Proposed Power Management Strategy Research Articles

Deterministic power management strategy for fast charging station with integrated energy storage system

With the increasing expansion of fast-charging stations (FCS) and the emergence of high-power electric vehicles (EVs), the development of management strategies to address potential grid disturbances becomes necessary. In this context, this paper proposes an optimized power management strategy for an FCS with integrated battery energy storage systems (BESS). The proposed strategy aims to monitor the variation in AC voltage at the point of common coupling (PCC) and the state of charge (SOC) of the BESS, with the objective of establishing a deterministic formulation to find the optimal instantaneous BESS power level. Therefore, by using the proposed approach, it is possible to compensate for potential fluctuations in AC voltage while maintaining the BESS SOC at appropriate levels. The results are obtained through a real-time hardware-in-the-loop (HIL) platform to verify the system behavior under distinct parameters or operating conditions. These results demonstrate the control flexibility by prioritizing the AC voltage regulation or the BESS charging. Furthermore, the impact of the proposed power management strategy is investigated by performing a comparison with the system without BESS integration. It is observed that the proposed control strategy effectively regulates the PCC voltage and maintains it within acceptable limits.

A fuzzy-logic-based smart power management strategy for reliability enhancement of energy storage system in a hybrid AC-DC microgrid with EV charging station

Abstract As Electric Vehicles (EVs) are considered as the future of the automobile industry, the demand for EV charging stations immensely rises. Though grid support can be expected for a microgrid (MG), it is highly advantageous to be self-reliant and be prepared for standalone operation for a long duration. This paper proposes a hybrid ac/dc microgrid (HMG) with an EV charging station in standalone mode with a smart power management strategy that increases the reliability of operation. Different modes of operation are designed according to power consumption levels and the operation of MG is switched between these modes considering the solar irradiance and battery bank state of charge. The decision-making system uses a combination of (a) fuzzy logic approach based on the state of charge of the energy storage system (ESS) and solar irradiance, adding to the intelligence of the system and providing smart reasoning capability and (b) logical decisions based on solar power and battery state of charge, considering all possible operating scenarios. With the proposed power management strategy in place, the standalone hybrid microgrid system is found to be able to prolong the discharge of the energy storage system under critical conditions of low battery state of charge and PV power availability and also under highly critical situations will be able to meet the critical load demand making use of the power available in the EV connected. Emphasis has also been given to maintaining the stability of the system while performing shifts in operating modes and during sudden changes in the scenario. Different operating conditions are recognized by the power management module in the model and an appropriate power management strategy is applied. Real-time simulation of the model was done in OPAL-RT real-time simulator using RT-LAB software and simulation results were reproduced.

An Efficient Supervisory Power Management Scheme for a Wind–Battery-Assisted Hybrid Autonomous System

This article presents an efficient power management and protection scheme for a wind–battery-assisted hybrid autonomous system. To operate the wind power generation system at the maximum power point (MPP), a drift-free perturb and observe algorithm is adapted. A unified controller for a bidirectional converter (BDC) is incorporated for the dc-bus voltage regulation along with the charge/discharge control of the battery. The small-signal analysis of the BDC and the buck converter is presented in detail. The performance of the proposed power management scheme (PMS) is validated through a time-domain simulation in MATLAB/Simulink along with the hardware implementation using the DS1103 digital platform. Initially, the PMS with only maximum power point tracking (MPPT) and pitch controller is unable to maintain the dc-bus voltage during the battery overcharge or deep-discharge condition. To address this issue, an <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> -MPPT controller is proposed to regulate the dc-bus voltage. The effectiveness of all the operating modes of the proposed PMS is verified in the developed laboratory prototype, and the performance is also compared with that of the classical droop control method. The experimental results confirm the ability of the PMS to handle power sharing and dc-bus voltage fluctuation during wind speed and load change and satisfy the load demand. This ensures that the improved performance of the developed hybrid autonomous system and changeover from the MPP to the generator or battery protection mode is seamless and fast.

