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Operating reserves (OR), alongside other ancillary services, are essential for mitigating the increasing uncertainty in generation from Intermittent Renewable Energy Sources (IRES). Traditional OR are determined based on rules of thumb, such as reliability considerations (N − 1 or N − k), and they neglect the uncertainty between dispatch intervals. Load–generation imbalances lead to excessive reliance on regulation reserves and out-of-merit dispatch. Highlighting this, recent literature has focused on developing models for dynamic reserve quantification. While the models significantly enhance the traditional static reserve quantification, they heavily rely on probabilistic approaches. Despite their effectiveness, probabilistic approaches are computationally complex and necessitate precise historical data. Furthermore, most studies do not consider the correlation between load and IRES when determining reserves, leading to inaccurate estimation and impacting the system's economics. Affine arithmetic-based models emerge as a promising solution, offering the ability to estimate correlated uncertainties with reduced computational complexity. This paper contributes to developing affine arithmetic models for OR quantification, considering IRES and load correlated uncertainties. Two distinct approaches to reserve quantification are explored: (1) deterministic scheduling with exogenous OR quantification, and (2) affine arithmetic-based scheduling framework to quantify and allocate OR endogenously. A comparative analysis is conducted with probabilistic scenario-based method and interval arithmetic scheduling. The effectiveness of the models is analyzed on the Great Britain test system with 40% renewable integration. Numerical results highlight that around 99% of the probabilistic net-load scenarios are within the net-load bounds generated by the proposed methodology. The reserve requirement is minimized by approximately 8% during peak hours and 35% during off-peak hours with correlated uncertainty. Furthermore, AA-based approach achieved a 15% reduction in total operating cost during the considered operational time-frame, compared to interval arithmetic optimization. • A multi-objective optimization model for reserve quantification and allocation. • Affine arithmetic based correlated uncertainties of renewable power and load. • Exogenous and endogenous methods for reserve assessment are presented. • Trade-offs between computational time and cost are deciding factors. [Display omitted] [ABSTRACT FROM AUTHOR]

State-estimation plays a vital role to monitor, observe and understand the combined heat and electric network. In this paper, a hybrid framework is presented to accurately estimate the system states of electric distribution network and heat network, using the limited non-redundant measurements obtained from supervisory control and data acquisition and advanced metering infrastructure systems. The presented hybrid framework involves two steps, namely, the state-forecasting and the state-estimation. The state-forecasting uses a deep neural network to forecast the system states at every fifteen minutes interval, while these forecasted states are further used by the hybrid estimator, which uses a robust extended Kalman filter to estimate the system states with help of both datasets corresponding to supervisory control and data acquisition and advanced metering infrastructure systems, at hourly interval. The proposed framework does not completely rely on the system model at different instants. The effectiveness of the method is validated through thorough comparisons with simulation studies carried out using the Barry Island test system, United Kingdom. Satisfactory performance is observed even with the presence of bad data in the measurements. • A framework to estimate states of electric and heat networks with SCADA and AMI data • A method of forecasting the CHEN states using only a limited SCADA dataset • A robust Kalman filter for accurate estimation under gross errors in data. • Test results that validate the method on Barry Island CHEN system. • Comparative results over the state-of-art WLS and ANN based estimators. [ABSTRACT FROM AUTHOR]

• Analytical method for estimating the size of a loss-of-generation disturbance. • Investigates a linear relationship between power and frequency deviations of CCGTs. • Improves disturbance size estimation accuracy with CCGT units' negative droop gain. • Estimates the disturbance size for pre-and post-event conditions. • Provide an approach that computes deviations in post-event demand-side consumption. With the substantial popularity of combined cycle gas turbine (CCGT) power plants in the nowadays power systems, special care must be taken to regulate frequency due to unique frequency response characteristic of the full-loaded CCGT units. This unique feature is documented in the literature; however, its effect on determining frequency response of the power systems was not addressed in detail. This study proposes a new analytical method to achieve a more accurate estimated size of a loss-of-generation disturbance. This method considers demand-side power deviations and transmission lines power loss as well as unique frequency response of the CCGT units following the event. Firstly, it is exposed that there is an approximately linear relationship between power and frequency deviations of these plants in a real-world power system despite the complexity of the CCGT model. This relationship may be represented by a negative droop gain. Next, the derived CCGT's linear characteristic is formulated in the disturbance size estimation process. Finally, the effectiveness of the proposed modifications is demonstrated through extensive simulations on a 36-zone Great Britain equivalent test system. [ABSTRACT FROM AUTHOR]

• EC-Earth large ensembles are used to drive hydrological models of catchments in Great Britain. • Estimates show the changing chance of unprecedented high temperatures and low rainfall. • Climate storylines describe changes in future dry spring-summers, autumn-winters and multiple dry winters. • Storylines can be used to stress test hydrological systems and inform decision-making. The UK has experienced recurring hydrological droughts in the past and their frequency and severity are predicted to increase with climate change. However, quantifying the risks of extreme droughts is challenging given the short observational record, the multivariate nature of droughts and large internal variability of the climate system. We use EC-Earth time-slice large ensembles, which consist of 2000 years of data each for present-day, 2°C and 3°C conditions relative to pre-industrial, to drive hydrological models of river catchments in Great Britain (GB) to obtain a large set of plausible droughts. Since future warming is certain, the uncertainty in drought is mainly associated with uncertainty in precipitation. Estimates of unprecedented extremes show that the chance of a summer month in a given year drier than the observed driest summer (1995) is projected to increase with future warming (from 9% in the present-day (PD) to 18% in a 3°C warmer world (3C) for southeast England). For winter, the chance of a dry winter month drier than the observed driest winter (1991–92) slightly decreases (from 10% - PD to 8% − 3C for southeast England) but the chance of the driest winter does not change significantly with future warming. We add value to these probabilistic estimates by sampling for physical climate storylines of drought sequences characterised by dry spring-summers, autumn-winters and consecutive dry winters. Dry spring-summers are estimated to become drier with future warming primarily driven by reduced precipitation in summer. Dry autumn-winters may become wetter mainly driven by the general trend of more precipitation in winter although drought conditions triggered by moderate autumn–winter precipitation deficits may worsen given the higher likelihood of being followed by a dry summer. Similarly, drought impacts of consecutive dry winters, a particular risk for slow-responding catchments in the English Lowlands, may worsen with future warming as the intervening summer is projected to become hotter and drier. These storylines can be used to stress-test hydrological systems and inform decision-making. [ABSTRACT FROM AUTHOR]

• Proposes a modified start and end-time netload uncertainty based multi-interval FRP design. • FRPs are modeled from energy storage systems, demand response and conventional units. • Flexibility constrained scheduling co-optimizes FRPs with energy and ancillary services. • Netload scenarios generated using Markov Chain Monte Carlo technique. • 15%--20% reduction in FRP procurement with proposed FRP design. Flexible Ramp Products (FRPs) can address netload variability and uncertainty caused by increasing renewable energy integration. They secure additional ramping capability to follow netload variations of upcoming dispatch intervals. The challenges in current practice of FRP procurement are - 1) Neglecting start-time uncertainty of netload results in inaccurate ramp requirement estimation 2) Inadequate ramping capacity in real-time market for FRP provision leads to the commitment of out-of-merit units 3) Priority in dispatch of renewable energy sources de-commits Conventional Generation (CG) units, thus reducing available ramping capacity. Hence, scheduling FRPs in day-ahead market from CG units and flexible resources along with an appropriate estimation of FRP requirement is essential to ensure ramping sufficiency. In this context, the paper proposes a modified start-time and end-time netload uncertainty based FRP design and a two-stage scheduling framework to integrate FRPs in day-ahead market. Further, system-wide benefits of integrating flexible resources, FRPs and improved operations are highlighted. The proposed framework is simulated on IEEE RTS-24 and reduced Great Britain test systems and proficiency of the method is compared with existing FRP designs. Numerical results indicate a reduction in operating costs and ramping insufficiency with coordinated operation of flexible resources and FRP deployment. [ABSTRACT FROM AUTHOR]

