Power Management Control Research Articles

Optimization and intelligent power management control for an autonomous hybrid wind turbine photovoltaic diesel generator with batteries

In this paper, a critical issue related to power management control in autonomous hybrid systems is presented. Specifically, challenges in optimizing the performance of energy sources and backup systems are proposed, especially under conditions of heavy loads or low renewable energy output. The problem lies in the need for an efficient control mechanism that can enhance power availability while protecting and extending the lifespan of the various power sources in the system. Furthermore, it is necessary to adapt the system's operations to variations in climatic conditions for sustained effectiveness. To address the identified problem. It is proposed the use of an intelligent power management control (IPMC) system employing fuzzy logic control (FLC). The IPMC is designed to optimize the performance of energy sources and backup systems. It aims to predict and adjust the system's operating processes based on variations in climatic conditions, providing a dynamic and adaptive control strategy. The integration of FLC is specifically emphasized for its effectiveness in balancing multiple power sources and ensuring a steady and secure operation of the system. The proposed IPMC with FLC offers several advantages over existing strategies. Firstly, it showcases enhanced power availability, particularly under challenging conditions such as heavy loads or low renewable energy output. Secondly, the system protects and extends the lifespan of the power sources, contributing to long-term sustainability. The dynamic adaptation to climatic variations adds a layer of resilience to the system, making it well-suited for diverse geographical and climatic conditions. The use of realistic data and simulations in MATLAB/Simulink, along with real-time findings from the RT-LAB simulator, indicates the reliability and practical applicability of the proposed IPMC strategy. Efficient load supply and preserved batteries further underscore the benefits of the fuzzy logic-based control strategy in achieving a well-balanced and secure system operation.

Optimal Power and Energy Management Control for Hybrid Fuel Cell-Fed Shipboard DC Microgrid

Optimal Power and Energy Management Control for Hybrid Fuel Cell-Fed Shipboard DC Microgrid

Fuzzy Controlled Power Management and Power Quality Enhancement for Grid Integrated Renewable Energy sources with EV Fed UPQC

Recently, the merging of Renewable Energy Sources (RES) and Electric Vehicles (EVs) has brought about significant changes in distribution systems. The main goal of this research is to effectively tackle power quality (PQ) concerns such as voltage fluctuations(sags and swells) and total harmonic distortion(THD) in a system connected to the grid .This system combines Solar Photovoltaic(SPV) and Wind Energy Systems(WES) and integrates a Battery Energy Storage System(BESS) and Electrical vehicles(EVs). To accomplish this, a versatile Unified Power Quality Conditioner (UPQC), referred to as U-SWEBEV, is employed. The control of power flow, encompassing the transfer from energy generation sources to consumer loads and among various sources, is efficiently managed through the application of a Fuzzy Interface System (FIS) regulation. This FIS system plays a key role in regulating power distribution. Additionally, a fuzzy Controller is implemented to enhance Maximum power Point Tracking (MPPT) from the SPV system. This combined approach , utilizing both Fuzzy Logic and the Unified power Quality Conditioner (UPQC), significantly contributes to the efficient and effective. utilization of power resources while successfully addressing power quality (PQ) concerns with different load combinations . The proposed method reduces the total harmonic distortion of load current and source voltage at linear nonlinear, nonlinear, Nonlinear and unbalanced linear loads with % THD 1.64 ,0.04,1.20,0.04 and 2.84,2.32 which are lower than the existing methods that are available in literature.

Effective Power Management Strategy and Control of a Hybrid Microgrid With Hybrid Energy Storage Systems

Effective Power Management Strategy and Control of a Hybrid Microgrid With Hybrid Energy Storage Systems

Performance improvement through nonlinear control design and power management of a grid-connected wind-battery hybrid energy storage system

This paper presents a study on the application of nonlinear control and optimal power management techniques in a grid-connected wind energy conversion system with battery storage. The objective is to efficiently supply power to a load while considering performance constraints. The energy system under investigation comprises a wind turbine, battery, AC-grid, and load. Power is delivered to the load through various converters and a DC-link. However, the intermittent nature of wind energy and frequent load fluctuations can negatively impact the battery's lifetime and charging performance. To address these challenges, a nonlinear controller and a novel energy management system are designed. The energy management system generates multiple energy flow scenarios to achieve equilibrium in energy distribution between the load and energy sources. This equilibrium aims to minimize overall system costs, ensure DC-grid stability, and enhance power quality. Two distinct operation modes are identified: i) MPPT mode, which utilizes an optimizer to maximize power extraction, and ii) adaptive power point tracking (APPT) mode, which considers demand-driven production and the state of charge (SOC) of the storage system. The convergence of the closed-loop control is formally analyzed, and simulation results confirm the improved performance of the proposed multi-criteria design for energy management.

