Optimizing integrated energy systems with a virtual energy station framework: Exergy-based scheduling and multi-energy integration

Key findings from this study

• The study found that the Virtual Energy Station framework reduces operational costs by improving exergy utilization across integrated electricity, heat, gas, and hydrogen systems.
• The researchers demonstrate that the Improved Walrus Optimization Algorithm achieves convergence 27% faster than standard implementations while maintaining solution stability.
• The authors report that the framework maintains profit prediction accuracy within 3.6% mean absolute percentage error despite uncertainties in renewable generation and market prices.

Overview

This study develops a Virtual Energy Station framework that integrates electricity, heat, natural gas, and hydrogen within a unified bi-level exergy-based optimization model. The approach addresses limitations in conventional Virtual Power Plant designs by employing equal-exergy representation of multi-energy flows and stochastic scheduling. An Improved Walrus Optimization Algorithm serves as the solution engine, incorporating adaptive search dynamics and chaotic parameter tuning.

Methods and approach

The framework combines bi-level exergy-based optimization with stochastic scheduling to account for uncertainties in renewable generation, demand fluctuations, and day-ahead market prices. The methodology integrates an Energy Quality Coefficient for evaluating multi-energy interactions and optimal energy conversion pathways. Simulation testing occurs on a coupled IEEE 33-bus electrical distribution system paired with a 6-node gas network to validate performance under realistic conditions.

Results

The VES framework reduced operational costs while improving exergy utilization and load flexibility compared to baseline approaches. Realized profits deviated from expectations by less than 4% (mean absolute percentage error approximately 3.6%), demonstrating prediction accuracy under uncertainty. The Improved Walrus Optimization Algorithm achieved convergence in 27% fewer iterations relative to the standard algorithm. Solution variance across independent runs decreased by approximately 50%, indicating enhanced stability and reproducibility.

Implications

The framework addresses fundamental gaps in multi-energy system management by moving beyond electricity-centric design paradigms. By unifying diverse energy carriers within a single optimization model, the approach enables more efficient resource allocation across coupled infrastructure systems. The demonstrated computational efficiency gains suggest feasibility for real-time or near-real-time operational scheduling in complex integrated energy systems.

The Energy Quality Coefficient integration provides a mechanism for systematically evaluating trade-offs between different energy conversion pathways. This metric-based approach facilitates transparent decision-making regarding energy mix optimization across multiple sectors. The exergy-based formulation captures thermodynamic quality differences that conventional energy balance methods overlook.

The robustness metrics reported (low deviation, rapid convergence, reduced variance) establish quantitative performance benchmarks for future algorithm development in this domain. These results validate the use of metaheuristic optimization for multi-energy scheduling problems at realistic system scales. The framework's performance characteristics position it as a viable tool for operators managing geographically distributed energy infrastructure.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.