Power Management Of Microgrid Research Articles

Dynamic Power Control In Utility-Connected Microgrids: A Back To Back Converter Approach For Efficient Integration Of Solar And Wind Energy

This research directs the imperative need for robust power management and control in utility-connected microgrids featuring solar and wind energy sources. The suggested method employs back to back converters for precise control on the transfer of both active and reactive power between the microgrid and the utility. The new control strategy operates in two states, effectively sharing power among the utility and the microgrid. An innovative arrangement among DGs ensures seamless load sharing in both grid-connected (GCM) and islanded modes (IM). The back-to-back converters provide complete frequency isolation, protecting the microgrid from voltage or frequency fluctuations from the utility side. The study introduces synchronized relay-breaker operation during faults, along with disabling back to back converters, ensuring smooth resynchronization after fault clearance. System stability is confirmed under different load changes. The proposed control setup proves resilient to changes in grid-side voltage and frequency, ensuring a stable and reliable power-sharing mechanism under various operating conditions. Simulations conducted in MATLAB/SIMULINK validate the efficacy of the proposed control strategy, highlighting its ability to enhance the stability and reliability of utility-connected microgrids incorporating solar and wind energy sources. This study contributes to advancing sustainable energy solutions, advocating for resilient and eco-friendly power systems.Top of Form.

Intelligent Fault Detection, Compensation, and Power Management in Microgrids: A Synchrophasor-Enhanced Approach Using Fuzzy Logic Controllers and RTDS

Intelligent Fault Detection, Compensation, and Power Management in Microgrids: A Synchrophasor-Enhanced Approach Using Fuzzy Logic Controllers and RTDS

Review on the Electric Spring Connected to the Renewable Energy System to Minimize Harmonics and Voltage Profile Enhancement

The emerging smart grid technology known as Electric Spring (ES) has been employed previously to maintain voltage and power stability in renewable energy-based grids that are minimally regulated or self-contained. This study thoroughly investigated the foundational theories, modeling approaches, practical uses, and restrictions associated with Electric Springs by analyzing existing Scholarly works in this domain Electric Springs implement a direct power control approach. solution for the next-generation single-phase Systems, addressing the limitations of current ES control methods. This solution aims to improve power management in microgrids, where integrating renewable energy sources (RESs) poses challenges due to the unpredictable power output. The challenge involves managing two aspects: the variable Power consumed by ES and the power fed into the grid. The suggested straightforward and accurate signal manipulation method, namely, ANN-based Electric Spring (ES), is set to be deployed. This technique can adeptly manage these aspects in both stable conditions and during transitions in Renewable Energy Sources (RES).

Advanced Control Technique for Optimal Power Management of a Prosumer-Centric Residential Microgrid

Advanced Control Technique for Optimal Power Management of a Prosumer-Centric Residential Microgrid

The Role of Artificial Intelligence in Enhancing Energy Management in Microgrid Systems

This look at investigates the effect of artificial intelligence (AI) on microgrid overall performance thru a quantitative evaluation of strength performance, reliability, and real-time optimization metrics. Two hypothetical microgrid systems, System A and System B, are examined, revealing that AI-pushed power management in System A outcomes in superior effects in comparison to System B. Descriptive facts exhibit that System A reveals higher strength efficiency (85.2%), increased reliability indices, and more advantageous actual-time adaptability, showcasing the capacity advantages of AI integration. These findings align with current literature, emphasizing the transformative position of AI in optimizing decentralized energy structures. The look at indicates that making an investment in AI technologies for microgrid power management holds promise for attaining sustainability and resilience. Future research need to consciousness on empirical research with actual-global facts to validate these findings.

