Due to their considerable projected installed capacity and rapid deployment time, PEM electrolyzers hold significant potential for participation in the frequency reserve market. Although numerous studies have examined the economic benefits for electrolyzer owners in this market, a comprehensive evaluation of the economic and operational implications of utilizing these devices alongside other reserve providers from a system-wide perspective is a gap in the current literature. Such an analysis is key for guiding policymakers and must account for potential loss of power infeed and associated reserve, diverse hydrogen supply chains, reserve pricing mechanisms, and hydrogen price scenarios. Consequently, this work addresses these factors by using a Mixed Integer Second Order Cone (MISOC) multi-energy unit commitment model that incorporates frequency stability constraints. The results obtained on the GB power system demonstrate that low hydrogen prices and different reserve pricing mechanisms significantly impact system inertia, which does not pose an issue for grid stability if sufficient fast frequency reserve is available. These results further suggest that the proposed 140 GW & sdot;s inertia lower bound for the GB system may be an overestimation if the planned electrolyzer projects are realized within the next five years.
Behind-the-meter (BTM) distributed energy resources and flexible electrified loads, such as photovoltaic (PV) systems and heat pumps (HPs), are expanding rapidly, thereby increasing the complexity and uncertainty of the distribution grid. This paper proposes a probabilistic methodology to disaggregate BTM energy components based on a multivariate conditional diffusion model. Leveraging low-frequency (LF) smart meter data and conditioning signals such as irradiance and temperature, the model jointly reconstructs PV generation and HP consumption, distinguishing between domestic hot water and space heating. Evaluated on real residential data from the Netherlands, the methodology demonstrates strong deterministic and probabilistic performance, providing reliable uncertainty estimates across multiple time scales. The model exhibits robustness to reduced training sets, and the impact of seasonality on disaggregation performance is analyzed. By jointly disaggregating BTM PV generation and HP loads while quantifying uncertainty, the proposed approach provides distribution system operators with a practical tool to improve observability, planning, and flexibility management under LF metering constraints.
Respiration and ethylene biosynthesis rates change dynamically in maturing and ripening climacteric fruit. However, typically in modelling work, under low-temperature controlled atmosphere storage, the temporal changes of these metabolic processes have often been neglected. This paper proposes a simplified modelling approach to describe and integrate the temporal changes of respiration and ethylene biosynthesis during storage and subsequent shelf-life. The model starts from transcriptome levels of the involved enzymes and incorporates enzyme synthesis and degradation. The interaction between oxygen and ethylene metabolism at the pathway and signalling level is explicitly incorporated in the kinetic equations. These dynamic models of respiration and ethylene biosynthesis were calibrated using mainly shelf-life data (18 °C in regular air) obtained after storage under various conditions. The models were then validated using data from a storage experiment under standard CA (-1 °C, 3 kPa O2, 0.7 kPa CO2) and DCA (-1 °C) conditions in both laboratory and industry settings. The goodness of fit, expressed by the adjusted coefficient of determination R2adj, for the coupled model of ethylene and respiration was 0.68. Overall, the model was able to capture the dynamic pattern of respiration and ethylene biosynthesis, aligning acceptably with the measured data in shelf-life. Specifically, the exchange rate of respiratory gases generally increases and stabilises after one week in shelf-life, while the peak of the ethylene exchange rate usually shifts earlier with later shelf-life stages from different storage conditions.
Small-signal stability assessment and monitoring in converter-dominated grids can be performed using frequency-response (FR) data that can be acquired online with minimal perturbation. This paper applies complementary binary codes as excitations for simultaneous multi-input multi-output (MIMO) frequency scanning. These codes are also referred to as Golay-Rudin-Shapiro (GRS) sequences, after their inventors. We exploit their complementary autocorrelation and composite spectral flatness to derive a correlation- and cross-spectral FR estimator, and include a guard interval to capture the impulse-response tail during admittance estimation. Hadamard modulation preserves complementarity and yields orthogonal channels for MIMO identification. A unified simulation framework benchmarks GRS against the more common pseudorandom binary sequences and multisines under identical constraints, such as injection time, signal energy and realistic noise levels. Accuracy and efficiency are compared using the log-integrated normalized mean square error (NMSE) and computation time. The admittance estimation is demonstrated for three systems including different passive networks with background harmonics and a power converter. The proposed GRS approach consistently shows comparable or better metrics with less injected energy. Therefore, these features, combined with its DSP-friendly implementation and short injection time, make GRS a practical and efficient solution for online FR acquisition and system monitoring.