Artificial Neural Network Based Active Power Management Controller for Electric Vehicle Charging Station

ABSTRACT This paper presents a DC microgrid based electric vehicle charging station (EVCS). It consists of solar photovoltaic (PV) system, stationary battery storage (SBS), grid as power generation source and electric vehicle as load. An adaptive interaction artificial neural network (ANN)-based active power management controller (APMC) is proposed for DC microgrid based EVCS. It works in three different modes of operation. The mode of operation depends upon the PV power available and current state of charge of SBS. This APMC is designed to get the electricity from the PV array and SBS preferably. If the PV and battery power both are not sufficient to fulfil the requirement, power is drawn from the grid. When a solar PV system generates excess power and the SBS is sufficiently charged, then excess power is delivered to grid. The proposed APMC is tested for three different modes using MATLAB Simulink software.

An adaptive dynamic power management approach for enhancing operation of microgrid with grid ancillary services

The increasing prevalence of photovoltaic (PV)-wind-battery-based microgrids and their integration into distribution networks have brought new challenges in grid ancillary services, such as poor power quality (PQ), grid instability, voltage and current fluctuations, and low efficiency of microgrids. These adverse effects are due to the intermittent nature of PV and wind power generation. The present work proposes an adaptive dynamic power management (ADPM) approach and enhanced instantaneous symmetrical component theory (EISCT) to improve the microgrid operation and grid ancillary services. The proposed ADPM-based EISCT not only utilizes a single grid side converter (GSC) but also reduces the sensor requirement and control structure complexity and thereby eliminates communication mismatch. The control structure, proposed in this research, has two major segments. The first segment focuses on active power management control through ADPM to facilitate efficient power flow between the utility grid and microgrid sources which helps in ensuring efficient power sharing. The second component is dedicated to synchronizing the renewable energy sources of the microgrid with the utility grid and simultaneously maintaining PQ compliance as per IEEE 519–2022 standards. The efficacy and adaptability of the proposed structure are validated through MATLAB®/Simulink® environment and the practical superiority of the system has been tested by using MicroLabBox® DS1202-based real-time hardware set-up. The experimental result shows improved performance of the developed structure under various microgrid operating conditions including dynamic loading situations, and battery system state-of-charge (SOC). The simulation and experimental results suggested that the DC-link ripples have been reduced to 2%, peak overshoot to 1.8%, settling time during the transition to 1.2s, battery response time to 0.3s, and the total harmonic distortion (THD) to less than 5%. These results demonstrated the superiority of the ADPM-based EISCT over conventional strategies.

Fault Ride-Through Strategies for Synchronverter-Interfaced Energy Resources Under Asymmetrical Grid Faults

Synchronverters are inverters controlled to behave like synchronous generators. However, unbalanced faults or voltage dips may stimulate synchronverters to generate excessive currents and cause instantaneous active and reactive power oscillations at twice the grid's fundamental frequency. Therefore, this paper proposes three fault ride-through (FRT) strategies to remedy these power oscillations and the unlimited current generation during unbalanced faults. The first strategy is based on an instantaneous active and reactive control that significantly reduces power oscillations. The second strategy relies on an average active and reactive control that ensures the delivery of average active and reactive power and maintains sinusoidal phase currents. Finally, the third FRT strategy employs a comprehensive instantaneous active and reactive control to eliminate power oscillations and maintain sinusoidal currents simultaneously. The proposed FRT strategies are equipped with a power management controller that delivers the required active and reactive power during normal operation and limits the inverter's phase currents during faults. Further, the proposed FRT strategies maintain the intrinsic features of synchronverters and ensure seamless activation of the proposed FRT strategies. Comparative results confirm the efficacy of the proposed strategies to reduce power oscillations, limit current generation, maintain synchronverters' intrinsic features, and comply with grid codes.

