Techno-economic based static and dynamic transmission network expansion planning using improved binary bat algorithm

[Display omitted] •Applying the IBBA for solving STNEP and DTNEP problems considering different OFs.•Proposing two modifications in the BBA to enhance the solution quality.•Applying the ANFIS to the WDN for finding the LTLF up to 2039.•Assessing the ANFIS based LTLF using standard evaluation tests.•...

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Vydané v:Alexandria engineering journal Ročník 61; číslo 2; s. 1383 - 1401
Hlavní autori: Mouwafi, Mohamed T., El-Ela, Adel A. Abou, El-Sehiemy, Ragab A., Al-Zahar, Waleed K.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.02.2022
Elsevier
Predmet:
Adaptive neuro-fuzzy inference system
Improved binary bat algorithm
Long-term load forecasting
Reactive power compensation
Transmission network expansion planning
Transmission network expansion planning
Reactive power compensation
Improved binary bat algorithm
Long-term load forecasting
Adaptive neuro-fuzzy inference system
ISSN:1110-0168
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Abstract [Display omitted] •Applying the IBBA for solving STNEP and DTNEP problems considering different OFs.•Proposing two modifications in the BBA to enhance the solution quality.•Applying the ANFIS to the WDN for finding the LTLF up to 2039.•Assessing the ANFIS based LTLF using standard evaluation tests.•Using standard and realistic networks to prove the robustness of the proposed IBBA. This paperproposes an improved binary bat algorithm (IBBA) for solving static and dynamic transmission network expansion planning (TNEP) problems for standard and realistic networks considering different objective functions (OFs).The proposed IBBA has two modifications to enhance the solution quality based on multi V-shaped transfer function and adaptive search space (ASS). The IBBAis applied to solve the static TNEP problem. A two-stage procedure is employed to solve the dynamic TNEP problem. In stage-1, the adaptive neuro-fuzzy inference system (ANFIS) is utilized to find the long-term load forecasting (LTLF) up to 2039. In stage-2, the IBBA is used to solve the dynamic TNEP problem.Two OFs are considered for solving TNEP problems. The first OF achieves the investment cost reduction. The second OF aims to minimize the total costs, which include the investment cost and the total costs of energy losses and reactive power compensation (RPC). The proposed procedure is applied to Garver’s 6-bus system and the West Delta Network (WDN) as a part of the Unified Egyptian Transmission Network (UETN) to solve TNEP problems. The obtained results are compared with other methods to show the robustness of the proposed procedure for solving TNEP problems.
AbstractList This paper proposes an improved binary bat algorithm (IBBA) for solving static and dynamic transmission network expansion planning (TNEP) problems for standard and realistic networks considering different objective functions (OFs). The proposed IBBA has two modifications to enhance the solution quality based on multi V-shaped transfer function and adaptive search space (ASS). The IBBA is applied to solve the static TNEP problem. A two-stage procedure is employed to solve the dynamic TNEP problem. In stage-1, the adaptive neuro-fuzzy inference system (ANFIS) is utilized to find the long-term load forecasting (LTLF) up to 2039. In stage-2, the IBBA is used to solve the dynamic TNEP problem. Two OFs are considered for solving TNEP problems. The first OF achieves the investment cost reduction. The second OF aims to minimize the total costs, which include the investment cost and the total costs of energy losses and reactive power compensation (RPC). The proposed procedure is applied to Garver’s 6-bus system and the West Delta Network (WDN) as a part of the Unified Egyptian Transmission Network (UETN) to solve TNEP problems. The obtained results are compared with other methods to show the robustness of the proposed procedure for solving TNEP problems.
[Display omitted] •Applying the IBBA for solving STNEP and DTNEP problems considering different OFs.•Proposing two modifications in the BBA to enhance the solution quality.•Applying the ANFIS to the WDN for finding the LTLF up to 2039.•Assessing the ANFIS based LTLF using standard evaluation tests.•Using standard and realistic networks to prove the robustness of the proposed IBBA. This paperproposes an improved binary bat algorithm (IBBA) for solving static and dynamic transmission network expansion planning (TNEP) problems for standard and realistic networks considering different objective functions (OFs).The proposed IBBA has two modifications to enhance the solution quality based on multi V-shaped transfer function and adaptive search space (ASS). The IBBAis applied to solve the static TNEP problem. A two-stage procedure is employed to solve the dynamic TNEP problem. In stage-1, the adaptive neuro-fuzzy inference system (ANFIS) is utilized to find the long-term load forecasting (LTLF) up to 2039. In stage-2, the IBBA is used to solve the dynamic TNEP problem.Two OFs are considered for solving TNEP problems. The first OF achieves the investment cost reduction. The second OF aims to minimize the total costs, which include the investment cost and the total costs of energy losses and reactive power compensation (RPC). The proposed procedure is applied to Garver’s 6-bus system and the West Delta Network (WDN) as a part of the Unified Egyptian Transmission Network (UETN) to solve TNEP problems. The obtained results are compared with other methods to show the robustness of the proposed procedure for solving TNEP problems.
Author El-Ela, Adel A. Abou
Mouwafi, Mohamed T.
Al-Zahar, Waleed K.
El-Sehiemy, Ragab A.
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Issue 2
Keywords Transmission network expansion planning
Reactive power compensation
Improved binary bat algorithm
Long-term load forecasting
Adaptive neuro-fuzzy inference system
Language English
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Snippet [Display omitted] •Applying the IBBA for solving STNEP and DTNEP problems considering different OFs.•Proposing two modifications in the BBA to enhance the...
This paper proposes an improved binary bat algorithm (IBBA) for solving static and dynamic transmission network expansion planning (TNEP) problems for standard...
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SubjectTerms Adaptive neuro-fuzzy inference system
Improved binary bat algorithm
Long-term load forecasting
Reactive power compensation
Transmission network expansion planning
Title Techno-economic based static and dynamic transmission network expansion planning using improved binary bat algorithm
URI https://dx.doi.org/10.1016/j.aej.2021.06.021
https://doaj.org/article/47e2f94dc6bd4751be7bebf33a6443c5
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linkProvider Directory of Open Access Journals
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