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This paper considers an electric-hydrogen hybrid energy storage system composed of supercapacitors and hydrogen components (e.g., electrolyzers and fuel cells) in the context of a microgrid with photovoltaic generators. To manage the power and hydrogen flows within the microgrid and coordinate the coupling between the microgrid and a hydrogen refueling station, this paper proposes an energy management framework. The outer layer of the framework takes the hydrogen compressor power as part of the microgrid load and optimizes the hydrogen flow from the microgrid to the hydrogen refueling station. The inner layer develops a two-stage scheme to optimize the power allocation between the electric and hydrogen systems within the microgrid. A low pass filter with an adaptive cutoff frequency is utilized to split the net load power of the microgrid into high-frequency and low-frequency components, which are then assigned to the electric and hydrogen systems, respectively. The adaptive cutoff frequency is determined based on the power split factor tuned twice during the two stages of the power allocation scheme based on the supercapacitor state of charge. The effectiveness of the proposed framework is validated by performing real-time simulations using a dSPACE platform. • Proposes an energy management framework for electric-hydrogen systems. • Optimizes the hydrogen flow from the microgrid to the hydrogen refueling station. • Develops a two-stage power allocation scheme based on artificial potential field. • Validates the framework using a dSPACE DS1202 real-time simulation system. [ABSTRACT FROM AUTHOR]

Effective energy management within microgrids is crucial, especially given system uncertainties. This study presents a novel Energy Management System (EMS) designed for microgrids with diverse energy sources, notably hydrogen and fuel cells. The EMS integrates artificial intelligence algorithms to predict and adapt to rapid changes, enhancing energy resource efficiency and minimizing wastage. Responsive in nature, the EMS enhances efficiency, reliability, and cost reduction in microgrid energy, dynamically responding to varying conditions for improved stability and environmental protection. Employing a random pattern approach, the EMS focuses on operational efficiency and reliability in supplying electrical and thermal loads. Addressing uncertainties in the electricity market and renewable production, a stochastic framework with scenario generation enables operation in grid-connected and island modes. Enhanced optimization through an improved particle swarm algorithm ensures cost-effectiveness and system reliability in dynamic environmental conditions. This research pioneers innovative microgrid energy management, emphasizing hydrogen and fuel cell technologies to elevate efficiency and performance reliability. • Renewable integration: Emphasis on renewables and storage for sustainable energy. • Micro-CHP importance: Dual function for reliable energy supply. • Advanced EMS algorithms: Cutting-edge tech for enhanced microgrid operations. • Holistic energy management: Comprehensive strategy for reliability and sustainability. • Practical implementation insights: Theory-practice bridge for adaptive strategies. [ABSTRACT FROM AUTHOR]

With the increase in the number of microgrids in the same distribution area usually belong to different subjects of interest, forming a multi-subject game pattern. Considering the interests of distribution networks and microgrids, a distribution network-multi-microgrid master-slave game model is established by selecting distribution networks as game masters and microgrids as game slaves. A master-slave game equilibrium algorithm based on a Kriging metamodel is proposed. The method replaces the microgrid energy internal management model with a proposed Kriging metamodel. In the iterative optimization process, the particle swarm optimization algorithm is used to generate new sampling points and modify the model in a targeted way so as to quickly and accurately obtain the transaction price and output plan of each microgrid. The algorithm does not need all the parameters of the microgrid, which both achieves the purpose of protecting the privacy of the microgrid and avoids a large number of calls to the lower optimization model, effectively reducing the amount of computation and improving the efficiency of the solution. The results show that the overall operating costs of the three microgrids used in the case study are reduced by 1.4%, 4.6%, and 1.6%, respectively, which effectively balances the interests of multiple parties in the microgrid system; the revenue of the distribution network is increased by 50.6%. [ABSTRACT FROM AUTHOR]

The transformer mineral oil is generally hydrocarbon-based and is not environmentally friendly, so it holds a significant share in environmental pollution. As an alternative to conventional transformer oil, plant-based insulation oil has been investigated globally in the past few decades. Even though vegetable oils are considered an environmentally viable alternative to mineral oil, the extensive utilization of vegetable oils could create a threat to Indian food security. The present work focuses on using Pongamia Pinnata oil (PPO) as a significant alternative to conventional transformer oil (CTO) in distribution transformers. The effectiveness of the proposed PPO is experimentally verified using thermal studies and electrical studies through a low-level distribution transformer of 1 kVA. A comparative analysis was carried out between the proposed and conventional oil regarding physical, chemical, and thermal properties. Also, scanning electron microscopy (SEM) and Energy Dispersive X-ray spectroscopy analysis have been carried out for fibreglass cloth insulation material with PPO and CTO to ensure the intrinsic structural strength of the insulation material. The structural strength ensures the bonding and life span of the insulation in the transformer. The cost–benefit analysis is also favourable for the proposed oil as a better green liquid dielectric for transformers. [ABSTRACT FROM AUTHOR]

Community microgrids hold significant promise to address the challenges posed by the growing electrification of transportation and other energy-intensive demands, such as the electrification of heating. This potential is further supported by rigorous scientific research, which highlights their capacity to enhance energy resilience, reliability, and sustainability. In the specific context of Aotearoa New Zealand, community microgrids exhibit the potential to significantly improve energy resilience and self-sufficiency. This article outlines the evidence-based benefits, challenges, and high-potential use cases of community microgrids in Aotearoa New Zealand, drawing on both domestic and international research. [ABSTRACT FROM AUTHOR]

