This article presents a mixed-integer linear stochastic model for the optimal expansion planning of electricity distribution networks and distributed generation (DG) units. In the proposed framework, autonomous DG units are aggregated and modeled using the well-known energy hub concept. In this model, the uncertainties of heat and electricity demand as well as renewable generation are represented using various scenarios. Although this is a standard technique to capture the uncertainties, it drastically increases the dimensions of this optimization problem and makes it practically intractable. In order to address this issue, a novel iterative method is developed in this article to enhance the efficiency of the optimization model. The proposed framework is further utilized to assess the benefits of the collaborative distribution network and autonomous distributed generation planning through various case studies performed on the 24-node distribution test grid. With 5.93% cost reduction, the obtained results indicate the importance of such collaborations in reaching an efficient network expansion solution. Moreover, the total planning cost for the stochastic model is 1.23% lower than the deterministic case. Various sensitivity analyses are also carried out to investigate the impacts of parameters of the proposed model on the optimal planning solution. The scalability of the model is also assessed by its implementation on the 54-node distribution test network.
- Collaborative planning
- Distributed generation (DG)
- Electricity distribution system planning
- Energy hub (EH)
- Stochastic programming