Peak Power Reduction Research Articles

Integrating Cold Thermal Energy Storage for Air Conditioning Demand in a CO2 Refrigeration System at a Supermarket

A common configuration for transcritical CO2 booster systems in supermarkets involves air conditioning (AC) supplied by cooling a water-glycol circuit. The design capacity of the refrigeration unit must handle all refrigeration loads and the AC load during the hottest summer day, leading to overcapacity and part-load operation for most of the year. A proposed design for implementing cold thermal energy storage (CTES) dedicated to AC demand in a supermarket located in the Oslo region is modeled in the object-oriented language Modelica. Simulation results demonstrate an electricity peak power reduction of up to 32.33%. Even though energy savings are not the primary objective of this project, they are achieved by producing and storing energy when the outdoor temperature is lower, and the coefficient of performance (COP) of the system is higher. The energy savings can reach up to 11.8%. Finally, the economic benefits of the system are assessed under the spot pricing system, revealing potential electricity cost savings of up to 12.56%.

ENHANCING OTFS MODULATION FOR 6G THROUGH HYBRID PAPR REDUCTION TECHNIQUE FOR DIFFERENT SUB-CARRIERS

Peak-to-average power ratio (PAPR) in orthogonal time frequency space (OTFS) is vital for optimizing signal efficiency, minimizing distortion, and enhancing the performance of a beyond fifth-generation (B5G) system. The paper focuses on lowering the PAPR while retaining the bit error rate (BER) and power spectral density (PSD) for 64 and 256 sub-carriers. The PAPR is minimized by using a fractal optimization process known as selective mapping (SLM) and partial transmission sequence (PTS) at the transmitting block of the OTFS framework. The combination of SLM and PTS reduces peak power by selectively modifying the phase of transmitted symbols. SLM generates multiple versions of the signal, while PTS allocates power to these versions optimally, collectively mitigating amplitude peaks and minimizing PAPR. According to the simulation results, the suggested SLM+PTS works better than the traditional SLM and PTS, achieving low spectrum leakage and PAPR improvements of 5.5 dB and 4.9[Formula: see text]dB as well as SNR gains of 2.4[Formula: see text]dB and 2.1[Formula: see text]dB. In the future, SLM+PTS may work on lowering PAPR by making computers faster, researching adaptive algorithms, and combining machine learning methods for real-time optimization in various communication settings. Complex modeling, including fractal-based SLM and PTS approaches, provides a detailed understanding of waveform behavior and interference patterns, enabling more accurate and effective hybrid PAPR reduction techniques. This combination enhances the ability to manage to mitigate non-linear distortion across various sub-carriers, crucial for the performance and efficiency of 6G OTFS modulation.

Reducing Peak Power in a Multiple Load System by Delaying the Activation of Electrical Loads Using a Filter Based on a PI Controller

In a power grid with multiple two-state loads, the total power can vary over a significant range. This results in the inability to supply this system from a low-power source. The solution is an algorithm that shapes energy demand depending on its availability. For this purpose, a new load distribution method is proposed based on introducing a buffer between the temperature controller output and the heater and filtering the load using a master Proportional–Integral (PI) controller. The aim of the work was to evaluate the quality of the developed algorithm for limiting power peaks in the power grid. The research was conducted on a model of the Creep Test Laboratory with 389 heaters simulated in MATLAB Simulink R2023b. The algorithm was tested with various settings of the master controller parameters. By experimentally adjusting these parameters, a ten-fold reduction in peak power was achieved. The standard deviation for the L1 phase was reduced from 7.6 kW to 0.6 kW. Similar results were obtained for phases L2 and L3. The tested control system tracked changes in the average power value by changing the number of loads switched on and by frequency-modulating the signal when the change was less than the power of a single load. It was demonstrated that the controlled delayed switching of electrical loads can modify the shape of the total electric power without affecting their operation. The proposed solution features a low computational complexity, which allows its implementation in various systems.

