Power Management Strategy Research Articles

Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System

Standalone microgrids with photovoltaic (PV) solutions could be a promising solution for powering up off-grid communities. However, this type of application requires the use of energy storage systems (ESS) to manage the intermittency of PV production. The most commonly used ESSs are lithium-ion batteries (Li-ion), but this technology has a low lifespan, mostly caused by the imposed stress. To reduce the stress on Li-ion batteries and extend their lifespan, hybrid energy storage systems (HESS) began to emerge. Although the utilization of HESSs has demonstrated great potential to make up for the limitations of Li-ion batteries, a proper power management strategy is key to achieving the HESS objectives and ensuring a harmonized system operation. This paper proposes a novel power management strategy based on an artificial neural network for a standalone PV system with Li-ion batteries and super-capacitors (SC) HESS. A typical standalone PV system is used to demonstrate and validate the performance of the proposed power management strategy. To demonstrate its effectiveness, computational simulations with short and long duration were performed. The results show a minimization in Li-ion battery dynamic stress and peak current, leading to an increased lifespan of Li-ion batteries. Moreover, the proposed power management strategy increases the level of SC utilization in comparison with other well-established strategies in the literature.

Fuel Consumption and Emission Reduction Power Management Strategy for Hybrid Electric Vehicles

Fuel Consumption and Emission Reduction Power Management Strategy for Hybrid Electric Vehicles

A Robust Power Management Strategy With Multi-Mode Control Features for an Integrated PV and Energy Storage System to Take the Advantage of ToU Electricity Pricing

In this paper, a power management strategy (PMS) for an integrated residential solar photovoltaic (PV) and energy storage unit (ESU) is proposed for both grid-connected and islanded operations to take the advantage of time-of-use pricing. This is an effective solution to integrate storage and renewable energy sources, such as solar PV, with the conventional grid to improve the reliability and efficiency of the power system and to reduce the total electricity cost for the consumer. An automatic switching control strategy is proposed to realize a smooth switching among the various operation modes of the proposed energy management strategy. The integrated PV-storage system is composed of a 5-kW PV array, a 3.5-kW·h ESU formed by 12-V single lead acid batteries, and three power converters. The PMS ensures seamless switching of the ESU converter between the charging and the discharging mode and seamless switching between the inverting and the rectifying mode of the grid converter. It also ensures that the local loads are supplied when the grid fails. An experimental setup has been implemented in the laboratory using the real-time field-programmable gate array general-purpose inverter control card from National Instrument to verify the practical feasibility of the proposed PMS under the various scenarios of the PV irradiation, load changings, and the state of charge of the ESU. The results show that the proposed PMS is effective and robust against various scenarios.

Decentralized Power Management of Multiple PV, Battery, and Droop Units in an Islanded Microgrid

In this paper, a decentralized power management strategy is proposed for multiple photovoltaic (PV), battery, and droop units in an islanded microgrid. The control strategy is developed to handle any combination of these units without modifying their control systems. This provides a more comprehensive and generalized approach to coordinate the three types of units, in comparison to the techniques in the literature that consider only two types, mainly PV and battery systems, or consider only a single unit of each type. The operation of each unit is autonomously coordinated to maintain the balance between generation and consumption, while ensuring controlled charging/discharging of the batteries in the microgrid. To achieve this coordination, the voltage and the power control loops, within each of the PV and battery units, are configured to follow the specifically designed multi-segment power/frequency ( P/f ) characteristic curves. These characteristics are designed to independently adapt to the microgrid operating conditions, without relying on any external communications and centralized management systems. Accordingly, the control system for each unit is able to autonomously and seamlessly switch in real-time between power control and frequency regulation based on the available PV power, the state-of-charge of the batteries, and the total load demand in the microgrid. The strategy is designed and implemented using multi-loop controllers, in contrast to the commonly adopted approach of using discrete operating modes and switching logics. The proposed strategy is validated using a microgrid simulated in PSCAD/EMTDC with detailed switching models of the power electronic converters.

