Integration Of Microgrids Research Articles

Smart micro-grid integration with bidirectional DC fast charging: Harnessing vehicle-to-grid technology

Smart micro-grid integration with bidirectional DC fast charging: Harnessing vehicle-to-grid technology

Retraction Note: Artificial intelligent controller-based power quality improvement for microgrid integration of photovoltaic system using new cascade multilevel inverter

Retraction Note: Artificial intelligent controller-based power quality improvement for microgrid integration of photovoltaic system using new cascade multilevel inverter

A Versatile Platform for PV System Integration into Microgrids

Advancing decarbonization critically depends on the integration of PV systems into microgrids. However, this integration faces challenges, including the variability of photovoltaic solar energy production, the demands of energy management, and the complexities of grid synchronization and communication. To address these challenges, a PV emulator platform is an essential tool. This paper presents a novel four-layer PV emulator platform that seamlessly integrates power systems, control systems, measurement instrumentation, and communication processes. The proposed platform enables the emulation of I-V curves and the dynamic adjustment of operating points—including both the maximum power point (MPP) and power reserve point (PRP)—as well as temperature and irradiance while providing sufficient power capacity for microgrid integration. To validate its effectiveness, the platform was assessed for its capability to adjust operating points, such as MPPs or PRPs, under varying irradiance and temperature conditions. The results show that the platform effectively adjusts operating points with a deviation of less than 5% from theoretical values and successfully tracks a sequence of operating points. This performance underscores the platform’s potential in integrating and managing PV systems within microgrid environments, thereby advancing the path to decarbonization.

Allocation of Microgrid-Forming resources for distribution systems reliability improvement

This paper proposes a novel method to allocate distributed generation and energy storage as Microgrid-Forming Resources (MFR) to improve the reliability of medium voltage (MV) grids. A graph-based approach is used, which simplifies the circuit analysis and reduces computational time when compared to exhaustive search methods. First, a medium voltage substation-level Nodal Fault-Effect Matrix (NFEM) is derived from the feeder’s topology, which is used for predictive reliability calculations, and calibrated with historical data. Second, a reduced set of scenarios of MFR allocation are generated using a graph community approach and evaluated in terms of reliability improvement and power and energy requirements of the distributed resources. Two case studies are presented based on real grids with different reliability characteristics and the results indicate that the proposed approach is an insightful tool for distribution system planners to evaluate the integration of microgrids to improve system reliability.

Using break-even analysis to explore the cost and carbon reduction benefits of solar and wind energy integration in microgrids for convenience stores

In response to the increasing demands for net-zero carbon emissions in Taiwan and globally, this study explores the feasibility of implementing microgrid technologies in convenience stores to reduce carbon emissions. The study utilizes the commercial simulation tool HomerPro to evaluate the performance of integrating solar and wind power into microgrid systems, aligning with Taiwan's current priorities in renewable energy development. The analysis incorporates carbon pricing scenarios, grid reliability considerations, and financial variables such as discount and inflation rates. We simulated 256 system configurations to identify the most economically viable solutions under various conditions. The results indicate that integrating solar and wind energy into microgrid systems can significantly lower energy costs and carbon emissions, especially in high carbon-price scenarios. These benefits provide strong incentives for businesses to adopt carbon-neutral strategies. These findings provide valuable insights for policymakers and businesses, highlighting the critical role of reducing green power purchase prices in improving economic feasibility. Future research explores the impacts of integrating other renewable energy sources, offering broader technical support for achieving carbon neutrality.

Robust integral super-twisting controller for enhanced photovoltaic integration with hybrid battery and supercapacitor storage in DC microgrid

This study relates to the enhancement in performance of a Hybrid Energy Storage System (HESS) over a DC microgrid, particularly toward a fast-accurate controller of the DC bus voltage. Conventional control methods are particularly difficult to implement in order to reduce start-up time with minimum overshoot, resulting in a significant disturbance of the DC bus voltage. To circumvent these problems, we propose an innovative hybrid controller method that combines the Integral Super-Twisting Algorithm (ISTA) controller technique with adaptive gain in regulating DC bus voltage and current. The capacity of ISTA to greatly enhance the system's dynamic responsiveness, guaranteeing steady operation with effective output voltage control, is by far its greatest benefit. Comparative testing found that the conventional PI controller had a rise time of 0.0666 s, a settling time of 0.165 s, and a 17 % overshoot with a steady-state error of 0.02 %. The proposed ISTA controller had a rise time of 0.0016 s, a settling time of 0.0667 s, zero percentage overshoot, and a minimal steady-state error of only 0.0003 %. The above-mentioned issues mostly focused on better sharing power in HESS and a more effective controller of the DC bus voltage. Furthermore, we evaluate the practical feasibility of the suggested controller through hardware-in-the-loop testing. This research lays a foundation for advancing renewable energy integration in DC microgrid, offering a promising avenue for sustainable and resilient power systems. Finally, the effectiveness and applicability of the proposed ISTA controller for DC microgrid use are proven with Controller Hardware-in-the-Loop (C-HIL) simulations.

