Power Management Approach Research Articles

Complex dual-tree wavelet transform and unified power management based control architecture for hybrid wind power system

This study proposes a complex dual-tree wavelet transform (CDTWT) based control technique for a combined sensitive load, battery, and hybrid wind power system (HWPS) to improve the power quality (PQ). To make the system hybrid both DC-grid and AC-grid are connected through two parallel inverters and to circumvent the sudden power demand, it is connected to the main grid. The CDTWT based control technique easily decomposes the non-linear load current into different frequency levels and extracts the fundamental line frequency component for the computation of the actual active current component. In addition to that, to improve the power reliability (PR) and generate the desired current signals, a novel centralized control and power management (CCPM) approach is proposed. The generated reference current is used to coordinate both of the parallel inverters for offering synchronize power flow, regulate the voltage and frequency and offer regardless of switching operation, at both grid supporting and islanding conditions. The MATLAB/Simulink software is used to develop and test the proposed HWPS at different working modes as the failure of one inverter and the sudden addition of two wind turbines at grid-supporting mode and to show battery functionalities at islanding modes of operation. To justify the necessity of the proposed controller, the total harmonic distortion (THD), stability, and advancement of CDTWT results are also presented at different operating modes. From the test results, it is found that with the presence of 20.06% non-linear load current, the overall improvement percentage of inverter and grid current is 96%-97% and 99% respectively. In addition to that, the proposed technique takes only 0.04 s-0.05 s transition time. The above percentages are well within the prescribed limit of IEEE-1541 and IEEE 1547–2018 and are suggested for real-time complex microgrid operation.

Component sizing and energy management for SOFC-based ship power systems

The shipping industry is facing increasing demands to reduce its environmental footprints. This has resulted in adoption of new and more environmental friendly power sources and fuels for on-board power generation. One of these novel power sources is the Solid Oxide Fuel Cell (SOFC) which has a great potential to act as a power source, thanks to its high efficiency and capability to handle a wide variety of fuel types. However, SOFCs suffer from low transient capabilities and therefore have never been considered to be used as the main power source for maritime applications. In this paper, novel component sizing, energy and power management approaches are proposed to enable the use of SOFCs as the main on-board power source for the first time in the literature and integrate them into the liquefied natural gas fueled Power and Propulsion System (PPS) of vessels. The proposed component sizing approach determines the power ratings of the on-board sources (SOFC, gas engine and battery) considering size and weight limits, while the energy and power management approaches guarantee an optimal power split between different power sources and PPS stability while looking after battery aging. The results indicate that the combined proposed optimization-based approaches can yield up to 53% CO2 reduction and 21% higher fuel utilization efficiency compared to conventional diesel-electric vessels.

Voltage synchronisation for hybrid-electric aircraft propulsion systems

AbstractIncreasing demand for commercial air travel is projected to have additional environmental impact through increased emissions from fuel burn. This has necessitated the improvement of aircraft propulsion technologies and proposal of new concepts to mitigate this impact. The hybrid-electric aircraft propulsion system has been identified as a potential method to achieve this improvement. However, there are many challenges to overcome. One such challenges is the combination of electrical power sources and the best strategy to manage the power available in the propulsion system. Earlier methods reviewed did not quantify the mass and efficiency penalties incurred by each method, especially at system level. This work compares three power management approaches on the basis of feasibility, mass and efficiency. The focus is on voltage synchronisation and adaptation to the load rating. The three methods are the regulated rectification, the generator field flux variation and the buck-boost. This comparison was made using the propulsion system of the propulsive fuselage aircraft concept as the reference electrical configuration. Based on the findings, the generator field flux variation approach appeared to be the most promising, based on a balance of feasibility, mass and efficiency, for a 2.6MW system.

Multi-Objective Optimization Dispatch Based Energy Management of A Microgrid Running Under Grid Connected and Standalone Operation Mode

This paper presents a novel optimization approach for a day-ahead power management and control of a DC microgrid (MG). The multi-objective optimization dispatch (MOOD) problem involves minimizing the overall operating cost, pollutant emission levels of (NOx, SO2 and CO2) and the power loss cost of the conversion devices. The weighted sum method is selected to convert the multi-objective optimization problem into a single optimization problem. Then, analytic hierarchy process (AHP) method is applied to determine the weight coefficients, according to the preference of each objective function. The system’s performance is evaluated under both grid connected and standalone operation mode, considering power balancing, high level penetration of renewable energy, optimal scheduling of charging/discharging of battery storage system, control of load curtailment and the system technical constraints. Ant lion optimizer (ALO) method is considered for handling MOOD, and the performance of the proposed algorithm is compared with other known heuristic optimization techniques. The simulation results prove the effectiveness and the capability of the developed approach to deal better with the coordinated control and optimization dispatch problem.They also revealed that economically running the MG system under grid connected mode can reduce the overall cost by around 4.70% compared to when it is in standalone operation mode.

