Peak Power Consumption Research Articles

A reconfigurable test method based on LFSR for 3D stacking integrated circuits

The specificity of the 3D integrated circuit and the changes in the design integration flow bring many new problems to the test process, such as reducing the controllability of the test and increasing the test cost. To address these problems, this paper proposes a low-power hybrid test scheme based on multiple reconfigurable linear feedback shift register(LFSRs) for 3D integrated circuit, which can reuse the test vector generator of the pre-binding test phase in the in-binding and post-binding test phase, reconfiguring a new test vector generator with lower test cost. Compared with the method of using the linear feedback shift register(LFSR) of each layer circuit independently as the test vector generator of the whole circuit in the in-binding test phase, the scheme in this paper can effectively utilize the original LFSR of each layer circuit and reduce the area overhead; the reconfigured 3D-LFSR can obtain better test data compression effect and lower test power consumption, improve test efficiency and reduce test time. Experiments have shown a 5% improvement in average test data compression, 53.1% and 9.6% reduction in average and peak power consumption respectively, 28% reduction in test area and 19.6% reduction in test time.

Design optimization of powered ankle prosthesis to reduce peak power requirement.

The aim of the prosthetic devices is to replicate the able-bodied angle-torque profile of a healthy human during locomotion. A lightweight and energy-efficient ankle joint is able to lower the actuator peak power and/or energy consumption per gait cycle, while adequately fulfilling the profile matching constraints. This study presents the design optimization of the prosthetic ankle joint containing an elastic element and actuator coupled with a rigid triangular part. The dimensions of the ankle joint triangular part were optimized to minimize actuator peak power and maximize spring energy within its elastic limits. As a result of series simulation tests, at 1.1 and 1.6 m/s walking speeds, the simulation of dorsi/plantar flexion shows up to 78.8% and 66.98% reduction in motor peak power compared to a direct drive system, respectively. Low power ankle-prosthetic device that closely matches the angle-torque profile of a healthy human's ankle, is one of the key parameters for the cost-effectiveness of lower limb prostheses.

Determination of the Electricity Storage Power and Capacity for Cooperation with the Microgrid Implementing the Peak Shaving Strategy in Selected Industrial Enterprises

The capacity limitations of distribution networks, the increasing demands for electricity installation in terms of electricity quality parameters, and the development of electromobility all increase the need to implement systems in order to stabilize and regulate loads for end users. Battery Energy Storage Systems (BESSs), that operate in internal microgrids of enterprises, allow smoothing of electrical power consumption profiles by actively reducing peak demand power. By using BESSs to implement a peak shaving strategy to reduce the peak power consumption of a company in an external network, energy efficiency can be improved by reducing contractual capacity. The aim of this study is to determine the conditions for the use of energy storage, in order to implement a peak shaving strategy for which the installation of the enterprise microgrid is economically efficient. The analysis of the operating conditions of the BESS should take into account the size of the energy storage, the characteristics of the demand profile for the demand systems, the charges related to electricity, and the costs of electricity storage. In the study, the size of the energy storage was related to the power and electrical capacity of the BESS that was used to implement the peak shaving strategy. The article presents the results of research on the method for determining an effective capacity and power of BESSs for enterprise microgrid systems. The technical and cost limitations of the actual microgrid system, which affected the decrease in economic efficiency of the peak shaving strategy in the company research, were taken into account. The simulations of the operation of the electricity storage system, based on real data of the demand of production companies, were based on the rules and market conditions in Poland.

35‐4: Student Paper: Systematic Comparisons on Display Performances including Halo Effect

We evaluate and compare the performance of a 15.6‐inch mini‐LED backlit LCD (mLCD) with a commercial OLED panel. Results show that the mLCD exhibits advantages over the OLED panel in peak brightness, gamma curve, color volume, power consumption, and ambient contrast ratio. The LMK imaging photometer with a high‐resolution CMOS sensor is employed to characterize the halo effect and reconstruct the real luminance profiles based on the non‐ideal profile of the OLED display. We have also proposed a novel strategy to measure the backlight's point spread function.

