Centralized control system for islanded minigrid

The integration of renewable energy sources (RESs) has become more attractive to provide electricity to rural and remote areas, which increases the reliability and sustainability of the electrical system, particularly for areas where electricity extension is difficult. Despite this, the integration of hybrid RESs is accompanied by many problems as a result of the intermittent and unstable nature of RESs. The extant literature has discussed the integration of RESs, but it is not comprehensive enough to clarify all the factors that affect the integration of RESs. In this paper, a comprehensive review is made of the integration of RESs. This review includes various combinations of integrated systems, integration schemes, integration requirements, microgrid communication challenges, as well as artificial intelligence used in the integration. In addition, the review comprehensively presents the potential challenges arising from integrating renewable resources with the grid and the control strategies used. The classifications developed in this review facilitate the integration improvement process. This paper also discusses the various optimization techniques used to reduce the total cost of integrated energy sources. In addition, it examines the use of up-to-date methods to improve the performance of the electrical grid. A case study is conducted to analyze the impact of using artificial intelligence when integrating RESs. The results of the case study prove that the use of artificial intelligence helps to improve the accuracy of operation to provide effective and accurate prediction control of the integrated system. Various optimization techniques are combined with ANN to select the best hybrid model. PSO has the fast convergence rate for reaching to the minimum errors as the Normalized Mean Square Error (NMSE) percentage reaches 1.10% in 3367.50 s.

Stochastic flexible transmission operation for coordinated integration of plug-in electric vehicles and renewable energy sources

Microgrids (MGs) are a critical component in modern power systems, comprising interconnected loads and distributed energy resources (DERs) that can operate autonomously or in conjunction with the main grid. This review examines the role of MGs in advancing smart grid technologies, emphasizing the optimal integration of renewable energy sources (RES) and energy storage systems (ESS) to enhance their resilience and efficiency. It provides a detailed analysis of various MG architectures, including AC, DC, and hybrid topologies, alongside their operational modes. The paper highlights the pivotal role of control systems in optimizing energy management within MGs. Additionally, the challenges associated with MG integration, such as harmonic distortion, system stability, and regulatory constraints, are discussed. The review further explores the application of intelligent control strategies, including Fuzzy Logic Controllers (FLCs) and Genetic Algorithms (GAs), to optimize battery energy storage systems (BESS) and improve overall MG performance. The findings emphasize the need for innovative optimization techniques and intelligent control systems to overcome current limitations, thus enhancing the potential of MGs in future energy systems

A reactive scheduling and control framework for integration of renewable energy sources with a reformer-based fuel cell system and an energy storage device

Voltage source converters (VSCs) have emerged as the key components in modern power systems, facilitating efficient energy conversion and flexible power flow control. Understanding the fundamental circuit model of VSCs is essential for their accurate modeling and analysis in power system studies. A basic voltage source converter circuit model connected to an LC filter is essential because it lowers the harmonic distortions and enhances the overall power quality of the micro-grid. This guarantees a clean and steady power supply, which is necessary for the integration of multiple renewable energy sources and sensitive loads. A comprehensive methodology for developing a basic circuit model of VSCs, focusing on the key components and principals involved, is presented in this paper. The methodology includes the modeling of space vector pulse-width modulation (SVPWM) as well as the direct quadrature zero synchronous reference frame. Different design controls, including the design of current control loop in the S-domain, the design of the direct current (DC) bus voltage control loop in the S-domain, and the design of the alternating current (AC) voltage control loop in the S-domain, are explored to capture the dynamic behavior and control strategies of VSCs accurately. The proposed methodology provides a systematic framework for modeling VSCs, enabling engineers and researchers to analyze their performance and assess their impact on power system stability and operation. Future studies can be conducted by using case studies and simulation scenarios to show the efficiency and applicability of the developed models in analyzing VSC-based power electronics applications, including high-voltage direct current (HVDC) transmission systems and flexible alternating current transmission systems (FACTS). The significance of this work lies in its potential to advance the understanding and application of VSCs, contributing to more resilient and efficient power systems. By providing a solid foundation for future research and development, this study supports the ongoing integration of renewable energy sources and the advancement of modern electrical infrastructure.

Advancing sustainable energy systems—especially in rural areas and underdeveloped countries—depends on the integration of renewable energy sources into microgrids. This paper looks at the challenges connected to the integration of renewable energy sources—such as solar, wind, and hydro power—into microgrids as well as the recommended solutions to counterbalance these challenges. Along with innovative technologies and strategies that can enhance the efficiency and reliability of microgrid systems, issues such as intermittency, energy storage, grid stability, and system optimization are discussed. This paper also looks at the economic, technological, and regulatory factors influencing the establishment of renewable-powered microgrids and provides case examples demonstrating successful integration. Stressing the future potential for general adoption of renewable energy microgrids, the report concludes by underlining the significance of continuous research and government assistance in addressing present challenges.

Power electronics plays a crucial role in the automation of industrial power systems, ensuring efficient energy conversion, control, and distribution in manufacturing and other large-scale operations. With the integration of renewable energy sources, industrial automation requires advanced power electronics technologies that provide enhanced flexibility, reliability, and optimization. This paper explores key power electronics components, their applications in industrial power system automation, and the challenges and opportunities they present in the context of modern industrial environments. The role of intelligent systems, grid integration, and energy storage solutions will also be discussed.

ABSTRACTThe advancement of technologies in combined cooling, heat, and power (CCHP) systems has significantly driven the development of energy hubs (EHs), offering an efficient platform to integrate diverse energy carriers. This study proposes a comprehensive framework for interconnected energy hubs within networked microgrids (NMGs), aiming to improve the efficiency of multi‐carrier energy systems through optimal coordination among the involved entities. In the proposed model, both microgrids and the distribution network are treated as autonomous agents, each managing its own local scheduling challenges. To achieve seamless coordination between these entities, a distributed approach based on the alternating direction method of multipliers (ADMM) is utilised to mitigate power exchange conflicts between multiple microgrids and the distribution network. Additionally, energy systems often operate under uncertain conditions due to the integration of renewable energy sources (RES) and their interactions with the electricity market. To address these uncertainties, this study introduces a novel decentralised optimisation framework that integrates robust optimization (RO) with information gap decision theory (IGDT). The proposed model is evaluated on multi‐carrier energy systems within networked microgrids, and the results demonstrate its low computational complexity and guaranteed convergence of the distributed algorithm.

The resilience level of networked microgrids (NMGs) has become a critical topic due to the increasing frequency of extreme weather events and the growing integration of renewable energy sources. Existing resilience evaluation and quantification methods primarily employ performance curve-based metrics or graph theory-based metrics. However, metrics such as expected load shedding or other outcome-based indices often fail to capture the security margin of current operating points, offering limited guidance for operators during extreme events. To address this gap, this paper proposes a region-based resilience evaluation and quantification method for NMGs that enables more intuitive resilience assessment and supports proactive dispatch. Specifically, the concept of the networked microgrids resilience region (NMRR) is introduced. The NMRR is constructed by projecting the intersection of feasible regions under all foreseeable failure-induced topology reconfiguration scenarios onto dispatchable variables accessible to system operators. A theoretical derivation of the NMRR's analytical expression is provided. To efficiently compute the NMRR within dispatch timescales, an outer progressive approximation algorithm is developed. Two NMRR-based metrics are then proposed to quantify the resilience level of NMGs and the security margin of operating points, offering actionable dispatch insights. Case studies show that the proposed method aligns with traditional resilience indices while offering greater intuitiveness and operational relevance.

Electrification of the transportation sector can play an essential role in curbing fossil fuel scarcity and oil shortages in the world. Electric vehicles (EVs) can significantly reduce CO2 emissions, improve urban pollution. Apart from these advantages of EVs, they may also pose challenges to the distribution grid. Increasing penetration of EVs puts an extra burden and leads to affect the grid severely. Load congestions, voltage drops/regulation, overloading are some of the issues that might incur in the grid because of improper charging of EVs. The uncertain nature of EV owners makes it very difficult to predict the charging pattern. So the grid operator may face a daunting task to avoid overloading if huge EVs are charged without following any smart charging management strategies. We propose a framework that allows EV users to follow a well-coordinated charging schedule and fulfill various objectives like load variance minimization and social welfare maximization for both electric utility and EV owners. A proof of concept of distributed resource allocation for EV charging is implemented using the output consensus approach. Microgrids are components of a smart power distribution grid that can operate as a decentralized/localized grid when disconnected from the main grid. A microgrid can enhance the grid resiliency and provide adequate power to a community or a region in case of grid failure caused by natural disasters or any other disruptions. We also propose the optimal scheduling model for Distributed Energy Resources (DERs) including an EV charging station and battery energy storage system (BESS) in a community microgrid. This model also minimizes the cost incurred in operating the DERs, charging station, BESS, and the grid load. Due to the two-way power flow in the smart grid because of the EVs and distributed energy resources (DERs) involvement, the distribution system operator (DSO) must provide control & protection to equipment and manage the reverse power flow without violating system constraints. Artificial Intelligence (AI) leads the path for a promising smart grid future and helps the DSO tackle such challenges. Given the large size of data flow in the smart grid due to phasor measurement units (PMUs) and smart sensors, AI-based techniques help the grid operator to manage and analyze such data effectively and improve grid resiliency. Machine learning can help the grid operator forecast the uncertainty in supply-demand because of the integration of renewable energy sources. For system planning and operation, the utility needs to perform the

