Three-Stage Stochastic Modeling for Optimal Scheduling of a Flexible Virtual Power Plant Considering the Inter-Day Market of Responsive Loads

Nokandi, Ehsan; Vahedipour-Dahraie, Mostafa; Goldani, Saeed Reza; Rashidizadeh-Kermani, Homa

doi:10.22075/jme.2026.39607.2927

Three-Stage Stochastic Modeling for Optimal Scheduling of a Flexible Virtual Power Plant Considering the Inter-Day Market of Responsive Loads

Articles in Press

Document Type : Research Paper

Authors

Ehsan Nokandi ¹

Mostafa Vahedipour-Dahraie ²

Saeed Reza Goldani ²

Homa Rashidizadeh-Kermani ³

¹ Department of Industrial Economics and Technology Management, Faculty of Economics, Trondheim, Norway

² Department of Power Electrical Engineering, University of Birjand, Birjand, Iran

³ Research and Innovation,- Nowocert- Dublin- Ireland

10.22075/jme.2026.39607.2927

Abstract

This paper proposes a novel framework for the optimal bidding strategy of a flexible virtual power plant (VPP) in the day-ahead market while considering the participation of responsive loads in local energy markets. In the proposed structure, a co-opimization model for energy and reserve scheduling is developed to analyze the impact of inter-day trading of responsive loads on the VPP’s profit, taking into account the operators’ risk-aversion indices. Moreover, the effect of VPP participation in the local market on its operational behavior in other markets is examined. The results demonstrate how such participation can compensate for the imbalances caused by forecasting errors and significantly reduce the VPP’s transactions in the real-time market. The proposed model is formulated as a three-stage stochastic bi-level optimization problem and solved using a stochastic dual dynamic programming approach. Simulation results reveal that participation in the inter-day market can increase the VPP’s profit by up to 2.5%, while notably decreasing its real-time market transactions—sometimes nearly to zero.

Keywords

Flexible virtual power plant

demand response

electricity market

stochastic programming

load aggregators.

Subjects