Hybrid Microgrid Research Articles

Smart Control Strategy for Adaptive Management of Islanded Hybrid Microgrids

This research paper presents a smart power control approach specifically designed for an independent microgrid. The proposed hybrid system consists of various crucial components, including a PV array, super capacitor, DC bus, battery bank, and AC bus working together to generate and store electricity within the microgrid. To address the challenges arising from random fluctuations in ecological parameters and changes in load demand, a supervisory controller is developed to enhance the standalone hybrid microgrid. This allows for optimized power management within the micro grid. The Liebenberg Marquardt algorithm is used to retrieve the trained ANN machine. The two and three hidden layered ANN machines have 96% accuracy on an average, whereas the single-layer ANN machine have poor predictive ability. The proposed model is implemented and analysed using MATLAB/Simulink. The observed results from the simulation experiments validate the effectiveness of integrating available resources in ensuring the resilience and reliability of microgrids.

Optimizing Renewable Energy Integration through Innovative Hybrid Microgrid Design: A Case Study of Najran Secondary Industrial Institute in Saudi Arabia

Amidst a growing global focus on sustainable energy, this study investigates the underutilization of renewable resources in the southern region of Saudi Arabia, with a specific emphasis on the Najran Secondary Industrial Institute (NSII). This research presents an in-depth analysis of installing a hybrid microgrid (MG) system on the roofs of NSII buildings, exploring six cases with varying tilt and azimuth angles. The study innovatively integrates architectural design and system administration, a novel approach for this location, and benchmarks the optimal angles against Hybrid Optimization of Multiple Energy Resources (HOMER) software defaults. The proposed system consists of solar photovoltaic (PV) panels, a battery storage system (BSS), a converter, a diesel generator (DG), and a grid. The selected model balances technological and economic viability with environmental considerations, ensuring a reliable power supply within the NSII’s roof area constraints. An extensive sensitivity analysis evaluates the system’s resilience across different scenarios. The current system, which is grid-only, has an estimated Net Present Cost (NPC) of about USD 7.02M and emits 1.81M kg/yr of CO2. The findings point to installing a microgrid with a 20.97° tilt and 50° azimuth angle as optimal, demonstrating 54.69% lower NPC and 92% lower CO2 emissions, along with zero kWh/year unmet electrical load when applying the resilience assessments. This outcome highlights Saudi Arabia’s southern region’s renewable energy potential, aligning with national mega-projects and energy initiatives.

Design Optimization of a Grid-Tied Hybrid System for a Department at a University with a Dispatch Strategy-Based Assessment

In this research project, the optimal design and design evaluation of a hybrid microgrid based on solar photovoltaics, wind turbines, batteries, and diesel generators were performed. The conventional grid-tied mode was used in addition to dispatch strategy-based control. The study’s test location was the loads in the Electrical, Electronic and Communication Engineering (EECE) department at Pabna University of Science and Technology (PUST), Pabna, Bangladesh. DIgSILENT PowerFactory was employed to determine the power system-based behaviors (electrical power, current, voltage, and frequency) of the proposed hybrid system, while a derivative-free algorithm was used for the expense, optimal size, and emission assessments. While developing the microgrid, load following (LoF) and cycle charging (CyC) control were employed. The microgrid is supposed to have a 23.31 kW peak load requirement. The estimated microgrid’s levelized cost of energy (LE), its net present cost (NC), its operating cost, and its annual harmful gas emissions were estimated in this work. Additionally, since the microgrid is grid-connected, the amount of energy output that might be exported to the grid was also estimated, which will potentially increase during blackouts. The power system responses found in this study ensure that the various microgrid components’ voltage, frequency, current, and power outcomes are steady within the designated range, making the microgrid practical and robust.