This paper proposes a novel configuration for a multiport boost converter (MPBC) with a single inductor (SI), accounting for equivalent series resistances (ESRs) and minimizing input switching stress. The MPBC performance is evaluated and compared with other established topologies. The proposed MPBC interfaces two unidirectional input DC power ports and a rechargeable port for an energy storage element (ESE) with two output ports. The design integrates two renewable sources with the ESE as a third source. One output is for higher voltage, linked to a single‐phase inverter for AC loads. The other output is for lower DC voltage, used for DC loads. The configuration can be adjusted based on requirements. This converter has numerous applications in renewable energy systems, electric vehicles, and agriculture. The steady‐state and small signal modeling of MPBC has been done to derive the mathematical expressions for analyzing stability, stresses (both voltage and current), and performance considering ESRs. A 240 W, MPBC is fabricated along with improved switching strategies using DSP TMS320F28379D. Experimental and simulation results are compared to show the effectiveness of proposed scheme on stability, stresses, and efficient power transfer. Output power is regulated effectively by sharing the input power thereby reducing voltage stress on switches. [ABSTRACT FROM AUTHOR]

The Rockaways Peninsula faces issues related to congestion and power outages during times of peak usage. Additionally, it is susceptible to disruptions caused by disasters such as hurricanes and storms. In this paper, we propose a new methodology that employs multi-paradigm modelling and control for the design and implementation of interconnected microgrid systems in the Rockaways. Microgrids are small-scale power networks that incorporate renewable energy technologies for power generation and distribution to enhance the control of energy supply and demand. Multi-paradigm modelling is employed to describe microgrids' dynamic behavior more accurately by integrating system dynamics, agent-based modelling, as well as discrete event and continuous time simulation. We use agent-based models to describe the behavior of separate microgrid elements and the microgrid as a whole. Discrete event/continuous time simulation is used to analyze real-time operation of electrical parameters, such as voltage, current and frequency. Thus, the design, analysis and performance of microgrids are improved. Also, control strategies are used for the purpose of enabling the microgrids to operate effectively by responding to changes in power supply and demand and minimizing the effects of disturbances. The findings of this study demonstrate the feasibility and resilience benefits of incorporating multi-paradigm modelling and control in the design and management of microgrid systems in the Rockaways, which can result in the development of more durable, efficient, and sustainable energy systems in the region. [ABSTRACT FROM AUTHOR]

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system's ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures. [ABSTRACT FROM AUTHOR]

This paper aims to apply data-driven maximum power point tracking (MPPT) techniques specifically tailored for fuel cell vehicle (FCV) supported hybrid DC microgrids to enhance the power harvesting capability of fuel cell (FC) stacks. Compared to existing MPPT techniques, the current study focuses on developing and evaluating data-driven approaches for maximum power extraction by dynamically determining the operating point of FC power sources through a Zeta converter. An in-depth analysis is conducted by considering parameters such as efficiency, tracking accuracy, response time, and robustness to variations in load demand and operating conditions. The performance results validate that the developed three-layer neural network (TNN)-based MPPT gives better performance findings than Gaussian process regression (GPR), support vector regression (SVR), decision tree regression (DTR), and bagging ensemble decision tree (BEDT). In the performance evaluation phase, a vehicular FC with a rating of 1.26 kW is designed and operated within the temperature range of 320 K to 343 K for hydrogen pressure values ranging from 1 bar to 1.8 bar. For these operational conditions, the prediction accuracy value of the proposed TNN method is 99.6% while the performance values GPR, SVR, DTR, and BEDT are 99%, 98.6%, 97.2%, and 96%. In addition, system efficiency is increased by 0.98%, 1.25%, 2.51%, and 3.02% compared to GPR, SVR, DTR, and BEDT, respectively. [Display omitted] • Developing data-driven algorithms-based MPPT techniques for vehicular FCs. • Providing advantages regarding high-speed tracking capability and tracking accuracy. • Providing stable DC bus voltage operation. • Improving system performance under dynamic load changes and operating conditions. [ABSTRACT FROM AUTHOR]

This paper investigates the feasibility and benefits of integrating hydrogen storage systems into off-grid power systems. As a case study, a stand-alone microgrid located on a small island in southeastern Sardinia (Italy) and already equipped with a photovoltaic (PV) system coupled with batteries is chosen. To evaluate the integration benefits of the two storage systems (hydrogen and batteries) and the optimal sizing of the hydrogen storage section, a parametric analysis with a simulation model implemented in the MATLAB environment has been carried out. Results show that the optimal integration between the two storage systems is found by imposing a share of the batteries (18 kWh, 50% of the overall battery capacity) to exclusively supply the load demand (called battery energy buffer). In these conditions, an almost 100% self-sufficiency of the microgrid can be achieved by a hydrogen generator with the lowest size considered (2.4 kW), a hydrogen storage volume of 10 m3 and a fuel cell, mainly able to completely cover the night loads, of 1.5 kW. This sizing leads to a Levelized Cost of Electricity (LCOE) for the hydrogen section of about 10.5 €/kWh. [Display omitted] • Investigation of a hydrogen storage into off-grid systems characterized by seasonal loads. • Determination of the hydrogen storage sizing through parametric analysis. • Optimal integration with batteries found by imposing an energy buffer equal to 50%. • Through the hydrogen storage, self-sufficiency higher than 99% is achieved. • Significant energy surcharges detected to ensure 100% green electricity. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) with renewable energy technologies is revolutionizing modern power systems by enhancing efficiency, reliability, and sustainability. This paper examines the role of the IoT in optimizing the integration and management of renewable energy sources, such as solar and wind power, into the electrical grid. The IoT enables real-time monitoring, data analysis, and automation, facilitating advanced load management, demand response, and energy storage solutions. Key advancements in IoT technologies, including smart grids and energy management systems, are discussed, highlighting their impact on improving grid stability and promoting the use of renewable energy. The paper also finds some challenges such as data security, privacy, and the need for standardized communication protocols. Furthermore, it finds how the IoT optimizes electric vehicle performance through advanced battery management, real-time energy consumption monitoring, and improved interaction with the electrical grid. Future research directions emphasize the potential of the IoT to further enhance renewable energy integration through artificial intelligence and machine learning, driving the transition towards a more sustainable and resilient energy future. [ABSTRACT FROM AUTHOR]

