期刊
IEEE TRANSACTIONS ON SMART GRID
卷 13, 期 6, 页码 4610-4623出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2022.3181987
关键词
Planning; Investment; Reliability; Uncertainty; Transformers; Stochastic processes; Costs; Active distribution network; distribution network operator (DNO); expansion planning problem; multi-agent; reliability; stochastic adaptive robust approach
资金
- Iran National Science Foundation (INSF) [TSG-00179-2022]
In this paper, a multi-agent framework is proposed to address the expansion planning problem in a restructured active distribution network. The framework considers the objectives and constraints of participating agents and applies optimization methods to achieve network and asset expansion planning as well as optimal operation management.
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.
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