Multi-Objective Power Management for EV Fleet With MMC-Based Integration Into Smart Grid

Modular multilevel converter (MMC) is a possible solution for integrating an electric vehicle (EV) fleet into the smart grid. Considering the differences of initial state of charge (SOC) and the inherent randomness in charge/discharge demands from the various EVs connected to the MMC, it becomes challenging to control the MMC in order to satisfy the network and EV fleet requirements. This paper proposes a multi-objective power management strategy for a MMC-Based EV fleet to be integrated into the smart grid using a virtual SOC model and coordinated control of the power flow among the arms of the same and different phase-units by taking advantage of circulation current. The model of the MMC-based EV fleet system is developed in MATLAB/Simulink and simulation results show that the multi-objectives of differential charging/discharging power control and interactive power management between EVs and utility can be effectively realized, while the three-phase output power of MMC is kept in balance and circulation current is controlled, thus, mitigating the negative impact of large-scale application of EVs on the utility grid.

Automated power management strategy for wind power generation system using pitch angle controller

In this literature, a new automated control strategy has been developed to manage the power supply from the wind power generation system to the load. The main objective of this research work is to develop a fuzzy logic–based pitch angle control and to develop a static transfer switch to make power balance between the wind power generation system and the loads. The power management control system is a progression of logic expressions, designed based on generating power and load power requirement. The outcome of this work targets at an improved power production, active and reactive power compensation and ensures system load constraints. To validate the proposed control strategy, a detailed simulation study is carried out on a 9-MW wind farm simulation simulated in MATLAB/Simulink environment.

Mitigation of Solar PV Intermittency Using Ramp-Rate Control of Energy Buffer Unit

The unpredictability of the solar irradiance fluctuations can result in both photovoltaic (PV) power output fluctuations and high ramp rates. This can lead to significant voltage fluctuations with high ramp rates at the point of common coupling. Traditionally, the energy buffer units (EBUs) are used to smooth out the power fluctuations using the moving average method or the PV power gradient control. However, these methods can only smooth out the power at the output of grid inverter without considering the local load. This paper proposes a power management strategy to address this issue with two main operation modes: first, the load-feeding mode to minimize the grid power consumption by maximizing the local load consumption from both the PV and the EBU; second, the grid-feeding mode to smooth out the PV output fluctuation and to control the ramp rate to a desired value when supplying the grid. Furthermore, the EBU can be fully charged during the off-peak period or at night by the grid to take advantage of the time-of-use electricity price. The proposed control strategy is first simulated using MATLAB/Simulink and then a laboratory prototype platform is built and tested to verify the feasibility and the effectiveness of the proposed power management strategy with ramp-rate control.

Power management system for a fuel cell/battery hybrid vehicle incorporating fuel cell and battery degradation

Optimization of fuel cell/battery hybrid vehicle systems has primarily focused on reducing fuel consumption. However, it is also necessary to focus on fuel cell and battery durability as inadequate lifespan is still a major barrier to the commercialization of fuel cell vehicles. Here, we introduce a power management strategy which concurrently accounts for fuel consumption as well as fuel cell and battery degradation. Fuel cell degradation is quantified using a simplified electrochemical model which provides an analytical solution for the decay of the electrochemical surface area (ECSA) in the fuel cell by accounting for the performance loss due to transient power load, start/stop cycles, idling and high power load. The results show that the performance loss based on remaining ECSA matches well with test data in the literature. A validated empirical model is used to relate Lithium-ion battery capacity decay to C-rate. Simulations are then conducted using a typical bus drive cycle to optimize the fuel cell/battery hybrid system. We demonstrate that including these degradation models in the objective function can effectively extend the lifetime of the fuel cell at the expense of higher battery capacity decay resulting in a lower average running cost over the lifetime of the vehicle.

Analysis of incremental LMS adaptive algorithm over wireless sensor networks with delayed-links

Wireless sensor networks (WSN) are used for many delay-sensitive applications, e.g. military surveillance, emergency response, city management, etc. Therefore, modeling of delay is very important. This paper presents a study of the impact of sensors' sensing delay, process delay and transmission delay in the estimation of the desired unknown parameter in the WSN. Recently proposed wireless sensor networks, in the literature, assume perfect nodes and links, in view of delay. It means that no consideration has been made about the delay in the sensing, processing and, transmission procedures. The proposed method in this paper analyzes the behavior of the distributed incremental estimation algorithm in the presence of delay in wireless sensor networks. Weighted spatio-temporal energy conservation method is used to evaluate the transient and steady state behavior of the wireless sensor networks with delay without putting any restriction on regressor's distribution. The equations that illustrate mean square deviation (MSD), excess mean square error (EMSE) and mean square error (MSE) behavior of individual nodes, are driven. Also, simulations show that overall delay could be calculated to turn off nodes in some iterations without affecting the performance of the distributed estimation algorithm or adding extra latency to the network, which can improve power management strategies by modifying sleep-wake scheduling protocols. Eventually, it is shown that simulation results have a good match with derived theoretical expressions.