Improved power flow management with proposed fuzzy integrated hybrid optimized fractional order cascaded proportional derivative filter (1+ proportional integral) controller in hybrid microgrid systems

The primary goal of power management in a hybrid microgrid is to maintain the active power balance among renewable energy sources, energy storage system, and loads. In a HMG system, an ESS is required to reduce power fluctuations caused by the intermittent behavior of RESs. A bi-directional dc–dc converter has been used to connect a battery to the dc bus. This paper proposes an improved power management scheme where a novel controller named as hybrid fuzzy integrated fractional order cascaded proportional derivative filter (1+proportional integral) is designed and implemented in a voltage-controlled loop of the BDC. This controller is introduced in this work to achieve optimum power flow management improves voltage stabilization, and enhances the system dynamic response. A hybrid modified sine cosine algorithm-pattern search algorithm is implemented here to tune the parameters of the proposed controller. The extensive time-domain analysis and robustness studies of the proposed controller have been performed and compared with the other conventional control methods Variable solar irradiance, solar temperature, wind speed, and load are used to test the effectiveness of proposed power management strategy. The proposed power management scheme with several case studies has been performed in MATLAB/Simulink and also validated through a Real-time digital platform (Opal-RT).

Secure power management strategy for direct torque controlled fuel cell/ supercapacitor electric vehicles

High reliability is recommended in hybrid electric vehicle applications. In this study, a secure power management strategy has been developed for a fuel cell—supercapacitor hybrid electric vehicle. In addition to its ability to detect the occurrence of failures in vehicle power sources, the proposed power management strategy isolates the faulty source and reconfigures the control scheme to always guarantee bus voltage stability and vehicle traction even in faulty situations. The developed power management strategy enhances vehicle comfort and prevents exhausting one source over another by allowing the fuel cell and the supercapacitor to operate at different power levels. The multiloop control scheme associated with the power sources is highly reliable since both sources can run the vehicle alone and regulate the bus voltage. Vehicle speed and torque controllers are simultaneously tuned using a particle swarm optimization algorithm. Torque and speed ripples are automatically minimized via the use of a new proposed cost function. This approach made the controller design easier and gave the designer the possibility to tradeoff between the variables to be minimized.

A Novel Agent-Based Power Management Scheme for Smart Multiple-Microgrid Distribution Systems

In this work, a novel agent-based day-ahead power management scheme is proposed for multiple-microgrid distribution systems with the intent of reducing operational costs and improving system resilience. The proposed power sharing algorithm executes within each microgrid (MG) locally, and the neighboring MGs cooperate via a multi-agent system cooperation scheme, established to model the communication among the agents. The power management for each agent is modeled as a multi-objective optimization problem (MOP) including two objectives: maximizing load coverage and minimizing the operating costs. The proposed MOP is solved using the Nondominated Sorting Genetic Algorithm (NSGA-II), where a set of Pareto optimal solutions is obtained for each agent through the NSGA-II. The final solution is obtained using an Analytical Hierarchical Process. The effectiveness of the proposed scheme is evaluated using a benchmark 4-MG distribution system. It is shown that the proposed power management scheme and the cooperation of agents lead to a higher overall system resilience and lower operation costs during extreme events.

Power management for hybrid AC/DC microgrid with multi-mode subgrid based on incremental costs

The hybrid AC/DC microgrid is attracting more and more attention, which can accommodate multiple subgrids. So, the flexible power interaction and economical operation in the hybrid AC/DC microgrid are complicated. A power management strategy for hybrid AC/DC microgrid considering multi-mode subgrid and generation cost is proposed in this paper. First, three operational modes for subgrids are defined, which include subgrid power autonomy (SPA), subgrid power sharing (SPS), and subgrid-ESU power coordination (SEPC). With the multi-mode subgrid, greater operating flexibility and less exchanged power can be facilitated among subgrids. Second, event-driven mode switching rules are established. The rules set thresholds for different events to meet different power quality requirements of subgrids. Besides, the virtual incremental costs are introduced for the designed events to avoid mode switching repeatedly. Third, the exchanged power is calculating by equalizing the nominal value of frequencies and voltages. In this way, the incremental costs of subgrids and ESUs can reach equality and reduce the total operating cost. Finally, simulations are carried out to demonstrate the effectiveness and superiority of the proposed power management strategy.

