Peak Power Consumption Research Articles

Inhibiting the current spikes within the channel layer of LiCoO2-based three-terminal synaptic transistors

Synaptic transistors, which emulate the behavior of biological synapses, play a vital role in information processing and storage in neuromorphic systems. However, the occurrence of excessive current spikes during the updating of synaptic weight poses challenges to the stability, accuracy, and power consumption of synaptic transistors. In this work, we experimentally investigate the main factors for the generation of current spikes in the three-terminal synaptic transistors that use LiCoO2 (LCO), a mixed ionic-electronic conductor, as the channel layer. Kelvin probe force microscopy and impedance testing results reveal that ion migration and adsorption at the drain–source-channel interface cause the current spikes that compromise the device's performance. By controlling the crystal orientation of the LCO channel layer to impede the in-plane migration of lithium ions, we show that the LCO channel layer with the (104) preferred orientation can effectively suppress both the peak current and power consumption in the synaptic transistors. Our study provides a unique insight into controlling the crystallographic orientation for the design of high-speed, high-robustness, and low-power consumption nano-memristor devices.

Considering the peak power consumption problem with learning and deterioration effect in flow shop scheduling

This paper investigates the permutation flow shop scheduling problem with peak power constraints under sequence-dependent setup time, learning, and deterioration effects to minimize the makespan, where the peak power consumption satisfies a given upper bound at any time. We establish relevant mathematical models based on the characteristics of the scheduling environment and set up five setup time-based heuristics, including the earliest start time, the latest setup time based on balance job–machine, latest setup time based on balance machine–job, latest setup time insert based on balance job–machine, and latest setup time insert based on balance machine–job. Similarly, a hybrid genetic algorithm combined with simulated annealing is proposed to prevent premature trapping in local optima. The algorithms are evaluated through a large number of data experiments, and the results show that it can effectively solve this scheduling problem.

IoT Weather Station Optimized for Energy Efficiency

This work presents an economical IoT weather station with energy-efficient capabilities. It offers a multi-sensor platform, with balanced functionality and power optimization that ensures an extended- wireless range. The developed system features diverse sensors measuring temperature, humidity, pressure and light intensity. Employing power-saving techniques like sleep/wake cycles and controlled sampling rates makes the station economical and power-efficient. This setup allows distant observation via a web browser interface, addressing the challenge of low power and extended-range deployments of embedded systems. The adopted energy optimization strategy enabled autonomous long-range functioning, with real-time data collection and visualization. The recoded peak power consumption is only 2022 A for all the embedded operations.

A MATLAB APP FOR BALANCING A CAPACITOR BANK IN THE DOUBLE STAR CONNECTION - NEUTRAL, NOT CONNECTED

This study presents a methodology for maintaining and rebalancing Double Star configuration capacitor banks, which are critical for managing power consumption peaks. As electricity demand is projected to rise by 21% by 2040, efficient maintenance methods are essential to ensure energy security. The paper introduces a novel approach to rebalance capacitor banks using a Matlab application, which simulates the unbalance current that may occur due to capacitive unit failures. Key aspects of capacitor banks, such as construction, connections, and protection methods, were reviewed to inform the methodology. Capacitor banks are used to control voltage, correct power factor, and reduce losses, but they can experience imbalances due to natural variances in capacitance or failures. The study developed a mathematical model to calculate the unbalance current and validated this model through simulations in LTspice software. The resulting rebalancing flowchart provides a step-by-step guide for technicians to follow during maintenance. The project achieved its goals, demonstrating through 24 simulations that the developed methodology and Matlab application could successfully predict unbalance currents with a negligible margin of error. The paper concludes that the methodology is ready for implementation, and each capacitor bank will require a tailored rebalancing application based on the presented calculations.

