A three-stage multi-year transmission expansion planning using heuristic, metaheuristic and decomposition techniques

Oliveira, Luiz Eduardo de; Saraiva, João Tomé; Gomes, Phillipe Vilaça; Freitas, Francisco Damasceno

doi:10.1109/ptc.2019.8810478

Cited by 1 publication

(1 citation statement)

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“…Therefore, by setting multiple target years and taking TNEP for each target year, the flexibility of the static planning method is improved to form the sequential static planning approach. In [14], to solve threestage TNEP with the objective of minimizing load shedding, an algorithm was proposed by combing the Harmony Search (HS) metaheuristic with the Branch & Bound (B&B). Although the multi-stage TNEP methods loss the perception of some changes in the system, they retain the high efficiency of static planning.…”

Section: ) Sequential Static Planning Approachmentioning

confidence: 99%

Transmission Network Dynamic Planning Based on a Double Deep-Q Network With Deep ResNet

Wang

Zhou

et al. 2021

IEEE Access

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Based on a Double Deep-Q Network with deep ResNet (DDQN-ResNet), this paper proposes a novel method for transmission network expansion planning (TNEP). Since TNEP is a large scale and mixedinteger linear programming (MILP) problem, as the transmission network scale and the optimal constraints increase, the numerical calculation and heuristic learning-based methods suffer from heavy computational complexities in calculation and training. Besides, due to the black box characteristic, the solution processes of the heuristic learning-based methods are inexplicable and usually require repeated training. By using DDQN-ResNet, this paper constructs a high-performance and flexible method to solve large-scale and complex-constrained TNEP problem. Firstly, we form a two-objective TNEP model, in which one objective is to minimize the comprehensive cost, and another objective is to maximize the transmission network reliability. The comprehensive cost takes into account the expansion cost, the network loss cost, and the maintenance cost. The transmission network reliability is evaluated by the expected energy not served (EENS) and the electrical betweenness. Secondly, from the TNEP model, the TNEP task is constructed based on the Markov decision process. By abstracting the task, the TNEP environment is obtained for DDQN-ResNet. In addition, to identify the construction value of lines, an agent is establish based on DDQN-ResNet. Finally, we perform the static planning and visualize the reinforcement learning process. The dynamic planning is realized by reusing the training experience. The validity and flexibility of DDQN-ResNet are verified on RTS 24-bus test system.

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Section: ) Sequential Static Planning Approachmentioning

confidence: 99%