In recent years, global efforts towards a future with sustainable energy have intensified the development of renewable energy sources (RESs) such as offshore wind, solar photovoltaics (PVs), hydro, and geothermal. Concurrently, green hydrogen, produced via water electrolysis using these RESs, has been recognized as a promising solution to decarbonizing traditionally hard-to-abate sectors. Furthermore, hydrogen storage provides a long-duration energy storage approach to managing the intermittency of RESs, which ensures a reliable and stable electricity supply and supports electric grid operations with ancillary services like frequency and voltage regulation. Despite significant progress, the hydrogen economy remains nascent, with ongoing developments and persistent uncertainties in economic, technological, and regulatory aspects. This paper provides a comprehensive review of the green hydrogen value chain, encompassing production, transportation logistics, storage methodologies, and end-use applications, while identifying key research gaps. Particular emphasis is placed on the integration of green hydrogen into both grid-connected and islanded systems, with a focus on operational strategies to enhance grid resilience and efficiency over both the long and short terms. Moreover, this paper draws on global case studies from pioneering green hydrogen projects to inform strategies that can accelerate the adoption and large-scale deployment of green hydrogen technologies across diverse sectors and geographies. [ABSTRACT FROM AUTHOR]

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHMs) present a promising solution to enhance the efficiency of conventional microgrids (MGs) and facilitate higher integration of Distributed Energy Resources (DERs), simultaneously managing active and reactive power dispatch. However, MHMs face challenges in resource management under uncertainty and control of electronic converters linked to the ST and DERs, complicating the pursuit of optimal system performance. This paper introduces a Data-Driven Distributionally Robust Optimization (DDDRO) approach for day-ahead operation planning in ST-based MHMs, focusing on minimizing network losses, voltage deviations, and operational costs by optimizing the reactive power dispatch of DERs. The approach accounts for uncertainties in photovoltaic generator (PVG) output and demand. The Column-and-Constraint Generation (C&CG) algorithm and the Duality-Free Decomposition (DFD) method are employed. The initial mixed-integer non-linear planning problem is also reformulated into a mixed-integer (MI) Second-Order Cone Programming (SOCP) problem using second-order cone relaxation and a positive octagonal constraint method. Simulation results on a connected MHM system validate the model's efficacy and performance. The study also highlights the advantages of the meshed MG structure and the positive impact of integrating the ST into MHMs, leveraging the multi-stage converter's flexibility for optimal energy management under uncertain conditions. [ABSTRACT FROM AUTHOR]

The rapid expansion of energy storage integration has not provided sufficient time to strengthen and expand the transmission and distribution network. This issue can lead to PCC congestion in green multi-microgrid (MMG) systems. In these systems, microgrids operate independently and connect to the grid at a point of common coupling (PCC) without sharing operational data with neighboring microgrids. To address this issue, this paper proposes a bi-level optimization model designed to reschedule hydrogen storage systems. The first level allows each microgrid to optimize its energy transactions with the grid and communicates any unbalanced energy to the second level, where a hydrogen management system (HMS) is introduced. The HMS optimizes virtual hydrogen prices to address the PCC congestion and maximize the MMG's profit. These virtual prices are then sent to the first level, allowing the microgrids to reschedule the hydrogen storage systems based on these virtual prices. Finally, the MMG's profit is fairly allocated among the microgrids using the Shapley value method. The proposed method's effectiveness is demonstrated using simulations, which show a six percent increase in MMG profit compared to scenarios that only share PCC capacity while maintaining the data privacy of all the involved microgrids. [ABSTRACT FROM AUTHOR]

Substantial breakthroughs in renewable energy have been made in order to reduce energy-induced climate change. Yet our reliance on these sources is still insufficient. The UK's objective of attaining net-zero emissions by 2050 is highly dependent on shifting to an electrical system that exclusively relies on zero-carbon generation. This entails integrating renewable energy sources, along with other low-carbon sources such as nuclear power, into the energy mix. However, the primary barrier to incorporating additional renewable energy sources into the grid is their intermittent and volatile nature. Therefore, there is a pressing need to stabilise the generation of renewables and manage this volatility by enhancing the balancing mechanism between microgrids and the national grid. This paper examines previous research on microgrids and smart grids, specifically from a supply chain perspective. It has been observed that the majority of the current literature focuses on documenting selfish microgrids that strive to optimise performance at the microgrid level. However, there is an alternative approach that draws inspiration from the field of supply chain management. Consequently, it is possible to enhance a microgrid's performance within the broader system that it belongs to by reconsidering the timing and location of storage utilisation. [ABSTRACT FROM AUTHOR]

With the explosion in energy consumption demand, the deep penetration of renewable energy into the grid is inevitable and has become trend across the world today. Microgrids with integrated renewable energy are the core components of smart grids and will permeate all areas of human activity. Although this grid has a very flexible working principle, its heavy reliance on renewable energy sources can cause significant disturbances to the electric transmission system. Therefore, the control and monitoring processes for microgrids must be implemented through various mechanisms to ensure the microgrid system operates safely, stably, and effectively. In this paper, the research team will introduce and synthesize the multilevel control scheme of current types of microgrids. We will evaluate the advantages and disadvantages of each type of MG, providing a reference for further research in the field of microgrid control applications, both current and in the near future. [ABSTRACT FROM AUTHOR]

This document explores the challenges and solutions related to the sustainable development of new power systems under dual carbon goals. The integration of renewable energy sources has impacted the stability of power systems, requiring measures such as grid control, optimization, and accurate forecasting of renewable energy generation. The accurate prediction of photovoltaic and wind power is crucial, and deep learning-based models have shown superior forecasting accuracy. Control and optimization strategies, such as voltage feedforward control and adaptive controllers, enhance system stability. Effective control of the power grid also has economic benefits, and planning and scheduling play a significant role in power system reliability and stability. System state management and intelligent question-answering systems contribute to efficient system operation. The advancement of artificial intelligence technology and big data analytics is crucial for enhancing power system stability and efficiency. Overall, these measures are essential for the sustainable development of power systems. [Extracted from the article]

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal. [ABSTRACT FROM AUTHOR]

In isolated operation, DC microgrids require multiple distributed energy storage units (DESUs) to accommodate the variability of distributed generation (DG). The traditional control strategy has the problem of uneven allocation of load current when the line impedance is not matched. As the state-of-charge (SOC) balancing proceeds, the SOC difference gradually decreases, leading to a gradual decrease in the balancing rate. Thus, an improved SOC droop control strategy is introduced in this paper, which uses a combination of power and exponential functions to improve the virtual impedance responsiveness to SOC changes and introduces an adaptive acceleration factor to improve the slow SOC balancing problem. We construct a sparse communication network to achieve information exchange between DESU neighboring units. A global optimization controller employing the consistency algorithm is designed to mitigate the impact of line impedance mismatch on SOC balancing and current allocation. This approach uses a single controller to restore DC bus voltage, effectively reducing control connections and alleviating the communication burden on the system. Lastly, a simulation model of the DC microgrid is developed using MATLAB/Simulink R2021b. The results confirm that the proposed control strategy achieves rapid SOC balancing and the precise allocation of load currents in various complex operational scenarios. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) microgrids comprise a multitude of small PV power stations distributed across a specific geographical area in a decentralized manner. Computational services for forecasting the output power of power stations are crucial for optimizing resource deployment. This paper proposes a deep-learning-based architecture for short-term prediction of PV power. Firstly, in order to make full use of the spatial information between different power stations, a spatio–temporal feature fusion method is proposed. This method is capable of exploiting both the power information of neighboring power stations with strong correlations and meteorological information with the PV feature data of the target power station. By using a multiscale convolutional neural network–long short-term memory (CNN-LSTM) network model, it is capable of generating a PV feature dataset containing spatio–temporal attributes that expand the data source and enhance the feature constraints. It is capable of predicting the output power sequences of power stations in PV microgrids with high model generalization and responsiveness. To validate the effectiveness of the proposed framework, an extensive numerical analysis is also conducted based on a real-world PV dataset. [ABSTRACT FROM AUTHOR]

In this paper, demand response for isolated industrial micro-grid is investigated, in which the typical manufacturing assembly process, battery, conventional generator and PV generation are contained. The typical manufacturing assembly process is modelled by fully considering the coupling relationships between tasks and buffers. On the other hand, the uncertainty for PV generation is taken into account, where the data-driven distributionally robust chance constraints are designed and the error between day-ahead forecast and real-time operation is dealt with by the reserve capacity of conventional generator. In addition, the distributionally robust chance constraints and the expectation operator are reformulated as linear constraints, which can be solved by commercial solver directly. Finally, a case study using lithium-ion battery factory is presented to prove the superiority and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems. [ABSTRACT FROM AUTHOR]

This paper introduces static and dynamic competitive optimal control of DC/DC converters in DC microgrids based on game theory considering mutual effects. Using static and dynamic game theories, the conditions for competitive optimal control of DC/DC converters in Nash equilibrium with static and dynamic states are determined. Also, to reduce the complexity in the design of competitive optimal control of DC/DC converters due to the bilinear dynamic model of the system, generalized objective functions are considered. The competitive optimal controls using the Lyapunov and Riccati equations are determined to track the system trajectory to Nash equilibrium. Also, the numerical and experimental results are presented and compared in several cases. The results show that the competitive optimal control of the DC/DC converters in the DC microgrids can effectively improve the performance of the system. [ABSTRACT FROM AUTHOR]

The droop control strategy is popularly employed in DC microgrids. However, its virtual resistance will cause voltage deviation and reduce transient response. The DOB‐based method is proven to improve transient response in literature. However, it is analyzed in this study that this method will negatively influence the current sharing when employed in droop control. A weakened disturbance observation (WDOB) is proposed in this work to improve the drawbacks. To employ the proposed method, the equivalent models of the droop controller and the physical system are separately established, and several transformations are conducted. An auxiliary compensation is added and the current loop is considered as a whole to be transmitted into the control plant, making the traditional DOB method successfully adopted. It is obvious that the dynamic performance is improved, but it disabled virtual resistance in the steady‐state. And the current sharing cannot be achieved in a multi‐converter parallel system. The reason for this problem is analyzed from the control process and transfer function, and the WDOB solution is finally proposed. Through the proposed method, both aims of improving dynamic response and current sharing can be achieved, and the steady voltage deviation is much less than that of the traditional droop controller. [ABSTRACT FROM AUTHOR]

The widespread integration of wind turbines poses voltage stability challenges to power systems. To enhance the ability of wind power systems to actively support grid voltage, grid-forming control techniques are increasingly being employed. However, current research primarily focuses on voltage stability challenges at the point of common coupling in wind power systems, lacking thorough investigation into system voltage response characterization. This paper establishes the voltage response model of a grid-forming wind power system. Based on this model, mathematical derivation and theoretical analysis are conducted, and the effect factors of the voltage at the point of common coupling are investigated. Furthermore, a voltage stabilization method is explored by adjusting the above effect factors. Finally, based on the MATLAB/Simulink platform, the simulation verification of each effect factor is carried out. The results indicate that voltage response characterization obtained by the theoretical analysis and simulation is similar and that the proposed method is valid. [ABSTRACT FROM AUTHOR]