Enhanced energy balancing and optimal load curtailment strategy for DC microgrid integration in hybrid AC/DC distribution networks

AbstractUnleashing the potential of distributed renewable energy sources (RESs), intelligent and autonomous microgrids are becoming pivotal in attaining net‐zero carbon emission goals. Hybrid AC/DC microgrids rise as cutting‐edge microgrid topologies, capitalising on the best of both AC and DC systems. However, the integration of intermittent renewables and uncertainties in loading poses stability challenges. Advanced bidirectional converter controls provide efficient power exchange, but in extreme contingencies, a resilient supervisory control framework (load management/load curtailment approach) is inevitable to withstand/avoid unplanned renewable disruptions/blackouts. Moreover, the operational paradigm shift towards achieving net‐zero emissions, isolated operation of RESs, and conventional load shedding methods are anticipated to encounter substantial challenges, necessitating the development of alternative strategies. In order to improve the stability of hybrid microgrid systems in islanding scenarios, this research presents an energy balancing and load curtailment strategy. The proposed method aims at optimising resource utilisation, prioritising essential loads, and executing an optimal load curtailment strategy (if required), thereby augmenting the stability of systems. Unlike a meta‐heuristic or exhaustive search, which depends on 2n − 1 possible combinations and become unworkable as load numbers increase, the suggested methodology is based on a mathematically modelled load restriction method. By including load criticality, this strategy effectively prevents blackouts even with an increasing number of loads, providing a significantly more useful and practical solution. Additionally, the proposed charging algorithm ensures that the energy storage system imports energy from the grid during off‐peak hours and maximises power generation from the DC subgrid. The efficacy of the proposed strategy is validated using a modified IEEE‐33 bus system as a test case for a hybrid AC/DC microgrid. Simulation results demonstrate the effectiveness of the MILP‐based load curtailment approach in maintaining system stability and preventing blackouts during unforeseen events.

Distributed Optimal Control of AC/DC Hybrid Microgrid Groups with Interlinking Converters

Distributed Optimal Control of AC/DC Hybrid Microgrid Groups with Interlinking Converters

Research on autonomous operation and mutual aid strategy of AC/DC hybrid microgrid cluster

In order to address the challenge of ensuring convergence and stability in AC/DC hybrid microgrid cluster with self/mutual communication delay, this study proposes a hierarchical control strategy. By leveraging the finite-time consistency algorithm, this strategy facilitates autonomous operation of sub-microgrids and enables mutual aid and assistance within the microgrid cluster. The proposed control strategy consists of two levels: the sub-microgrid level and the microgrid cluster level. Both levels utilize primary control along with a secondary adjustment term based on improved finite-time consistency. This strategy aims to ensure frequency and voltage stability, power equalization in the sub-microgrids, and economic allocation of power in the microgrid cluster. The proposed control strategy improves the traditional finite-time consistency protocol, considers the effect of time delay on system stability. It also introduces the convergence coefficient to strike a balance between system economy and stability objectives. Through a comparison with the traditional consistency algorithm, simulation results demonstrate that the proposed control strategy enhances the robustness of the system, enables coordinated control of frequency and voltage in sub-microgrids, facilitates economic power allocation in the microgrid cluster, and achieves faster convergence speed.

Reliability enhancement of hybrid microgrid protection against communication data loss and converter faults using cubic-spline interpolation, Savitzky Golay filtering and GRU network

A primary challenge towards adoption of hybrid AC-DC microgrid pertains to the complexity of the protection scheme. Among other challenges, the high resemblance of voltage and current dynamics for converter faults and line faults, hinders use of threshold-based protection schemes. Further the high dependence of the protection mechanism on the communication network results in reduced reliability during data packet loss in the communication channel. In this regard, a protection scheme is proposed based on the combination of cubic spline interpolation, Savitzky-Golay (SG) filtering, maximum overlap discrete wavelet packet transform (MODWPT) and gated recurrent unit (GRU)-neural network. The impact of data packet loss (modelled using Markov chain model) in the communication channel has been minimized by reconstructing the original signal from lossy data using spline interpolation and SG filter. With the reconstructed signal, significant attributes are extracted using MODWPT, which are further utilized to perform fault detection, fault classification and faulty section identification using a set of GRU classifiers. Unlike the reported techniques whose fault detection and classification accuracy are severely compromised during packet loss, the signal reconstruction module in the proposed framework makes it immune to packet loss. The inclusion of a module for distinguishing between converter faults and line faults allows for avoiding relay maloperation during switching failures. The proposed scheme has been extensively validated for a wide range of operating and fault scenarios under converter faults, PV array faults, varying degrees of packet loss, burst length and dynamic loading conditions. For packet loss rate ranging from 5 % to 30 % and burst length from 0.0167 s to 0.1 s, the proposed scheme achieves dependability and security in excess of 99.85 % and 99.46 % respectively.