Performance Analysis of Multiple Energy-Storage Devices Used in Electric Vehicles

Considering environmental concerns, electric vehicles (EVs) are gaining popularity over conventional internal combustion (IC) engine-based vehicles. Hybrid energy-storage systems (HESSs), comprising a combination of batteries and supercapacitors (SCs), are increasingly utilized in EVs. Such HESS-equipped EVs typically outperform standard electric vehicles. However, the effective management of power sources to meet varying power demands remains a major challenge in the hybrid electric vehicles. This study presents the development of a MATLAB Simulink model for a hybrid energy-storage system aimed at alleviating the load on batteries during periods of high power demand. Two parallel combinations are investigated: one integrating the battery with a supercapacitor and the other with a photovoltaic (PV) system. These configurations address challenges encountered in EVs, such as power fluctuations and battery longevity issues. Although batteries are commonly used in conjunction with solar PV systems for energy storage, they incur higher operating costs due to the necessity of converters. The findings suggest that the proposed supercapacitor–battery configuration reduces battery peak power consumption by up to 39%. Consequently, the supercapacitor–battery HESS emerges as a superior option, possibly prolonging battery cycle life by mitigating stress induced by fluctuating power exchanges during the charging and discharging phases.

Explorations of Integrated Multi-Energy Strategy under Energy Simulation by DeST 3.0: A Case Study of College Dining Hall

Buildings characterized by high energy consumption necessitate the implementation of efficient multi-energy complementary systems to achieve energy conservation and emission reduction objectives. College dining halls use a lot more electricity than typical residential buildings, despite their relatively small size. The dining hall at the Dongshan Campus of Shanxi University is employed as a representative case study in this research. By utilizing DeST 3.0 software, a comprehensive dynamic load analysis is conducted to estimate the annual energy consumption of the dining hall, with the ultimate goal of an energy-saving system being proposed based on the analysis results. Leveraging DeST 3.0 software, dynamic load characteristics were assessed, revealing an annual energy consumption of 2.39 × 106 kWh for the dining hall. Cooling accounted for 0.91 × 106 kWh, while heating requirements amounted to 0.24 × 106 kWh. These findings illustrate peak power consumption trends, seasonal variations, and potential avenues for energy conservation. To satisfy the heating, cooling, and electricity demands of the dining hall, an integrated energy system incorporating solar and wind energy, as well as utilizing restaurant kitchen garbage as a biomass source, was proposed. This study compares two solar energy utilization systems: photothermal and photovoltaic, with total capacities of 2.375 × 106 kWh and 2.52 × 106 kWh, respectively. The research outcomes underscore that Strategy 2, which integrates a photovoltaic system with wind and biomass energy, emerges as the optimal approach for load management. Ultimately, this investigation demonstrates the feasibility and promise of constructing a hybrid renewable energy system within a college dining hall setting, aligning with sustainability objectives and global trends toward environmentally responsible energy solutions.

Energy-efficient scheduling in an identical parallel machine environment with peak power consumption and deadline constraints

Energy-efficient scheduling is an essential means of achieving sustainability in manufacturing systems. This paper defines and addresses the problem of scheduling n jobs on m identical parallel machines in which peak power consumption and deadline constraints exist simultaneously. The objective is to maximize the total value of the selected jobs. We show that this problem is equivalent to a special case of the rectangular knapsack problem, based on which four properties are observed. To solve the problem, an effective mixed integer linear programming model is proposed based on the properties, and it is much more efficient compared to the performance of modeling methods inspired by other works. Furthermore, three effective decoding methods are proposed and embedded into genetic algorithms (GAs). Comparisons with the exact algorithm (i.e., the proposed model) show that our GAs can lead to good-quality solutions within one second for small instances. Meanwhile, experimental results for large instances indicate that the proposed GAs can obtain near-optimal or satisfactory solutions. Finally, results also show that the proposed GA-DD significantly outperforms the existing matheuristic algorithm.