Multi-objective multi-verse optimization of renewable energy sources-based micro-grid system: Real case

Hybrid micro-grid systems (HMGS) are small scale power system where the energy sources are installed to supply local customers. These systems may be considered as promising energy solution to meet the increased in energy demand and traditional sources depletion. Cost of electricity, system reliability, and environmental impacts of the system are three design criteria that must be considered in obtaining the accurate parameters of hybrid renewable energy system components. In this paper, hybrid micro-grid renewable energy system includes photovoltaic system, (PV) wind energy system, (WES) battery bank, (BB) and conventional diesel generator (DG) are proposed to meet the energy requirements in remote area, located in Red Sea called city of Bernice, Egypt, at 23° 54′ 31″ N, 35° 28′ 21″ E. Optimization of Cost of Electricity (COE), Renewable Factor (RF), and Loss of Power Supply Probability (LPSP) are main objective of the designing process of the hybrid system considered as the objective functions. Then, Multi-objective multi-verse optimization (MOMVO) algorithm is used with considering two scenarios, the first one is renewable sources and the second is renewable/diesel energy source. All the possible HMGS configurations namely: PV/battery, wind/battery, PV/wind/battery and PV/battery/diesel, wind/battery/diesel, PV/wind/battery/diesel are studied and analyzed. Moreover, one year hourly meteorological weather data for case study are recorded. Reverse osmosis desalination (ROD) is considered in conjunction with the residential load. The proposed power management strategy is used to manage the system operation when supplying the load. A linear fuzzy membership function is used for purpose of decision making. The simulation results show that MOMVO produces appropriate components size and the PV/wind/battery/diesel is the optimum configuration with values of COE = 0.2720$/KWh, LPSP = 0.1397, and RF = 92.37% at w 1 = 0.5, w 2 = 0.3, and w 3 = 0.2. Sensitivity analysis is performed to show the effect of changing system parameters on the objective functions. It is also shown that the techno-economic feasibility of using HMGS for rural electrification systems and enhance energy access.

Multiagent Distributed Power Management of DC Shipboard Power Systems for Optimal Fuel Efficiency

Medium-voltage dc power systems have been recognized as a promising electrical architecture for marine vessels. Such shipboard power systems (SPSs) require advanced control architectures to ensure reliability and flexibility, as well as achieve fuel-efficient operation. With this regard, this article presents a multiagent distributed power management scheme to achieve voltage regulation and economic power-sharing of diesel generators (DGs) for optimal fuel efficiency in dc SPSs. Based on the power management signal, each DG will adjust its operating speed to minimize fuel oil consumption. All functions are realized in a distributed manner with only neighboring communications, which offers more reliability and flexibility compared to conventional centralized approaches. The proposed power management scheme is validated by the controller hardware-in-the-loop (CHIL) experiment. A dc SPS test system is developed in the OPAL-RT-based real-time simulator. The B&R programmable logic controllers and network switches are set up to realize the multiagent-based distributed control. The CHIL experimental results under different operation conditions demonstrate the feasibility and advantages of the proposed method.

Fractional order PI control combined with improved frequency droop method for power management in standalone LVDC microgrids

According to increasing growth of DC loads, DC microgrids (MGs) have an effective role in future power systems. Improvement of power management is one of the important issues in DC MGs. In this paper, in order to provide an accurate power management in standalone low-voltage DC (LVDC) MG, including photovoltaics (PVs) and battery energy storage systems (BESSs), an AC component is superimposed to the output voltage of DC-DC converters by inspecting the frequency droop control of the synchronous generators. In addition, two corrective terms based on the calculation of AC active and reactive powers are added to this AC component, which is named improved frequency droop method (IFDM). Different operating modes for PV system comprising maximum power point tracking (MPPT) and voltage control mode (VCM); and BESS consisting of charge, discharge, and idle are considered in the proposed power management scheme based on the state of charge (SoC) of local BESS. On the other hand, the conventional proportional-integral (PI) controllers in DC-DC converters' control structure present poor transient dynamic performance and low loading level. Hence, fractional order PI (FOPI) controllers are designed by optimization of integral time absolute error (ITAE) criteria. The effectiveness of the proposed FOPI controllers combined with IFDM scheme is evaluated by simulations conducted in MATLAB/SIMULINK software. The FOPI-IFDM approach provides accurate current sharing and better voltage regulation without dependence on communication link in comparison with PI-IFDM under load, line impedance, and solar irradiance variations.