Optimization of DC Microgrid Power and Energy Management in the Presence of Small-Scale Wind Turbine Integration and Renewable Load Shedding

Improvement of Direct Current Microgrid (DCM) Power and Energy Management is the methodical process of increasing the DCM system’s performance, dependability, and effectiveness. In this study, the suggested a Stochastic Dove Swarm Optimized Deep Reinforcement Learning Algorithm approach to estimate the availability of wind energy, allowing for proactive management tactics. Initially, the data was collected and preprocessed by performing min–max normalization to clean the unwanted data. Recursive Feature Elimination is used to determine the greatest informative attributes. This study analyzes the use of significant performance metrics, including accuracy (91%), precision (92%), recall (93%), Root Mean Square Error (0.154), Normalized Root Mean Square Error (0.0526), and Mean Absolute Percentage Error (4.21). The use of optimization methodology focuses on the seamless integration of SWTs and the application of renewable LS methods to ensure the efficient management of power and energy in DC microgrids. The intention is to keep operational expenses to a minimum while providing a stable and robust energy supply. To ensure power balance, which serves as the primary objective of the monitoring, it is considered imperative for carrying out electricity limitations in this instance of the energy excess generated by solar power plants and the small-scale wind turbine.

Techno-economic analysis of reactive power management in a solar PV microgrid: A case study of Sunyani to Becheam MV feeder, Ghana

Solar photovoltaic (PV) electricity offers a promising solution for the increasing demand for clean and sustainable energy, particularly in developing countries like Ghana. Power utility operators maintain power quality as their utmost priority per benchmarks established by regulators. Some researchers argue that increasing variable renewable sources in the grid compromises its stability, leading to poor power quality. This paper aims to evaluate the suitability of reactive power management (technical aspect) in a solar PV microgrid in meeting regulators' power quality benchmarks. It also presents the economics associated with reactive power management. The study uses Python optimisation algorithms to achieve the technical objective. The net present value (NPV), profitability index (PI) and discounted payback period (DPP) are the econometrics used to assess the economic viability of reactive power management in this study. The results show that optimising reactive power in microgrids reduces power losses, increasing the utility's energy sales. An annual energy savings of 1,985,600 MWh was made due to the reactive power optimisation, leading to significant economic returns for the microgrid. The NPV, PI and DPP calculated from the study were GHS 12,528,583,512, 10,404.3, and one year, respectively. Therefore, it suggests reactive power optimisation to microgrid and grid operators as a viable solution to meet regulators' benchmarks.

Modeling and Management of Hybrid System Photovoltaic-FC-Ultracapacitors Power Systems

This study shows an improved DC microgrid power management technique. The value of utilizing renewable energy has long been debatable, and this article suggests a typical DC microgrid because of the advantages it has over AC technology. A solar cell (PV), fuel cell (FC) and storage component - an ultracapacitor (UC)-make up the standard microgrid. The most important goal of the paper is to optimize the management strategy for the bus voltage with arranged power-sharing between these types of renewable energy that are used here. For variable solar irradiance and various loading conditions, the proposed management seeks to deliver high-quality energy to the load while taking into consideration the FC state. The ultracapacitor was prepared to furnish the transient period due to the FC's slow dynamics. To maintain the DC bus voltage steady in the face of load variations, a management algorithm is put into practice. To generate the references, the management controller relies on a differential flatness method. The electricity from the ultracapacitor controls the DC bus. Earlier studies showed that the circuit of the fuel cell was integrated with a standalone photovoltaic system and discovered that the fuel cell doesn't have the same properties. In the current research, observations were made on power quality elements like voltage, current, and power for energy storage systems. With several self-definition elements of the photovoltaic, fuel cell, and ultracapacitor, along with their parameters, simulation is used as the study method medium. The outcome demonstrates that since the ultracapacitor serves as the primary storage, the fuel cell integration has little impact on it, and only small changes in voltage and current were observed. Due to the fuel cell integration characteristic of providing the system a major perturbation, most perturbation from the system had been absorbed while in an ultracapacitor

Efficient power management and control of DC microgrid with supercapacitor-battery storage systems

This paper introduces a novel power management strategy (PMS) that aims to facilitate power-sharing between battery and supercapacitor (SC) energy storage systems. The proposed technique is employed to resolve the discrepancy between power demand and generation, as well as to regulate the voltage of the dc bus. The implementation of a hybrid adaptive fuzzy integrated fractional order PD-PI controller (HAFI FPD-PI controller), in the hybrid energy storage system (HESS) results in enhanced regulation of the dc bus, while simultaneously minimising the levels of stress imposed on the battery. Moreover, the proposed PMS improves the lifespan of batteries by redirecting unwaged battery currents to SCs for rapid compensations. In addition, the stability study of dc microgrid is performed. The efficacy of the proposed PMS is evaluated by the utilisation of varying solar irradiation, wind speed, and load. The effectiveness of the proposed PMS was examined by simulations, which involved a comparison of peak deviations in the dc bus voltage with conventional PMS techniques used in literature. The real-time verification of the results has been conducted using OPAL-RT real-time simulator.

Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System

Growing Electric vehicle (EV) ownership leads to an increase in charging stations, which raises load demand and causes grid outages during peak hours. Microgrids can significantly resolve these issues in the electrical distribution system by implementing an effective energy management approach. The suggested hybrid optimization approach aims to provide constant power regardless of the generation discrepancy and should prevent the early deterioration of the storage devices. This study suggests using a dynamic control system based on the Fuzzy-Sparrow Search Algorithm (SSA) to provide a reliable power balance for microgrid (MG) operation. The proposed DC microgrid integrating renewable energy sources (RES) and battery storage system (BSS) as sources are designed and evaluated, and the findings are further validated using MATLAB Simulink simulation. In comparing the hybrid SSA strategy with the most widely used Particle Swarm Optimization (PSO)-based power management, it was observed that the hybrid SSA approach was superior in terms of convergence speed and stability. The effectiveness of the given energy management system is evaluated using two distinct modes, the variation of solar irradiation and the variation of battery state of charge, ensuring the microgrid’s cost-effective operation. The enhanced response characteristics indicate that the Fuzzy-SSA can optimise power management of the DC microgrid, making better use of energy resources. These results show the relevance of algorithm configuration for cost-effective power management in DC microgrids, as it saves approximately 7.776% in electricity expenses over a year compared to PSO.

A multifunctional complex droop control scheme for dynamic power management in hybrid DC microgrids

It is crucial for hybrid DC microgrids (MGs) to employ power management strategies that take into account the dynamic characteristics of all energy sources in the system. This paper proposes a novel complex droop control (CDC) for voltage control units in hybrid DC MGs, which enables flexible power management by providing the capability to adjust steady-state and dynamic power sharing in addition to damping provision by leveraging a multifunctional droop function. To accurately tune the splitting frequencies that determine the performance of dynamic power sharing, a novel design procedure based on a reduced-order DC MG model is proposed. To gain insight into the dominant oscillatory modes and the impact of the proposed control on the stability of the hybrid DC MG, a system-level small-signal analysis is also conducted. Offline and real-time simulations of the hybrid DC MG subjected to pulsed-power load variations reveal that the CDC can effectively share dynamic power, while improving voltage regulation and damping.

Optimization Technique for Renewable Energy Storage Systems for Power Quality Analysis with Connected Grid

Power quality is the main problem with the power system network. Poor electricity quality may cause disruptions and financial challenges for consumers. Additionally, it could cause electronic gadgets to overheat, be damaged, or operate inadvertently. Transformers and other components of the power distribution system may also overheat and experience core saturation. This study investigates potential problems with the quality of the electricity in a photovoltaic linked power system. This paper suggests a new optimization method for day-ahead trading and control in DC microgrid power management (MG). The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. Using the weighted sum approach, the multiobjective optimization problem is reduced to a single optimization problem. The analytical hierarchy process (AHP) method is then used to calculate the weight coefficients while accounting for each objective function’s preferences. Power balancing, high levels of renewable energy penetration, the most effective scheduling of battery charging and discharging, control of load curtailment, and the technical limitations of the system are all taken into account when evaluating the system’s performance in both grid-connected and standalone operation modes. The ant lion optimizer (ALO) technique is taken into account to tackle MOOD, comparing the effectiveness of the proposed method to other well-known heuristic optimization techniques. The simulation’s results demonstrate how effectively the proposed strategy can address the coordinated control and optimization dispatch difficulties. They also found that operating the MG system economically in grid-connected mode can save overall costs by roughly 4.70% compared to doing it in independent mode.