Effects of concurrent heat and hypoxic training on cycling anaerobic capacity in men

Physical training in heat or hypoxia can improve physical performance. The purpose of this parallel group study was to investigate the concurrent effect of training performed simultaneously in heat (31 °C) and hypoxia (FIO2 = 14.4%) on anaerobic capacity in young men. For the study, 80 non-trained men were recruited and divided into 5 groups (16 participants per group): control, non-training (CTRL); training in normoxia and thermoneutral conditions (NT: 21 °C, FIO2 = 20.95%); training in normoxia and heat (H: 31 °C, FIO2 = 20.95%); training in hypoxia and thermoneutral conditions (IHT: 21 °C, FIO2 = 14.4%), and training in hypoxia and heat (IHT + H: 31 °C, FIO2 = 14.4%). Before and after physical training, the participants performed the Wingate Test, in which peak power and mean power were measured. Physical training lasted 4 weeks and the participants exercised 3 times a week for 60 min, performing interval training. Only the IHT and IHT + H groups showed significant increases in absolute peak power (p < 0.001, ES = 0.36 and p = 0.02, ES = 0.26, respectively). There were no significant changes (p = 0.18) after training in mean power. Hypoxia appeared to be an environmental factor that significantly improved peak power, but not mean power. Heat, added to hypoxia, did not increase cycling anaerobic power. Also, training only in heat did not significantly affect anaerobic power. The inclusion of heat and/or hypoxia in training did not induce negative effects, i.e., a reduction in peak and mean power as measured in the Wingate Test.

Risk-aware scheduling and dispatch of flexibility events in buildings

Residential and commercial buildings, equipped with systems such as heat pumps (HPs), hot water tanks, or stationary energy storage, have a large potential to offer their consumption flexibility as grid services. In this work, we leverage this flexibility to react to consumption requests related to maximizing self-consumption and reducing peak loads. We employ a data-driven virtual storage modeling approach for flexibility prediction in the form of flexibility envelopes for individual buildings. The risk-awareness of this prediction is inherited by the proposed scheduling algorithm. A Mixed-integer Linear Program (MILP) is formulated to schedule the activation of a pool of buildings in order to best respond to an external aggregated consumption request. This aggregated request is then dispatched to the active individual buildings, based on the previously determined schedule. The effectiveness of the approach is demonstrated by coordinating up to 500 simulated buildings using the Energym Python library and observing about 1.5 times peak power reduction in comparison with a baseline approach while maintaining comfort more robustly. We demonstrate the scalability of the approach by solving problems with 2000 buildings in about 21 s, with solving times being approximately linear in the number of considered assets.

Modelling a DC Electric Railway System and Determining the Optimal Location of Wayside Energy Storage Systems for Enhancing Energy Efficiency and Energy Management

Global demand for fossil fuels is highly increasing, necessitating energy efficiency to be enhanced in transitioning to low-carbon energy systems. Electric railways are highly efficient in reducing the transportation demand for fossil fuels as they are lightweight and their energy demand can be fed by renewable energy resources. Further, the regenerative braking energy of decelerating trains can be fed to accelerating trains and stored in onboard energy storage systems (ESSs) and stationary ESSs. It is fundamental to model electric railways accurately before investigating approaches to enhancing their energy efficiency. However, electric railways are challenging to model as they are nonlinear, resulting from the rectifier substations, overvoltage protection circuits, and the unpredictability and uncertainty of the load according to the train position. There have been few studies that have examined the ESS location’s impact on improving the energy efficiency of electric railways while using specialised simulation tools in electric railways. However, no single study exists that has studied the location impact of stationary ESSs on the energy efficiency of electric railways while the trains are supported by onboard ESSs. Given these goals and challenges, the main objective of this work is to develop a model using commercial software used by industry practitioners. Further, the energy saving is aimed to be maximised using stationary ESSs installed in optimal locations while trains are supported by onboard ESSs. The model includes trains, onboard ESSs, rail tracks, passenger stations, stationary ESSs, and traction power systems involving power lines, connectors, switches, sectioning, and isolators. In this article, a test scenario is presented comprising two trains running on a 20 km with three passenger stations and two substations. The trains and track are modelled in OpenTrack simulation software (Version 1.9) while the power system is modelled in OpenPowerNet simulation software (Version 1.11). The two simulation tools are used in the railway industry and can produce realistic results by taking into account the entire electrical network structure. A stationary ESS is added on the wayside and moved in steps of 1 km to obtain the optimal location before investigating the impact of stationary ESSs on the performance and energy management of onboard ESSs. It is found that the energy saving when installing a stationary ESS at the optimal location is 56.05%, the peak-power reduction of Substation 1 is 4.37%, and the peak-power reduction of Substation 2 is 18.67%.