Hybrid Renewable Energy Systems (HRES) have recently been proposed as a way to improve dependability and reduce losses in grid-connected load systems. This research study suggests a novel hybrid optimization technique that regulates UPQC in order to address the Power Quality (PQ) problems in the HRES system. The load system serves as the primary link between the battery energy storage systems (BESS), wind turbine (WT), and solar photovoltaic (PV) components of the HRES system. The major objective of the study is to reduce PQ issues and make up for the load requirement inside the HRES system. The addition of an Optimized Fractional Order Proportional Integral Derivative (O-FOPID) controller improves the efficiency of the UPQC. The Crow-Tunicate Swarm Optimization Algorithm (CT-SOA), an enhanced variant of the traditional Tunicate Swarm Optimization (TSA) and Crow Search Optimization (CSO), is used to optimize the control parameters of the FOPID controller. Utilizing the MATLAB/Simulink platform, the proposed method is put into practice, and the system's performance is assessed for sag, swell, and Total Harmonic Distortion (THD). The THD values for the PI, FOPID, and CSA techniques, respectively, are 5.9038%, 4.9592%, and 3.7027%, under the sag condition. This validates the superiority of the proposed approach over existing approaches. [ABSTRACT FROM AUTHOR]

In DC transmission and distribution systems, both unipolar and bipolar transmission modes exist, and DC transformers used in these systems are also available in either unipolar or bipolar configurations. In actual systems, due to requirements such as economy, land occupation, and reliability, there is a tendency to use a system with unipolar input and bipolar output. However, the bipolar loads, if unbalanced, will lead to increased equipment costs and voltage imbalance, causing power quality problems. This paper defines the Power Unbalance Factor (PUF) to describe the power quality of the studied DC transmission system and presents an improved DC transformer topology based on a power balancing system. This topology realizes bipolar voltage balance and improves the power quality of the DC transmission system when the load is unbalanced. The influence of the proposed solution on the power design of the DC system is demonstrated through theoretical analysis, and its effectiveness for improving the DC power quality is verified by both simulations in MATLAB/Simulink environment and physical experiments. When the power electronic transformer needs to be overloaded, the proposed topology can reduce the design power of the two branches by using the difference power, which is economical. [ABSTRACT FROM AUTHOR]

With advancements in communication systems and measurement technologies, smart grids have become more observable and controllable, evolving into cyberphysical-power systems (CPPS). The impact of network security and secondary equipment on power system stability has become more evident. To support the existing grid toward a smart grid scenario, smart metering plays a vital role at the customer end side. Cyber-Physical systems are vulnerable to cyber-attacks and various techniques have been evolved to detect a cyber attack in the smart grid. Weighted trust-based models are suggested as one of the most effective security mechanisms. A hardware-in-loop CPPS co-simulation platform is established to facilitate the theoretical study of CPPS and the formulation of grid operation strategies. This paper examines current co-simulation platform schemes and highlights the necessity for a real-time hard-ware-in-the-loop platform to accurately simulate cyber-attack processes. This consideration takes into account the fundamental differences in modeling between power and communication systems. The architecture of the co-simulation platform based on RT-LAB and OPNET is described, including detailed modeling of the power system, communication system, and security and stability control devices. Additionally, an analysis of the latency of the co-simulation is provided. The paper focuses on modeling and implementing methods for addressing DDOS attacks and man-in-the-middle at-tacks in the communication network. The results from simulating a 7-bus system show the effectiveness and rationality of the co-simulation platform that has been designed. [ABSTRACT FROM AUTHOR]

This study aimed to examine the ability of adolescents to maintain breathing rhythm while swimming with and without goggles, in the context of pedagogical interventions for implementation of water competence skills, rather than simply teaching swimming technique (strokes). 25 females and 25 males, 12–13 years old, swam the front crawl both with goggles and without goggles. Distance covered and the ability to maintain breathing rhythm were evaluated by experts. For both girls and boys, the lack of goggles reduced the breath control. The boys in contrast to the girls, could "swim" (cover a distance) but did not have the "competence" to swim effectively/safely—with breathing rhythm—regardless of the goggle factor. Goggle-free swimming as an autonomous component of water competence is highly recommended in elementary swimming education. The following elements for pedagogical intervention in the area of water competence development are proposed: (1) the formatting of breath control on the basis of the student's preferred, simplest form of swimming (not strokes); (2) the a priori treatment of swimming goggles as an unnecessary teaching aid; (3) the gender differences in area of both adaptation in visual perception (the goggles factor) and motor control (breath control factor) should be considered. [ABSTRACT FROM AUTHOR]

Recent advances in distribution networks, driven by the integration of renewable energy sources, have spurred the emergence of microgrids, elevating concerns regarded reliability and stability. In this context, precise monitoring of events, particularly those elusive to detection like high-impedance faults (HIFs), becomes imperative. The development of phasor measurement units (PMUs) with their harmonic synchronized measurements has enhanced the monitoring task and fostering the application of synchrophasors even on microgrids. This work introduces a novel method for event classification in microgrids, utilizing combined low-rate PMU data and harmonic synchrophasor time series. Central to our approach is the usage of a state-of-the-art transformer neural network, based on the attention mechanism, to effectively discern HIFs from other faulty and non-fault events. Notably, this methodology accounts for prevalent PMU data quality issues, including noise, missing data, and synchronism errors. Results from real-world HIF data demonstrate a robust performance, with an accuracy rate of approximately 98% in event classification. This harmonic synchrophasor-based strategy showcases promise as an original approach for handling commercial PMU data, offering sufficient robustness for deployment in real-world applications. [ABSTRACT FROM AUTHOR]

Hybrid microgrid (HMG) control strategies reveal several critical research gaps that must be addressed. Current approaches often fall short in efficiency and reliability due to the inherent complexities of transitioning between grid-connected mode (GCM) and islanded mode (IM), resulting in increased losses and operational difficulties. Additionally, managing overloads, especially in IM, poses significant challenges for maintaining stable voltage and frequency regulation. This paper seeks to address these issues by introducing a novel universal control structure (UCS) for HMGs, which includes a frequency compensation unit (FCU) and a three-stage bidirectional AC/DC droop. It presents a streamlined yet robust solution for handling overloads, maintaining power balance, and ensuring precise voltage and frequency regulation while promising smooth maneuver in both GCM and IM. The proposed UCS provides flexible and reliable operation, allowing for the versatile placement of DC and AC sources and loads. This will reduce power transformation stages thereby lowering cost and increasing efficiency of system. Through experimental validation, this study demonstrates the effectiveness of the proposed control in addressing these critical research gaps and advancing the field of HMG control strategies. [ABSTRACT FROM AUTHOR]

In building a smarter and more flexible low‐carbon smart grid system, alternating current (AC) microgrids using virtual synchronous generator (VSG) technology are viewed as a key link in integrating distributed renewable energy access into the main grid. Given that renewable energy sources (such as solar, hydroenergy, and wind) do not have sufficient capacity for reactive power when not available, AC microgrids face challenges in maintaining stable operation. In order to overcome this difficulty, it is hoped that digging deeper and applying more system information can significantly improve the overall performance of the microgrid. This paper proposes a novel method based on macroscopic state dynamic modeling. This method expands the understanding of the inherent rational control mechanism within the microgrid, enabling the overall control objective of the microgrid to be expressed in a more abstract and direct manner. Additionally, by implementing additional convergence constraint conditions on the macroscopic state dynamics, such as based on some optimality criteria, a set of macroscopic state controllers can be obtained to meet specific performance indicators. Theoretical analysis combined with simulation validation demonstrate the effectiveness of this macroscopic state based control strategy. It proves that when meeting the predefined design requirements, the designed controller can enhance the transient response of microgrids in practical applications, thus supporting higher rate of renewable energy access and promoting the development of the smart grid. [ABSTRACT FROM AUTHOR]

Aiming at the solid oxide fuel cell (SOFC) applied to the ship DC microgrid in the face of pulse load disturbance is prone to make the SOFC voltage drop too large leading to the DC grid oscillation problem. In this paper, a stability criterion method for SOFC-Li battery DC system based on hybrid potential function is proposed. Firstly, a mathematical model of shipboard DC microgrid with SOFC-Li battery is established and the accuracy of the model is verified. Then, the stability criterion of the system based on the hybrid potential function under large disturbances is constructed. Subsequently, the effects of system stability under impulse load conditions were analysed under different parameters. Based on the constructed criterion, simulation verification of the stability boundary conditions of the SOFC system operating independently or jointly with a lithium battery system is carried out. The experimental results show that the proposed stability criterion and control strategy are effective in accurately predicting the system stability boundary. The experimental results verify the effectiveness of the proposed method in improving the stability of the system and provide a theoretical basis for further research on the dynamic characteristics of SOFC systems under complex load conditions. [ABSTRACT FROM AUTHOR]

In the multi‐paralleled converter microgrid system, the traditional hierarchical control strategy can eliminate the bus voltage amplitude and frequency deviation from the rated value. However, isolated AC microgrids may face extreme scenarios such as communication delays and interruptions in data transmission due to the use of low‐bandwidth communication (LBC) lines. Additionally, the inconsistent line impedance of each distributed generation (DG) unit may result in the inaccurate division of reactive power in multi‐paralleled converter systems, thus affecting system stability. To address these issues, this paper presents a distributed coordinated control strategy for isolated AC microgrids based on the consensus algorithm. The proposed strategy first replaces LBC lines with a filter to alleviate the effects of communication delays. A small‐signal model is established in its state space, and stability of the microgrid system under the proposed control strategy is verified through eigenvalue analysis. Furthermore, based on the above theoretical analysis, a consensus algorithm is introduced, and a distributed control strategy for isolated AC microgrids based on the consensus algorithm is proposed to solve the issue of inaccurate equalization of the system's reactive power. Finally, factors that influence the dynamic convergence performance of the consensus algorithm are analyzed through simulation in PLECS software. Also, the maximum tolerable communication delays of the microgrid system under different communication topologies are also compared, and the system's robustness is evaluated under the condition of sudden communication interruption in a DG unit and sudden weather variation. These analyses confirmed the robustness of the proposed strategy against communication delays, output power fluctuation, and communication interruptions. [ABSTRACT FROM AUTHOR]

As renewable energy resources dwindle and the world's population grows, the balance between energy supply and demand is being adversely affected. The rapid increase in the number of EVs causes power quality problems in existing conventional grids. Topologies such as V2G, G2V, and V2H are being studied in order to overcome these problems. In this study, a microgrid is designed for power flow in single-phase V2G topology. In this microgrid, there is one EV, one home, and one PV panel. The EV is charged by the PV panel during the daytime when it is in the parking lot. When there is a power quality problem in the microgrid, the V2G topology is activated and power flows from the EV battery to the grid. A single-phase grid-connected inverter and a bidirectional DC-DC converter were designed to operate this system. The simulations of the designed microgrid were performed in the MATLAB/Simulink program. According to the simulation results, installing this system in homes or workplaces with EVs, charging the EV battery with PV panel, and using the EV battery to eliminate power quality problems that may occur in the existing microgrid provides a solution for single-phase V2G topology. [ABSTRACT FROM AUTHOR]

The telecommunication industry relies heavily on a reliable and continuous power supply. Traditional power sources like diesel generators have long been the backbone of telecom infrastructure. However, the growing demand for sustainable and eco-friendly solutions has spurred interest in renewable energy sources. Proton exchange membrane (PEM) fuel cell-based systems, integrated with solar and wind energy, offer a promising alternative. This review explores the potential of these hybrid systems in stationary telecom applications, providing a comprehensive overview of their architecture, energy management, and storage solutions. As the demand for telecommunication services grows, so does the need for a reliable power supply. Diesel generators are linked with high operational costs, noise pollution, and significant greenhouse gas emissions, prompting a search for more sustainable alternatives. This review analyzes the current state of PEM fuel cell systems in telecom applications, examines the architecture of microgrids incorporating renewable energy sources, and discusses optimization methods, challenges, and future directions for energy storage systems. Critical findings and recommendations are presented, highlighting objectives and constraints for future developments. Leveraging these technologies can help the telecom industry reduce fossil fuel reliance, lower operational costs, minimize environmental impact, and increase system reliability. [ABSTRACT FROM AUTHOR]

