Times Of Peak Demand Research Articles

Vehicle-to-Vehicle Energy Trading Framework: A Systematic Literature Review

As transportation evolves with greater adoption of electric vehicles (EVs), vehicle-to-vehicle (V2V) energy trading stands out as an important innovation for managing energy resources more effectively as it reduces dependency on traditional energy infrastructures and, hence, alleviates the pressure on the power grid during peak demand times. Thus, this paper conducts a systematic review of the V2V energy trading frameworks. Through the included article analysis (n = 61), this paper discusses the state-of-the-art energy trading frameworks’ structure, employed methodologies, encountered challenges, and potential directions for future research. To the best of the authors’ knowledge, this is the first review explicitly focused on V2V energy trading. We detail four critical challenges to face while establishing the framework in current research, providing an overview of various methodologies, including auctions, blockchain, game theory, optimisation, and demand forecasting, that are used to address these challenges and explore their integration within the research landscape. Additionally, this paper forecasts the evolution of V2V energy trading, highlighting the potential incorporation of advanced and established technologies like artificial intelligence (AI), digital twins, and smart contracts. This review aims to encapsulate the existing state of V2V energy trading research and stimulate future advancements and technological integration within the field.

Deep neural network optimization of a continuous solar-geothermal-driven plant with integrated thermal and mechanical energy storage: Incorporating bypass mechanism

In this current investigation, optimizing the cost and technological aspects of a novel integrated dual energy storage system embedded in a solar-geothermal-driven plant is proposed to assist in generating inexpensive and continuous power, fresh water, and hydrogen. the devised compressed air energy storage unit, aims to store a portion of the compressed air in the storage tank to use later for low-priced electricity production in peak demand times, and via a Bypass, the rest of the compressed air is used to run the downward sub-cycles even during the charging phase of the compressed air energy storage tank. The novel introduction of a Bypass in the compressed air energy storage unit, accelerates the synergy of the system by enabling the compressed air energy storage unit to continuously provide pressurized air to the system, instead of only supplying pressurized air for a limited time. Therefore, this will largely contribute to an increase in the production of the system. The introduction of the Bypass in the CAES unit has resulted in a significant 20.36 % increase in its round-trip efficiency, with only a negligible 2.8482 % rise in the overall cost rate, and a major 44.05 % increase in the rate of hydrogen production. The heated compressed air by the heliostat field is stored in the phase change material tank to supply the gas turbine with a constant heat load. Additionally, geothermal energy as an auxiliary energy source helps to generate desired products. A parametric study investigates how design parameters affect system performance. Through deep learning optimization which involves artificial neural networks and genetic algorithms, reduces the time used in optimization as it helps in finding the best solution fast and efficient. Based on multi-objective optimization, the system achieves a round-trip efficiency of 46 %, produces 6.956 kg/day of hydrogen, and has a total cost rate of 1.46 $/s.

Membrane capacitive deionization (MCDI): A flexible and tunable technology for customized water softening

There is a growing demand for water treatment systems for which the quality of feedwater in and product water out are not necessarily fixed with “tunable” technologies essential in many instances to satisfy the unique requirements of particular end-users. For example, in household applications, the optimal water hardness differs for particular end uses of the supplied product (such as water for potable purposes, water for hydration, or water for coffee or tea brewing) with the inclusion of specific minerals enhancing the suitability of the product in each case. However, conventional softening technologies are not dynamically flexible or tunable and, typically, simply remove all hardness ions from the feedwater. Membrane capacitive deionization (MCDI) can potentially fill this gap with its process flexibility and tunability achieved by fine tuning different operational parameters. In this article, we demonstrate that constant-current MCDI can be operated flexibly by increasing or decreasing the current and flow rate simultaneously to achieve the same desalination performance but different productivity whilst maintaining high water recovery. This characteristic can be used to operate MCDI in an energy-efficient manner to produce treated water more slowly at times of normal demand but more rapidly at times of peak demand. We also highlight the “tunability” of MCDI enabling the control of effluent hardness over different desired ranges by correlating the rates of hardness and conductivity removal using a power function model. Using this model, it is possible to either i) soften water to the same hardness level regardless of the fluctuation in hardness of feed waters, or ii) precisely control the effluent hardness at different levels to avoid excessive or insufficient hardness removal.