Power Management Strategy for a Novel Hybrid Electric Bus with Dual Planetary Gears

Hybrid electric vehicles with power split system have been widely used during recent years due to its huge potential to reduce the fuel consumption and enhance the drivability. This paper presents a novel powertrain architecture for a hybrid electric bus (HEB) based on dual planetary gear. The configuration and operating modes are described elaborately in this paper and a rule-based power management strategy is adopted to the HEB. Meanwhile, dynamic programming (DP) algorithm is also used as a benchmark to verify the effectiveness of this system. In order to validate the fuel-saving ability, both the rule-based and DP-based power management strategy are simulated based on the same typical driving cycle. As a result, the low fuel consumption achieved by DP verifies the effectiveness of this novel powertrain architecture. Moreover, the fuel consumption of the rule-based strategy is merely 11.29% higher than that on DP, which proves the feasibility of the rule-based strategy.

Power Management Strategy for the 48 V Mild Hybrid Electric Vehicle Based on the Charge-Sustaining Control

To enhance the 48 V mild hybrid electric vehicle performance using a smaller capacity and lower voltage battery than the full hybrid electric vehicle, a novel power management strategy needs to be established that considers the characteristics and limitations of the components. This paper proposes a charge-sustaining control strategy as a ground principle of the 48 V hybrid electric vehicle control for managing the battery state-of-charge (SOC) to stay near the most efficient regime. The base efficiency characteristics of the component models including engine, motor/generator, and battery are determined in the form of efficiency maps using the powertrain analysis tool. Then the control strategy is formulated as a nonlinear optimal regulation problem that meets two conflicting control objectives, such as fuel efficiency improvement and state-of-charge maintenance. The optimal regulation problem implements a discrete-time Hamilton-Jacobi-Bellman approach. The proposed strategy is evaluated by comparing with the reference strategy applying the Dynamic Programming (DP), i.e. a global optimal result, under urban dynamometer driving schedule and worldwide harmonized light duty test cycle. Through the evaluation, the fuel efficiency of the proposed strategy with three different electrical loads is slightly deteriorated at most by 5.03 % from the DP results with staying within a desirable SOC. This suggests that the proposed strategy is operating very closely to global optimal performances.

Tribotronics for Active Mechanosensation and Self‐Powered Microsystems

AbstractTribotronics has attracted great attention as a new research field that encompasses the control and tuning of semiconductor transport by triboelectricity. Here, tribotronics is reviewed in terms of active mechanosensation and human–machine interfacing. As a fundamental unit, contact electrification field‐effect transistors are analyzed, in which the triboelectric potential can be used to control electrical transport in semiconductors. Several tribotronic functional devices have been developed for active control and information sensing, which has demonstrated triboelectricity‐controlled electronics and established active mechanosensation for the external environment. In addition, the universal triboelectric power management strategy and the triboelectric nanogenerator‐based constant sources are also reviewed, in which triboelectricity can be managed by electronics in the reverse action. With the implantation of triboelectric power management modules, the harvested triboelectricity by various kinds of human kinetic and environmental mechanical energy can be effectively managed as a power supply for self‐powered microsystems. In terms of the research prospects for interactions between triboelectricity and semiconductors, tribotronics is expected to demonstrate significant impact and potential applications in micro‐electro‐mechanical systems/nano‐electro‐mechanical systems (MEMS/NEMS), flexible electronics, robotics, wireless sensor network, and Internet of Things.