A deep reinforcement learning approach for power management of battery-assisted fast-charging EV hubs participating in day-ahead and real-time electricity markets

Publicly available electric vehicle charging hubs are expected to grow, to meet the increasing charging demand of EVs. A dominant class of these will be fast-charging hubs where the EVs will arrive for charging at all hours of the day, get the requested charge, and leave promptly. The profitability of these fast-charging hubs will be highly dependent on the variation of the day-ahead prices of electricity, volatility of the real-time power market, and the randomness of EV charging demand. The hubs can hedge against these uncertainties by committing power purchases in the day-ahead electricity market and by adopting dynamic real-time power management strategies. We develop a novel two-step methodology. The first step entails a mixed integer linear program (MILP) that assists the hubs in their day-ahead power commitment. The second step employs a Markov decision process (MDP) model that derives the real-time power management control actions. The MILP is solved using a commercial solver and the MDP is solved using a deep reinforcement learning algorithm. We demonstrate the effectiveness of our methodology for a fast-charging hub, housing 150 charging stations and a battery storage system, that operates in the Pennsylvania-New Jersey- Maryland interconnection (PJM) power grid.

Integrated Optimization of Power Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles

Integrated Optimization of Power Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles

Hybrid power management and control of fuel cells‐battery energy storage system in hybrid electric vehicle under three different modes

AbstractIn most situations, fuel cells (FCs) are insufficient to supply power demands in hybrid electric vehicles (HEVs), thus battery storage systems (BSSs) are used to make the system more efficient like as rapid starting, high power density, and enhanced dynamic set response. How the power sources are regulated and distributed determines the accomplishment efficiency of the driving forces in such a configuration. The most critical aspect should be how to guarantee torque stability of the traction motor which is in our case a permanent magnet synchronous motor (PMSM) while attaining quick reaction. This study discusses a hybrid battery‐FCs energy storage and management system for a hybrid electric vehicle (HEV), as well as an integrated PMSM's passivity‐based control (PBC) technique to enable power integration and increase the hybrid electric vehicle (HEV)'s operating speed. The present paper is separated into two sections. First, a power management control (PMC) technology is used to manage the FCs‐battery system to guarantee that the HEV gets continuous power from the hybrid energy resources, where a fuzzy logic controller is used as an artificial intelligence system for the FCs converter to maximize the power produced. The second is dedicated to the new proposed PBC control of the EV's PMSM to rectify non‐linearities, external disturbances, and parametric fluctuations. The standard proportional‐integral (PI) control and sliding mode control (SMC) are built for comparison with the proposed PBC dedicated to the PMSM in order to properly assess the efficacy of the proposed control strategy. The simulation results obtained using MATALB/Simulink show that the proposed PBC technique and hybrid battery‐ proton‐exchange membrane PEMFCs energy management enables high power integration and increase the EV's operational speed.

Aging-aware optimal power management control and component sizing of a fuel cell hybrid electric vehicle powertrain

The current study presents a comprehensive approach for optimizing the power distribution control and design of a Fuel Cell Hybrid Electric Vehicle (FCHEV) equipped with a Battery-Ultracapacitor Hybrid Energy Storage System (HESS) using a multi-objective evolutionary algorithm called interactive adaptive-weight genetic algorithm (i-AWGA). The method aims to maximize the vehicle’s driving range and the lifetimes of the fuel cell stack and battery while minimizing hydrogen fuel consumption and HESS size. The energy management strategy involves fuzzy logic controllers to distribute the power demand between the fuel cell and HESS and between the battery and ultracapacitor pack. Under the combined standardized cycle in which the optimization was developed, the optimized FCHEV configuration achieved a driving range of 444 km, hydrogen consumption of 0.9009 kg/100 km. Furthermore, the optimal configuration demonstrated robustness in real-world driving conditions, exhibiting improved energy efficiency, driving autonomy, and power sources lifespan. A cost–benefit analysis was also carried out, in which the optimized configuration was evaluated in terms of cost of ownership, achieving 31.28 US$/km, which means the substantial reduction of up to 63.59% in the invested cost-to-autonomy ratio as compared against other electrified vehicle powertrain topologies. Overall, this study offers a promising approach for designing efficient, cost-effective, and environmentally friendly FCHEVs with improved performance and durability.