Using Solar PV and Stationary Storage to Buffer the Impact of Electric Minibus Charging in Grid-Constrained Sub-Saharan Africa

Despite the unstoppable global drive towards electric mobility, the electrification of sub-Saharan Africa’s ubiquitous informal multi-passenger minibus taxis raises substantial concerns. This is due to a constrained electricity system, both in terms of generation capacity and distribution networks. Without careful planning and mitigation, the additional load of charging hundreds of thousands of electric minibus taxis during peak demand times could prove catastrophic. This paper assesses the impact of charging 202 of these taxis in Johannesburg, South Africa. The potential of using external stationary battery storage and solar PV generation is assessed to reduce both peak grid demand and total energy drawn from the grid. With the addition of stationary battery storage of an equivalent of 60 kWh/taxi and a solar plant of an equivalent of 9.45 kWpk/taxi, the grid load impact is reduced by 66%, from 12 kW/taxi to 4 kW/taxi, and the daily grid energy by 58% from 87 kWh/taxi to 47 kWh/taxi. The country’s dependence on coal to generate electricity, including the solar PV supply, also reduces greenhouse gas emissions by 58%.

Short-term load forecasting using an Artificial Neural Network for Battery Energy Storage System

Load levelling that use recent spotlighted the Battery Energy Storage System (BESS) saves spare electricity or low cost electricity. The saved electricity can be used for peak power demand time. In this paper, load is predicted for purpose of operating the BESS effectively. By analysing pattern of industrial load, it is classified into workdays, Saturday and holidays. Then apply ANN on workdays load pattern from classified load patterns to predict load. To increase accuracy, number of neurons in hidden layer and learning data period are changed for estimation of load. After that, error rate was calculated for comparison and analysis.

Wind generator and storage system scheduling for customer benefit and battery life

Due to increased fossil fuel use and fossil fuel limitations, the Indian energy industry is migrating to non-conventional energy resources such as solar power, wind production, and fuel cells, among others. The unpredictability of non-conventional energy sources makes it difficult to balance an electrical system when they are incorporated, necessitating the incorporation of a storage device into the grid. In a microgrid system with wind turbine generation (WTG) and a battery energy storage system (BESS), the BESS may reserve energy during periods of surplus generation and release it to the grid during times of peak demand. The suggested technique establishes the state of charge (SOC) schedule for the BESS by employing an artificial rabbit optimisation (ARO) algorithm that minimises energy costs for customers. The state of health (SOH) of the energy storage is incorporated as an ageing coefficient, which causes the BESS to behave conservatively in order to retain its lifespan. Using a time of use (TOU) tariff, simulation results suggest a substantial possibility to boost the savings of consumers in a grid-connected micro-grid. The simulation findings indicate that by efficiently scheduling the BESS power management technique, the proposed method improves a number of distribution system efficacy.

Cooperative generation of photovoltaic solar energy with adoption of microgrids in residential condominiums

The distributed generation of photovoltaic solar energy has grown significantly in recent years and a new opportunity has emerged which is cooperative generation in condominiums. Buildings can compose a microgrid and take advantage of joint operation benefits such as: minimization with shared costs, market opportunities and better protection in scarcity situations. This configuration increases localized energy sustainability and decongests distribution and transmission systems, especially during peak demand times. This research made a case study, in a residential condominium, with white energy tariff, photovoltaic solar energy generation and energy storage in batteries. The methodology included a survey of physical data from the system and stratified the calculations for peak, intermediate and off-peak hours of energy demand. It was concluded that the adoption of the cooperative micronetwork among the tenants, to meet peak consumption, reduced the total investment by 71.2% and payback time by 36.3%, when compared to the individual system and full service to the customer demand.

Energy saving through modifications of the parallel pump schedule at a pumping station: A case study

Diverse state-of-the-art methods can be utilized to reduce energy consumption in water supply and distribution systems, including using high-efficiency equipment, shifting energy use away from peak demand times, energy recovery and storage devices, and renewable energy. However, the benefit of using high efficiency equipment can sometimes never be completely realized, as the performance of the individual pumps within a site can be reduced from optimal owing to a variety of reasons: a lower operating rate of the water treatment plant than the design flow rate, hydraulic conditions such as pipeline resistance or determined pressure, and pump scheduling. This research focused on optimizing pump scheduling through real-time monitoring utilizing smart temperature and pressure sensors, wireless low-power data communicators, and a pump data analysis algorithm to determine the hydraulic efficiency from thermodynamic state variables with subsequent parallel pump optimization. Thermodynamic pump performance measurements provided real-time information, including flow rate, specific power, and pump efficiency, in both individual and parallel pump operations. Evaluation of the specific power under diverse parallel pump operation scenarios demonstrated a variation of 0.115–0.140 kWh/m3 in the range of 7800–10,100 m3/h. However, the deviation in specific power was more than 17 % when operating at less than 7800 m3/h and more than 10,000 m3/h. The summary of the pump combination operation suggested that energy could be saved by up to 15 % by optimum pump scheduling, that is, small capital investment, simple optimization of control through thermodynamic state variable measurement, analysis, and feedback.

