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Reliability-Based expansion planning studies of active distribution networks with multiagents

Kabirifar, M ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1109/TSG.2022.3181987
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2022
  4. Abstract:
  5. In this paper, a multi-agent framework is proposed to address the expansion planning problem in a restructured active distribution network. In this framework, the objective and techno-economic constraints of participating agents are addressed in the expansion planning of power network and DER assets as well as the network and DERs optimal operation management. The agents include distributed generator owners and load aggregators which participate along with the distribution network operator (DNO) in the active distribution network planning. The proposed framework is formulated as a bi-level optimization problem with multi-lower levels in which the DNO optimizes the network expansion planning and operation in the upper level where the network reliability is also modeled. In the lower level problems, other participating agents optimize the expansion planning and operation of their assets. The model addresses the optimal operation of resources by clearing the local energy market. The uncertainties in the upper and the lower level problems are effectively addressed using a stochastic adaptive robust optimization approach. The proposed model is implemented on the 54-bus and 138-bus distribution networks and the results demonstrate the effectiveness and scalability of the proposed framework. © 2010-2012 IEEE
  6. Keywords:
  7. Active distribution network ; Distribution network operator (DNO) ; Expansion planning problem ; Multi-agent ; Reliability ; Expansion ; Investments ; Multi agent systems ; Optimization ; Random processes ; Stochastic systems ; Active distribution network ; Active distributions ; Distribution network operator ; Distribution network operators ; Expansion planning problems ; Multi agent ; Robust approaches ; Stochastic adaptive robust approach ; Stochastics ; Transformer ; Uncertainty
  8. Source: IEEE Transactions on Smart Grid ; Volume 13, Issue 6 , 2022 , Pages 4610-4623 ; 19493053 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/9794335