Multi-scale coordinated optimization for power and carbon emission flow in multi-energy systems

The distribution network serves as a crucial linkage between the transmission grid and power consumers, with its carbon emission intensity varying based on operational modes. Power generation units with differing energy efficiencies, emission reduction potentials, and costs, along with new energy producers and consumers, significantly reshape the power flow distribution and carbon emission distribution across branches. However, the diverse characteristics of different loads, the varying demands of power generation and consumers, and the dynamic nature of power flow changes present challenges to the coordinated optimization of power and carbon emission flow (CEF). To address these challenges, this study proposes a multi-scale coordinated optimization framework to manage unit generation and power flow within a distribution system, aimed at minimizing the overall electricity and carbon emission cost. Specifically, a mixed-integer linear programming model is first formulated by predicting real-world renewable generations and base load data. Then, the optimization framework determines the optimal power flow for various loads hourly and calculates carbon flows, including CEF intensity and emission rates. The proposed approach is validated using an enhanced IEEE-14 test bus system, and the results demonstrate that system operation costs can be minimized while maintaining the system’s stable operation.