Optimal Microgrid–Interactive Reactive Power Management for Day–Ahead Operation

The replacement of conventional generation sources by DER creates the need to carefully manage the reactive power maintaining the power system safe operation. The principal trend is to increase the DER volume connected to the distribution network in the coming years. Therefore, the microgrid represents an alternative to offer reactive power management due to excellent controllability features embedded in the DER, which enable effective interaction between the microgrid and the distribution network. This paper proposes a microgrid–iterative reactive power management approach of power-electronic converter based renewable technologies for day-ahead operation. It is designed to be a centralised control based on local measurements, which provides the optimal reactive power dispatch and minimise the total energy losses inside the microgrid and maintain the voltage profile within operational limits. The proposed optimal-centralised control is contrasted against seven local reactive power controls using a techno-economic approach considering the steady–state voltage profile, the energy losses, and the reactive power costs as performance metrics. Three different reactive power pricing are proposed. The numerical results demonstrate the optimal microgrid–interactive reactive power management is the most suitable techno-economic reactive power control for the day–ahead operation.

Smartphone power management based on ConvLSTM model

The battery capacity of smartphones is limited, and power saving is crucial. In this paper, we propose a power management approach depends on the users’ usage patterns. To save power, we need to know in which time interval the most power is consumed. The convolutional long short-term memory network (ConvLSTM) model provides learning long sequence dependencies based on time series data. The battery usage data is processed with the ConvLSTM model to predict every 30 min of the next 24-h remaining battery capacity of the smartphone. Then, the rate of power consumption is calculated for each hour interval according to predicted values of the remaining battery capacity. Depending upon the rates of power consumption, the value of numeric features (screen brightness, media volume, etc.) is reduced and non-numeric features (GPS, Wi-Fi, etc.) are turned off. Our approach saved much power at some time interval, besides saved up to 21% power saving overall. The performances of CNN, RNN, LSTM, CNN-LSTM, ConvLSTM, and ARIMA were also evaluated. We developed our approach as a smartphone application to forecast the remaining battery capacity and reduce battery consumption effectively for any smartphone user without an initial condition or any other restriction.

Dynamic Power and Energy Management for NCFET-Based Processors

Power and energy consumption are the key optimization goals in all modern processors. Negative capacitance field-effect transistors (NCFETs) are a leading emerging technology that promises outstanding performance in addition to better energy efficiency. The thickness of the added ferroelectric layer as well as frequency and voltage are the key parameters that impact the power and energy of NCFET-based processors in addition to the characteristics of runtime workloads. Unlike existing CMOS technologies, operating NCFET-based processors at a higher frequency than the required minimum can result in power/energy minimization. The optimal operating point, however, strongly depends on dynamic workload characteristics and technology parameters. In this work, we propose and implement the first NCFET-aware power and energy management approach that minimizes the processor's power and energy through optimal voltage/frequency selection under different runtime scenarios. Such an NCFET-aware approach does not result in any tradeoff between power/energy and performance. Instead, it can achieve higher performance while minimizing energy. A comprehensive, simulation-based evaluation of our runtime management under realistic workloads demonstrates up to 58% energy saving with 2.1× higher performance, and 46% power saving compared to conventional NCFET-unaware management techniques, over the total execution of a benchmark. Compared to state-of-the-art NCFET-aware management techniques, our technique provides up to 49% energy saving and 32% power saving.

Optimal Energy Management for Microgrids Considering Uncertainties in Renewable Energy Generation and Load Demand

This paper proposes an efficient power management approach for the 24 h-ahead optimal maneuver of Mega–scale grid–connected microgrids containing a huge penetration of wind power, dispatchable distributed generation (diesel generator), energy storage system and local loads. The proposed energy management optimization objective aims to minimize the microgrid expenditure for fuel, operation and maintenance and main grid power import. It also aims to maximize the microgrid revenue by exporting energy to the upstream utility grid. The optimization model considers the uncertainties of the wind energy and power consumptions in the microgrids, and appropriate forecasting techniques are implemented to handle the uncertainties. The optimization model is formulated for a day-ahead optimization timeline with one-hour time steps, and it is solved using the ant colony optimization (ACO)-based metaheuristic approach. Actual data and parameters obtained from a practical microgrid platform in Atlanta, GA, USA are employed to formulate and validate the proposed energy management approach. Several simulations considering various operational scenarios are achieved to reveal the efficacy of the devised methodology. The obtained findings show the efficacy of the devised approach in various operational cases of the microgrids. To further confirm the efficacy of the devised approach, the achieved findings are compared to a pattern search (PS) optimization-based energy management approach and demonstrate outperformed performances with respect to solution optimality and computing time.