Optimization of Photovoltaic Capacity and Electric Vehicle Battery Charging at Electric Vehicle Charging Stations Using Particle Swarm Optimization

The transportation sector is one of the largest producers of carbon gas emissions. This encouraging electrification to reduce the impact of environmental damage. However, the impact of large-scale electrification causes an increase in peak load demand on the distribution network. This results in an energy crisis, economic losses, and reduces the reliability of the power system if not handled seriously. Important solutions involve optimizing electric vehicle charging stations (EVCS), accelerating the use of renewable energy for transportation and controlling electric vehicle charging. The hybrid microgrid system at EVCS can be an effective solution. This system is able to manage its own electricity needs through an energy management system with a combination of energy sources. This research discusses optimizing photovoltaic capacity and charging electric vehicle batteries at EVCS using particle swarm optimization (PSO). The aim of this research is to increase efficiency in investment and increase profits. The model is determined as an EVCS that is interconnected with photovoltaic (PV). The parameters determined include: charging load data, investment costs, power import tariff provisions and charging rates. PSO is used to size photovoltaic (PV) and battery energy storage systems (BESS). The results show that PSO can be used for optimization. This is proven by obtaining the position of the combination of PV and BESS with the best profit with a combination of PV of 494 kW and BESS of 581 kW, with an NPV of $1,652,604.89, an IRR of 46.60%, and a payback period of 2.15 years. The combination with the best PV contribution, namely PV of 3,146 kW and BESS of 1,811 kW, produces energy of 75,504 kWh per month, power import of 35,946 kWh and battery usage level of 58%. These two combinations of optimization results meet the investment acceptance requirements with an internal rate of return (IRR) of at least 9%.

Blockchain-Based energy trading in Renewable-Based community based Self-Sufficient Utility: Analysis of Technical, Economic, and regulatory aspects

In this ongoing research, a variety of sustainable energy sources, decentralized power generation entities, energy storage solutions, and hybrid electric vehicles with plug-in capability are taken into account. The aim is to present a dependable framework for managing large-scale energy, applicable to both isolated and network-connected modes of renewable hybrid microgrids (HMGs). This framework employs a novel adaptation of Cultural Algorithms (CA) with a two-step modification to minimize the operational expenses of the network. Additionally, it incorporates an innovative intrusion detection system (IDS) based on a deep learning model. The IDS is designed to identify cyber threats related to identity, such as Sybil attacks and masquerading attacks, within the cyber layer. The proposed IDS utilizes the real-time data gathered from the digital twin of the HMG and then uses the LSTM model to differentiate between various signal sources and identify instances of cyberattacks. The data transaction security over the messaging is first preserved by the blockchain technology and later is assessed by the proposed IDS system. To assess the reliability and effectiveness of the proposed framework, a real-time hybrid microgrid is simulated partly in physical layer and partly by digital twin of renewable units to check the model performance.

Optimal Resource Assignment in Hybrid Microgrids Based on Demand Response Proposals

The energy consumption of buildings has been affected by the increase in new loads, which is where emerging technologies have become important. In this sense, microgrids have become a solution that has reduced the loadability of power systems. Thus, the Salesian Polytechnic University in Quito has implemented a hybrid microgrid with three photovoltaic plants (PV), two battery storage systems (BESS), and a connection to the public grid. This research shows a methodology to minimize the energy consumption of the public grid by taking advantage of the existing resources in the microgrid through the allocation of resources and demand management, for which a domotic system based on a z-wave protocol was implemented to monitor and control the loads. The initial state and the state after the implementation of the management equipment were compared, and the reduction of electricity consumption in the public grid was quantified, which was around 63%.