Small-Scale Battery Energy Storage System for Testing Algorithms Aimed at Peak Power Reduction

Small-Scale Battery Energy Storage System for Testing Algorithms Aimed at Peak Power Reduction

PAPR reduction using model‐driven hybrid algorithms in the 6G NOMA waveform

AbstractIn the evolving landscape of sixth‐generation (6G) network technologies, Non‐Orthogonal Multiple Access (NOMA) systems are pivotal for achieving enhanced spectral efficiency and network capacity. However, a significant challenge in NOMA systems is the high Peak‐to‐Average Power Ratio (PAPR), which undermines system efficiency by necessitating high‐power amplifiers (HPAs) to operate in their less efficient, non‐linear range. Addressing this, we introduce a novel hybrid approach, the Selective Mapping‐Circular Transformation Method (SLM‐CTM), which ingeniously amalgamates the strengths of Selective Mapping (SLM) and the Circular Transformation Method (CTM) to mitigate PAPR issues. SLM is renowned for its peak power reduction capabilities without adding to system complexity, whereas CTM is valued for its simplicity and controlled signal distortion. The proposed SLM‐CTM strategy employs a blend of linear and nonlinear techniques to effectively lower PAPR in non‐orthogonal NOMA configurations, thereby reducing high‐power peaks while simultaneously enhancing signal quality. This paper delineates the application of the SLM‐CTM algorithm to evaluate critical NOMA parameters such as Power Spectral Density (PSD), Bit Error Rate (BER), and PAPR. Simulation results highlight the efficacy of SLM‐CTM over conventional SLM, demonstrating a significant throughput improvement of 3.2 dB and a PAPR reduction of 4.6 dB, underscoring the potential of SLM‐CTM in elevating the performance of NOMA systems within 6G network.

Multi-stage optimization for energy management and trading for smart homes considering operational constraints of a distribution network

The increase in the uptake of distributed energy resources (DERs) makes the distribution network more complex. Inadequate management of these DERs may lead to escalated power transmission, potentially resulting in challenges within the distribution network and provoking degradation of critical assets like distribution transformers. The smart home has a home energy management system (HEMS) to schedule the DERs at the home level based on the objectives of the end-user. However, HEMS is not coordinated with the distribution network operators, which can cause different network issues at the distribution level. To mitigate this, effective management of DERs across different levels of the distribution network is essential. This paper introduces a four-stage optimization within a three-level coordination framework that aims to maximize benefits for all stakeholders. A mixed integer linear programming (MILP)-based HEMS is formulated in the first stage to perform home energy management effectively. At the aggregator level, in the second stage, a MILP-enabled peer-to-peer (P2P) trading mechanism is designed. At the same level, a third-stage loss of life optimization is performed pertaining to the optimal power status of the HEMS and P2P trading. In the last stage, a three-phase optimal power flow-based optimization is proposed to maintain the operational constraints of the unbalanced distribution network. Optimizing HEMS and P2P trading while addressing transformer limitations, our proposed method reduces peak power by 38.85%. The transformer life loss using P2P is only 0.00059%, while in HEMS, it is 0.62% per day. Additionally, our method substantially lowers electricity costs for P2P prosumers. Thus, our proposed method outperforms other existing mechanisms from both financial and physical network operation suitability perspectives.