Performance Examination of Low-Power Thermoelectric Sensor Arrays for Energy Harvesting From Human Body Heat

Thermoelectric energy harvester is known as a type of energy harvesting technologies which extracts waste heat from a target device or object to generate electrical power. The low power generation from thermoelectric energy harvester, though, is always a critical consideration in designing a self-sustaining system. The energy harvesting system is usually aided by a power management solution to further enhance the power generation for better performance. Therefore, maximizing the power generated from the thermoelectric sensor itself is essential in order to select the most suitable power management approach. This paper presumed the methodology to maximize power generation of thermoelectric and further discussion is reviewed in the report.

Stochastic power management approach for a hybrid solid oxide fuel cell/battery auxiliary power unit for heavy duty vehicle applications

The present paper aims to develop an innovative real-time power management strategy dedicated to the efficient operation of an auxiliary power unit (APU) in a heavy-duty vehicle. Specifically, the APU comprises a Solid Oxide Fuel Cell (SOFC) system and a Lead-Acid battery pack. The power management strategy envisages optimal power sharing between the APU elements and it is defined based on Simultaneous Perturbation Stochastic Approximation (SPSA) principle, pursuing SOFC power profile smoothing in real-time. The SPSA-based algorithm introduced here overcomes real-time operation issues remarked in other implementations (fuzzy logic, genetic algorithms), accounting for a robust and less complex formulation. The power management strategy is implemented in a dynamic model developed in Matlab/Simulink, simulating SOFC-based APU behavior. Simulation outcomes highlight that the proposed strategy allows a global energy saving over 6% if compared to a conventional power management, based on power split control. Moreover, comparing the power profiles corresponding to the battery and the SOFC, it is remarked as SOFC power oscillations evaluated over 1 s timeframe are halved, achieving values lower than 4.5 W/s for more than 80% of the operation time.

An MPC-based power management of standalone DC microgrid with energy storage

Standalone dc microgrid faces a significant reliability risk due to continuous variation in power from renewable energy generation and the load demand. Usually, the output power from the renewable generators fluctuates with the weather conditions. The load is also varying all the time. This leads to a continuous risk of power mismatch in the system. Thus, a complementary generation or a storage system is required for maintaining the power balance in the system. This paper introduces a supervisory power management strategy (PMS) for a standalone dc microgrid with multiple distributed generations, load, and a battery energy storage system. The PMS is designed based on the model predictive control (MPC) approach. The complete mathematical model of the system has been developed, and it has been utilized in the MPC controller to solve an optimization problem with operating constraints. A forecasting technique is also incorporated in the proposed MPC to predict the environmental and load demand parameters. Simulation results illustrate the effectiveness of the presented MPC-based power management approach. Accordingly, the proposed approach effectively manages the power among the generators, load, and the battery and stabilizes the dc voltage. The paper also evaluates the effects of various prediction methods on the controller performance.

Autonomous Power Management With Double-QReinforcement Learning Method

Energy efficiency and autonomous power management are extremely important for mobile-edge computing. Reducing energy consumption of a number of applications running concurrently in mobile devices while maintaining performance poses a challenge to energy optimization due to the limited capacity of the embedded battery. To extend battery life and offer a long-lasting working energy, dynamic voltage and frequency scaling (DVFS) has been widely used in mobile devices for energy consumption minimization. However, most conventional DVFS techniques scale operating frequency based on static policies, and thus, they are difficult to be adapted to systems of varied conditions. In order to improve adaptivity, in this article, we proposed a Double- Q power management approach to scale operating frequency based on learning. The Double- Q method stores two Q tables and two corresponding update functions. In each decision point, either of Q tables is randomly chosen and updated, while the other is used for the measurement. This mechanism reduces the overestimation in Q values, consequently enhancing the accurateness of frequency predictions. To evaluate the effectiveness of our proposed approach, a Double- Q governor is implemented in the Linux kernel. Our approach is computationally light, and experimental results indicate that it achieves at least 5– $18\%$ total energy saving compared to ondemand and conservative governors, as well as Q learning-based method.

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

SummaryFor rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication, and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. By considering harvested energy as an energy source, certain considerations are different from that of battery‐operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences, which put a limit on the maximum rate at which energy can be used. Thus, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self‐powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy‐harvesting approach in WSNs.

Organisational ambidexterity, hard power management and smart power management at Amazon, a case study

This paper examines the management practices of Amazon one of the most successful companies in the world in the last 20 years. The paper presents first hand reviews from Amazon workers on working at Amazon. The employee reviews indicate that with regard to its blue collar workers, the workers in the fulfilment centres, Amazon follows a hard power management approach. With regard to its white collar workers, the workers in the high-tech units, Amazon attempts to implement smart power management by giving workers the ability to innovate and develop their ideas. This dichotomy is an example of organisational ambidexterity separation at Amazon. However, even by some of the white collar workers, the management practices are hard power management and not smart power management. Most likely, Amazon would be better served if it implemented a smart power management style throughout the company.