A Review of Phase Change Materials as a Heat Storage Medium for Cooling Applications in the Built Environment

The air conditioning demand varies significantly in the hot and desert climates of the UAE due to diurnal temperature variation, seasonal shifts, and occupancy patterns. One of the challenges faced by the relatively higher energy-consuming UAE building stock is to optimize cooling capacity utilization and prevent excessive energy loss due to undesired cooling. A potential route to achieving such a goal involves cooling energy storage during low demand and releasing the stored cooling at peak demand times via thermal energy storage (TES). Latent heat thermal energy storage (LHTES) employing phase change materials (PCMs) provides impactful prospects for such a scheme, thus gaining tremendous attention from the scientific community. The primary goal of the current article is to provide a comprehensive state-of-the-art literature review on PCM-based TES for cooling applications to understand its efficacy, limitations, and future prospects. The article involves various applications, designs, and validations. The article emphasizes the importance of material innovations and heat transfer augmentation strategies to render this technology feasible for real-life integration into cooling systems.

Can the Basis Lead to Arbitrage Profits on the MISO Exchange?

Previous research has shown the basis contains information about future spot prices in wholesale electricity markets. This paper examines the profitability of a trading rule that utilizes the basis on the Midcontinent Independent System Operator (MISO) exchange. 24 hourly time series regressions across each of MISO’s eight regional hubs are estimated to determine the rule’s effectiveness. 67 of the 192 total regressions yield statistically significant results. The number of significant results as well as the size of the returns tend to be highest during the afternoon through early evening hours, which calls into question the efficiency of the market during peak demand times.

Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system

Reducing electricity peak demand is essential to maintain the balance between supply and demand side in electricity power markets, as well as reduce utility costs and environmental impacts. With growth in adoption of Electric Vehicles (EVs), there is an emerging opportunity to balance electrical power demand of buildings by storing electricity in EVs during low demand periods and discharging electricity into buildings during peak demand periods. Due to uncertainty in time and magnitude of peak demand, decision makers are always faced with a challenging task to identify optimal schedules for charging and discharging EVs to minimize peak electricity demand. This paper presents the development of a novel system that is capable of predicting day-ahead building electricity demand profile and identifying optimum schedule of charging and discharging EVs to minimize electricity peak demand. The system is designed to comply with planned EV trip schedules and minimum state of charge (SOC). The system consists of (1) machine Learning (ML) model to predict electrical power demand, and (2) demand management optimization model to identify optimal schedule for charging and discharging EVs. Four methods are explored to develop the ML model, including histogram-based gradient boosting, random forest, deep artificial neural network (DNN), and long short-term memory (LSTM). A case study of multi-tenant commercial building is analyzed to evaluate the performance of the system and demonstrate its new capabilities. The results of the case study shows that LSTM has the best performance in terms of mean absolute error, root mean square error, and mean absolute percentage error with average values of 7.44, 17.78, and 20.08 %, respectively. Five scenarios for shaving peak electricity demand, including combinations of two electric vehicles, a stationary battery, and a PV system are investigated. Scenarios including the stationary battery and the PV system are considered to evaluate the full potential of peak demand reduction in the case study building. The results of the demand management optimization model show up to 36 % reduction in peak demand using two EVs, one stationary battery, and PV system of 40 kW capacity. The key contributions that this study adds to existing knowledge are: (1) developing machine learning models to predict day-ahead electricity demand in 15-minute intervals, (2) integrating machine learning and optimization algorithms in identifying EV charging and discharging schedules to minimize utility cost by shaving peak energy demand, and (3) considering planned EV trips and minimum SOC requirements in identifying optimal charging and discharging of EVS to shave peak energy demand in buildings. The implementation of this system provides practical solutions for managing electricity demand in commercial buildings using EVs. By reducing energy consumption and promoting the innovative use of EVs, this system offers a sustainable approach to managing electricity demand in commercial buildings.