A Smart Camera Trap for Detection of Endotherms and Ectotherms

Current camera traps use passive infrared triggers; therefore, they only capture images when animals have a substantially different surface body temperature than the background. Endothermic animals, such as mammals and birds, provide adequate temperature contrast to trigger cameras, while ectothermic animals, such as amphibians, reptiles, and invertebrates, do not. Therefore, a camera trap that is capable of monitoring ectotherms can expand the capacity of ecological research on ectothermic animals. This study presents the design, development, and evaluation of a solar-powered and artificial-intelligence-assisted camera trap system with the ability to monitor both endothermic and ectothermic animals. The system is developed using a central processing unit, integrated graphics processing unit, camera, infrared light, flash drive, printed circuit board, solar panel, battery, microphone, GPS receiver, temperature/humidity sensor, light sensor, and other customized circuitry. It continuously monitors image frames using a motion detection algorithm and commences recording when a moving animal is detected during the day or night. Field trials demonstrate that this system successfully recorded a high number of animals. Lab testing using artificially generated motion demonstrated that the system successfully recorded within video frames at a high accuracy of 0.99, providing an optimized peak power consumption of 5.208 W. No water or dust entered the cases during field trials. A total of 27 cameras saved 85,870 video segments during field trials, of which 423 video segments successfully recorded ectothermic animals (reptiles, amphibians, and arthropods). This newly developed camera trap will benefit wildlife biologists, as it successfully monitors both endothermic and ectothermic animals.

Cold thermal energy storage for industrial CO[formula omitted] refrigeration systems using phase change material: An experimental study

Refrigeration systems in industrial food processing plants are large users of electric energy and often show high peak power consumption. Cold thermal energy storage (CTES) technology integrated into refrigeration systems can reduce the peak power requirement and achieve peak shifting by decoupling the supply and demand of the refrigeration load. This paper presents the design and performance of a CTES unit consisting of a pillow plate heat exchanger (PP-HEX) immersed into a low-temperature phase change material (PCM) as the storage medium. It is one of the first experimental investigations featuring a large-scale technical solution that allows for coupling the evaporation and condensation processes of the refrigeration system with the melting and solidification of a low-temperature PCM in the same heat exchanger. The charging and discharging performance of the plates-in-tank CTES unit was extensively tested using CO2 as the refrigerant and a commercial PCM with phase change temperature of -9.6 °C. The charging time was found to be mainly affected by the refrigerant evaporation temperature, while the discharging rate and discharged energy over the cycle was higher when increasing the refrigerant condensing temperature. Using a plate pitch of 30 mm resulted in the highest mean discharge rate and total discharged energy over the cycle with 9.79 kW and 17.04 kWh, respectively. The flexible CTES-PCM unit can be adapted to fit several refrigeration load characteristics and temperature levels by changing the PP-HEX geometry and type of PCM used as the storage medium.

Peak Power Minimization for Commercial Thermostatically Controlled Loads in Multi-Unit Grid-Interactive Efficient Buildings

The load profiles of most commercial and industrial consumers are characterized by brief periods of very high power consumption followed by intervals of lower demand. To encourage such consumers to flatten their load profiles, power utilities around the world often levy a monthly demand charge (DC) on the peak demand measured over brief intervals. In this work, we consider the joint optimization of energy costs (EC) and the instantaneous peak power of a multi-unit building which uses a hydronic heating, ventilation and air-conditioning (HVAC) system and responds to a demand response (DR) program. Despite the apparent non-convexity of the control framework, we show how it may be transformed into a convex optimization problem. Next, we study the power demand patterns resulting from our proposed strategy for thermostatically controlled loads (TCLs), and evaluate the strategy’s performance for various climate zones in the US under both typical and atypical weather conditions. The results show that, depending on the ambient conditions and the tariff structure, our strategy can result in utility bill savings of up to nearly 19% compared to the baseline. The results also indicate that our power control strategy can significantly reduce the instantaneous peak power consumption in commercial TCLs.

Battery Sizing Optimization in Power Smoothing Applications

The main objective of this work was to determine the worth of installing an electrical battery in order to reduce peak power consumption. The importance of this question resides in the expensive terms of energy bills when using the maximum power level. If maximum power consumption decreases, it affects not only the revenues of maximum power level bills, but also results in important reductions at the source of the power. This way, the power of the transformer decreases, and other electrical elements can be removed from electrical installations. The authors studied the Spanish electrical system, and a particle swarm optimization (PSO) algorithm was used to model battery sizing in peak power smoothing applications for an electrical consumption point. This study proves that, despite not being entirely profitable at present due to current kWh prices, implanting a battery will definitely be an option to consider in the future when these prices come down.