Risk assessment of zero-carbon salt cavern compressed air energy storage power station

While exploiting natural resources, human beings have also left irreversible damage to the environment. The salt caverns left behind by the mining of salt are one of them. With the proposal of the “dual carbon” background, clean power and energy storage power stations have also become one of the focuses of sustainable development. The abandoned salt cavern is combined with the energy storage power station, and the excess electric energy is used to compress the air during the low power consumption period through the non-supplementary combustion mode, and the air kinetic energy is converted into electric energy during the peak power consumption period to realize the zero-carbon salt cavern energy storage conversion. This method can not only achieve zero carbon utilization of the abandoned environment, but also achieve energy storage and conversion, which is a very meaningful research. Based on spherical fuzzy sets, cumulative prospect theory and VIKOR, this paper constructs a novel combined research framework to analyze the risk of zero-carbon salt cavern compressed air energy storage (SAES) power station, and takes China ‘s first zero-carbon SAES power station project as an example. The results show that the overall risk of the zero-carbon SAES power station is 0.3467, which is a low risk. The key risks are non-supplementary combustion thermal energy storage technology risk, salt cavern creep and leakage risk, and the risk tolerance limit is +31.54 %.

Effective incentive based demand response with voltage support capability via reinforcement learning based multi-agent framework

With the ongoing advancements in power grids, it is crucial to maintain a balance between energy generation and usage to ensure the stability of power systems. Any disparity between the supply and demand of energy can lead to increased expenses for utility companies and consumers, which might potentially result in a widespread failure of the grid. To tackle this problem, this study offers the implementation of the Voltage Capability Incentive-Based Demand Response (VC-IBDR) system, which utilizes a multi-agent framework. The main goal is to reduce power usage by providing financial incentives, while also ensuring that voltage standards are satisfied at each node. In this method, the aggregator agent coordinates demand response operations by interacting with household agents that are equipped with home energy management systems. Every household utilizes a disjunctively constrained knapsack problem optimization method to effectively organize the consumption of home appliances, considering customer preferences and dissatisfaction. During demand response events, the aggregator agent works together with household agents to optimize appliance schedules, ensuring that voltage levels at each node remain within acceptable levels. The simulation is performed to assess the efficacy of VC-IBDR on a feeder that provides service to 25 residential households. The findings indicate substantial decreases in power usage, with an average decline in demand of 4.20% at the feeder level and a 10.41% decrease in peak power consumption throughout the day. Furthermore, the voltage values at each node stay consistently steady within the range of 0.96 to 1.0 per unit throughout the day.

Experimental study of an on-grid hybrid solar air conditioner with evaporative pre-cooling of condenser inlet air

This paper presents an experimental study of a split type solar air conditioning system with evaporative pre-cooling at the condenser. The main objective of this research is to evaluate the thermal and electrical performance of the system under hot summer conditions in a Mediterranean climate. Previous studies have separately shown the benefits of both condenser inlet air pre-cooling and photovoltaic solar cooling. The novelty of this work will be the study of the coupling of these two techniques in a real installation, where the viability of the system depends on the component-level design and the operating conditions, since weather variables such as ambient temperature, relative humidity and solar radiation significantly affect the thermal behaviour of the refrigeration cycle, the evaporative pre-cooling capacity and the performance of the photovoltaic panels. Experimental tests were carried out over a summer season to evaluate the behaviour of the system and to obtain performance correlations validated with experimental data. These correlations will allow the modelling of the system and will be used to carry out an economic and environmental feasibility study of the proposed hybrid solar air conditioning system. The results show that a 100mm-thick evaporative pad achieved an average evaporative cooling efficiency for all tests performed of ɛevap=67.4%, reducing the air inlet temperature to the condenser by around 6°C. This resulted in an 18.3% improvement in the EER of the air conditioner. In addition, the use of evaporative pre-cooling resulted in a significant 23% reduction in peak power consumption. The study also evaluated the impact of the air pressure drop caused by the evaporative pad when not in use, with results favouring its installation. The system achieved a solar contribution of more than 30% under different solar radiation conditions when using pre-cooling, compared to 25% with a standard condenser. In addition, the combination of photovoltaic contribution and evaporative pre-cooling reduced energy consumption by 43%.