Plugged-in electric vehicle-assisted demand response strategy for residential energy management

Demand response (DR) management systems are a potentially growing market due to their ability to maximize energy savings by allowing customers to manage their energy consumption at times of peak demand in response to financial incentives from the electricity supplier. Successful execution of a demand response program requires an effective management system where the home energy management system (HEMS) is a promising solution nowadays. HEMS is developed to manage energy use in households and to conduct the management of energy supply, either from the grid or the alternative energy sources like solar or wind power plants. With the increase of vehicle electrification, in order to achieve a more reliable and efficient smart grid (SG), cooperation between electric vehicles (EVs) and residential systems is required. This cooperation could involve not only vehicle to grid (V2G) operation but a vehicle to home (V2H) too. V2H operation is used to transfer the power and relevant data between EVs and residential systems. This paper provides an efficient HEMS enhanced by smart scheduling and an optimally designed charging and discharging strategy for plugged-in electric vehicles (PEVs). The proposed design uses a fuzzy logic controller (FLC) for smart scheduling and to take the charging (from the grid)/discharging (supply the household appliances) decision without compromising the driving needs. Simulations are presented to demonstrate how the proposed strategies can help to reduce electricity costs by 19.28% and 14.27% with 30% and 80% state of charge (SOC) of the PEV respectively compared to the case where G2V operation only used along with the photovoltaic (PV) production, improve energy utilization by smoothing the energy consumption profile and satisfy the user’s needs by ensuring enough EV battery SOC for each planned trip.

Private and External Costs and Benefits of Replacing High-Emitting Peaker Plants with Batteries

Falling costs of lithium-ion (Li-ion) batteries have made them attractive for grid-scale energy storage applications. Energy storage will become increasingly important as intermittent renewable generation and more frequent extreme weather events put stress on the electricity grid. Environmental groups across the United States are advocating for the replacement of the highest-emitting power plants, which run only at times of peak demand, with Li-ion battery systems. We analyze the life-cycle cost, climate, and human health impacts of replacing the 19 highest-emitting peaker plants in California with Li-ion battery energy storage systems (BESS). Our results show that designing Li-ion BESS to replace peaker plants puts them at an economic disadvantage, even if facilities are only sized to meet 95% of the original plants' load events and are free to engage in arbitrage. However, five of 19 potential replacements do achieve a positive net present value after including monetized climate and human health impacts. These BESS cycle far less than typical front-of-the-meter batteries and rely on the frequency regulation market for most of their revenue. All projects offer net air pollution benefits but increase net greenhouse gas emissions due to electricity demand during charging and upstream emissions from battery manufacturing.

Energy shifting in wastewater treatment using compressed oxygen from integrated hydrogen production

Integrating renewable hydrogen production via electrolysis with wastewater treatment is an opportunity to manage environmental resources more sustainably while providing a pathway to producing sustainable hydrogen at industrial scale. The synergies of integrating oxygen production from water electrolysis and oxygen use in wastewater treatment benefit both hydrogen production and wastewater industries. However, the understanding of the most suitable integrated process configuration and scale of renewable equipment is not known. A novel energy shifting process is proposed here using compressed and stored oxygen produced by water electrolysis and used in the activated sludge process, replacing traditional aeration in the wastewater treatment plant and eliminating the high energy consuming blowers supplying air to submerged fine bubble diffusers. In the proposed energy shifting process, excess oxygen produced by water electrolysis at times of peak renewable electricity production is stored and used for wastewater treatment at times of peak oxygen demand. Wastewater treatment data from the activated sludge process was used to calculate oxygen demand in 1-h intervals over a 24-h period, and the system response of the integrated plant was simulated at hourly intervals and equipment size determined according to an optimisation algorithm that balances oxygen and electricity supply and demand over a 24-h period. Sensitivity analysis of operational parameters is assessed and the case for replacing traditional WWTP aeration with newer technologies is quantified using a high efficiency oxygen transfer system such as a Speece cone as an example that is shown to be a prerequisite for the feasibility of the process. The results produced by this study provide valuable information to the hydrogen and wastewater industries on how an integrated plant could be configured. Besides the environmental advantages of sustainably produced hydrogen, using oxygen as a biochemical energy storage medium in this configuration means WWTP powered from renewable electricity becomes more viable reducing the industries reliance on fossil fuels.