Hybrid stochastic/robust flexible and reliable scheduling of secure networked microgrids with electric springs and electric vehicles

Electric spring (ES) as a novel concept in power electronics has been developed for the purpose of dealing with demand-side management. In this paper, to conquer the challenges imposed by intermittent nature of renewable energy sources (RESs) and other uncertainties for constructing a secure modern microgrid (MG), the hybrid distributed operation of ESs and electric vehicles (EVs) parking lot is suggested. The proposed approach is implemented in the context of a hybrid stochastic/robust optimization (HSRO) problem, where the stochastic programming based on unscented transformation (UT) method models the uncertainties associated with load, energy price, RESs, and availability of MG equipment. Also, the bounded uncertainty-based robust optimization (BURO) is employed to model the uncertain parameters of EVs parking lot to achieve the robust potentials of EVs in improving MG indices. In the subsequent stage, the proposed non-linear problem model is converted to linear approximated counterpart to obtain an optimal solution with low calculation time and error. Finally, the proposed power management strategy is analyzed on 32-bus test MG to investigate the hybrid cooperation of ESs and EVs parking lot capabilities in different cases. The numerical results corroborate the efficiency and feasibility of the proposed solution in modifying MG indices.

Modelling and simulation of microgrid power system including a hybrid energy storage system

Microgrid technology is evolving rapidly with increased use Renewable energy (RE) in electricity sector. In this paper, an isolated DC microgrid is simulated with solar photovoltaic (PV) as the RE source to supply power to resistive DC charges along with a hybrid energy storage system (HESS) for battery and supercapacitor. Various cases of load and solar insolation variability are simulated to validate the proposed power management strategy for controlling the DC bus voltage. The output waveforms are compared with and without HESS, and beneficial reduction in transient voltage is observed when using the HESS. A case using IEEE 9bus is also simulated to evaluate device output with a constant demand for load. It is found that in all cases HESS helps to minimize transients of the DC bus voltage effectively. The HESS also compensates for very large transients resulting in sudden changes in the output of PV or load demand.

Stochastic Drift Counteraction Optimal Control of a Fuel Cell-Powered Small Unmanned Aerial Vehicle

This paper investigates optimal power management of a fuel cell hybrid small unmanned aerial vehicle (sUAV) from the perspective of endurance (time of flight) maximization in a stochastic environment. Stochastic drift counteraction optimal control is exploited to obtain an optimal policy for power management that coordinates the operation of the fuel cell and battery to maximize the expected flight time while accounting for the limits on the rate of change of fuel cell power output and the orientation dependence of fuel cell efficiency. The proposed power management strategy accounts for known statistics in transitions of propeller power and climb angle during the mission, but does not require the exact preview of their time histories. The optimal control policy is generated offline using value iterations implemented in Cython, demonstrating an order of magnitude speedup as compared to MATLAB. It is also shown that the value iterations can be further sped up using a discount factor, but at the cost of decreased performance. Simulation results for a 1.5 kg sUAV are reported that illustrate the optimal coordination between the fuel cell and the battery during aircraft maneuvers, including a turnpike in the battery state of charge (SOC) trajectory. As the fuel cell is not able to support fast changes in power output, the optimal policy is shown to charge the battery to the turnpike value if starting from a low initial SOC value. If starting from a high SOC value, the battery energy is used till a turnpike value of the SOC is reached with further discharge delayed to later in the flight. For the specific scenarios and simulated sUAV parameters considered, the results indicate the capability of up to 2.7 h of flight time.