The Dynamic Power Control Technology for the High Power Lithium Battery Hybrid Rubber-Tired Gantry (RTG) Crane

In order to ensure the operation safety of the lithium battery hybrid rubber-tired gantry (RTG) crane, the dynamic power control of the lithium battery hybrid RTG crane is studied. This paper introduces the construction and switching modes of the lithium battery hybrid RTG crane. The generator set will not turn on / off frequently using the power management strategy with max on and off , especially in the hybrid system with high-capacity battery and small size of diesel engine generator set. The high-power lithium battery is a better choice for RTG because it reduces emissions on the port. The dynamic model of the hybrid RTG crane is established, and the operation characteristics in different modes are analyzed. The safe operation is realized through the coordinated control load power, battery power, and the diesel-generator power. The warm-up time and cool down time of generator set are considered in the power management strategy with max on and off for a series hybrid system for the first time. The hybrid RTG crane characteristic and the energy saving are verified through the experimental data. The cost of charging the battery is analyzed, revealing that the new RTG saves 70% compared to the conventional RTG crane.

Design Optimization and Model Predictive Control of a Standalone Hybrid Renewable Energy System: A Case Study on a Small Residential Load in Pakistan

Renewable energy sources (RESs) offer a promising prospect for covering the fundamental needs of electricity for remote and isolated regions. To serve the customers with high power quality and reliability, design optimization methodology and a possible power management strategy (PMS) for wind-diesel-battery-converter hybrid renewable energy system (HRES) is proposed in this paper. The analysis is applied to a real case study of a standalone residential load located in a remote rural area in Pakistan. Firstly, optimal component sizing is investigated according to actual meteorological and load profile data. Different hybrid configurations are modeled, analyzed, and compared in terms of their technical, economic and environmental metrics with the aid of HOMER® software. The main objective is to determine the most feasible and cost-effective solution with least life-cycle cost, keeping in view the impact of carbon emissions. Secondly, a suitable PMS based on the state of charge (SOC) of the battery is proposed and implemented in MATLAB/Simulink® software for the designed HRES. The PMS is targeted to maintain load balance and extract maximum wind power while keeping the battery SOC within the safe range. Model predictive control (MPC) approach is applied to improve the output voltage profile and reduce the total harmonic distortion (THD). The boost converter is used for maximum power extraction from the wind. The DC-DC buck-boost battery controller is utilized to stabilize the DC bus voltage. The design optimization results show that the hybridization of wind, battery, and converter presents optimal configuration plan with minimum values of total net present cost (14,846 $) and cost of energy (0.309 $/kWh), which means 76.7% reduction in both total system cost and energy cost and 100% saving in harmful emissions when compared to the base case using diesel generator. The proposed system is able to support hundred percent of the load demand with excess energy of 30.1%. Performance analysis of PMS under variable load and fluctuating wind power generation is tested, and promising results with efficient load voltage profile is observed. Further, THD is reduced significantly to 0.26% as compared to 2.62% when the conventional PI controller is used. The output of this work is expected to open a new horizon for researchers, system planners for efficient design and utilization of HRES to curb drastic increase in load demand for urban as well as rural areas.

Power quality enhancement in electricity grids with wind energy using multicell converters and energy storage

In recent years, the wind power industry is experiencing a rapid growth and more wind farms with larger size wind turbines are being connected to the power system. While this contributes to the overall security of electricity supply, large-scale deployment of wind energy into the grid also presents many technical challenges. Most of these challenges are one way or another, related to the variability and intermittent nature of wind and affect the power quality of the distribution grid. Power quality relates to factors that cause variations in the voltage level and frequency as well as distortion in the voltage and current waveforms due to wind variability which produces both harmonics and inter-harmonics. The main motivation behind work is to propose a new topology of the static AC/DC/AC multicell converter to improve the power quality in grid-connected wind energy conversion systems. Serial switching cells have the ability to achieve a high power with lower-size components and improve the voltage waveforms at the input and output of the converter by increasing the number of cells. Furthermore, a battery energy storage system is included and a power management strategy is designed to ensure the continuity of power supply and consequently the autonomy of the proposed system. The simulation results are presented for a 149.2 kW wind turbine induction generator system and the results obtained demonstrate the reduced harmonics, improved transient response, and reference tracking of the voltage output of the wind energy conversion system.

