avoid the overfitting problem, trapping in local minima and dead bands. Based on this learning algorithm, the suggested neural network can efficiently extract the input/output mapping function of the forecast process leading to high STLF accuracy. The proposed approach is tested on two practical power systems and the results obtained are compared with the results of several other recently published STLF methods. These comparisons confirm the validity of the developed approach.

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“…such as those presented in [3,25]. The key aspects of implementing a meta- In the beginning, the algorithm performs the pre-process stage described as number of added line limits (14e) and network interconnectivity (14f).…”

mentioning

confidence: 99%

A Less-Effort Method for Spatial Network Planning

et al. 2017

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This paper presents a method to solve spatial network planning (SNP) in power systems expansion. SNP is an integration of transmission expansion planning (TEP) and optimal line routing problems. In fact, SNP considers the impacts of lines routing on the network power flow. Therefore, it can exploit such impacts to attain a significant techno-economic solution. In this work, a preprocess stage is employed to reduce the computational effort demanded by line routing.Furthermore, a meta-heuristic algorithm is engaged to solve such a complicated problem. Applying these modifications made it possible to solve SNP for largescale systems with high-resolution rasterized maps. The proposed method is applied to a 3-bus system, an 18-bus test system as well as the original and modified versions of the IRAN southeast high-voltage transmission network to demonstrate the capability of the proposed methodology.

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Harmony search algorithm for transmission network expansion planning

Cited by 114 publications

References 22 publications

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

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

A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems

A Less-Effort Method for Spatial Network Planning

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