Physics-Based Data-Driven rSOC Modeling for Power System Operation: An Interpretability-Enhanced Hybrid Neural Network Agent (Supplementary Information)

1 Abstract-- This supplementary material provides model parameters for the Case Study in the Letter to facilitate reproducibility. Index Terms-- Reversible solid oxide cell, neural network, operation region, power system operation. I. PARAMETERS IN CASE STUDY Fig. S1 presents the modified IEEE 33-node system structure used in the Case Study. Nodes 6, 18, 24, and 32 are equipped with 3 MW wind turbines (WTs) and 0.5 MWh batteries (BTs, maximum charge/discharge duration of 2 hours). The rSOC system deployed at node 10 comprises 20 stacks, each consisting of 60 cells connected in series. It is accompanied by 1.5 tons of hydrogen storage (HS, maximum charge/discharge time of 8 hours) and 60 MWh of thermal storage (TS, maximum charge/discharge time of 6 hours). The energy balance cycle for BTs and TS is intraday (24 hours), while HS operates on a weekly cycle (168 hours). The capacity factor of WTs and power load are shown in Fig. S2. Additionally, Tables SⅠ and SⅡ provide parameters of the case study and rSOC module. Fig. S1. The modified IEEE 33-bus system structure. This work was supported in part by the National Natural Science Funds for Distinguished Young Scholar (52325703), the National Natural Science Foundation of China (52407085), the Natural Science Foundation of Jiangsu Province (BK20241322), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX24_0392) (Corresponding Author: Guangsheng Pan.) Zhongfan Gu, Guangsheng Pan, Wei Gu, Qinran Hu, Linwei Sang, and Shuai Lu are with the School of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, China (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]). Hanyang Liu is with State Key Laboratory of Technology and Equipment for Defense against Power System, Operational Risks State Grid Electric Power Research Institute, Nanjing, China (e-mail: [email protected]). Fig. S2. The capacity factor of WTs and the power load. TABLE SⅠ PARAMETERS OF CASE STUDY [S1], [S2] Parameters Value Parameters Value stack N 20 LHV 33.3 kWh/kg cell N 60 tm C 10 kWh/K min T 1023 K ,min ec cell P 0.0717 kW max T 1123 K ,min ec cell P 1.6 kW avr T 1073 K ,min fc cell P 0.035 kW amb T 298 K ,max fc cell P 0.4 kW TABLE SⅡ PHYSICAL PARAMETERS OF THE RSOC MODULE [S1], [S2] Parameters Value Fuel electrode support layer (Ni-YSZ) 250 μm Electrolyte layer (YSZ) 8 μm Oxygen electrode layer (LSCF-CGO) 55 μm Effective area 414 cm2 SOEC Fuel Electrode Feed 10/90 %vol H2/H2O SOFC Fuel Electrode Feed 96/4 %vol H2/H2O rSOC Oxygen Electrode Feed Dry Air SOEC Oxygen/Fuel Electrode Flow Ratio 1 SOFC Oxygen/Fuel Electrode Flow Ratio 13 SOEC Current Density 0-2 A/cm2 SOFC Current Density 0-1.2 A/cm2 SOEC Steam Utilization 90 % H2O SOFC Hydrogen Utilization 85 % H2 rSOC Pressure 1 atm II. REFERENCES [S1] F. R. Bianchi et al., “Modelling and optimal management of renewable energy communities using reversible solid oxide cells,” Appl. Energy, vol. 334, p. 120657, Mar. 2023. [S2] C. Huang et al., “Modeling and optimal operation of reversible solid oxide cells considering heat recovery and mode switching dynamics in microgrids,” Appl. Energy, vol. 357, p. 122477, Mar. 2024. Physics-Based Data-Driven rSOC Modeling for Power System Operation: An InterpretabilityEnhanced Hybrid Neural Network Agent (Supplementary Information) Zhongfan Gu, Graduate Student Member, IEEE, Guangsheng Pan, Member, IEEE, Wei Gu, Senior Member, IEEE, Qinran Hu, Senior Member, IEEE, Linwei Sang, Member, IEEE, Shuai Lu, Member, Hanyang Liu