Privacy-preserving mechanism for collaborative operation of high-renewable power systems and industrial energy hubs

Nowadays, achieving operational solutions to boost the flexibility of bulk power systems has become one major challenges in both industry and academia. The outcomes recent studies demonstrate that deployment large-scale energy hubs can help enhance systems. However, centralized management networked may not be compatible with system operator when they are managed by private owners. Motivated this observation, a privacy-preserving decision-making structure is proposed paper for collaborative operation industrial renewable considering high penetration sources. drawn up based on decentralized two-stage robust–stochastic approach solved using Benders decomposition algorithm relying ownership various entities. main objectives study lie (1) decreasing curtailment (2) minimizing total costs To achieve these objectives, effects multi-energy demand response program conversion facilities investigated context developed model. competency robustness examined IEEE 30-bus test GAMS DIgSILENT PowerFactory software. Results show if successfully deployed parks, cost decreases 16.33%, reduces 92.9%, enhances increasing spinning reserve.