EV Charging Scheduling Under Demand Charge: A Block Model Predictive Control Approach

This paper studies the online scheduling of electric vehicle charging by a service provider subject to a demand charge in a distribution system. Demand charge imposes a penalty on the peak power consumption over each billing period, representing a substantial cost for the service provider with a large number of clients. Because the demand charge is calculated at the end of the billing period, it poses challenges in real-time scheduling when energy demand forecasts are inaccurate, resulting in either overly conservative power consumption or substantial demand charge. We propose a block model predictive control approach that decomposes the demand charge into a sequence of stage costs. Optimality conditions on demand patterns are also presented and analyzed. Numerical simulations demonstrate the efficacy of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper addresses a significant practical problem of minimizing the demand charge on the real-time scheduling of deferrable demands. In particular, we consider a setting where a commercial electric vehicle (EV) charging service provider has to manage the online scheduling of a large number of arriving EVs at a charging facility subject to a maximum charging power constraint and a tariff with the demand charge. A major practical challenge is to balance the tradeoff between maximizing profit in scheduling as much EV charging as possible and the need to minimize penalty on the peak charging power. We propose a model predictive control strategy that decomposes the overall demand charge into a sequence of terminal costs. Also addressed is the practical constraint arising from the mismatched EV charging decision period and the power measurement period used to compute the demand charge. Using real data collected at the Adaptive Charging Network (ACN) testbed in simulations, the proposed approach yields 8-12% improvement in operational profit over existing benchmarks, while it has yet been tested in actual charging systems. In the future research, we will address the charging scheduling under demand charge over multiple charging stations.

Monolithic back-end-of-line integration of phase change materials into foundry-manufactured silicon photonics

Monolithic integration of novel materials without modifying the existing photonic component library is crucial to advancing heterogeneous silicon photonic integrated circuits. Here we show the introduction of a silicon nitride etch stop layer at select areas, coupled with low-loss oxide trench, enabling incorporation of functional materials without compromising foundry-verified device reliability. As an illustration, two distinct chalcogenide phase change materials (PCMs) with remarkable nonvolatile modulation capabilities, namely Sb2Se3 and Ge2Sb2Se4Te1, were monolithic back-end-of-line integrated, offering compact phase and intensity tuning units with zero-static power consumption. By employing these building blocks, the phase error of a push-pull Mach–Zehnder interferometer optical switch could be reduced with a 48% peak power consumption reduction. Mirco-ring filters with >5-bit wavelength selective intensity modulation and waveguide-based >7-bit intensity-modulation broadband attenuators could also be achieved. This foundry-compatible platform could open up the possibility of integrating other excellent optoelectronic materials into future silicon photonic process design kits.

Modeling, design and optimization of integrated renewable energy systems for electrification in remote communities

Integrated renewable energy systems are becoming a promising option for electrification in remote communities. Integrating multiple renewable energy sources allows the communities to counteract the weaknesses of one renewable energy source with the strengths of another. This study aims to model, design and optimize integrated renewable energy systems consisting of solar photovoltaic (PV) panels, wind turbines, a biomass power generator, and storage batteries for applications in remote communities in Canada. Biomass is used as a fuel to produce electricity during periods when solar power and wind power are not capable of meeting the power demand. A methodology is developed to optimize the integrated renewable energy systems design, with the aim of minimizing the net present cost (NPC) and the levelized cost of electricity (LCOE) of the energy systems. Results show that the NPC is $3.61 M and the LCOE is $0.255/kWh for an optimized integrated renewable energy system in a sample remote community that has a peak power consumption of 238.7 kW and an average load demand of 2230 kWh/day. Through the present research, the integrated energy systems are evidenced to be an effective option for electrification in remote communities.