Organisational ambidexterity, hard power management and smart power management at Amazon, a case study

This paper examines the management practices of Amazon one of the most successful companies in the world in the last 20 years. The paper presents first hand reviews from Amazon workers on working at Amazon. The employee reviews indicate that with regard to its blue collar workers, the workers in the fulfilment centres, Amazon follows a hard power management approach. With regard to its white collar workers, the workers in the high-tech units, Amazon attempts to implement smart power management by giving workers the ability to innovate and develop their ideas. This dichotomy is an example of organisational ambidexterity separation at Amazon. However, even by some of the white collar workers, the management practices are hard power management and not smart power management. Most likely, Amazon would be better served if it implemented a smart power management style throughout the company.

Design and analysis of a novel power management approach, applied on a connected vehicle as V2V, V2B/I, and V2N

Design and analysis of a novel power management approach, applied on a connected vehicle as V2V, V2B/I, and V2N

Adaptive Predictive Power Management for Mobile LTE Devices

Reducing the energy consumption of mobile phones is a crucial design goal for cellular modem solutions for LTE and 5G NR standards. Most dynamic power management techniques targeting mobile devices proposed so far, however, are purely reactive in powering down and up system components. Promising approaches extend this, by predicting information from the cell and the communication protocol to take decisions proactively. In this paper, we present a complete proactive power management approach for the modem based on on-line grant prediction. In this context, we define proactive policies that allow a mobile device to go to sleep states more often compared to reactive power management systems, e.g., in time slots of predicted transmission inactivity in a cell. Furthermore, we propose and compare two algorithmic solutions to this proactive grant prediction problem, one a feed-forward neural network and one a SARSA-λ reinforcement agent. As the implementation of these machine learning techniques also creates additional energy and resource costs, both approaches are carefully designed, optimized, and evaluated not only in terms of prediction accuracy, but also in terms of overall energy savings. Notably, our predictor implementations are able to achieve up to 17 percent in overall energy savings on real-world traces.

Collaborative Power Management Through Knowledge Sharing Among Multiple Devices

Rapidly evolving embedded applications continuously demand more functionalities and better performance under tight energy and thermal budgets, and maintaining high energy efficiency has become a significant design challenge for mobile devices. Learning-based methods are adaptive to dynamic conditions and show great potential for runtime power management. However, with the ever-increasing complexity of both hardware and software, it is a challenging issue for a learning agent to explore the state-action space sufficiently and quickly find an efficient management policy. In this paper, we propose a reinforcement learning-based multi-device collaborative power management approach to address this issue. Multiple devices with different runtime conditions can acquire related knowledge during the learning process. Efficient knowledge sharing among these devices can potentially accelerate the learning process and improve the quality of the learned policies. We integrate the proposed method with dynamic voltage and frequency scaling on the multicore processors in mobile devices. Experimental results on realistic applications show that the collaborative power management can achieve up to a $7 \times$ speedup and 10% energy reduction compared with state-of-the-art learning-based approaches.

An Adaptive Approach for Effective Power Management in DC Microgrid Based on Virtual Generation in Distributed Energy Sources

This paper proposes a new control algorithm for effective power management in the dc microgrid with renewable energy sources (RES) and energy storage system (ESS). This scheme overcomes the average power sharing and bus voltage regulation problems with maximum utilization of source power. In the source, the additional power available beyond its average rated value is treated as virtual generation. The new references are generated considering virtual generation. The novelty of the proposed algorithm is to bring the virtual generation effectively in operation. The virtual generation of the individual source is based on available power source. The proposed algorithm reduces the charging/discharging cycles of ESS and, hence, enhances the life span of ESS. It reduces the stress on interfaced converters, regulates the system bus voltage, reduces power fluctuations on the sources, and enhances stability. The proposed control algorithm is verified in MATLAB simulation and in real-time simulator.

Coordinated control and power management of diesel‐PV‐battery in hybrid stand‐alone microgrid system

Coordinated control and power management of distributed energy resources, such as diesel generators (DGs), photovoltaics (PVs) and battery energy storage systems (BESSs), is vital for secure, reliable and economical operation of a stand-alone microgrid system. This paper proposes a coordinated control and power management approach for a hybrid stand-alone microgrid system containing DGs and PVs and also BESSs, which commonly servers as a power supply solution for remote areas such as islands. The proposed approach takes into account the maximum utilisation of PV power, economical operation condition of diesel generators, status of battery energy storage system and their operation constraints to share the load among them. Efficacy of the proposed approach is validated through a RTDS-based simulation test and an actual project practice.