The current study proposes a strategy for sensing fault detection in the secondary control of an isolated Microgrid based on a high-order Sliding Mode Robust Observers design. The proposed strategy's main objective is to support future diagnostic and fault tolerance systems in handling these extreme situations. The proposal is based on a generation system and a waste management system. Four test scenarios were generated in a typical Microgrid to validate the designed strategy, including two Battery Energy Storage Systems in parallel, linear, and non-linear loads. The scenarios included normal grid operation and three types of sensing faults (abrupt, incipient, and random) directly affecting the secondary control of a hierarchical control strategy. The results showed that the proposed strategy could provide a real-time decision for detection and reduce the occurrence of false alarms in this process. The effectiveness of the fault detection strategy was verified and tested by digital simulation in Matlab/Simulink R2023b. [ABSTRACT FROM AUTHOR]

This paper reviews the use of game theory tools to study the operation and design of modern power grids. The contribution of this work is to summarize the literature to highlight the versatile solution capability of game theory by focusing on the interconnected objectives of energy trading and energy management. This review was conducted with a focus on various applications in energy systems, including general energy markets, micro grids (MGs), virtual power plants (VPP), electric vehicles (EVs), and smart homes, and explores how game theory can summarize the solutions for pricing, bidding, demand side management, and resource optimization. A key finding is the suitability of game theory for modeling decentralized energy systems where strategic incentives can lead to outcomes that benefit both individuals and society. It also discusses the limitations, challenges, and potential benefits of game theory in complex power systems. This study provides researchers and policy makers with a comprehensive overview of current research and insights into the potential of game theory to shape the future of energy systems. [ABSTRACT FROM AUTHOR]

This paper provides a power-sharing strategy designed for an islanded grid that becomes isolated from the main grid due to faults or natural disasters. The proposed topology is introduced for post-disaster scenarios where the restoration process may be time-consuming (from a few hours to a few months). This system is equipped with a step-by-step power-sharing strategy based on priority, inputs from photovoltaic sources, and a diesel generator to enhance reliability. The DC–AC inverter control and AC–DC–AC control for the diesel generator are presented, which provide flexible real and reactive control. The system is divided into three priority load areas, and power sharing is conducted based on these priorities. The distinctive feature of the proposed strategy lies in its ability to manage power-sharing under different power generation conditions, prioritizing critical loads. The proposed method is implemented using the MATLAB Simulink environment. Simulation studies are performed under different solar irradiances and variations in the diesel generator's output to validate the performance of the proposed method. The results demonstrate the practicality of the proposed algorithm in harnessing renewable resources and diesel generators and dynamically managing the energy consumption in loads. [ABSTRACT FROM AUTHOR]

This paper proposes a sophisticated, fault-tolerant, and centralized secondary controller that is designed for inverter-based, islanded microgrids. The proposed controller enhances system resilience to unexpected network partitions, which typically occur due to the tripping of protective devices under fault conditions. In typical radially configured MGs, a line fault can cause protective devices to isolate the faulted line, thereby splitting the MG into two electrically independent sub-microgrids (SMGs), while retaining the existing communication and control framework. In contrast to traditional centralized and distributed secondary controllers, which often fail to restore the frequency to the nominal value (50 Hz) in split SMGs, the proposed controller exhibits exceptional performance. Through simulation studies on 6-bus and 13-bus islanded MG setups, the controller has not only demonstrated its ability to swiftly restore the nominal frequency in both SMGs within a few seconds (specifically 5 s), but also to ensure fair power distribution among the distributed generators (DGs) supplying the SMGs. This rapid frequency stabilization underscores the controller's effectiveness in maintaining stable frequency levels immediately following a fault. In contrast, the use of traditional centralized and consensus controllers typically results in a frequency deviation of about 3 Hz from the nominal value in one of the SMGs during the microgrid's partition. [ABSTRACT FROM AUTHOR]

Swarm electrification-driven communities face significant challenges, including implementing advanced distributed control in areas with limited ICT access and establishing trust among villagers hesitant to grant access to their assets. This paper proposes a distributed DC microgrid architecture for P2P energy exchange in these communities, ensuring stability and an effective exchange operation. By implementing a Blockchain marketplace specifically designed to suit the rural context, the proposed architecture ensures tracing of exchange transactions to fairly settle participants. Validation experiments demonstrate its efficacy in achieving peak shaving. It provides 11% of the requester's total demand from the community even while maintaining the constraint of reducing discharge–charge cycles to one per day, thereby preserving battery life. Additionally, the solution reduces prosumer production losses by 16% of the total PV production. [ABSTRACT FROM AUTHOR]

Being multivariable in nature, voltage and current control loops have controllers in the forward and cross-coupling paths. Most methods discussed in the literature focus on tuning the controllers in the forward paths to reduce the dq coupling. A modified pole-zero cancellation (MPZC) technique has recently been discussed, which uses the concepts of pole-zero cancellation and particle swarm optimization to effectively tune the forward path controllers. However, given the fixed gains in the cross-coupling paths, it is not possible to realize a superior transient response from this technique. Therefore, to achieve enhanced vector control of VSIs under transient conditions, this paper proposes a hybrid MPZC (HMPZC) method, which incorporates multivariable control along with the MPZC technique for both voltage/current control loops. In the proposed HMPZC method, the MPZC method is used to tune the forward path controllers, and multivariable control-based PI controllers are assigned in the cross-coupling paths of dq-axes loops rather than fixed gains. In this paper, these multivariable control-based PI controllers are designed using direct synthesis method-based internal model control (IMC). From the simulation results, it is verified that the proposed HMPZC method has reduced the coupling between the d- and q-axes loops of the current/voltage, leading to the improved transient response and power delivery capability of VSIs. [ABSTRACT FROM AUTHOR]