Assessing the value of information for electric vehicle charging strategies at office buildings

Smart charging strategies for electric vehicles (EVs) require as input information such as energy requirement and dwell time. In practice, that information is often not available. However, estimations may be obtained from historical charging behavior. This paper investigates the added value of historical information for EV charging strategies based on real-world EV charging data collected in an office building parking lot with 125 chargers. Furthermore, it provides valuable insights into EV charging behavior at office building parking lots, based on a statistical analysis of the data. The added value of data availability in EV charging strategies for day-ahead planning and real-time operation of office building parking lots is assessed via a set of quality metrics that measure user satisfaction and impact on the local grid. Offline charging strategies under various degrees of available information are validated by comparing their performance with the real-time operation of the parking lot. Results show a power peak reduction of more than 50% using historical data and simple estimations of arrival times, dwell times, and energy requirement. A trade-off between power peaks and service quality (on average 4.4kWh energy not served) is observed. It was found that knowledge of individual average energy provides higher added value compared to knowing individual average dwell time in both offline planning and real-time operation of the parking lot.

Sustainable electric vehicle charging coordination: Balancing CO2 emission reduction and peak power demand shaving

Although electric vehicles (EVs) eliminate CO2 emissions on the road, the upstream emissions from electricity generation still affect the emissions benefit brought by vehicle electrification. Coordinating EV charging to match the low marginal emission factors (MEFs) of electricity generation could reduce emissions, while aggregating the charging events may create a higher power demand undermining the grid stabilization goal. To address the trade-off between emission reduction and peak power demand shaving, this study developed a bi-objective optimization model for quantifying the relationship between minimizing CO2 emissions and peak power demand. Using one-year EV charging data from a workplace parking lot with 50 charging piles in California, USA, the results show a non-linear relationship between the two objectives throughout the year. Compared to uncoordinated charging, coordinated charging can reduce CO2 emissions by 18% annually while also reducing peak power demand by 33%. However, further reducing emissions by around 1% requires a significant increase in peak power demand by 84%, which is 23% higher than the peak power demand without coordination. These findings provide quantitative insights for policymakers and other stakeholders to balance emissions reduction and grid stabilization goals toward sustainable EV charging coordination.

A framework to formulate and aggregate performance indicators to quantify building energy flexibility

Building energy flexibility is being considered an emerging strategy allowing the demand side actively support the operation and control of variable resources-based energy supply. This study presents the development of a framework to formulate and aggregate energy flexibility indicators (EFIs) to evaluate building energy flexibility. EFIs were developed/selected using the important characteristics of building energy flexible sources, performance factors linked with flexibility services and penalty factors posed by the grid. A multicriteria decision analysis method was then used to develop an Aggregated Energy Flexibility Potential (AEFP) function that can consider the interactions between the power grid, buildings and building energy systems to represent the building's overall energy flexibility potential with a single dimensionless value. AEFP can help determine cost-effective energy flexible sources and solutions for increased energy flexibility. The performance of the EFIs and AEFP developed was evaluated via a case study with two distinct scenarios through simulations. The photovoltaic system provided a peak power demand reduction of 94%, emissions reduction of 46% and energy cost reduction of 54% to the building with an AEFP value of 0.74 (i.e. 74%), whereas a thermal energy storage system coupled with an air conditioning system helped reduce peak power demand by 56.4%, CO2 emissions by 14.5% and energy cost by 32% with an AEFP value of 0.54 (i.e. 54%). Both scenarios verified the effectiveness of the developed EFIs and AEFP for evaluating building energy flexibility.