Numerical simulation of convective heat transfer in the discharging process of external ice-melting ice storage systems

The external ice-melting ice storage system was widely used because of its advantages of reducing peak power consumption, reducing energy consumption and emissions, and fast ice melting speed. However, designing the external ice-melting ice storage system proved to be a challenge, and the structure and flow of the water distribution system needed to be fully considered. In particular, the problem of unstable outlet water temperature limited the practical application of the external melting ice storage system. Aiming at the problem of unstable outlet water temperature, a three-dimensional CFD phase change model was constructed in this paper. By comparing with the experimental data, the impact of varying water flow rates on the thermal and cooling properties of the was investigated. The results indicated that when the water flow rate was 226 ml/min for the system with an ice thickness of 74 mm, the outlet temperature of the ice storage tank was consistently below 5°C, the Nusselt number of ice and water remained constant, and the convective heat transfer effect was improved. The numerical simulation results provided guidance for the design and practical application of the water distribution system of the external melting ice storage system.

Renewable Energy in Portugal - Legislation, Incentives and Suggestions.

This paper presents the evolution of renewable energy generation in Portugal in the last decade, and explains the legislation and incentives in existence. The paper also presents suggestions that could incentive small and medium consumers to install renewable energy power plants (namely of wind power and photovoltaic types) in their facilities. These power plants would have to accomplish present legislation regarding “reactive energy” production during peak power consumption period, and besides, would have to assure power quality levels. On the other hand, reversible energy meters should be used, so that it would be registered the consumption of energy from the energy company, or the delivery of energy to the energy company. The equipments which could implement these characteristics are briefly described.

The design and analysis of a hydro-pneumatic energy storage closed-circuit pump control system with a four-chamber cylinder

A decentralized variable electric motor and fixed pump (VMFP) system with a four-chamber cylinder is proposed for mobile machinery, such that the energy efficiency can be improved by hydro-pneumatic energy storage, and problems of closed-circuit pump-controlled systems including asymmetrical flow and speed limitation are addressed. One of the chambers is arranged to the energy storage accumulator to increase energy efficiency, while the other chambers are flexibly connected to the pump ports to achieve variable transmission ratios. The piston areas of the multiple chambers are designed at first to permit a symmetric single-rod cylinder, and secondly a switching system with three operating modes is presented to expand force-velocity capabilities. Then, the four-chamber cylinder system with three solenoid valves is designed to substitute for the traditional two-chamber boom cylinder in a 6-ton excavator. A valve switching logic, as well as a feedforward and feedback compound speed controller, are presented. Simulations conducted in a 6-ton excavator model show that the maximum velocity increases by 66 %, together with asymmetrical flows to diminish the low-pressure charging accumulator. Peak power and energy consumption are both reduced by more than 20 % compared with the VMFP using the two-chamber cylinder. The energy-saving characteristics of the 6-ton excavator are emphatically analyzed considering energy storage and re-utilization. At last, experiment verifications are conducted in a lifting mechanism to verify the proposed energy-storage system.

Research on Energy Efficiency Characteristics of Mining Shovel Hoisting and Slewing System Driven by Hydraulic-Electric Hybrid System

Mining shovel is a crucial piece of equipment for high-efficiency production in open-pit mining and stands as one of the largest energy consumption sources in mining. However, substantial energy waste occurs during the descent of the hoisting system or the deceleration of the slewing platform. To reduce the energy loss, an innovative hydraulic-electric hybrid drive system is proposed, in which a hydraulic pump/motor connected with an accumulator is added to assist the electric motor to drive the hoisting system or slewing platform, recycling kinetic and potential energy. The utilization of the kinetic and potential energy reduces the energy loss and installed power of the mining shovel. Meanwhile, the reliability of the mining shovel pure electric drive system also can be increased. In this paper, the hydraulic-electric hybrid driving principle is introduced, a small-scale testbed is set up to verify the feasibility of the system, and a co-simulation model of the proposed system is established to clarify the system operation and energy characteristics. The test and simulation results show that, by adopting the proposed system, compared with the traditional purely electric driving system, the peak power and energy consumption of the hoisting electric motor are reduced by 36.7% and 29.7%, respectively. Similarly, the slewing electric motor experiences a significant decrease in peak power by 86.9% and a reduction in energy consumption by 59.4%. The proposed system expands the application area of the hydraulic electric hybrid drive system and provides a reference for its application in oversized and super heavy equipment.