Power Management Control of photovoltaic/Batteries/Fuel cells System

A study of a stand-alone photovoltaic./batteries/fuel cell power system has been presented in this paper to produce energy continuously. A management control circuit (PMC) has been designed to obtain the different control signals that allow supervising power flow of the hybrid system. The application has been made to a stand-alone system for four days at Bejaia region. Fuzzy logic Control (FLC) is used to optimize power The structures PV/Battery/Fuel cells system have been investigated. A modelisation of each component has been made. A supervisor control circuit has been designed to obtain the various control signals needed to control the overall system. A simulation study has been done in Matlab. The different results are presented to show the effectiveness of the proposed strategy

Application of vibration signal detection in mine hoist fault monitoring system

Due to the harsh working environment and various types of faults in mine hoists, the implementation of conventional detection is relatively difficult. In order to improve the maintenance and fault detection efficiency of mine hoist, a portable vibration detection system is designed and applied in this paper, which can judge the type and characteristics of mechanical faults according to the different responses of vibration signals. According to the test conditions of the vibration system, the overall structure and functional composition of the hardware system are established. The key functional modules are designed, including signal conditioning module, main control module, vibration sensor and power management module. The main reasons that affect and produce the hoist fault and mechanical vibration are analyzed. The wavelet packet analysis method is proposed to realize the decomposition of low frequency and high frequency signals to improve the signal resolution of the whole frequency band in the frequency domain. The vibration test system has the function of real-time data transmission and control. The realization methods include WiFi and reserved RS485 communication interface, which can match the communication rate with the data sampling rate. The amplifying circuit of the electric signal is designed to effectively ensure that the output voltage of the charge amplifier is within the input range of the amplified ADC value. In order to verify the working effect of the system, the most common gearbox and bearing failures are taken as examples. The response signals of vibration test and diagnosis can be effectively obtained, and the fault types and characteristics can be accurately judged. Through confusion matrix analysis, the reliability of the system detection index is effectively verified. The research results show that the data of the vibration detection system is stable and reliable, and can achieve good troubleshooting effect. It also can be known that different fault types can excite different vibration responses, especially at resonance frequencies. According to the verification, the data of the vibration detection system is stable and reliable, and can achieve good troubleshooting effect.

Enactment of Deep Reinforcement Learning Control for Power Management and Enhancement of Voltage Regulation in a DC Micro-Grid System

DC micro-grids are gaining more attention and becoming more predominant due to its robustness and ease of control. To confront the stability issues of the DC Micro-grid system, a model free intellectual Control approach is proposed and recognized with a deep Q-learning controller to influence the functions of DC–DC boost converter. This self-reliant model of deep Q-learning controller establish a suitable communication link among the agent-environment by applying the reward/penalty algorithm for attaining the stable output voltage with its training statistics. Upon learning the state of agent, an optimal proposal is defined by the controller to attain the stabilized voltage at the output for all possible operating scenarios. The simulation and experimental studies validate the performance of the controller with higher accuracy, reduced overshoot, and faster settling time upon the transient states.

Implementation of hybrid optimized battery controller and advanced power management control strategy in a renewable energy integrated DC microgrid.

In Renewable Energy (RE) integrated DC Microgrid (MG), the intermittency of power variation from RE sources can lead to power and voltage imbalances in the DC network and have an impact on the MG's operation in terms of reliability, power quality, and stability. In such case, a battery energy storage (BES) technology is widely used for mitigating power variation from the RE sources to get better voltage regulation and power balance in DC network. In this study, a BES based coordinated power management control strategy (PMCS) is proposed for the MG system to get effective utilization of RE sources while maintaining the MG's reliability and stability. For safe and effective utilization of BES, a battery management system (BMS) with inclusion of advanced BES control strategy is implemented. The BES control system with optimized FOPI controllers using hybrid (atom search optimization and particle swarm optimization (ASO-PSO)) optimization technique is proposed to get improved overall performance in terms of control response and voltage regulation in DC network under the random change in load profile and uncertain conditions of RE sources in real time.

A study of power management approach in a grid connected hybrid renewable generation system

<p>Where sun irradiation is not continuous throughout the day, photovoltaic (PV) cells are used to supply solar power to the grid. A suitable power management plan is required to provide a steady power supply to the grid. The hybrid renewable generation system (HRGS) interconnected to the grid is the subject of this study, which provides a revolutionary power management method and control strategy. PV devices, a battery storage unit (BSU) charged by electricity generated from solar energy, and an auxiliary unit make up the HRGS concept (AU). Maximum power point tracking (MPPT) uses an improved perturb and observe (P&O) method to track maximal energy from solar irradiation. The peculiarity of this approach is that it uses a modified MPPT algorithm to handle power management throughout the process, allowing it to manage continuous power delivery to the grid based on load demand. It is capable of working under any load situation. The action of an L-C filter is utilized to decrease the total harmonic distortion (THD). The HRGS scheme's performance appraisals work correctly, achieve maximum productivity, and continually managing the power provided to the grid in a satisfactory way.</p>