Study on the performance enhancement of ice storage and melting processes in an ice-on-coil thermal energy storage system

Air conditioners equipped with an ice storage system store a large amount of latent heat during the off-peak period at night, and use the stored cold energy for the air conditioner during the peak period of the day, thereby greatly reducing peak power consumption. In this study, an experimental analysis was conducted to evaluate the cold storage and cooling characteristics of an ice-on-coil storage system. Firstly, the influence of the inlet brine temperature, flowrate, and concentration on the discharge rate was studied. It was observed that all parameters were directly proportional to the discharge rate, and the effect of brine inlet temperature was more pronounced. Secondly, the alumina nanofluid as a phase change material was employed in the system for enhancing the thermal performance. The experimental results showed that using nanofluid shortened the freezing time, while it had a negative impact on the thermal behavior during the ice melting. Thirdly, the ice storage coils with spiral fins were used. It was concluded that the employment of finned coils reduced the melting time by 24.0 %, increased the average discharge rate by 74.4 %, and raised the cumulative discharge capacity by 63.5 % compared with original bare coil design.

Adaptive DFT-s-OFDM employed novel multi layered scheme for reduction of PAPR for mMTC node in 5G (NR)

In 5G New Radio, The DFT-s-OFDM is a promising multiplexing technique for enhancing the average power of subcarriers to reduce the overall peak-to-average power ratio (PAPR). The peak of subcarrier is too high then the impact of DFT-s-OFDM is quite inferior. An Adaptive DFT-s-OFDM employed multilayered novel mechanism has been proposed in this research article to reduce the PAPR. The extent of sub-band is varied in accordance with predefined threshold value to enhance average power, meanwhile clipping is applied to shape the peak value of subcarrier. The clipping technique reduced the subcarrier’s peak power. The proposed approach has the greater impact in order to enhance the efficiency of non-linear Power Amplifier (PA). The PAPR is suppressed by the multilayered strategy in two ways. The first is the numerator element, which is the reduction of peak power by clipping operation, and the second is the denominator element, which is the improvement of average power through K-point DFT sub-band. The superior time–frequency localization of DFT-s-OFDM across uplink transmissions reduces the requirement for difficult node-to-node synchronization, which is potentially used by the mMTC nodes. The proposed adaptive DFT-s-OFDM modulation scheme performs better than conventional OFDM modulation, which is approximately 24% more effective at 6 dB.

Electrically Evoked Isotonic Plantar Flexion Contractions Are Impaired Less than Voluntary After a Dynamic Fatiguing Task.

Evaluating central and peripheral processes responsible for reduced power after dynamic fatiguing tasks are often limited to isometric torque, which may not accurately reflect dynamic contractile performance. Here, we compare voluntary and electrically evoked peak power (and its determinants: dynamic torque and velocity) and rate of velocity development (RVD) before and after a dynamic fatiguing task using concentric Plantar flexion contractions. Young (18-32 yr) males ( n = 11) and females ( n = 2) performed maximal-effort isotonic Plantar flexion contractions using a load of 20% isometric torque until an approximately 75% reduction in peak power. Voluntary and electrically evoked (300 Hz tibial nerve stimulation) contractions loaded to 20% and 40% isometric torque through 25° ankle joint range of motion were compared before and 0, 2.5, 5, and 10 min after task termination. At task termination, peak power and RVD of voluntary contractions at both loads were reduced more (~40% to 50% reduction) than electrically evoked (~25% to 35% reduction) contractions ( P < 0.001 and P = 0.003). Throughout the recovery period, electrically evoked peak power and RVD returned to baseline sooner (<5 min) than voluntary contractions, which were still depressed at 10 min. Reductions in peak power for the 20% load were equally due to impaired dynamic torque and velocity, whereas velocity was impaired more than dynamic torque ( P < 0.001) for the 40% load. The relative preservation of electrically evoked power and RVD compared with voluntary contractions at task termination and quicker recovery to baseline indicates that the reductions in dynamic contractile performance after task termination are due to both central and peripheral processes; however, the relative contribution of dynamic torque and velocity is load dependent.

