Long-term transmission system expansion planning with multi-objective evolutionary algorithm

Sousa, Aldir Silva; Asada, Eduardo N.

doi:10.1016/j.epsr.2014.09.013

Cited by 25 publications

(16 citation statements)

References 15 publications

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“…Static long-term TNEP is solved by an enhanced NSGA-II algorithm in 1 study 20 and by strength Pareto evolutionary algorithm 2 in previous study. 21 Both of these methods produce several Pareto-optimal solutions, which are then considered by a decision maker on the basis of their merits. In 1 study, 22 authors have used adaptive multi-operator evolutionary algorithm to solve contingency constrained TNEP problems.…”

Section: Critical Overview Of the Existing Methodsmentioning

confidence: 99%

“…These multiple generation scenarios are governed by the availability of generators, weather conditions, fluctuation of fuel prices, etc. Static long‐term TNEP is solved by an enhanced NSGA‐II algorithm in 1 study and by strength Pareto evolutionary algorithm 2 in previous study . Both of these methods produce several Pareto‐optimal solutions, which are then considered by a decision maker on the basis of their merits.…”

Section: Introductionmentioning

confidence: 99%

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Transmission network expansion planning using a modified artificial bee colony algorithm

Das

Verma

Bijwe

2017

Int Trans Electr Energ Syst

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Summary Transmission network expansion planning (TNEP) problem is an essential part of power system expansion planning, and it is an extremely complex nonlinear, nonconvex, mixed‐integer optimization problem. Solution to such a computationally intensive problem is a challenge for any optimization algorithm. Consideration of security constraints makes the problem even more formidable. Although various conventional and metaheuristic methods have been used in the past to solve such problem, scope for better optimization techniques always remain. The artificial bee colony (ABC) algorithm is one of the newest swarm intelligence‐based optimization algorithms, which has delivered promising results in solving numerical optimization problems. However, the algorithm is quite less efficient in solving real‐life constrained engineering problems. In this paper, a modified ABC (MABC) algorithm is formulated by incorporating the idea of global attraction, universal gravitation, and by introducing modified ways of searching in various bees' phases of the ABC algorithm. The MABC is able to get better results in a very efficient manner, when used for solving various benchmark functions. The efficiency and effectiveness of the MABC algorithm in solving constrained engineering problems is demonstrated by solving TNEP problems for different systems. The proposed method is tested on IEEE 24 bus system, South Brazilian 46 bus system, Colombian 93 bus system for direct current TNEP model, and Garver 6 bus system for alternating current TNEP model. Results confirm that MABC can be an attractive alternative to the existing optimization algorithms for solving very complex nonlinear engineering optimization problems in a real‐world situation.

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Section: Critical Overview Of the Existing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transmission network expansion planning using a modified artificial bee colony algorithm

Das

Verma

Bijwe

2017

Int Trans Electr Energ Syst

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“…On the basis of the sorting results, tournament selection, single-point crossover and mutation are implemented to generate offspring. In this paper, the mutation manipulation is to add or remove a line for a selected right-of-way with 50/50 probability [5,6].…”

Section: The Initialization Of Populationmentioning

confidence: 99%

“…The parameters are set as follows: the range of cluster number is set to [2,500]; the repetition s n for cluster number analysis is set at 40, the repetition s m for scenario sampling is set at 5000; penalty factor β is set as 10 6 $/MW; the population number is set as 60 based on [5,8]; the maximum number of iterations is set as 1500, which is a relatively large value to guarantee the optimality of the Pareto front. Wind Farm Figure 5.…”

Section: Case Descriptionmentioning

confidence: 99%

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Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

Wang

2015

Applied Sciences

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This paper presents a flexible transmission network expansion planning (TNEP) approach considering uncertainty. A novel hybrid clustering technique, which integrates the graph partitioning method and rough fuzzy clustering, is proposed to cope with uncertain renewable generation and load demand. The proposed clustering method is capable of recognizing the actual cluster distribution of complex datasets and providing high-quality clustering results. By clustering the hourly data for renewable generation and load demand, a multi-scenario model is proposed to consider the corresponding uncertainties in TNEP. Furthermore, due to the peak distribution characteristics of renewable generation and heavy investment in transmission, the traditional TNEP, which caters to rated renewable power output, is usually uneconomic. To improve the economic efficiency, the multi-objective optimization is incorporated into the multi-scenario TNEP model, while the curtailment of renewable generation is considered as one of the optimization objectives. The solution framework applies a modified NSGA-II algorithm to obtain a set of Pareto optimal planning schemes with different levels of investment costs and renewable generation curtailments. Numerical results on the IEEE RTS-24 system demonstrated the robustness and effectiveness of the proposed approach.

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Transmission expansion planning of large power networks via constructive metaheuristics with security constraints and load uncertainty analysis

Assis

Silva

Manso

et al. 2020

Int Trans Electr Energ Syst

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Long-term transmission system expansion planning with multi-objective evolutionary algorithm

Cited by 25 publications

References 15 publications

Transmission network expansion planning using a modified artificial bee colony algorithm

Transmission network expansion planning using a modified artificial bee colony algorithm

Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

Transmission expansion planning of large power networks via constructive metaheuristics with security constraints and load uncertainty analysis

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