Loading...

Maximizing the utilization of existing grids for renewable energy integration

Ranjbar, H ; Sharif University of Technology | 2022

108 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.renene.2022.03.035
  3. Publisher: Elsevier Ltd , 2022
  4. Abstract:
  5. This paper presents a new model to maximize the utilization of existing transmission system infrastructure by optimally sizing and siting the future developments of variable renewable energy sources (VRES). The model tries to maximize the integration of VRES in power systems with minimum expected energy curtailment without relying on new investments in the transmission systems. The proposed model is formulated as a linear stochastic programming optimization problem where VRES output scenarios are generated such that their spatio-temporal correlations are maintained. The Progressive Hedging Algorithm (PHA) with bundled scenarios is utilized to solve the proposed model for large-scale cases. The proposed model is tested on the modified Garver 6-bus and IEEE 118-bus test systems, and its results are compared with the results of the conventional VRES integration model. These results and comparisons illustrate the effectiveness of the proposed approach in terms of maximizing VRES integration and enhancing computational performance. © 2022 Elsevier Ltd
  6. Keywords:
  7. Copula method ; Renewable energy integration ; Electric power transmission ; Integration ; Investments ; Stochastic models ; Stochastic programming ; Stochastic systems ; Transmissions ; Correlated uncertainty ; Generation and transmission planning ; Progressive hedging algorithm ; Renewable energy integrations ; Renewable energy source ; Sources integrations ; System infrastructure ; Transmission systems ; Variable renewable energies ; Renewable energy resources ; Alternative energy ; Geogrid ; Infrastructure ; Optimization
  8. Source: Renewable Energy ; Volume 189 , 2022 , Pages 618-629 ; 09601481 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0960148122003123