Real-Time Energy Management Strategy for Parking Lot Considering Maximum Penetration of Electric Vehicles

With the rapid increase of electric vehicles (EVs), the uncoordinated charging of large-scale EVs will inevitably form a new peak of power consumption, which will put forward new requirements for the adjustment of EV charging behavior and the power supply capacity of the distribution network. However, upgrading the infrastructure of the distribution network is very expensive, so it is of great value to research on how to realize the optimal integration of EVs without infrastructure upgrading. In this paper, an intelligent grouping method is proposed considering the coupling relationship among EV travel information, battery status and other characteristics, and a charging/discharging priority model is established based on the contribution index of charging process. Finally, a smart real-time energy management strategy is formed in order to maximize the penetration level of EVs under current conditions. With the strategy proposed, the maximum penetration level of EVs is increased from 20% to 60% in the simulation. The strategy has good adaptability to the gradually growth of the base load, which can effectively delay infrastructure upgrading of the distribution network and reduce the overall operation cost of the station. It could furtherly provide a reference for the operation and upgrading of the parking lot charging station.

Real-Time Demand Response Management for Controlling Load Using Deep Reinforcement Learning

With the rapid economic growth and improved living standards, electricity has become an indispensable energy source in our lives. Therefore, the stability of the grid power supply and the conservation of electricity is critical. The following are some of the problems facing now: 1) During the peak power consumption period, it will pose a threat to the power grid. Enhancing and improving the power distribution infrastructure requires high maintenance costs. 2) The user's electricity schedule is unreasonable due to personal behavior, which will cause a waste of electricity. Controlling load as a vital part of incentive demand response (DR) can achieve rapid response and improve demand-side resilience. Maintaining load by manually formulating rules, some devices are selective to be adjusted during peak power consumption. However, it is challenging to optimize methods based on manual rules. This paper uses Soft Actor-Critic (SAC) as a control algorithm to optimize the control strategy. The results show that through the coordination of the SAC to control load in CityLearn, realizes the goal of reducing both the peak load demand and the operation costs on the premise of regulating voltage to the safe limit.

Design and Performance Evaluation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring

This article proposes a proof-of-concept device to continuously assess the usage of handheld power tools and detect construction working tasks (e.g., different drilling works) along with potential misusages, e.g., drops, with an energy-efficient architecture design. The designed device is based on Bluetooth low energy (BLE) and NFC connectivity. BLE is used to exchange data with a gateway, whereas NFC has been chosen as an energy-efficient wake-up mechanism. A temperature and humidity sensor is embedded to monitor storage conditions and an accelerometer for tool usage monitoring. The ARM Cortex-M4 core embedded in the BLE module is exploited to process the information at the edge. A Tiny Machine Learning (TinyML) algorithm is proposed to process the data directly on board and achieve low latency and high energy efficiency. The TinyML algorithm has been developed embedded in the proposed device to detect four different usage classes (tool transportation, no-load, metal, and wood drilling). A dataset containing more than 280 min of three-axis accelerations during different activities has been acquired with the device attached to a construction rotary hammer drill and used to train and validate the algorithm. A neural architecture search has been performed to optimize the trade-off between accuracy and complexity, achieving an accuracy of 90.6% with a model size of roughly 30 kB. The experimental results showed an ultralow power consumption in sleep mode of 550 nA and a peak power consumption of 8 mA while running TinyML on the edge. This results in a balanced combination of edge processing capabilities and low power consumption, enabling to obtain a smart Internet of Things (IoT) device in the field with a long lifetime of up to four years in operation and 17 years in shelf mode with a standard 250-mAh coin battery. This work enables a long battery lifetime operation of device degradation and utility analysis, further closing the gap between edge processing and fine granularity data evaluation.

A Novel Category-Specific Pricing Strategy for Demand Response in Microgrids

Setting different electricity prices for different types of loads can effectively reduce the peak power consumption in microgrids (MGs). This paper proposes a category-specific pricing strategy for demand response program in dynamic MGs that can efficiently utilize renewable energy to achieve peak shaving and valley filling via establishing a Stackelberg game model. A state characteristic clustering (SCC) based non-intrusive load monitoring (NILM) scheme is first proposed, by which both the MG market operator (MMO) and users can access the detailed power consumptions of shiftable and non-shiftable loads. MMO then specifies detailed electricity prices dynamically based on user-side demand and satisfaction feedback, while users adjust their shiftable loads in a timely manner accordingly. Through solving the game optimization problem, the uniqueness and existence of the Stackelberg equilibrium is derived. Moreover, a distributed solution algorithm is presented to seek the unique equilibrium. Finally, a real residential power dataset is used to verify the effectiveness of the proposed category-specific pricing strategy. Numerical results show that the strategy reduces the peak-valley difference significantly, mitigates the power imbalance, and improves the utility of MG participators.

