TY - JOUR
T1 - A Model for Stochastic Planning of Distribution Network and Autonomous DG Units
AU - Jooshaki, Mohammad
AU - Farzin, Hossein
AU - Abbaspour, Ali
AU - Fotuhi-Firuzabad, Mahmud
AU - Lehtonen, Matti
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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.
AB - 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.
KW - Collaborative planning
KW - Distributed generation (DG)
KW - Electricity distribution system planning
KW - Energy hub (EH)
KW - Stochastic programming
UR - http://www.scopus.com/inward/record.url?scp=85081538855&partnerID=8YFLogxK
U2 - 10.1109/TII.2019.2936280
DO - 10.1109/TII.2019.2936280
M3 - Article
AN - SCOPUS:85081538855
VL - 16
SP - 3685
EP - 3696
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
SN - 1551-3203
IS - 6
M1 - 8807219
ER -