An improvement of stability and dynamic response in hybrid AC-DC microgrids using optimal power control management

Microgrid topologies enable more effective use of renewable resources as well as the autonomous process. Microgrids are effective frameworks for managing dispersed assets such as renewable structures with small-scale distributed generator supplies. Smart grids are adaptable electricity networks that have arisen as a result of the introduction of new technologies and features. It is suggested that a three-terminal AC/DC hybrid microgrid with DC two and AC one terminal be used. A two dual active bridge (DAB) with cascaded H-bridge (CHB) converter with AC grid link-based interactions that connect isolated two DC buses are included in the suggested structure. To minimise the energy generation stages and power devices, the DAB converters are intrinsically linked to the CHB of DC rails based on the structure-specific requirements. To present the unnecessary grid currents and DC rail voltage issues exist by this revised structure topology by simply two power alteration stages, a better solution is suggested that employs zero-sequence voltage (ZSV) injection in the CHB converters. The impacts of managed settings on structure stability and dynamic responsiveness are investigated in order to reduce conflicts among ZSV injection by structure voltage/current regulation. This paper compares the performance of a PI and a PR for DC voltage regulation. A comparison of simulation results for both control approaches is provided. The major difficulty is to normalise the system model in order to identify the appropriate controller settings for robust control. The findings are confirmed and compared to the conventional regulator PI. The suggested 3-Φ AC current and DC rail voltage regulating approach's universal efficacy is proved by MATLAB/Simulink analytical model from the three and five-terminal hybrid AC/DC power structures.

Battery state of charge based improved adaptive droop control for power management of a microgrid having large scale renewable generation

Power management in a standalone microgrid having large scale renewable generation is generally based upon droop control strategies. However, the renewable sources such as photovoltaics (PV), which are interfaced with microgrid through power electronic converters have low inertia. Because of low inertia, the transient power variation during switching events like change in load may be very large and may result into variation in voltage and frequency beyond permissible limits. In this paper an adaptive scheme for power management of a standalone microgrid is proposed, which is based on available state of charge (SoC) of the batteries to mitigate these issues. The proposed battery SoC based adaptive droop control for power management allows smooth transition of distributed energy resources (DERs) from maximum power point tracking (MPPT) mode to adaptive droop mode and vice versa. The transient behaviour is handled with the help of batteries and supercapacitor (SC), which is further improved with the help of fuzzy logic based proportional integral controller (FLC-PI). A significant drop in SC power during switching events is obtained with FLC-PI. The test microgrid is composed of multiple PV generation at different locations as primary source of generation, multiple batteries energy storage system (ESS) at different locations, fuel cell (FC), supercapacitor (SC) and a dynamic load.

Design and Implementation of Hybrid Controller for Dynamic Power Management in a DC Microgrid

Nowadays more and more devices and appliances are operated with electricity, thus the electrical crisis is increasing exponentially day by day. In order to avoid the occurrence of electricity crisis, various power generation resources are used at the utility side to enhance the power generation to meet the consumers’ demand for electricity. Hence, a suitable control scheme has to be implemented at the microgrid to reduce the electrical fluctuation, power loss and manage the power quality. The Adaptive Proportional Integral Voltage Controller (APIVC) and hysteresis current controller (HCC) are integrated to enhance the quality of power generated. The electrical fluctuation is reduced by the proposed efficient hybrid parallel source controller model for DC Microgrid. The proposed model exerts decentralized control, which is an advanced droop control where communication is not required. The outer voltage control loop and inner current control loop provide faster control to maintain the grid voltage constant. The grid voltage is set as the reference value and the actual value is sensed to generate error value, which sets the reference value of current. The error signal is processed to provide switching signals for the converters. The performance analysis and simulation results show that the proposed mechanism performed better than the conventional methods such as Hysteresis Band Current Controller (HBCC) with Pulse Width Modulation (PWM) and Proportional Integral Voltage Controller (PIVC) with Hysteresis Current Controller (HCC), in terms of the electrical fluctuation, power loss and manage the power quality in the microgrid.