The increasing integration of renewable energy sources and the deep fusion with power electronics have introduced new challenges to the stability of power systems, making traditional prediction methods inadequate for real‐time, high‐precision control. This paper presents a power system‐specific methodology designed to solve high‐dimensional, nonlinear dynamic prediction problems, which is demonstrated through an application to rotor‐angle trajectory prediction in power systems. Our proposed method, which combines model‐based approaches with data‐driven techniques, leverages the strengths of both paradigms. At its core, the method employs the Transformer deep learning architecture to capture the temporal dynamics of system transients. Additionally, physics‐informed neural networks are integrated to ensure that key physical principles are embedded within the model, enhancing both accuracy and convergence speed. While the proposed methodology has the potential for broader applications in dynamic modeling, this paper focuses on its application to rotor‐angle trajectory prediction, which is a critical task in transient stability assessment. This scenario was chosen as a representative use case to validate the effectiveness and feasibility of the approach under realistic power system conditions. The method was tested on the modified IEEE‐39 bus system, with results showing that it delivers fast, accurate, and physically consistent predictions without a large amount of labelled data. This demonstrates the method's potential for supporting online control and maintaining system stability in future power grids.

GA-based optimization for integration of DGs, STATCOM and PHEVs in distribution systems

This paper introduces a Modified Reptile Search Algorithm (MRSA) designed to optimize the operation of distribution networks (DNs) considering the growing integration of renewable energy sources (RESs). The integration of RESs‐based Distributed Generation (DG) systems, such as wind turbines (WTs) and photovoltaics (PVs), presents a complex challenge due to its significant impact on DN operations and planning, particularly considering uncertainties related to solar irradiance, temperature, wind speed, consumption, and energy prices. The primary objective is cost reduction, encompassing electricity acquisition, PV and WTs unit costs, and annual energy losses. The proposed MRSA incorporates two strategies: the fitness‐distance balance method and Levy flight motion, enhancing its searching capabilities beyond standard Reptile Search Algorithm and mitigating local optima issues. The uncertainties in load demand, energy prices, and renewable energy generation are represented through probability density functions and simulated using Monte Carlo methods. Evaluation involves typical bentchmark functions and a real 112‐bus Algerian DN, comparing MRSA's efficacy with other optimization techniques. Results indicate that the proposed DN optimization program with WTs and PVs integration reduces annual costs by 21.43%, from 6.2715E + 06 to 4.9270E + 06 USD, reduce voltage deviations by 21.67%, from 77.1022 to 60.4007 USD, and enhance system stability by 2.59%, from 2.3699E + 03 to 2.4314E + 03 USD, compared with the base case.

The article analyzes the development trends for renewable energy sources in 2010-2021. The growing use of renewable energy sources in the global energy balance in developed and developing countries was revealed. The share of renewable sources in the production of electricity in 2021 was determined. The results presented in the form of approaches to the integration of renewable sources in order to replenish the production processes for the provision of energy services are based on an extensive assessment of scientific Russian and foreign literature. Integration of renewable energy sources will contribute to the sustainability of existing technologies and risk neutralization. The integrated approach will contribute to the interconnected provision of electricity, heat, cold and mobility

The integration of renewable energy sources is one of the key challenges for the transition of the energy supply chain towards a low carbon society. Due to the intermittency of wind and solar power additional storage and flexibility is required for their integration into the electricity system. Based on a scenario analysis for Germany, the economic and environmental effects of flexibility from electrical cooling as an example for demand side management in a smart grid as well as traditional flexibility from pumped storage power plants is evaluated with the PowerFlex model developed by Ö ko-Institut. In the 2030 scenario with new flexibility, about 500GWh of renewable electricity could be integrated in addition which leads to cost reduction effects as well as a decrease of carbon dioxide emissions.