Adaptive RoCoX droop control strategy for AC/DC hybrid microgrid

With the increasing application of virtual inertia in microgrid design, AC and DC subgrids in hybrid microgrids (HMG) present diverse inertia characteristics. However, an HMG cannot achieve an optimal dynamic response by interconnecting multiple AC and DC virtual inertia scenarios. Therefore, a droop controller with a normalized rate of the change of DC voltage and AC frequency (RoCoX) is proposed to minimize the HMG's steady and dynamic deviations and reduce the power oscillation of the interlinking converter (ILC). The effect of the proposed controller on dynamic performance and equalization of RoCoX is analyzed using a small-signal model of an HMG with various inertias applied at both subgrids. The coefficient of the RoCoX controller is adaptively adjusted with the constraint of equalization of RoCoX and dynamic performance. The proposed method is verified using a Starsim hardware-in-the-loop experimental platform under various working conditions.

OPTIMAL SIZING OF SOLAR-WIND HYBRID MICROGRID USING IMPROVED GREY WOLF OPTIMIZATION ALGORITHM A CASE STUDY OF KADUNA - NIGERIA

This paper presents an improved grey wolf optimization algorithm (IGWOA) for optimal sizing of an isolated photovoltaic (PV), wind turbine (WT), and battery energy storage (BES) hybrid microgrid. To demonstrate the effectiveness of the proposed approach, atmospheric data sets comprising of wind, solar, and temperature of Kaduna International Airport were collected from Nigerian Meteorological Agency while the load demand data was collected from Kaduna International Airport Electricity Distribution Center. The microgrid optimal sizing was formulated as a constrained single objective optimization problem. Constraints including, loss of power supply probability (LPSP), power balance, generation limits and battery state of charge (SOC) were imposed. Three simulation scenarios were considered. Firstly, the target allowable maximum LPSP was fixed at 25% and the algorithm was able to determine the optimal sizing of the hybrid microgrid components and minimize the initial cost from 169,880.00 USD to 112,356.40 USD per annum resulting in 34% savings in cost. Secondly, the effect of the target allowable maximum LPSP variation was investigated, and it was found that the total installed capacity of the system decreases with increase in LPSP thereby decreasing the total cost. Additionally, a novel electricity price index (EPI) was introduced in order to quantify the degree of optimality of the solution. The EPI was found to increase exponentially with increase in LPSP, resulting in an EPI of < 0.05USD/kWh at 20% LPSP. Lastly, to validate the proposed approach, a comparative analysis between the IGWOA and other algorithms was carried out, and the proposed IGWOA proved applicable.

On Hybrid Nanogrids Energy Management Systems—An Insight into Embedded Systems

In recent years, the growing demand for efficient and sustainable energy management has led to the development of innovative solutions for embedded systems. One such solution is the integration of hybrid nanogrid energy management systems into various applications. There are currently many energy management systems in different domains, such as buildings, electric vehicles, or even naval transport. However, an embedded nanogrid management system is subject to several constraints that are not sufficiently studied in the literature. Indeed, such a system often has a limited energy reserve and is isolated from any energy supply for a long time. This paper aims to provide a comprehensive overview of the current state of research, advancements, and challenges in the field of hybrid nanogrid energy management systems. Furthermore, it offers a comparative analysis between hybrid nanogrids and microgrids and the implications of their integration in embedded systems. This paper also discusses the key components, operation principles, optimization strategies, real-world implementations, challenges, and future prospects of hybrid nanogrid energy management systems. Moreover, it highlights the significance of such systems in enhancing energy efficiency, reducing carbon footprints, and ensuring reliable power supply.

Optimal performance of stand-alone hybrid microgrid systems based on integrated techno-economic-environmental energy management strategy using the grey wolf optimizer.