Economic and strategic challenges in microgrid integration: Insights from operational dynamics and renewable energy potential

With the integration of a large number of microgrids in the power distribution network operation, economic and strategic challenges arise. To address these challenges, this research provides a comprehensive investigation into the operational, economic, and strategic dynamics of microgrids. Through careful data analysis, the study interprets the complications of microgrid responses to seasonal changes. It also investigates energy consumption patterns and production dynamics. The detailed analysis of microgrid configurations reveals the unique attributes and challenges of PV, wind, and hydropower microgrids. Moreover, the research explains the financial implications of microgrid integration, from setup costs to potential ROI. It is also examining the cooperative relationship between microgrids and conventional grids. Key findings highlight that solar microgrids contribute 3.2% to 5.3%, wind microgrids provide 5.9% to 7.4%, and hydropower microgrids contribute 24.4% of total power. Energy purchase peaks at 850,000 kWh in August and declines to 580,000 kWh in May. 170,000 kWh of energy is sold back to the grid in May. Moreover, the grid energy costs reduced from $0.178 kWh to $0.30 kWh. The total net present cost of the system is achieved at $37,880,023.33. Renewable energy production ranges from 480 kW to 2,300 kW, with a penetration level reaching 160%.

Optimizing Distributed Generation in DC Microgrids: A Comprehensive Study

This thorough examination offers a critical analysis of the intricate relationship between Distributed Generation (DG) and DC microgrids. It provides a thorough analysis of basic ideas, sophisticated control techniques, technological developments, and useful applications in actual situations. In the framework of a paradigm shift towards decentralized energy solutions, this study investigates the efficacy of Direct Current (DC) microgrids in integrating and optimizing diverse distributed generation sources. The study conducts a critical analysis of the challenges and possibilities related to various distributed generation technologies and renewable energy systems. For scientists, engineers, and policymakers engaged in the challenging task of incorporating dispersed energy into current systems, it offers crucial perspectives. This review is an important tool that advances our knowledge of distributed generation in DC microgrids. It contributes significantly to the development of discussions on resilient and sustainable energy solutions. In addition to discussing the challenges associated with DG integration, this study highlights real-world examples that highlight the adaptability and efficiency of DC microgrid designs. Because it includes a thorough examination of power electronics, energy storage, and advanced control systems, this study is an invaluable resource for stakeholders hoping to take advantage of DC microgrids. Through the integration of analytical analysis and pragmatic application, this review provides insightful analysis and field assistance. The entire study contributes significantly to the advancement of distributed generation (DG) integration, which is necessary to establish a sustainable and resilient energy environment. It offers the fundamental knowledge required to accomplish successful integration. This review paper offers an in-depth analysis of DG integration in DC microgrids. This review is to provide a comprehensive overview of the dynamic landscape where distributed energy generation and DC microgrids interact, starting with the foundational ideas and moving on to a close examination of the difficulties, innovations in technology, and useful applications.

A Novel Technique for the Optimization of Energy Cost Management and Operation of Microgrids Inspired from the Behavior of Egyptian Stray Dogs

Managing costs in microgrids presents a formidable challenge due to the intricate blend of renewable and non-renewable energy sources that underpin their power generation. Ensuring seamless integration of microgrids with the national grid is pivotal for continuous load demand satisfaction and adherence to liberalized energy market mandates. To address this challenge, this paper introduces a new optimization technique for the Cost Management and Operation System (CMOS) of multi-source microgrids through a smart management unit. The cornerstone of this unit is the Egyptian Stray Dog Optimization (ESDO) algorithm, meticulously designed to optimize operational costs in line with load demands, energy cost dynamics, and generation proficiencies. Rigorous testing of the proposed system was conducted on a multi-resource microgrid using MATLAB, encompassing various operational scenarios. The simulation outcomes consistently highlighted the unit’s capability to achieve optimal cost-efficiency. Comparative analysis with other optimization techniques, particularly Particle Swarm Optimization (PSO), demonstrated the superior performance of the Egyptian Stray Dog algorithm, underscoring its potential as a leading solution in this domain.