Designing Reconfigurable Manufacturing Systems to Minimize Power Peak

Industry is more and more urged towards energy efficiency by the increasing societal and environmental concern about energy. This is also true for more recent production system types, like Reconfigurable Manufacturing Systems (RMS), which are gaining momentum due to the advent of Industry 4.0 and the growing uncertainty of markets. In this work, we consider energy efficiency in the design stage of an RMS. The tasks of a production process must be assigned to the machines of a Parallel-Serial manufacturing line, and scheduled, so that the related power consumption peak is minimized. An Integer Linear Programming formulation is proposed and tested on a set of benchmark instances. Numerical results are discussed.

Reduction of the environmental impacts of the hydropower plant by microalgae cultivation and biodiesel production

Hydropower plants supply their energy needs for electricity generation from rivers or water canals, so these power plants cannot be used for sustainable electricity generation, and the best time to use these power plants is during peak power consumption. These power plants are less polluting in terms of environment than other power plants, but they also have negative environmental effects, such as freshwater eutrophication and water salinity or microalgae. This study focused its attention on microalgae extraction as an environmentally friendly method to reduce water salinity and how they can be used for biodiesel production as an auxiliary fuel to enhance the energy production by hydropower plants. The information of the sample hydroelectric power plant (Gotvand Dam) that was required for the processing and simulation process is stated. The step-by-step simulation is reviewed and the results and optimizations are described. The highest separation of microalgae for 1 min electrolysis with distance of 1 cm between the two electrodes is 90%, which reduces the salt content of the water in which the microalgae is grown by 13%. The maximum separation of salt from water is 19.5%, which is reduced to 9.5% in the centrifuge method. Water salinity reduction to microalgae extraction ratio is 14.45%. The optimal combination of diesel and biodiesel is 80%–20%. As can be seen from the results it is recommended to use microalgae for reducing negative environmental impacts in addition to increasing the power generation capacities of hydropower plants. Also more specific studies on terms of the culture of the microalgae and its individual cultivation methods for hydropower plants beneficial programs should be taken into account and be used by policy makers in the future.

Predictive Algorithm for Handover Decisions between LTE and LTE-A Networks

A heterogeneous wireless network needs to maintain seamless mobility and service continuity; for this reason, we have proposed an approach based on the combination of particle swarm optimization (PSO) and an adaptive neuro-fuzzy inference system (ANFIS) to forecast a handover during a movement of a mobile terminal from a serving base station to target base station. Additionally, the handover decision is made by considering several parameters, such as peak data rate, latency, packet loss, and power consumption, to select the best network for handover from an LTE to an LTE-A network. The performance efficiency of the new hybrid approach is determined by computing different statistical parameters, such as root mean square error (RMSE), coefficient of determination (R2), mean square error (MSE), and error standard deviation (StD). The execution of the proposed approach has been performed using MATLAB software. The simulation results show that the hybrid PSO-ANFIS model has better performance than other approaches in terms of prediction accuracy and reduction of handover latency and the power consumption in the network.

Method for power reduction of demodulation driver circuit in indirect time-of-flight CMOS image sensor.

To improve the depth accuracy of an indirect time-of-flight CMOS image sensor, high modulation frequency is often adopted. It will result in high power consumption of an on-chip demodulation driver, and this problem will be much more serious when the resolution of the sensor is much higher. In this paper, a power reduction method that can lower the power consumption of the demodulation driver circuit during the integration time while obtaining accurate high-resolution depth maps is proposed and analyzed theoretically. The method decreases the number of driven pixels at a high-modulation frequency by a programmable resolution adjustment circuit to obtain an accurate low-resolution depth map. A low-depth accuracy high-resolution depth map is obtained at a low modulation frequency, and then a modified super-resolution algorithm is used to obtain an accurate high resolution solution depth map. To demonstrate the effectiveness of the proposed method, a model is established based on the actual indirect time-of-flight sensor architecture, then the depth error and power consumption are analyzed by the simulation results of the model. In the simulation, 25 MHz and 100 MHz are used as the low modulation frequency and high modulation frequency, respectively. With the best scenario in depth accuracy kept, average power consumption decreases 38.47% and peak power consumption decreases 49.83% while the depth error that is represented by RMSE merely increases 8.08%.