Emissions and prices are anticorrelated in Australia’s electricity grid, undermining the potential of energy storage to support decarbonisation

Recent work has shown that energy storage operating in a CO2 intensive grid can increase greenhouse gas (GHG) emissions. In this paper we sought to characterise the emissions of Australia’s electricity grid to inform the planning and operation of energy storage with the goal of minimising emissions associated with energy storage and supporting the rapid decarbonisation of Australia’s energy system.To do so, a marginal emissions factor (MEF), representative of the emissions intensity of the marginal generator (the generator with the highest bid price) in each time period, was calculated for the Australian National Electricity Market (NEM) from 2013 to 2021. Through analysis, significant variation was discovered in the MEF’s intra-day variability, with high MEF values occurring overnight and low MEF values occurring during times of peak demand. Compared against the average 30-min spot price across the day, a strong anti-correlation was calculated between the MEF and the spot price.Using these results, the importance of energy storage operated to minimise both costs and emissions was highlighted. By taking the key finding of its anti-correlation with price, the MEF can be simply implemented in the real world, including through dynamic carbon incentives and market tariffs, to ensure emissions are being reduced both in the short and long-term.

Investigating Impacts of CVR and Demand Response Operations on a Bi-Level Market-Clearing With a Dynamic Nodal Pricing

This paper investigates the impacts of conservation voltage reduction (CVR) on electricity prices, the local market, and technical issues in distribution networks. An increase in electricity demand is one of the key challenges for developing sustainable societies. An increase in electric consumption puts immense pressure on electricity providers, which forces them to apply for load reduction programs during peak-demand time intervals. The CVR is one of the popular methods for load reduction, but how it would impact the pricing process and electricity market at the distribution level needs further investigation. The proposed methodology includes a power tracing and loss allocation-based pricing method. Since the distribution networks are going to be confronted by penetration of distributed energy resources (DER), prosumers, and microgrids, it is important to have a comprehensive methodology. This paper deploys a bi-level optimization algorithm to consider the financial benefits of all participating agents. In addition to CVR, the demand response (DR) programs are considered to shift and curtail flexible loads by the distribution system operator (DSO) and prosumers, respectively. The price sensitivity of prosumers toward change in the network’s voltage for better planning is calculated. The operation costs/profits of DSO/prosumers decrease/increase during CVR and DR programs by 4.63% / 3%, respectively.

Unlocking demand response in commercial buildings: Empirical response of commercial buildings to daily cooling set point adjustments

Buildings represent over half of global electricity demand. Cooling buildings already accounts for over 9% of global electricity demand and is expected to grow rapidly due to climate-change induced hot-spells and increasing prosperity in developing economies. In the US, commercial buildings represent 35% of nationwide electricity consumption. Increased electricity demand for cooling services will challenge already stressed power grids, particularly during times of peak demand. This work explores the flexibility and demand response potential of large Heating, Ventilation and Air Conditioning (HVAC) systems based on an extensive set of measurements from six commercial buildings in a Warm-summer Mediterranean climate. Over a three-month summer period, zone-level temperature set points were adjusted daily in six commercial buildings to determine the effect on chilled water and electricity loads, as well as on zone-level temperatures. External weather conditions were measured continuously during the testing period. The experimental data that were collected are published with this article. These experiments confirmed the potential to provide flexibility by reducing energy demands based on modest zone-level temperature set point adjustments. A two-degree Fahrenheit increase of the cooling set point resulted in a 13–28% reduction in daily building-level cooling loads on average for four office buildings and 3–4% for two laboratory buildings. The impact on electric loads was less than 2% (excluding for cooling water but including for ventilation). Zone-level temperature increases were measurable but temperatures remained within the target ranges. By collecting 385 experiment-days of experiment data, we were able to parameterize statistical models for the response of the buildings. These models provide statistical guarantees on the reliability of thermal demand response. This work provides a blueprint for constructing building and zone-level energy-response functions and highlights the value of testing buildings repeatedly and across a range of weather conditions. Providing statistical performance guarantees will be critical for widespread adoption of demand response technology to provide the flexibility needed to meet peak electricity demands. Combined with thermal storage, the daily flexibility studied here would also unlock daily and sub-daily electrical flexibility, and can also be integrated with sub-daily flexibility from building-level electrical loads.