Recently, global interest in organizing the functioning of renewable energy resources (RES) through microgrids (MG) has developed, as a unique approach to tackle technical, economic, and environmental difficulties. This study proposes implementing a developed Distributable Resource Management strategy (DRMS) in hybrid Microgrid systems to reduce total net percent cost (TNPC), energy loss (Ploss), and gas emissions (GEM) while taking the cost-benefit index (CBI) and loss of power supply probability (LPSP) as operational constraints. Grey Wolf Optimizer (GWO) was utilized to find the optimal size of the hybrid Microgrid components and calculate the multi-objective function with and without the proposed management method. In addition, a detailed sensitivity analysis of numerous economic and technological parameters was performed to assess system performance. The proposed strategy reduced the system's total net present cost, power loss, and emissions by (1.06%), (8.69%), and (17.19%), respectively compared to normal operation. Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) techniques were used to verify the results. This study gives a more detailed plan for evaluating the effectiveness of hybrid Microgrid systems from a technical, economic, and environmental perspective.

Energy management strategy for a hybrid micro-grid system using renewable energy

This paper introduces an energy management strategy for a hybrid renewable micro-grid system. The efficient operation of a hybrid renewable micro-grid system requires an advanced energy management strategy able to coordinate the complex interactions between different energy sources and loads. This strategy must consider some factors such as weather fluctuations and demand variations. Its significance lies in achieving the overarching objectives of these systems, including optimizing renewable energy utilization, reducing greenhouse gas emissions, promoting energy independence, and ensuring grid resilience. The intermittent nature of renewable sources necessitates a predictive approach that anticipates the energy availability and adjusts the system operation. The aim of this study was to develop an energy management system for a hybrid renewable micro-grid system to optimize the deployment of renewable energy resources and increase their integration in the power system. Therefore, the main objective of this work was to develop an energy management strategy that controls the flow of energy between the hybrid micro-grid system and the load connected directly as well as the load connected to the utility grid using MATLAB/Simulink software. The second objective was to control the charging and discharging of the battery. The results show that the developed algorithm was able to control the energy flow between the hybrid micro-grid system and the utility grid and also to ensure a proper relation between the charging /discharging rate of the battery based on their operating conditions. In this application, the battery was charged at higher power. It was seen that a higher charging power enables to fully recharge the battery in a shorter amount of time than usual. The results have shown that it is possible to maximize the charging time by using a greater power and this algorithm ensures the state of charge (SOC) of battery to remain in the admissible limits (between 20 and 100%).

Design of optimized flatness based active disturbance rejection controller for communication time delayed hybrid microgrid

In an islanded hybrid microgrid, frequency deviation is caused by the intermittent nature of renewable resources and load variations. Maintaining nominal frequency requires the secondary controller to send a signal to the controllable energy sources so that supply and demand are synchronized. Time delays in the system's communication channels, however, can reduce its efficiency. To compensate for the system uncertainties, modeling error and variant nature of renewable resources, an optimized flatness based active disturbance rejection controller have been proposed. Mitigating the effects of communication delay in the system and to synchronize the inputs to the extended state observer, a smith predictor based low pass filter is employed. Furthermore, an improved beluga whale optimization (IBWO) technique is employed to tune the proposed controller gains. The comparative analysis of the proposed IBWO is performed with existing algorithms. Moreover, the proposed controller superiority over the standard PID and LADRC approach has been investigated by perturbation in renewable resources, uncertainty in parameters, variation and mismatch in communication delay. Simulation results provide the testimony for the efficient performance of the designed controller. Lastly, the proposed controller is further evaluated in multi microgrid environment.

Optimal sizing of a hybrid microgrid system using solar, wind, diesel, and battery energy storage to alleviate energy poverty in a rural area of Biskra, Algeria

This paper presents a model for designing a stand-alone hybrid system consisting of photovoltaic sources, wind turbines, a storage system, and a diesel generator. The aim is to determine the optimal size to reduce the cost of electricity and ensure the provision of electricity at lower and more reliable prices for isolated rural areas. Three scenarios for five, fifteen, and twenty rural homes were sized and analyzed using actual weather data in the rural and pastoral area of Biskra, Algeria. The SFS and SOS algorithms were used for the first time, in addition to the commonly used PSO algorithm, to compare the performance of each algorithm and achieve the goal of meeting all energy load requirements at the lowest cost. Three factors have been taken into consideration within the proposed objective function, which are the levelized cost of energy (LCOE), loss of power supply probability LPSP, and the amount of energy consumed by the dummy load. The results of the scenarios showed that the SFS algorithm outperformed the other algorithms, with the lowest cost of electricity for each of the mentioned scenarios. The levelized cost of energy (LCOE), respectively, was 0.09138 $/kWh, 0.16588 $/kWh, and 0.24862 $/kWh.

