Social spider algorithm for solving the transmission expansion planning problem

ElBages, Mohamed S.; El-Sayed, Wael T.

doi:10.1016/j.epsr.2016.09.002

Cited by 44 publications

(9 citation statements)

References 22 publications

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“…The authors show that with the integration of wind power and plug-in electric vehicle along with demand response allows for a lower investment cost in the overall planning. Relatively new techniques have also been used to solve static TNEP, such as social spider algorithm 24 and tree searching heuristic algorithm combined with GA. 25 The social spider algorithm shows its effectiveness in finding optimal solutions for various sized systems. However, as is evident from the cost convergence curves, the number of iterations required to reach the solutions are quite large, and security constraints are also not considered.…”

Section: Critical Overview Of the Existing Methodsmentioning

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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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%

Transmission network expansion planning using a modified artificial bee colony algorithm

Das

Verma

Bijwe

2017

Int Trans Electr Energ Syst

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“…The above equations manipulate a set of spiders with the number of steps toward obtaining optimal results to a given optimization problem. SSA can solve some real-world issues such as transmission expansion planning [37], railroad operation plan [38], and economic load dispatch [39]. An improvement of SSA can be found in [40], which considers the vibration triggered by trapped prey on the web.…”

Section: Firefly Algorithm (Fa)mentioning

confidence: 99%

“…In [79], PSO was also applied but to optimize the scale and location of ESS to enhance the dependability of the radial distribution hybridization system. SSA was utilized in [37] to tackle the transmission expansion planning issue, which identifies a collection of additional power lines to expand the electric grid capacity. In [39], a variant of SSA was developed to address an economic load dispatch issue, which determines the optimum scheduling of electricity generator, taking into account fuel consumption and power unit generator restrictions.…”

Section: Commercial and Industrialmentioning

confidence: 99%

Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges

Nguyen

Jung

et al. 2020

Sustainability

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Sustainable energy development consists of design, planning, and control optimization problems that are typically complex and computationally challenging for traditional optimization approaches. However, with developments in artificial intelligence, bio-inspired algorithms mimicking the concepts of biological evolution in nature and collective behaviors in societies of agents have recently become popular and shown potential success for these issues. Therefore, we investigate the latest research on bio-inspired approaches for smart energy management systems in smart homes, smart buildings, and smart grids in this paper. In particular, we give an overview of the well-known and emerging bio-inspired algorithms, including evolutionary-based and swarm-based optimization methods. Then, state-of-the-art studies using bio-inspired techniques for smart energy management systems are presented. Lastly, open challenges and future directions are also addressed to improve research in this field.

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“…Moradi et al [438] developed an algorithm technique to resolve the TEP problems. A social-spider algorithm is used by [439] to solve TEP problems. Rathore and Roy [440] introduced a STEP energy approach to discuss the impacts of plug-in electronic vehicles.…”

Section: Rajesh Et Al [349] Established a Multi-stage Mixedmentioning

confidence: 99%

Recent Updates on Energy Planning Models - A Review

Hafeez

Gulshan

Tahira

2020

Journal of Mechanical and Energy Engineering

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Energy is the most important part for community and financial growth of any country. Energy planning models play a vital role in strategy formulation and power sector progress. In this paper a systematic review and comparison of energy planning models developed and applied from 1977-2019 has been presented. The review indicates that most of the energy planning models has been presented and applied in developed nations. Only few numbers of energy planning models have been presented and applied in under developing countries. The review also shows the comparison of energy planning models applied in developed and under-developed countries. This review article will assistance the energy managers, scholars and strategy makers broadly.

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Social spider algorithm for solving the transmission expansion planning problem

Cited by 44 publications

References 22 publications

Transmission network expansion planning using a modified artificial bee colony algorithm

Transmission network expansion planning using a modified artificial bee colony algorithm

Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges

Recent Updates on Energy Planning Models - A Review

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