Machine learning based demand response scheme for IoT enabled PV integrated smart building

The short-term forecasting of electric power consumption and renewable energy generation with high efficiency and advanced demand side management is essential for improving the energy flexibility provided by the smart buildings. However, the recent literature fails to present the mechanism for developing demand side management for energy flexibility. This article addresses the importance of developing an electric power consumption and renewable energy generation short-term forecasting model using a supervised machine learning approach. In this work, random forest and decision tree regression models are developed and used to forecast future electric power consumption and renewable energy generation. To validate the forecasting model performance using the MAE, RMSE, MAPE, and R2. This article proposes a day ahead dynamic pricing model for the demand response scheme. The day ahead dynamic pricing minimizes the peak time demand and end-user electricity tariff. A smart grid system must know the future short-term electric power consumption and renewable energy generation to enable the demand response scheme. The educational institute smart substation and weather station are integrated into the internet of things devices to enable real-time monitoring and control of the electric power consumption and renewable energy generation. The results show that the proposed DR scheme benefits end electricity users and smart grid operators.

Economic viability of using thermal energy storage for flexible carbon capture on natural gas power plants

Fossil fuel-based power plants generate 80 % of the electricity in the United States and provide a reliable generation source for both base and peak power demands. These plants are expected to adapt to changes in environmental policies that will require carbon management with carbon capture and storage (CCS) representing a possible solution. Current solvent-based CCS has a detrimental impact on a power plant's performance due to large heat loads required for carbon capture solvent regeneration. This parasitic load restricts the power plant's output and operation flexibility. Therefore, this study evaluates the feasibility of using thermal storage technologies for natural gas combined cycle (NGCC) power plants coupled with CCS to minimize the impact of solvent regeneration and enable the plant to operate at peak power output. Thermal storage can minimize the impact of CCS on the power plant by providing the heat load required for solvent regeneration during times of peak demand which will allow the plant to operate unrestricted and at full power. In total, fifteen unique thermal storage configurations were evaluated from three thermal storage categories: Brayton cycle heat pump, vapor compression heat pump, and heat recovery steam generator steam extraction for storage. The viability of these systems was determined by evaluating each configuration on thousands of real-world Locational Marginal Pricing (LMP) profiles from the New York Independent System Operator and California Independent System Operator electricity markets using a techno-economic analysis. Results were compared to the performance of a base power plant (NGCC with CCS and no thermal storage) to determine the impact of thermal storage on power plant economics. Overall, six of the thermal storage configurations performed better than base CCS enabled power plant on between 11.5 % and 38.7 % of the LMP signals evaluated. The best performing configuration was a vapor compression heat pump that used flue gas as the working fluid and had both hot and cold thermal storage units. This configuration performed better than the base CCS power plant on 38.7 % of the LMP profiles. The results of this study show thermal storage can mitigate the economic impact of carbon capture solvent regeneration on NGCC power plants. Discussion focuses on the impact of electricity pricing on the optimal thermal storage system, the advantages and disadvantages of the systems evaluated, and identifies limitations with the study.

A bump in the night: a 15-year retrospective analysis of urgent inpatient and emergency CT reporting out of hours in a tertiary referral centre

To assess the impact on specialty trainee (ST) experience of out-of-hours (OOH) working, focusing on what might be improved with both patient safety and staff wellbeing in mind. The number of acute computed tomography (CT) examinations reported OOH over the last 15 years (2007-2021) at Oxford University Hospitals NHS Foundation Trust was analysed. Qualitative data from the radiology STs participating in the acute OOH rotas were obtained using questionnaires during winter months in 2019 and 2021, before and after the introduction of an OOH CT outsourcing service in 2020. Overnight acute CT has increased over 10-fold over the last decade to almost 50 CT examinations in 2021, and similar increases were observed during evening and weekend shifts. The option to outsource acute CT on an ad hoc basis was introduced in 2020 to manage the increase in demand. This resulted in a statistically significant improvement in the STs' level of reported satisfaction for OOH shifts (p<0.018), despite significantly increased perception of how busy the shifts were (p<0.035). OOH acute CT reporting at Oxford NHS Foundation Trust has increased dramatically over the previous 15 years. Working patterns and resources have changed incrementally to absorb this increase in demand, most recently with the option for outsourcing at times of peak demand. The trend for increasing OOH CT demand has considerable implications for future resource planning.