A Multichannel Electrochemical Sensor Interface IC for Bioreactor Monitoring.

This research article introduces a novel integrated circuit (IC) designed for bioreactor applications catering to multichannel electrochemical sensing. The proposed IC comprises 2x potentiometric, 2x potentiostat, 2x ISFET channels and 1x temperature channel. The potentiostat channel utilizes a current conveyor-based architecture with a programmable mirroring ratio, enabling an extensive measurement range of 114 dB. The potentiometric channel incorporates a customized electrostatic discharge (ESD) protection circuit to achieve ultra-low input leakage in the picoampere range, while the ISFET channel employs a constant-voltage, constant-current topology for accurate pH measurement. Combined with the die temperature sensor, this IC is well-suited for monitoring bioreactions in real-time. Additionally, all channels can be time-multiplexed to a reconfigurable analog backend, facilitating the conversion of input signals into digital codes. The prototype of the IC is fabricated using 0.18 μm standard CMOS technology, and each channel is experimentally characterized. The interface IC demonstrates a peak power consumption of 22 μW.

A Generalized Residue Number System Design Approach for Ultralow-Power Arithmetic Circuits Based on Deterministic Bit-Streams

The peak power consumption has become an important concern in the hardware design process of some of today’s applications, such as Energy Harvesting (EH) and Bio-Implantable (BI) electronic devices. The limited peak harvested power in EH devices and heating concerns in BI devices are the main reasons for power control’s importance in these devices. This paper proposes a generalized design approach for ultra-low-power arithmetic circuits. The proposed circuits are based on Residue Number System (RNS) combined with deterministic bit-streams. The resulting circuits can be used in systems with a restricted power budget. We suggest several approaches to design generic hardware-efficient adders, multipliers, multiply-accumulate units (MAC), forward converters (FC), and reverse converters (RC). Using the proposed approach, designing these components for any moduli of the RNS can be performed through simple bit-width adjustments in the circuits. The synthesis results show that the proposed adder achieves, on average, 69% and 2% lower area compared to the bit-serial and a state-of-the-art RNS adder, respectively. Furthermore, the proposed multiplier outperforms the bit-serial, interleaved, and a state-of-the-art design for multiplying RNS numbers by, on average, 57%, 60%, and 77% in terms of power consumption, respectively. The efficiency of our approach is shown via two essential applications, digital signal processing and machine learning. We implement an FFT engine using the proposed method. Compared to prior RNS implementations, our design achieves 47% lower power consumption. We also implement a CNN accelerator’s Processing Element (PE) with the proposed computation elements. Our design provides considerable speedup and lower power consumption compared to a state-of-the-art ultra-lower-power design.

Container Power Consumption Prediction Based on GBRT-PL for Edge Servers in Smart City

Edge computing and IoT devices have been widely deployed in smart city applications due to the rapid promotion and implementation of 5G communication technology. Limited by power supply and hardware computing capability, applications on edge servers are mostly deployed and run in the form of container micro-services to improve resource utilization. Therefore, identifying the power consumption at the container granularity is of great importance for the load scheduling and service quality assurance of edge servers. In this paper, a GBRT-PL-based container power prediction method is proposed. Performance metrics with a strong correlation between container and server power consumption are selected for power consumption modeling. To effectively suit the nonlinear relationship between container performance metrics and server power consumption, the integrated predictive capabilities of multiple regression trees and the segmented linear model of single regression tree leaf nodes are applied. The data from the experiments prove that the GBRT-PL model predicts power consumption more accurately than other models for single and multiple container groups. The highest average relative error rate in the four multi-container group tests is 6.72%, whereas the highest relative error rate in the 90% quantile is 11.66%. In addition, it can accurately predict the majority of power consumption peaks, which contributes to the precise detection of power consumption anomalies.