Attack-aware blockchain-based privacy-preserving synergy policy for distributed big data prediction in city green smart energy community considering cloud server and social-security welfare

This paper addresses the challenge of coordinated operation, risk management, and data security in urban multi-carrier energy systems, where energy distribution, transportation dynamics, and digital intelligence are often modeled in isolation. However, the growing penetration of mobile energy resources, cyber–physical coupling, and uncertainty have revealed structural limitations in existing frameworks, particularly with respect to social security welfare and privacy-preserving coordination. This paper presents a comprehensive City Green Smart Energy Community (CGSEC) architecture to bridge these gaps by integrating energy, mobility, and digital decision layers within a unified optimization structure. The framework combines a full multi-energy distribution network with a spatiotemporal graph-based forecasting transportation network to capture gas–electric dual-mode plug-in Vehicles mobility and its feedback on energy scheduling explicitly. A blockchain-based privacy-preserving synergy mechanism supports distributed big data prediction through cloud server coordination and digital twin synchronization, ensuring secure and scalable information exchange. Decision-making is formulated through a hierarchical game-theoretic structure that couples Stackelberg leadership, mechanism design, and general-sum Nash games, while uncertainty is handled via a two-stage scheme that integrates a scenario-based stochastic method with adaptive robust optimization using Conditional Value-at-Risk (CVaR) and p-robust constraints. Numerical studies on electricity-gas-heating-hydrogen Energy Distribution Networksdemonstrate that the proposed CGSEC reduces carbon emissions by more than 13% under robust operation compared with baseline configurations, while maintaining feasible computation times as system scale increases. Compared with CVaR, the p-robust formulation preserves higher profit levels under elevated risk settings. These results establish a unified, secure, and risk-aware framework that supports scalable, welfare-oriented operation of future urban energy communities under uncertainty.