Transmission Expansion Planning Using Teaching and Learning Based Optimization Approach

Jangid, Jitesh; Mehta, Aishwarya; Saxena, Akash; Shekhawat, Shalini; Kumar, Rajesh; Sharma, Ajay

doi:10.1007/978-981-33-4604-8_12

Cited by 1 publication

(1 citation statement)

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“…In [3], a bi-level evolutionary optimization using a genetic algorithm is used for a coordinated generation and TNEP through an independent system operator standpoint. The work in [4] proposes a teaching learning-based optimization method to solve the TNEP problem for two different transmission systems. In [5], Bender's decomposition approach is implemented to optimize a securityconstrained co-planning of transmission line expansion and energy storage to accommodate the penetration of renewable energy.…”

Section: Introductionmentioning

confidence: 99%

Hybrid-Adaptive Differential Evolution with Iterated Local Search for Long- Term Transmission Network Expansion Planning Optimization

Almeida,

Lezama,

Soares

et al. 2024

Preprint

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The increasing sophistication of transmission network expansion planning (TNEP) can be attributed to several evolving factors. These include the uncertain nature of renewable energy sources, the introduction of new market regulations and participants, and ongoing demand amplification in line with the increasing integration of electric vehicles and energy storage systems. The TNEP is a complex optimization problem that finds the optimal number and location of new transmission lines to satisfy system demand. In this work, we propose a hybrid-adaptive differential evolution with an iterated local search algorithm to solve this combinatorial problem and evaluate the performance of multiple acceptance criteria for solution selection in the iterative process for an 87-bus north-northeast Brazilian transmission system. Results showed that the HyDE-ILS strategies reduced the total costs by around 5% to 6% compared to HyDE on average.

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Section: Introductionmentioning

confidence: 99%