Experimental characterisation of a cold thermal energy storage unit with a pillow-plate heat exchanger design

• Design of a novel cold thermal energy storage unit for CO 2 refrigeration systems. • Investigation of the charging and discharging performance of the unit. • Variation of the heat exchanger geometry to adapt to different refrigeration load. • Condensation and evaporation temperature of CO 2 are the most critical parameters. • Maximum discharged energy is 35.29 kWh over 4.5 h with a mean rate of 7.90 kW. Cold Thermal Energy Storage (CTES) technology can be introduced to refrigeration systems for air conditioning and process cooling to reduce the peak power consumption by decoupling the supply and demand of refrigeration. In these systems, the refrigeration demand can vary significantly over a day, resulting in a challenging peak and valley load pattern on the electrical grid. This paper presents the design, development, and experimental performance investigations of a novel plates-in-tank CTES unit design intended for integration into pump-circulated CO 2 industrial refrigeration systems. The CTES unit is composed of a stainless steel container filled with water as the latent storage medium and fitted with a pillow plate heat exchanger. The refrigerant (CO 2 ) circulates within the heat exchanger to transfer heat with the storage medium. The current study demonstrates the feasibility of implementing a latent CTES unit directly into the primary refrigerant circuit for peak shaving of the refrigeration load. The results show that the evaporation and condensation temperatures of the refrigerant are the most critical parameters influencing the performance of the charging and discharging cycles, respectively. The unit demonstrated a mean discharge rate of 7.90 kW over a total discharging cycle time of approximately 4.5 h. The resulting maximum discharged energy was calculated to 35.29 kWh.

Method for Determining the Optimal Capacity of Energy Storage Systems with a Long-Term Forecast of Power Consumption

The unevenness of the electricity consumption schedule at enterprises leads to a peak power increase, which leads to an increase in the cost of electricity supply. Energy storage devices can optimize the energy schedule by compensating the planned schedule deviations, as well as reducing consumption from the external network when participating in a demand response. However, during the day, there may be several peaks in consumption, which lead to a complete discharge of the battery to one of the peaks; as a result, total peak power consumption does not decrease. To optimize the operation of storage devices, a day-ahead forecast is often used, which allows to determine the total number of peaks. However, the power of the storage system may not be sufficient for optimal peak compensation. In this study, a long-term forecast of power consumption based on the use of exogenous parameters in the decision tree model is used. Based on the forecast, a novel algorithm for determining the optimal storage capacity for a specific consumer is developed, which optimizes the costs of leveling the load schedule.

A Bionic-Intelligent Scheduling Algorithm for Distributed Power Generation System

With the introduction of the new power system concept, diversified dis-tributed power generation systems, such as wind power, photovoltaics, andpumped storage, account for an increasing proportion of the energy supplyside. Facing objective issues such as distributed energy decentralization andremote location, exploring what kind of algorithm to use to dispatch nearbydistributed energy has become a hot spot in the current electric power field. Inview of the current situation, this paper proposes a Bionic Intelligent Schedul-ing Algorithm (DWMFO) for distributed power generation systems. On thebasis of the Moth Flame Algorithm (MFO), in order to solve the problemof low accuracy and slow convergence speed of the algorithm in schedulingdistributed energy, we use the adaptive dynamic change factor strategy todynamically adjust the weighting factor of the MFO. The purpose is to assistthe power dispatching department to dispatch diversified distributed energysources such as wind power, photovoltaics, and pumped storage in a timelymanner during the peak power consumption period. In the experiment, wecompared with 4 algorithms. The simulation results of 9 test functions showthat the optimization performance of DWMFO is significantly improved, theconvergence speed is faster, the solution accuracy is higher, and the global search capability is stronger. Experimental test results show that the proposedbionic intelligent scheduling algorithm can expand the effective search spaceof distributed energy. To a certain extent, the possibility of searching for theglobal optimal solution is also increased, and a better flame solution can befound.

Stochastic programming approaches for an energy-aware lot-sizing and sequencing problem with incentive

Motivated by real challenges on energy management faced by industrial firms, we propose a novel way to reduce production costs by including the pricing of electricity in a multi-product lot-sizing problem. In incentive-based programs, when electric utilities face power consumption peaks, they request electricity-consuming firms to curtail their electric load, rewarding the industrial firms with incentives if they comply with the curtailment requests. Otherwise, industrial firms must pay financial penalties for an excessive electricity consumption. A two-stage stochastic formulation is presented to cover the case where a manufacturer wants to satisfy any curtailment request. A chance-constrained formulation is also proposed, and its relevance in practice is discussed. Finally, computational studies are conducted to compare mathematical models and highlight critical parameters and show potential savings when subscribing incentive-based programs. We show that the setup cost ratio, the capacity utilisation rate, the number of products and the timing of curtailment requests are critical parameters for manufacturers.