Stochastic Optimal Strategies and Management of Electric Vehicles and Microgrids

This study combines the Nash–Cournot competition model and the stochastic optimization model to examine the impact of electric vehicle (EV) quantity fluctuations on microgrid operations, aiming to optimize energy usage in a competitive electricity market. Integrating distributed energy resources and bidirectional charging, microgrids offer a novel approach for energy optimization, aiding in renewable energy generation, peak demand management, and emission reduction. Empirical evidence highlights benefits in Taiwan’s electricity market and net-zero emissions target by 2050, with a case study demonstrating enhanced local renewable energy generation due to EVs and microgrid integration. As the number of EVs increases, electricity sales from microgrids decline, but electricity purchases remain stable. The degree of electricity liberalization also influences the supply and demand dynamics of the electricity market. Microgrids selling electricity only to the main grid increases total power consumption by 65.55 million MWh, reducing the market share of the state-owned utility (Taipower). Conversely, allowing retailers to purchase from microgrids increases total consumption by 30.87 million MWh with a slight market share decrease for Taipower. This study contributes to providing an adaptable and flexible general model for future studies to modify and expand based on different scenarios and variables to shape energy and environmental policies.

Sustainable energy integration and optimization in microgrids: enhancing efficiency with electric vehicle charging solutions

Sustainable energy integration and optimization in microgrids: enhancing efficiency with electric vehicle charging solutions

ANFIS-based power management and islanding detection utilizing permeation rate(γ) and relaxation parameter(ζ) for optimal operation of multiple grid-connected microgrids

Microgrid(s) integration into distribution networks and the growing use of Distributed Generation Systems necessitate the switch from traditional fossil fuel-based power generation methods to renewable resource-based ones. To achieve optimal power delivery, an adaptive method for solving power quality delivery problems posed by this transition is required. Unexpected islanding is an important power security concern that can lead to equipment damage, electrical risks, and decrease in power quality delivery. To address this problem, ANFIS-Based power management and islanding detection utilizing permeation rate(γ) and relaxation parameter(ζ) is proposed in this paper based on a rate of change of output voltage(ROCOV) of power sources at the point of common coupling in a 34-bus distribution network. Modeling of the distribution network, distributed generators, ANFIS training, and simulations was done using MATLAB/Simulink software. Results show that a 0.3 and 1.01 value indicates permeation rate of Microgrid(s) in a grid-tied mode whereas a 0 value indicates relaxation parameter of Microgrid(s) disconnected from the main distribution network. Islanding detection time of 0.02 sec was recorded when all Microgrids were disconnected during disturbances, and a 0.04 sec islanding detection time for disconnecting individual power sources at a time was recorded respectively. In general, islanding causes a system nominal voltage(400 V) deviation of 7 %. However, this was quickly restored without causing network voltage fluctuations due to the adaptive nature of ANFIS. Comprehensive simulations were used to validate the suggested method. The outcomes demonstrated that the proposed approach effectively distinguishes between islanding and non-islanding events and can quickly and accurately identify islanding as compared to other related works.

Energy management of greenhouses in micro grids: A case study

The nexus of population growth, constrained access to agricultural land, and enhanced living standards has necessitated the expansion of commercial greenhouses. These controlled environments are vital for shielding produce from unpredictable weather patterns and ensuring the availability of high-quality fresh produce throughout the year. Greenhouses maintain precise control over key parameters such as indoor temperature, humidity, CO2 concentration, and lighting, which are pivotal for optimal plant growth and product quality. However, the stringent control of these variables often results in substantial energy consumption, posing significant environmental and economic challenges. This research explores optimizing the internal environment of greenhouses while curbing energy usage, thereby mitigating potential environmental impacts. By focusing on grid-connected modes of operation, this study addresses the unique challenges posed by limited access to local energy resources. It explores the integration of microgrids, renewable energy sources, and combined heat and power systems to maintain operational efficacy and sustainability.

Review Study on Recent Advancements in Islanding Detection and Diagnosis in Microgrids Using Signal Processing and Machine Learning Techniques