Low-disturbance automatic bias point control for optical IQ modulator using digital chaotic waveform as dither signals.

A low-disturbance automatic bias point control (ABC) method for optical in-phase and quadrature modulators (IQM) is proposed using digital chaotic waveform as dither signals. Two distinct chaotic signals, each with unique initial values, are introduced to the direct current (DC) port of IQM in conjunction with a DC voltage. Due to the robust autocorrelation performance and exceptionally low cross-correlation of chaotic signals, the proposed scheme is capable of mitigating the impact of low-frequency interference, signal-signal beat interference, and high-power RF-induced noise on transmitted signals. In addition, due to the broadwidth of chaotic signals, their power is distributed across a broad frequency range, resulting in a significant reduction in power spectral density (PSD). Compared to the conventional single-tone dither-based ABC method, the proposed scheme exhibits a reduction in peak power of the output chaotic signal by over 24.1 dB, thereby minimizing disturbance to the transmitted signal while maintaining superior accuracy and stability for ABC. The performance of ABC methods, based on single-tone and chaotic signal dithering, are experimentally evaluated in both 40Gbaud 16QAM and 20Gbaud 64QAM transmission systems. The results indicate that the utilization of chaotic dither signals leads to a reduction in measured bit error rate (BER) for 40Gbaud 16QAM and 20Gbaud 64QAM signals, with respective decreases from 2.48% to 1.26% and from 5.31% to 3.35% when the received optical power is -27dBm.

Comparison of Strength and Power Characteristics Before ACL Rupture and at the End of Rehabilitation Before Return to Sport in Professional Soccer Players.

Strength and power is often reduced on the involved versus contralateral limb and healthy controls after anterior cruciate ligament (ACL) reconstruction, but no study has compared with preinjury values at the time of return to sport (RTS). Divergent recovery patterns in strength and power characteristics will be present at RTS relative to preinjury baseline data and healthy matched controls. Cohort study. Level 3. Isokinetic strength tests, bilateral and single-leg countermovement jumps (CMJ; SLCMJ) were measured before ACL rupture in 20 professional soccer players. These then had surgical reconstruction (ACL group) and completed follow-up testing before RTS. Healthy controls (uninjured group) were tested at the same time as the ACL group preinjury. Values recorded at RTS of the ACL group were compared with preinjury. We also compared the uninjured and ACL groups at baseline and RTS. Compared with preinjury, ACL normalized quadriceps peak torque of the involved limb (difference = -7%), SLCMJ height (difference = -12.08%), and Reactive Strength Index modified (RSImod) (difference = -5.04%) were reduced after ACL reconstruction. No significant reductions in CMJ height, RSImod, and relative peak power were indicated at RTS in the ACL group when compared with preinjury values, but deficits were present relative to controls. The uninvolved limb improved quadriceps (difference = 9.34%) and hamstring strength (difference = 7.36%) from preinjury to RTS. No significant differences from baseline were shown in SLCMJ height, power, and reactive strength of the uninvolved limb after ACL reconstruction. Strength and power in professional soccer players at RTS after ACL reconstruction were often reduced compared with preinjury values and matched healthy controls. Deficits were more apparent in the SLCMJ, suggesting that dynamic and multijoint unilateral force production is an important component of rehabilitation. Use of the uninvolved limb and normative data to determine recovery may not always be appropriate.

Constrained control allocation for dynamic ship positioning using deep neural network

Dynamic positioning (DP) is one of the key technologies towards ship autonomy. Ships in DP mode use thruster devices to maintain position and perform low-speed maneuvering. The motion controller issues force requests according to the measured ship state and motion objectives. These requests must be translated into individual thruster commands. Due to constraints in the thrusters, such as inertia and limited angles of operation, state-of-the-art control allocation methods apply constrained optimization techniques. Although such methods readily capture the handling of constraints, they may require significant computational resources in searching for optimized commands in real time. Here we show that a neural network may be applied to offer an effective evaluation of the mapping between motion controller requests and executable thruster commands. An Autoencoder-like neural network is trained with data generated using knowledge about the configuration of the thrusters. Custom loss functions shape the weights of the network, such that the overall motion objectives and thruster constraints are met. Then, the network is applied to perform low-speed maneuvering and stationkeeping in a simulator. Comparison relative to a state-of-the-art variable angle, constrained control allocator indicate similar dynamic performance with reduced peak power consumption.

Peak Shaving of a District Heated Office Building with Short-Term Thermal Energy Storage in Finland

Short-term thermal energy storage techniques can be effective to reduce peak power and accommodate more intermittent renewable energies in district heating systems. Centralized storage has been the most widely applied type. However, in conventional high-temperature district heating networks, substations are typically not equipped with short-term thermal energy storage. Therefore, this paper investigated its peak shaving potential. A 5 m3 thermal storage tank directly charged by the district heating supply water was integrated into a substation of a Finnish office building. The substation with the stratified storage tank and the office building were modeled and simulated by IDA ICE. Different storage tank temperature control curves were designed to charge the tank during off-peak hours and discharge to reduce the high-peak-period heating power. Moreover, the peak power was further dimensioned by reducing the mass flow of the primary district heating supply water. The results indicate that the storage tank application significantly decreases the office building daily peak power caused by the ventilation system’s morning start during the heating season. It reflected a higher peak shaving potential for colder days with 31.5% of maximum peak power decrease. Cutting the mass flow by up to 30% provides an additional peak power reduction without sacrificing thermal comfort.

The Efficacy of a Multimodal Recovery Strategy Implemented After a High-Intensity Rugby League Training Session.

Aben, HGJ, Hills, SP, Higgins, D, Cooke, CB, Davis, D, Jones, B, and Russell, M. The efficacy of a multimodal recovery strategy implemented after a high-intensity rugby league training session. J Strength Cond Res 37(8): 1634-1642, 2023-The efficacy of a multimodal recovery strategy implemented within 4 hours of rugby league (RL) training was investigated using repeated-measures, randomized, crossover methods in 10 professional academy RL players (age: 17 ± 1 years). Following standardized training (5,383 m covered, 350-m high-speed running, 28 repeated high-intensity efforts, 24 collisions), players completed a multimodal recovery (REC) strategy (i.e., ∼640 kcal meal + ∼1,285 kcal snacks or drinks, cold-water immersion, sleep hygiene recommendations) or control (i.e., ∼640 kcal meal: CONT) practices. Isometric mid-thigh pulls (IMTP), countermovement jumps (CMJ), and wellness questionnaires were completed before (-3 hours) and after (+24, +48 hours) training. The recovery strategy influenced IMTP peak force ( p = 0.026), but between-trial differences were undetectable. No other between-trial effects (all p > 0.05) were seen for IMTP, CMJ, or wellness variables. Training-induced reductions in CMJ peak power (-4 ± 6% vs baseline: 4,878 ± 642 W) at +24 hours ( p = 0.016) dissipated by +48 hours. Fatigue and lower-body soreness reduced by 16 ± 19% ( p = 0.01) and 32 ± 44% ( p = 0.024) at +48 hours versus +24 hours, respectively. Relative to CONT (i.e., posttraining nutrition), the effects of a single bout of recovery practices appeared limited when implemented after RL-specific training. Therefore, when training included limited collisions, balanced postexercise meals appeared equally effective relative to a multimodal recovery strategy. Transient changes in performance and wellness variables after training may have implications for practitioners. Consecutive training sessions, including a high frequency and intensity of eccentric muscle actions, should be carefully planned, especially near match-play.