Complex network algorithm and robustness of enterprise energy economic planning model under carbon neutrality goal

Against the global carbon neutrality goal, existing enterprise energy economic planning models struggle to balance multi-objective optimization and robustness. In complex dynamic environments with volatile energy prices, shifting carbon policies, and fluctuating demand, they fail to form stable trade-offs—either focusing on single-objective optimization or neglecting the energy system’s complex network and dynamic traits, leaving key energy flow paths and vulnerable links unidentified, resulting in poor scheme adaptability. To address this, this paper proposes an innovative model integrating two core innovations: an improved weighted complex network algorithm and a scenario-based robust optimization framework. The improved algorithm innovatively combines node degree centrality, edge weight analysis, clustering coefficient, and average path length, as well as a dynamic weight adjustment mechanism and multi-indicator comprehensive evaluation system, enabling accurate characterization of the energy system’s topology and dynamic features to pinpoint key paths and vulnerable links. The robust framework embeds carbon quota/trading constraints, defines uncertainty sets, and couples them with dynamic weight adjustment to realize real-time multi-objective balance. Verified via high-energy-consuming Enterprise J’s 2018–2024 data and virtual scenarios, the model reduces average energy costs by ∼6.9% and carbon emissions by 7.1% vs the baseline, with an ROI of up to 9.3%; under uncertainty, its objective function standard deviation is only 120 000 yuan. This model resolves the multi-objective-robustness contradiction and provides technical support for enterprise energy transformation and sustainable development.