# An optimization scheduling model of multi-energy virtual power plants considering uncertainty constraints and multi-energy coupling characteristics

## TL;DR

This paper introduces a new model for scheduling multi-energy virtual power plants that reduces emissions and costs by integrating energy coupling and uncertainty.

## Contribution

The novel model integrates biomass, power-to-ammonia, and urea synthesis in a stochastic VPP framework with multi-energy coupling.

## Key findings

- The model reduces CO₂ emissions by 38.5% and total costs by 75.1% compared to a baseline scenario.
- Emission reductions of 30–38% are achievable through carbon trading price sensitivity adjustments.
- The model supports low-carbon energy transitions and helps meet China’s dual-carbon goals.

## Abstract

Existing research on virtual power plants (VPPs) has not fully integrated the coupling relationships among electricity, heat, hydrogen, and carbon, and scheduling strategies under uncertainty conditions remain imperfect. To address this gap, this paper proposes an optimization scheduling model for a multi-energy virtual power plant (MEVPP) that incorporates uncertainty constraints and multi-energy coupling characteristics. The proposed model integrates biomass co-combustion carbon capture power plants (BCCPP), power-to-ammonia (P2A), and low-carbon chemical production (urea synthesis) within a unified stochastic VPP scheduling framework, achieving multi-energy synergy and flexible coupled operation involving electricity, heat, hydrogen, and carbon. A scenario generation method based on Latin hypercube sampling (LHS) is adopted to formulate a stochastic scheduling model aimed at maximizing the expected total system revenue under wind and solar uncertainties. Simulation results demonstrate that compared to the baseline scenario without carbon capture, the proposed model reduces CO₂ emissions by 38.5% (from 10,000 t to 6,150 t) and total costs by 75.1% (from $800,000 to $199,200) in the optimal scenario. Carbon trading price sensitivity analysis shows that emission reductions can reach 30–38% through constraint adjustments. These findings provide practical insights for system operators and policymakers in advancing low-carbon energy transitions, particularly for China’s dual-carbon goals.