• The welfare of broiler chickens affects the pattern of their flock movements. • Cameras can 'read' these movements to predict future mortality and hockburn. • Differences between flocks are apparent in chicks of only a few days old. • Flocks carry their history with them throughout life. • Results are repeatable in the different conditions of the UK and Switzerland. Although systems for automated assessment of broiler chicken welfare have now been developed on a small scale, none are currently in widespread commercial use. We addressed this gap between research and uptake by field testing a camera system that uses the optical flow patterns made by the movements of flocks to monitor bird welfare. We tested the hypothesis that the movement patterns made by flocks of broiler chickens are correlated with two key welfare outcomes – mortality and hockburn. Life-long CCTV monitoring was carried out on 74 commercial broiler flocks (UK = 31; Switzerland = 43) and the resulting data analysed to give daily values of 4 optical flow descriptors: mean, variance, skew and kurtosis. Flock mortality and hockburn data were obtained from information routinely collected on the farms and at abattoirs. Bayesian multivariate regression models were used to analyse data with all 4 optical flow descriptors as input variables, first day-by-day and then cumulatively using information from each day and all previous days. For both the UK and Swiss flocks, the cumulative regression showed that optical flow was significantly (p < 0.01) correlated with % total mortality (by day 1 for the UK flocks and by day 4 for the Swiss flocks). Optical flow was also significantly (p < 0.01) correlated with % birds with end-of-life hockburn (by day 3 for UK flocks and day 2 for Swiss flocks). This ability to distinguish between flocks that will have different final welfare outcomes when the birds are only a few days old is a particularly useful property of this system as it potentially provides farmers with an early warning of problem flocks that are still young enough for interventions to be possible. In conclusion, the optical flow patterns made by the movements of broiler chicken flocks were reliably correlated with two key welfare outcomes - mortality and hockburn - obtained by cumulating information over days as the birds grow. Differences between flocks became apparent within the first few days of life and were similar in the UK and Switzerland, despite differences in environment and management practices, suggesting that this approach to automated welfare assessment has the potential for widespread commercial use. [ABSTRACT FROM AUTHOR]

This paper assesses how operational flexibility and the curtailment of renewable energy are connected using a unit commitment and economic dispatch model that includes operational characteristics of conventional power plants and system constraints. A Great Britain test system is analysed under different scenarios of wind (onshore and offshore) and solar installed capacity, showing that an increase in curtailment is mostly expected as wind deployment increases. This curtailment reaches 17% of the annual available variable renewable electricity generation at high wind and solar installed capacities and is mainly driven by the inertial requirement. The best approach to reducing curtailment is, therefore, to reduce the inertia floor by relaxing Rate of Change of Frequency limits. For the assumed curtailment costs, onshore wind presents a stronger correlation with overall curtailment than offshore wind and solar, albeit influenced by the levels of solar installed capacity. Significant reductions in curtailment can be achieved if wind contributes to system balancing requirements. This emphasizes the importance of ensuring that variable renewables are technically able to contribute to system balancing, wherever feasible, and of improving access to revenue streams that incentivise flexible operation of variable renewable generation. • The inertia floor is the main driver of curtailment in this work. • This curtailment can reach 17% of annual available renewable generation. • Onshore wind has stronger correlation with curtailment than offshore wind or solar. • Curtailment is needed to ensure reserve services when the inertia floor is lowered. • Curtailment can be reduced by 88% if wind contributes to system inertia and reserve. [ABSTRACT FROM AUTHOR]