A High Voltage Gain Interleaved DC-DC Converter Integrated Fuel Cell for Power Quality Enhancement of Microgrid

Fuel cells have drawn a lot of interest in recent years as one of the most promising alternative green power sources in microgrid systems. The operating conditions and the integrated components greatly impact the quality of the fuel cell’s voltage. Energy management techniques are required in this regard to regulate the fuel cell’s power in a microgrid. The active/reactive power in the microgrid should be adjusted in line with US Energy Star’s regulations whereas the grid current needs to follow the standard set by IEEE 519 2014 to enhance the power quality of the electrical energy injected into the microgrid. Uncontrolled energy injection from the fuel cell can have serious impacts including superfluous energy demand, overloading, and power losses, especially in high power and medium voltage systems. Although fuel cells have many advantages, they cannot yet produce high voltages individually to compensate for the demand of a microgrid system. Due to these reasons, the fuel cell must be interfaced with a DC-DC converter. This research proposes a novel high voltage gain converter integrated 1.26 kW fuel cell for microgrid power management that can boost the fuel cell’s voltage up to 20 times. Due to this high voltage gain, the voltage and current ripple of the fuel cell is also reduced substantially. According to the analysis, the proposed converter demonstrated optimal performance when compared to the other converters due to its high voltage gain and extremely low voltage ripple. As a result, the harmonic profile of the microgrid current persists with a reduced THD of 3.22% and a very low voltage ripple of 4 V. To validate the converter’s performance, along with extensive simulation, a hardware prototype was also built. The voltage of the fuel cell is regulated using a simplified proportional integral controller. The operating principle of the converter integrated fuel cell along with its application in microgrid power management is demonstrated. A comparative analysis is also shown to verify how the proposed converter is improving the system’s performance when compared against other converters.

Power Management in DC Microgrid by the DC-DC Converters

Abstract: DC loads have proliferated rapidly on the market today and DC micro grids with renewable energies are being built as a potential solution to meet the rising demand for electricity. As different energy sources such as solar, wind, fuel cell, and diesel generators can be incorporated into the DC grid, it is important to control the power flow between the sources. An attempt is made in this paper to study the hybrid system consisting a three energy sources, namely wind energy, photovoltaic power source and Battery. Each of the three energy sources is controlled so as to deliver uninterrupted power supply to the load. A control strategy for the management of power flows with solar and wind energy sources in DC micro grid are discussed. Given that voltage profile regulation is critical in a standalone system, a dedicated converter should be used to maintain the voltage of the DC connection. The battery circuit regulates DC charging voltage, while the full power is derived from Solar and Wind to power the attached DC bus charges. An algorithm is developed to manage power flow between three outlets. The algorithm is evaluated in MATLAB / SIMULINK environments for different charging conditions and variations in solar and wind energy.

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.

Energy and power management system for microgrids of large‐scale building prosumers

AbstractA method for optimal energy and power management of microgrids consisting of mega buildings, plug‐in electric vehicles (PEVs) and renewable energy sources (RES) with low computation requirements is proposed by the authors. Thermal and electrical loads are considered for the operation scheduling of the microgrid. In case of non‐interconnected operation of the microgrid with the main power grid, the proposed method allows the microgrid to meet the power demand by the buildings and distribution loads exploiting only the hosted PEVs, the integrated RES and, if it is necessary or financially optimal, building auxiliary diesel generators. The primary goal of the suggested algorithm is to significantly reduce the overall daily cost of the microgrid's operation while simultaneously meeting a wide range of constraints. The implementation of the method is based on the exploitation of a two‐level hierarchical multi‐agent system (MAS) at the level of the microgrid. Suitably defined flexibilities of the microgrid's components to change their power are used to implement optimal power dispatch to them. Detailed simulation results indicated that a remarkable cost reduction of 27% can be achieved.

Real‐time resilient microgrid power management based on multi‐agent systems with price forecast

AbstractMicrogrids have emerged to diversify conventional electric generation using small‐scale distributed generation. Large efforts have been put into designing control strategies to optimise the power schedules of microgrids, however, verification that such control systems also are reliable in terms of stability during normal operation and fault conditions is needed. This study presents a hierarchical distributed control system that fulfils these conditions for an AC microgrid. The stability maintained by proposed controller, considering the large signal model, is analysed with the use of Lyapunov's direct method. Resilient control distribution is achieved by the implementation of suitable forecast models and fault‐tolerance mechanisms to avoid single points of failure. The resilience of the control system is verified with the use of graph theory. The stable and resilient operation of the proposed control system is tested by a real‐time microgrid model implemented with an OPAL‐RT real‐time simulator, combined with a communication network built with Raspberry Pis, testing the control system presented under normal and faulty conditions. Simulation results show a stable operation in terms of voltage and frequency in both conditions, resilient operation is shown for the faulty condition case. Additionally, cost minimisation performance is included to validate optimal power management capabilities.

Contributions to Power Management in AC Microgrids Based on Concepts of Microeconomics Theory

Renewable energy sources are growing in importance and reshaping power systems through microgrid (MG) networks. Some relevant issues in the management of an MG need to be addressed, such as voltage and frequency control, power balance between sources (sharing) and distributed loads, stability, security, power quality, etc. As a contribution, this manuscript presents a power management of AC MGs based on concepts of microeconomics theory for market equilibrium. The prices of active and reactive energy are informed through the AC voltage parameters such as magnitude, frequency and angle. This approach allows several sources and loads to operate with no extra communication interface. Experiments are provided to validate operation in many different operational scenarios using a hardware-in-the-loop environment.

Power Management, Control and Optimization of Photovoltaic/Battery/Fuel Cell/Stored Hydrogen-Based Microgrid for Critical Hospital Loads

Renewable-energy-based DC microgrids are acting as a possible replacement for the diesel-generator-based stand-alone power systems for critical loads as they are environment friendly, efficient and reliable. Research attention is required on optimization and effective power management of DC microgrids to address power failures and affordability issues. This paper performs optimization and proposes a power management strategy for a DC microgrid consisting of Solar PV/battery/fuel cell and stored hydrogen. A suitable case study is implemented in HOMER (Hybrid Optimization of Multiple Energy Resources) to check the feasibility of the proposed system configuration for the remote location of North India (74.75∘ E and 34.05∘ N). The HOMER is used to evaluate the best system configuration in size and cost per unit of energy. A custom-based power management algorithm is suggested to enhance the proposed microgrid topology scope for any given site. The custom-based power management algorithm helps in effectively sharing the critical load demand among the various renewable power sources, considering the various parameters like battery state-of-charge (SOC) and availability of solar photovoltaic power. Time-domain simulations are performed to check the proposed custom-based power management algorithm’s microgrid response and effectiveness considering random solar irradiance profile and various operating modes. It is assumed that hydrogen is available in the tank and supplied to the fuel cell at a constant flow rate for the time-domain simulations. The contribution of this work is to describe the application of renewable-energy-based DC microgrid in electrifying ventilator critical loads, where load shedding is not feasible and neither allowed. The optimization results indicate the economic feasibility with the best optimal system configuration providing electricity at $ 0.186 with a total NPC of $83103. Time-domain simulations are performed in MATLAB/Simscape software and the results indicate the technical feasibility of electrifying the ventilator loads through the proposed renewable-energy-based DC microgrids.

A nonlinear double‐integral sliding mode controller design for hybrid energy storage systems and solar photovoltaic units to enhance the power management in DC microgrids

In this paper, a nonlinear decentralized double-integral sliding mode controller (DI-SMC) is designed along with an energy management system (EMS) for the DC microgrid (DCMG). This DCMG includes having a hybrid energy storage system (HESS) that incorporates a battery energy storage system (BESS) and supercapacitor energy storage system (SCESS) while the load demand is met through the power generated from solar photovoltaic (SPV) units. First, dynamical models of each subsystem of DCMGs such as the SPV system, BESS, and SCESS are developed to capture highly nonlinear behaviors of DCMGs under various operating conditions. The proposed nonlinear DI-SMC is then designed for each power unit in DCMGs to ensure the desired voltage level at the common DC-bus and appropriate power dispatch of different components to fulfill the load requirement of the DCMG. On the other hand, an energy management system (EMS) is designed to determine the set point for the controller with an aim of ensuring the power balance within DCMGs under various operating conditions where the overall stability is assessed using the Lyapunov theory. Simulation studies along with the processor-in-loop validation, including a comparative study with a proportional-integral (PI) controller, verify the applicability and effectiveness of the EMS-based DI-SMC under different operating conditions of the DCMG.