Microgrids play a crucial role in modern energy systems by integrating diverse energy sources and enhancing grid resilience. This study addresses the optimization of microgrids through the deployment of high-efficiency converters, aiming to improve energy management and operational efficiency. This study explores the pivotal role of AC-DC and DC-DC bidirectional converters in facilitating energy conversion and management across various sources and storage systems within microgrids. Advanced control methodologies, including model-based predictive control and artificial intelligence, are analyzed for their ability to dynamically adapt to fluctuations in power generation and demand, thereby enhancing microgrid performance. The findings highlight that implementing high-efficiency converters not only enhances power stability and quality but also reduces operational costs and carbon emissions, thereby reinforcing microgrids as a sustainable and effective solution for contemporary energy management challenges. This research contributes to advancing the understanding and implementation of efficient energy systems in microgrids, promoting their widespread adoption in diverse applications. [ABSTRACT FROM AUTHOR]

To enhance the power supply reliability of the microgrid cluster consisting of AC/DC hybrid microgrids, this paper proposes an innovative structure that enables backup power to be accessed quickly in the event of power source failure. The structure leverages the quick response characteristics of thyristor switches, effectively reducing the power outage time. The corresponding control strategy is introduced in detail in this paper. Furthermore, taking practical considerations into account, two types of AC/DC hybrid microgrid structures are designed for grid-connected and islanded states. These microgrids exhibit strong distributed energy consumption capabilities, simple control strategies, and high power quality. Additionally, the aforementioned structures are constructed within the MATLAB/Simulink R2023a simulation software. Their feasibility is verified, and comparisons with the existing studies are conducted using specific examples. Finally, the cost and efficiency of the application of this study are discussed. Both the above results and analysis indicate that the structures proposed in this paper can reduce costs, improve efficiency, and enhance power supply stability. [ABSTRACT FROM AUTHOR]

Renewable energy‐powered DC microgrids have emerged as a sustainable alternative for standalone power systems in remote locations, which were traditionally reliant on diesel generators (DIG) only. To ensure power quality and reliability, energy storage systems (ESS) and demand‐side management (DSM) techniques are employed, addressing the intermittent nature of renewable energy sources (RES). This manuscript presents a novel multi‐objective optimisation framework to determine the equipment sizing, depth of discharge (DoD) of ESS, and share of controllable loads contributing to DSM in a standalone DC microgrid incorporated with RES as a primary energy source and a backup DIG. The proposed optimisation strategy utilises genetic algorithm with the objectives of minimizing lifecycle cost and carbon footprint. A novel battery energy storage system (BESS) management criterion is introduced, which accounts for battery degradation in the lifecycle cost calculation. The minimum allowable DoD of the BESS is considered a decision variable in the optimisation problem to assess the impact of higher DoD on lifecycle cost improvement. MATLAB simulation results demonstrate that the proposed optimisation model significantly reduces the levelized cost of electricity and per unit carbon footprint compared to previous models. Additionally, it identifies an optimal range of DoD for the BESS to enhance the lifecycle cost of a standalone DC microgrid. [ABSTRACT FROM AUTHOR]

There is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi‐area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi‐area MGs are regarded as cyber‐physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non‐linearities in the power system or server cyberattacks. [ABSTRACT FROM AUTHOR]

Microgrids play a pivotal role in modern power distribution systems, necessitating precise control methodologies to tackle challenges such as performance instability, especially during islanding operations. This paper introduces an advanced control strategy that employs artificial intelligence, specifically deep neural network (DNN) predictions, to enhance microgrid performance, particularly in an islanding mode where voltage and frequency (VaF) deviations are critical concerns. By utilizing real‐time data and historical trends, the proposed controller accurately forecasts power demand and generation patterns, enabling proactive planning and optimization of efficiency, reliability, and sustainability in microgrid management. One significant aspect of this approach is to establish an intelligent distributed control system that minimizes reliance on communication devices while ensuring that VaF remains within acceptable limits. Moreover, it consolidates the roles of primary and secondary controllers within the microgrid and facilitates the prediction of load changes and load injection processes. This capability significantly reduces microgrid VaF deviations, enhancing system performance through precise power distribution and balanced coordination among distributed generators. Consequently, it ensures the stability and reliability of the system. In summary, the integration of DNN‐based predictive control represents a significant advancement in microgrid management, providing a solution to address performance challenges and optimize operational efficiency, reliability, and sustainability. [ABSTRACT FROM AUTHOR]

For making the grid to be more intelligent, a unique design is required in this changing sphere. A better-automated grid is more important for controlling the variations in the entire system. This is possible by introducing secondary controllers where both power and voltage are shifted at necessary stages. Therefore, in this article, a Neural Fizzy Identification System with Partial Integral Controller (NFISPID) is introduced. Also, a secondary controller named Selective Voltage Pulse Width Modulation with Hybrid Artificial Bee Colony-Particle Swarm Optimization technique is incorporated for handling all false situations in the grid. The efficiency of the proposed system is checked with MATLAB environment where the results prove to be much efficient in terms of all parametric values, and the grid becomes stable within a short span of time than other existing controllers. [ABSTRACT FROM AUTHOR]

In view of the low carbon economy of energy supply and the stable operation of "heat to power" of cogeneration units in offshore islands, an energy management strategy of island microgrid of photothermal power stations with carbon trading mechanism is proposed. Based on the multi energy island microgrid system integrating power collection, heat and water, such as solar thermal power station, heat pump, wind power, micro gas turbine unit and seawater desalination equipment, the heat pump achieves the bidirectional conversion of electric thermal energy with solar thermal power station to improve the power generation capacity of solar thermal power station and meet part of the heat load demand. Carbon trading mechanism is introduced to limit carbon emissions, and low carbon economic model of island microgrid is established to study the economy of different operation modes of island micro-grid with the participation of photothermal power stations. The multi scenario comparison experiment verifies that the island microgrid low carbon economic dispatching can better balance economy and environmental protection, and generate less carbon emissions while operating at a lower economic cost. [ABSTRACT FROM AUTHOR]