Smart V2G/G2V Charging Strategy for PHEVs in AC Microgrids Based on Maximizing Battery Lifetime and RER/DER Employment

Nowadays, the problem of the proper charging of plug-in hybrid electric vehicles (PHEVs) has become increasingly difficult because there is high uncertainty on both the demand and supply sides of microgrid (MG) systems, which may reduce efficiency and increase the cost of MG systems. Available solutions to these challenges are the application of energy storage systems and energy exchange with the upstream utility grid (UUG). However, uncoordinated PHEV charging could have adverse effects on MGs, such as increasing the amounts of energy exchanged between the MGs and the UUG as well as decreasing the battery energy storage systems (BESSs) lifetime. This article proposes a smart charging scheme for PHEVs in ac MGs that can simultaneously minimize the energy drawn from the UUG to charge PHEVs and maximize the lifetime of BESSs based on a multiobjective optimization algorithm. For proving the validity of the proposed power management strategy (PMS), offline digital time-domain simulation studies are conducted based on the modified version of the IEEE 33-bus test system. The obtained results are compared with the results of other previously reported PMSs, which show that by increasing the output power of DER units (even when the penetration level of PHEVs increases), the proposed PMS can ensure proper charging of PHEVs while improving the lifetimes of BESSs and decreasing the energy drawn from the UUG.

Power Management of Microgrids Including PHEVs Based on Maximum Employment of Renewable Energy Resources

Recently, the penetration rate of plug-in hybrid electric vehicles (PHEVs) and renewable/distributed energy resources (RERs/DERs) has increased in microgrids (MGs). However, due to the high uncertainties on both the demand (i.e., PHEV) and the supply (i.e., RER) sides, it is an indisputable fact that these components should be coordinated intelligently with the rest of the grid. In this article, a smart charging scheme is proposed for PHEVs that can minimize the energy drawn from the main grid, and this minimizes the dependence of MGs on the main grid by maximizing the use of RERs/DERs. Two scenarios are conducted on the modified version of the IEEE 33-bus test system to verify the effectiveness of the proposed power management strategy for different penetration levels of PHEVs, and the results of the proposed scheme are compared with the results of other, previously reported strategies. The obtained results from offline digital time-domain simulations and software verification indicate that the energy exchanged between the MG and the main grid to charge PHEVs is decreased since the RER/DER generation is increased. Therefore, the obtained results reveal the superiority of the proposed power management strategy over other, previously reported strategies.

Multitoken-Based Power Management for NAND Flash Storage Devices

NAND flash-based storage devices (NFSDs) have been widely employed in various systems, including cloud servers as well as mobile devices. The core component of NFSDs is NAND flash memory (NFM) which has several advantages over the conventional hard disk drives (HDDs). An NFSD typically adopts a bunch of NFMs which are operated in parallel for maximizing the I/O throughput. However, optimizing for performance may not be desirable from the power budget (PB) perspective. In other words, concurrent operations of NFMs often drain inordinate current, which leads to the violation of the PB allocated for a storage device. In this article, we propose a novel power management scheme which maximizes concurrent operations of NFMs under the given power constraint. The proposed method quantizes the given power constraint of an NFSD. A quantum also called token is the basic unit of power management. The proposed power management scheme allocates tokens to NFMs and only the NFMs having enough tokens can perform their operations. We call this method multitoken-based power management (MTPM). The critical issue of MTPM is a deadlock which is resolved with the key allocation scheme. Furthermore, we enhance MTPM to improve performance. The extended method called keyless MTPM (KMTPM) improves the overall performance by relaxing the key acquisition requirement and allowing subatomic operations. In the experimental results, we confirm that the proposed methods always meet the given power constraint. The proposed KMTPM improves throughput by 22.85% compared to state of the art technique. In addition, KMTPM only incurs 3.8% of performance overhead and 0.015% of area overhead.

