Ryosuke Shikuma

As inverter-based renewable generation increases, load modeling from ambient measurements is important for power-system stability assessment. Under ambient conditions, voltage and frequency variations are small, whereas time-varying nominal power can substantially affect load-power variation. If nominal-power variation is not considered, it may be misattributed to voltage- and frequency-dependent load responses, resulting in biased parameter estimates. Because this misattribution depends on the variations within each time window, single-window estimation may suffer reduced accuracy. This study proposes a method that models time-varying nominal power using an autoregressive moving-average model to separate nominal-power variation from voltage- and frequency-dependent load responses and aggregates validation errors across multiple time windows to reduce dependence on any specific window. Numerical simulations under two nominal-power variation levels were compared with single-window estimation and a previously proposed sensitivity-based window-selection method. When the maximum 100 s peak-to-peak nominal-power variation was 1.0% of the mean nominal power, the proposed method achieved an overall error of 0.100, 86% lower than the mean single-window error and 79% lower than the best sensitivity-based result. Additional analyses under modified identification and data-generation conditions showed that the proposed method generally maintained comparatively low identification errors, although accuracy varied with the conditions.

The increasing penetration of inverter-based resources (IBRs) into electric power systems and the retirement of synchronous generators (SGs) both reduce system inertia and challenge frequency stability. This is particularly evident in terms of the rate of change of frequency (ROCOF) measured over short, practically measurable time windows (P-ROCOF). Conventional frequency-constrained unit commitment (FCUC) uses a theoretically idealized ROCOF (T-ROCOF) constraint that underestimates the effect of the fast frequency response (FFR) provided by virtual synchronous generators (VSGs) on P-ROCOF, leading to over-provisioning of generator capacity and higher operating costs. This paper presents a neural network-assisted FCUC (NN-UC) framework that formulates a mixed-integer ROCOF constraint based on an NNtrained to accurately predict P-ROCOF, and introduces a post-solution compensation procedure to correct NN prediction errors.We conducted one-year scenario studies on modified IEEE 9-bus and Japanese EAST 10-machine systems. Results show that NN-UC reduces both total operating cost and committed SG capacity compared to the conventional UC, which uses the T-ROCOF constraint. Even after worst-case compensation for NN’s ROCOF prediction errors, NN-UC still achieves net SG capacity reductions in most scenarios. These findings demonstrate that explicitly modeling the VSG’s FFR in FCUC yields both economic and operational benefits, while the compensation procedure ensures reliable operation.

Virtual synchronous generators (VSGs) have been developed to mitigate the increase in the rate of change of frequency (ROCOF) in power systems by replacing synchronous generators (SGs) with inverter-based resources. VSGs mimic the dynamics and control of SGs; however, the mechanical delay typical of an SG’s turbine is often excluded, limiting improvements to the VSG’s response. The fast frequency response (FFR) of VSGs can help reduce ROCOF and enhance emulated inertia. This implies that the effective inertia (EI) of VSGs can exceed the set inertia parameter, posing challenges for operators in allocating sufficient VSG capacity considering the inertia required for stable operation. In this study, we quantitatively analyzed the difference between the EI of a VSG and the set inertia parameter by separating the active power output into inertia and governor responses. The quantitative analysis revealed that when the VSG provides FFR within the inertia-time domain, the EI exceeds the set inertia parameter. Furthermore, the sensitivities of the VSG set parameters and VSG capacity ratio, which is related to synchronizing power coefficients and the initial sharing ratio, for the EI were analyzed. These factors were theoretically considered, and the simulations validated their characteristics.