A Multi-objective Optimization Algorithm for Multiple Home Users Intelligent Power Management and Control Based on Pareto and Nash Equilibrium Game

A multi-objective optimization model for multiple home users intelligent power management and control is proposed. A photovoltaic power model, an electric vehicle battery model and a load model are developed first, and then a strategy of home intelligent power management is presented based on battery operation and PV spontaneous self-use. Secondly, a multi-objective optimization model of multiple home users intelligent power management, including the user comfort, economy and optimization of load curve, is provided under the constraints. Then using a multi-objective optimization algorithm and Nash equilibrium game theory to solve the multi-objective problem. Finally, the 100-home power management and control simulation case show that the presented algorithm can improve the comfort and the economy of users effectively, but also help the power grid to peak load shifting.

Flow Network-Based Real-Time Scheduling for Reducing Static Energy Consumption on Multiprocessors

The energy management for embedded real-time systems is crucial due to their restricted power supplies. With the advancement of technologies, the static energy consumption of the embedded systems that is caused by their leakage power is growing. Thus, a number of research works have started focusing on reducing the static energy consumption by making the systems transit into low-power states, wherein some hardware components are temporarily shut down. Specifically, when a processor is idling, they attempt to set the processor into one of several low-power states. To make a processor remain in the low-power state as long as possible to minimize the energy consumption, the idle time should be maximally clustered. At the same time, in order to satisfy the real-time constraints of the tasks, the length of the clustered idle time should be estimated accurately. To achieve our objective, we propose energy-efficient real-time scheduling algorithms on symmetric homogeneous multiprocessors with a dynamic power management scheme for periodic real-time tasks. The proposed algorithms rely on a flow network model that effectively helps to cluster the idle time while respecting the real-time constraints. In our experimental evaluation, the proposed algorithms consume a comparable static energy to an existing off-line scheme that is the only suitable existing algorithm in the problem domain. Furthermore, we show that the proposed algorithms consume less static energy than the existing one in a case where the total workload of the given task set is low and the task completion is earlier than expected.

Do You Waste Laptop Battery to Light the Room?

Display Power Management That Detects User Intent

Structural and energy storage retrofit of an electric bus for high-power flash recharge

The activity here presented describes the retrofit performed on a small-size electric urban bus, which has been transformed in a laboratory vehicle for flash charge functionality using energy transfer between supercapacitors. The existing motor and driveline has been maintained, while the energy storage, originally based on lead batteries, has been modified reducing battery capacity and installing high voltage supercapacitors; such hybrid energy storage system is able to accept high recharge power (approximately about 150kW-peak in the used configuration), while smoothing power load on battery during traction. The system is therefore also able to extend the life of aged batteries. The so-called flash charge occurs through a pantograph connection to the infrastructure, typically in case of bus stops, during ascent and descent of passengers. The structure of the vehicle used for the transformation, which is still widely used worldwide, is constituted by as a glass fiber bodywork installed on a steel frame; since the body was not designed for the support of a significant mass as the one of the pantograph, it has been necessary to install an additional frame anchored to the main vehicle frame; such auxiliary frame has been designed also to substitute the internal fittings of the vehicle, in order to be used as stanchions for the standing passengers. In parallel, the battery compartment has been redesigned in order to install the supercapacitors and the new power converters needed. The contents of the article include the description of the mechanical components installed for the retrofit, the definition of the main power management strategy for the new powertrain and the results of preliminary testing activities.

Energy Management of a Stand-Alone DC Microgrid Based on PV/Wind/Battery/ Diesel Gen. Combined with Supercapacitor

Distributed generation system is a reliable, economical and efficient electrification option that interconnects different energy sources. Small and local distribution generation system including storage systems and load is defined as a microgrid. This paper presents an energy management strategy to supervise the power flows in a stand-alone DC microgrid power generation plant. The plant is composed of: a wind turbine, a photovoltaic generator, battery storage system and diesel generator combined with a supercapacitor. The DC microgrid is designed and modeled using Matlab/ Simulink/ SimPowSysâ„¢ environment. The supercapacitor is utilized to cover the deficiency of power shortage during the start-up of the diesel generator. It is also utilized to compensate the limitations on the charging/discharging current of battery. The power balance of the suggested system is the main objective of the proposed management strategy. Some performance indexes are considered: the frequency deviation, the stability of the DC bus voltage and the AC voltage total harmonic distortion. The performances of the proposed strategy are evaluated by simulation in different operating conditions. Simulation results confirm the effectiveness of the proposed power management strategy.