Optimal sizing and performance analysis of hybrid microgrid for remote island of developing country: Effect of sustainable parameters, benefits and installation barriers

Maintaining a continuous supply of power with a sustainable microgrid is still a difficult task for the off-grid area in a developing country. Using HOMER software, the techno-economic potentiality of various system configurations is demonstrated, and a PV/WT/battery/DG-based optimal system is chosen for the selected off-grid area based on Net Present Cost (NPC). The result of this optimal system is compared with the result obtained by Mixed Integer Linear Programming (MILP). The NPC ($1087520) and Renewable Fraction (RF) (90.7%) of MILP-based systems are 3.7% lower and 29.1% higher than HOMER-based systems. The amount of carbon emissions in the HOMER-based system is 79.7% larger than the MILP-based system, even though the difference in NPC is not excessive. Due to lower NPC and carbon emissions, MILP-based systems are more cost-efficient and eco-friendly. A sensitivity analysis of uncertain parameters, including annual load, discount rate, fuel price, and solar radiation, has been added to evaluate the effect of the variation of these parameters.Additionally, a few linguistic factors, such as land usage, future of technology, employment, comfortable living, and biological diversity, are also examined by normalization to demonstrate the efficiency of the optimal system. The optimal system achieved top ranking for its outstanding preference values in all criteria except land usage and is recommended in the environmental, technological, and social categories. This evaluation approach can aid researchers, decision-makers, and financiers in better planning microgrids focusing on sustainable features.

Performance Investigation of Hybrid Shipboard Microgrid using ESOGI-FLL Technique

The issue of reducing greenhouse gas (GHG) emissions from ships has garnered significant interest from stakeholders within the maritime industry. This work considers a hybrid shipboard microgrid powered by a battery energy storage (BES), solar photovoltaic array (SPVA), and diesel generator (DG) set. The enhanced second-order generalized integrator-based frequency-locked loop (ESOGI-FLL) technique synchronizes the DG set and the shore grid at different operating modes to supply uninterruptable power to loads. The propulsion motor, nonlinear service loads, DG set, and sources equipped with power-electronic inverter result in power quality challenges within a hybrid shipboard microgrid. Thus, the ESOGI-FLL algorithm is utilized for the mitigation of power quality issues of hybrid shipboard microgrid. The hybrid microgrid is modeled in MATLAB/Simulink at different operating conditions and validated on a real-time test simulator.

Power coordination control method for AC/DC hybrid microgrid considering demand response

Abstract AC–DC hybrid microgrid consists of a variety of energy resources, different types of loads and storage devices, and involves the interconnection between AC and DC power grids. This leads to increased complexity in system design and coordination control, which needs to take into account the interactions and coordination between individual components. Considering the demand response, the power coordination control method of AC/DC hybrid microgrid is studied, which can effectively adjust the power of microgrid in different operation modes and ensure its smooth operation. Combined with the topology of AC/DC hybrid microgrid and its bidirectional converter, the constraints such as AC-side load demand response and AC/DC-side power balance are set on the premise of considering demand response. Under such constraints, the microgrid is divided into two modes: sub-network independent operation and inter-network power exchange. Corresponding control sections are designed for the two modes. On the premise of the set constraints, the microgrid power in each mode is coordinated by coordinating the control methods of the converters. The results show that this method can respond to several mode switches of AC/DC hybrid microgrid in time and effectively coordinate and control the power during the switching process under the constraint conditions such as load demand response. In addition, in the single mode of inter-network power exchange, this method can also quickly respond to the load demand, effectively adjust the exchange power between AC and DC subnets of microgrid, realize the reasonable distribution of power, and make the hybrid microgrid run stably in different modes. During the 8 s to 10 s phase of the experiment, the load power of the DC subnetwork of the experimental microgrid did not change, while the load power of the AC subnetwork increased to 36 kW, the reference power and converter power remained unchanged, and the microgrid voltage remained stable.