Management and Distribution Strategies for Dynamic Power in a Ship’s Micro-Grid System Based on Photovoltaic Cell, Diesel Generator, and Lithium Battery

Combining new energy technology with electric propulsion technology is an effective way to decrease the pollution of water resources caused by cruise ships. This study examines the stable parallel operation of a ship’s micro-grid system through a dynamic power management strategy involving a step change in load. With cruise ships in mind, we construct a micro-grid system consisting of photovoltaics (PV), a diesel generator (DG), and a lithium battery and establish a corresponding simulation model. We then analyze the system’s operating characteristics under different working conditions and present the mechanisms that influence the power quality of the ship’s micro-grid system. Based on an analysis of the power distribution requirements under different working conditions, we design a power allocation strategy for the micro-grid system. We then propose an optimization allocation strategy for dynamic power based on fuzzy control and a load current feed-forward method, and finally, we simulate the whole system. Through this study we prove that the proposed power management strategy not only verifies the feasibility and correctness of the ship’s micro-grid structure and control strategy, but also greatly improves the reliability and stability of the ship’s operation.

Application and Thermal-reliability-aware Reinforcement Learning Based Multi-core Power Management

Power management through dynamic voltage and frequency scaling (DVFS) is one of the most widely adopted techniques. However, it impacts application reliability (due to soft errors, circuit aging, and deadline misses). However, increased power density impacts the thermal reliability of the chip, sometimes leading to permanent failure. To balance both application- and thermal-reliability along with achieving power savings and maintaining performance, we propose application- and thermal-reliability-aware reinforcement learning–based multi-core power management in this work. The proposed power management scheme employs a reinforcement learner to consider the power savings and variations in the application and thermal reliability caused by DVFS. To overcome the computational overhead, the power management decisions are determined at the application-level rather than per-core or system-level granularity. Experimental evaluation of proposed multi-core power management on a microprocessor with up to 32 cores, running PARSEC applications, was done to demonstrate the applicability and efficiency of the proposed technique. Compared to the existing state-of-the-art techniques, the proposed technique enables an average energy savings of up to ∼20%, up to 4.926°C temperature reduction without degradation in the application- and thermal-reliability.

State-of-Health-Oriented Power Management Strategy for Multi-Source Electric Vehicles Considering Situation-Based Optimized Solutions in Real-Time

This paper presents a novel situation-based power and battery health management strategy for fuel cell vehicles. In such hybrid powertrains, the synergy role of batteries is essential to minimize overall power consumption and maintain higher electric efficiency of the fuel cell. On the other side, lifetime degradation of the battery is associated to the recurrent charging/discharging cycles. The proposed power management strategy addresses the trade-off between these contradictive objectives. Vehicle states in each situation are defined in terms of driver-related identification parameters (power demand and speed) corporately with powertrain related ones (on-board battery’s state of charge). Optimal power handling solution for each situation is searched offline considering different optimizations criteria: range extension, lifetime maximization, or power consumption minimization. A weighted fusion of these optimized solutions can be implemented online based on desired driving strategy, leading to situation-based optimized solution. This contribution aims to provide flexible power handling options meeting performance requirements (energy efficiency and driveability) without scarifying battery’s lifetime. Simulation tests using different driving cycles are conducted for evaluation purpose.

Conservation voltage reduction based comprehensive power management scheme for an isolated solar photovoltaic battery based microgrid

Use of Battery in standalone system can offer number of benefits in terms of energy management, reduction of dependency on fossil source and voltage regulation. In this study, a power management technique considering conservation voltage reduction (CVR) along with droop control method is proposed for a standalone system supplied from Photovoltaic (PV) source and battery. The proposed power management scheme ensures that under all operating conditions, the battery is neither overcharged nor completely discharged. It further ensures that under no condition, the power limit of battery charging as well as battery discharging controller is exceeded. The proposed method also aims to minimize the size of overall Photovoltaic battery (PVB) system to reduce the total investment and operation cost considering energy saved achieved by CVR, while satisfying system operational constraints. The proposed method also reduces the number of charging/discharging operations of battery to enhance its life. The validity of the proposed power management scheme has been extensively tested on a European distribution system through PSCAD/EMTDC simulation studies as well as on real time digital simulator (RTDS). The simulation results indicate that the combination of CVR and Droop control can achieve appreciable energy savings.