Integrating electric vehicle and renewable energy uncertainties in demand response programs: a two-stage stochastic framework for distribution system planning and operation
Abstract
With the continuous rise in population growth, demand consumption increases in every sphere of life. Due to the wide spread of modern cutting-edge technologies, such as electric vehicles (EVs), the daily demand of retail customers increases many folds, which results in overburdening of the distribution system (DS). This paper deals with the solution of increment in overall loading by means of application of EVs considering uncoordinated and coordinated charging. EVs are also expected to participate in vehicle-to-grid (V2G) mode to counter peak loading during demand exigencies. Moreover, with the placement of optimal renewable energy resources (RERs) at specified buses, the overall requirement of grid demand can be minimized. So, in this process, this paper proposes a two-stage stochastic framework considering uncertainty in load demand and generated power by RERs. At the first stage, the optimal renewables capacity is determined on the basis of combined retail and EV demand, and in the second stage, operational scheduling of smart distribution company (SDISCO), renewables, and EV load is done to optimize the overall DS’s performance. Moreover, the peak loading is also taken care of by means of a demand response (DR) program by offering a dynamic time-of-use (TOU) price.
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BibTeX
@article{kansal2026integrating,
title = {{Integrating electric vehicle and renewable energy uncertainties in demand response programs: a two-stage stochastic framework for distribution system planning and operation}},
author = {Gaurav Kansal and Rajive Tiwari},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-70601-7},
url = {https://www.nature.com/articles/s41598-026-70601-7},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrating electric vehicle and renewable energy uncertainties in demand response programs: a two-stage stochastic framework for distribution system planning and operation
AU - Gaurav Kansal
AU - Rajive Tiwari
PY - 2026
DA - 2026/10/05
JO - Scientific Reports
DO - 10.1038/s41598-026-70601-7
UR - https://www.nature.com/articles/s41598-026-70601-7
AB - With the continuous rise in population growth, demand consumption increases in every sphere of life. Due to the wide spread of modern cutting-edge technologies, such as electric vehicles (EVs), the daily demand of retail customers increases many folds, which results in overburdening of the distribution system (DS). This paper deals with the solution of increment in overall loading by means of application of EVs considering uncoordinated and coordinated charging. EVs are also expected to participate in vehicle-to-grid (V2G) mode to counter peak loading during demand exigencies. Moreover, with the placement of optimal renewable energy resources (RERs) at specified buses, the overall requirement of grid demand can be minimized. So, in this process, this paper proposes a two-stage stochastic framework considering uncertainty in load demand and generated power by RERs. At the first stage, the optimal renewables capacity is determined on the basis of combined retail and EV demand, and in the second stage, operational scheduling of smart distribution company (SDISCO), renewables, and EV load is done to optimize the overall DS’s performance. Moreover, the peak loading is also taken care of by means of a demand response (DR) program by offering a dynamic time-of-use (TOU) price.
SN - 2045-2322
ER -