The integration of renewable energy sources and microgrids has become a key focus in the pursuit of sustainable and resilient power systems. Microgrids, being decentralized and often operating in isolation from the main grid, face unique challenges, including the need for accurate islanding detection and diagnosis to ensure safe and efficient operations. This review article comprehensively investigates and evaluates the application of signal processing and machine learning techniques in the context of islanding detection and diagnosis within microgrids. The significance of islanding detection and diagnosis is highlighted in this review study which emphasizes grid stability, safety risk mitigation, and energy efficiency enhancement during islanding. Further, the study explores the technical aspects, covering signal processing techniques and machine learning approaches used for islanding detection, including harmonic analysis, wavelet transforms, and neural networks. This research explores the most recent developments in the domain, encompassing a variety of strategies that harness sophisticated algorithms and data analysis to improve the dependability and effectiveness of microgrid operations. Subsequently, this review sheds light on the state-of-the-art methodologies, challenges, and promising avenues in islanding detection and diagnosis, ultimately contributing to the advancement of microgrid technology and the broader vision of sustainable and resilient energy systems. As the transition toward renewable energy sources accelerates, microgrids represent a promising solution for enhancing grid resilience and integrating distributed generation. However, ensuring the safety and efficiency of microgrid operations during islanding events is a critical concern. This study explores the intersection of signal processing and machine learning, offering a comprehensive examination of islanding detection and diagnosis techniques in microgrids.

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO’s superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness.

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.

Smart grid optimization considering decentralized power distribution and demand side management

AbstractThe increasing integration of microgrids into distribution networks has highlighted the significance of evaluating and managing intelligent microgrids from both technical and economic perspectives. In this paper, a decentralized approach using agents is employed to optimize the operation of an intelligent microgrid within the telecommunications platform. The decentralized control method comprises two layers. The first layer represents the main microgrid, which includes loads and their controllers, as well as renewable and conventional resources. In the secondary layer, there is a telecommunication platform in which agents can operate as a control processor along with the means of communication. It should be noted that agents interact with the primary layer and neighbouring agents and exchange information with each other. This exchange takes place until the best state of optimization for the power supply occurs. In this study, the operation cost is calculated for decentralized control rules and considering telecommunication links. Also, the effect of performance on cost reduction is examined and compared with normal conditions and centralized methods. It can be seen that the operation cost of the network has decreased to 9.034% after the implementation of the mentioned method in comparison with the normal condition and it has decreased to 6.957% in comparison with the centralized method. Then, using a demand side management program, the cost will be reduced by 2.5%. In the next step, the uncertainty of available resources is taken into account where the uncertainties increase the cost by 7.8%.

Analysis of Grid-Forming Inverter Controls for Grid-Connected and Islanded Microgrid Integration

Autonomous grid-forming (GFM) inverter testbeds with scalable platforms have attracted interest recently. In this study, a self-synchronized universal droop controller (SUDC) was adopted, tested, and scaled in a small network and a test feeder using a real-time simulation tool to operate microgrids without synchronous generators. We presented a novel GFM inverter control adoption to better understand the dynamic behavior of the inverters and their scalability, which can impact the distribution system (DS). This paper provides a steady-state and transient analysis of the GFM power inverter controller via simulation to better understand voltage and frequency stabilization and ensure that the critical electric loads are not affected during a prolonged power outage. The controllers of the GFM inverter are simulated in HYPERSIM to examine voltage and frequency fluctuations. This analysis includes assessing the black start capability for photovoltaic microgrids, both grid-connected and islanded, during transient fault conditions. The high photovoltaic PV penetration levels open exciting opportunities and challenges for the DS. The GFM inverter control demonstrated appropriate response times for synchronization, connection, and disconnection to the grid. The DS has become more resilient and independent of fossil fuels by increasing the penetration of inverter-based distributed energy resources (DERs).

Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm

In order to effectively improve the excessive load of microgrid during peak hours of urban electricity consumption, a multi-source and multi-level coordination method of energy Internet under V2G based on particle swarm optimization algorithm was proposed. First, a mathematical model for V2G energy integration in microgrids was developed and a scheduling concept based on a particle-by-particle optimization algorithm was used. Second, an improved PSO algorithm is proposed and experimentally validated, and the experimental results are compared with previous particle swarm optimization algorithms. Experiments have shown that as the number of iterations increases, the value of the objective function decreases and the optimal solution can be obtained until the maximum number of iterations is reached. The iteration speed and power processing cost of the improved PSO algorithm are better than before.The original load curve is the load trough period from 23:00 to 6:00, and two load peaks occur from 12:00 to 14:00 and 19:00 to 22:00. The V2G technology basically realizes the coordinated control of microgrid electric energy and achieves the effect of peaking and valley filling. The improved algorithm has obvious improvement compared with the original power grid state. Conclusion: The application of EV V2G technology can smooth the daily load curve of power grid and coordinate the electric energy of micro-grid to achieve ``peak cutting and valley filling'', and the effect of this algorithm is more outstanding than the previous algorithm. Finally, the future development direction and suggestions of V2G technology are put forward.The power grid with V2G discharge depth limit has the ability to basically reduce and eliminate the daily peak load, so the technology has broad research space and development prospects.

A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions

The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies.