Artificial intelligence applications for microgrids integration and management of hybrid renewable energy sources

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

The review found that the integration of renewable and sustainable energy sources with micro grids has gained significant attention in recent years. The studies reviewed highlighted the technical, economic, political, regulatory, social, geographic, technological, and environmental limitations associated with the integration of renewable energy sources into micro grids. Additionally, the review identified several research gaps, including the need for more research on the optimization of micro grids, the development of new technologies to improve energy storage, and the identification of best practices for electric Vehicle policy and regulation. The review study highlights the importance of continued research in the field of renewable and sustainable energy sources and their integration with micro grids. The integration of renewable energy sources with micro grids has the potential to significantly reduce carbon emissions and enhance energy security.

This paper provides a comprehensive review of hybrid energy systems (HESs), focusing on their challenges, optimization techniques, and control strategies to enhance performance, reliability, and sustainability across various applications, such as microgrids (MGs), commercial buildings, healthcare facilities, and cruise ships. The integration of renewable energy sources (RESs), including solar photovoltaics (PVs), with enabling technologies such as fuel cells (FCs), batteries (BTs), and energy storage systems (ESSs) plays a critical role in improving energy management, reducing emissions, and increasing economic viability. This review highlights advancements in multi-objective optimization techniques, real-time energy management, and sophisticated control strategies that have significantly contributed to reducing fuel consumption, operational costs, and environmental impact. However, key challenges remain, including the scalability of optimization techniques, sensitivity to system parameter variations, and limited incorporation of user behavior, grid dynamics, and life cycle carbon emissions. The review underlines the need for robust, adaptable control strategies capable of accommodating rapidly changing energy environments, as well as the importance of life cycle assessments to ensure the long-term sustainability of RES technologies. Future research directions emphasize the integration of variable RESs, advanced scheduling, and the application of emerging technologies such as artificial intelligence and blockchain to improve system resilience and efficiency. This paper introduces a novel classification framework, distinct from existing taxonomies, addressing gaps in prior reviews by incorporating emerging technologies and focusing on the dynamic nature of energy management in hybrid systems. It also advocates for bridging the gap between theoretical advancements and real-world implementation to promote the development of more sustainable and reliable HESs.

India is considering renewable energy resources (RES) like solar and wind as alternative for future energy needs. As on March 31, 2012 the grid interactive power generation from RES is 24914 MW i.e. around 12.1 % of the total installed energy capacity. Further Ministry of New and Renewable Energy (MNRE), Government of India is targeting to achieve 20000 MW grid interactive power through solar and 38500 MW from wind by 2022. However there are various issues related to grid integration of RES keeping in the view of aforesaid trends it becomes necessary to investigate the possible solutions for these issues. Integration of renewable energy sources to utility grid depends on the scale of power generation. Large scale power generations are connected to transmission systems where as small scale distributed power generation is connected to distribution systems. There are certain challenges in the integration of both types of systems directly. This paper presents the some issues and challenges encountered during grid integration of different renewable energy sources with some possible solutions.

Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach

In this chapter, new trends in power-electronic technology for the integration of renewable energy sources like wind/photovoltaic and energy-storage systems are presented along with the current technology and future trends in variable-speed wind turbines. Also, the research trends in energy-storage systems used for the grid integration of intermittent renewable energy sources are discussed in detail.

The integration of renewable energy sources (RESs) is a strategic goal in Saudi Arabia. The energy source diversification plan comprises the penetration of various technologies, including solar photovoltaic (PV) and wind energy. In this research, an optimal microgrid system design is proposed and analyzed at the Islamic University of Madinah. The research intends to facilitate the decision-making process in the incorporation of RESs in Saudi universities. A pilot project has been established at the Faculty of Engineering and the measured load profile has been incorporated. Three alternatives are investigated, and their technical and economic performance is determined (i.e., PV system, wind system, and hybrid system). To enhance the accuracy of the simulated models, on-the-ground weather data have been utilized to formulate a typical meteorological year profile. The results demonstrate that a PV system of 1.5 MW installed capacity can cover up to 3.03% of the university’s annual electrical consumption, with a levelized cost of energy (LCOE) of 0.051 USD/kWh. The PV alternative can generate annual energy of 2.68 GWh with a capacity factor of 20.2% and a simple payback period of 18.6 years. The wind energy system has a capacity factor of 1.1 MW and yields a higher ratio of energy production to installed capacity, owing to a higher capacity factor at 29.5%, and annual energy of 2.71 GWh. However, due to the higher initial cost and insufficiency of wind resources at the proposed location, this wind energy alternative results in higher LCOE at 0.064 USD/kWh and a simple payback period of 23.6 years. The hybrid alternative facilitates the integration of diverse RESs. It has a capacity factor of 1.37 MW, leading to an annual generation of 3.27 GWh and a renewable fraction of 3.7%. The LCOE of the hybrid option is determined to be 0.061 USD/kWh and the simple payback period at 20.7 years. All alternatives help in the reduction of carbon dioxide (CO2), sulfur dioxide (SO2), and nitric oxide (NOx) between 0.11 million kg and 54.6 million kg annually. Each of the systems can provide opportunities at the technical, economic, and environmental levels. The implications of this research facilitate Saudi universities in supporting the integration of RESs, considering the strategic goals of Saudi Arabia.

Optimized controller for renewable energy sources integration into microgrid: Functions, constraints and suggestions

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

SummaryIn this paper, an innovative switching scheme named simultaneous space vector modulation (SSVM) is proposed for integrating various AC sources in the energy industry using a unified multiport converter. The proposed SSVM technique is applied to the multileg topology of a multiport converter, which is an encouraging option for the grid integration of renewable energy sources and multimachine drives. Considering the shared leg in the multileg converter, the proposed SSVM can utilize the utmost simultaneous switching states between different ports, resulting in lower switching loss and better DC‐link voltage utilization compared with the conventional sequential space vector modulation approach. A novel decision matrix concept is introduced to identify the simultaneous switching states. For this aim, according to the number of ports of the multileg converter, decision matrices containing valid simultaneous switching states are first calculated. Then, they are defined as look‐up tables in the proposed SSVM to be retrieved and exploited in every sampling period. The effectiveness of the proposed SSVM for a seven‐leg version of the multileg converter is assessed using the simulation analysis and real‐time validation. The capability of the proposed SSVM‐based multiport converter in grid integration of AC renewable energy sources is also verified considering two permanent magnet synchronous generator (PMSG)‐based wind turbines with real wind speed patterns. The simulation results confirm that the proposed SSVM is properly able to manage the power flow between different ports and improve the DC voltage utilization and switching loss compared with the sequential SVM.

The potential benefits of employing dynamic line rating (DLR) for the grid integration of variable renewable energy sources (RES), such as wind and photovoltaic (PV) units, are presented and analyzed. Unlike nominal line rating (NLR), DLR takes advantage of the fact that the physical power transmission capacity of overhead lines is a function of ambient conditions (temperature, wind speed, wind angle and solar insolation). DLR is hence often less conservative than NLR, which assumes more challenging ambient conditions. A simulation study has been performed on a functionally modeled six-node benchmark power system loosely based on the German power system. Simulations with high time resolution were accomplished using a predictive power dispatch scheme that directly incorporates line constraint information. Historic load demand, wind and PV in-feed profiles, as well as scaled-up profiles for high RES scenarios are used. Dispatch impacts of line constraints derived via DLR and NLR are compared and their effect on RES grid integration is assessed.

Indeed, the rapid expansion of Renewable Energy Sources (RES) in recent years has brought about numerous benefits, including reduced carbon emissions, increased energy independence, and the creation of new economic opportunities. However, integrating these variable and intermittent sources into existing power systems poses several challenges for power system management. Faults in electrical networks are among the key factors and sources of network disturbances. Control and automation strategies are among the key fault clearing techniques responsible for the safe operation of the system. In recent years, the increasing penetration of wind energy in multi-machine power systems has posed unique challenges to power grid stability and reliability. Accurate assessment methodologies are required to ensure the effective integration of wind energy sources while maintaining grid stability. Several researchers have revealed various constraints of control and automation strategies such as a slow dynamic response, the inability to switch the network on and off remotely, a high fault clearing time and loss minimization. It's important to note that the impact of wind energy on system inertia is a complex and dynamic aspect of power system operation. Ongoing research and technological advancements aim to improve the integration of wind and other renewable energy sources while ensuring grid stability and reliability. As the energy transition continues, addressing these technical challenges is crucial for building a sustainable and resilient power system. In this paper, the influence of doubly-fed induction generator (DFIG) penetration is analyzed to examine the transient stability of power system networks. The concept of a Coupling Strength Index (CSI) derived from Network Structural Characteristics Theory sounds intriguing, especially in the context of identifying critical elements susceptible to the impact of a three-phase fault in a network. The investigation involves studying the transient stability of a power system under different conditions, specifically with and without doubly-fed induction generators (DFIGs) connected to a weak bus. Additionally, a three-phase fault is applied at the middle of the identified weakest line for both the IEEE 9 and 39 bus systems. The investigation of generator speed, rotor angle, and electric power during transient stability analysis provides a holistic view of how a power system responds to disturbances. This information is crucial for ensuring the reliability and stability of the system, especially when studying the integration of renewable energy sources and addressing potential challenges associated with faults and weak buses. This paper presents a pioneering non-iterative framework for dynamically assessing wind energy dominated multi-machine power systems. The proposed framework aims to address the shortcomings of traditional iterative methods, providing a more efficient and reliable approach to power system analysis.

Intelligent systems (Smart Systems) are an important element of modern electric power industry, which allows to increase the reliability, efficiency and stability of the functioning of electric networks. The article considers the implementation of intelligent technologies for monitoring, management and optimization of the operation of energy systems. The main attention is paid to the use of artificial intelligence (AI), Internet of Things (IoT) and automated control systems to solve the problems of accident prediction, adaptive management of energy flows and integration of renewable energy sources (RES). The results of the study confirm the effectiveness of intelligent systems in real conditions. In particular, the use of IoT sensors to monitor the condition of equipment allowed to significantly reduce the number of emergency situations by predicting possible failures. Artificial intelligence algorithms provided adaptive management of energy flows, which allowed to reduce energy losses by 15% and ensure the stability of the networks even under variable load conditions. The integration of renewable energy sources, such as solar and wind power plants, became possible thanks to the implementation of intelligent algorithms that take into account the variability of generation parameters and ensure a balance between energy production and consumption. The article also discusses mathematical modeling methods and MATLAB/Simulink software to assess the impact of intelligent systems on the operation of power grids. The results demonstrate that the implementation of intelligent systems allows not only to increase the reliability of power systems, but also to reduce maintenance costs and optimize resource use. The study showed that the key advantages of intelligent systems are their ability to adapt to unstable energy markets, rapid response to emergencies, and integration of new technologies. The article presents prospects for further research, including the development of more complex algorithms for integrating renewable energy sources and improving distributed energy management systems. Thus, the results of the work confirm that intelligent systems are a powerful tool for the modernization of power grids, contribute to the sustainable development of the energy sector, and ensure the efficient use of energy resources for the integration of renewable energy sources.

The paper presents a fault tolerant scheme for integrating renewable energy sources to HVDC system using a new modular resonant DC-DC converter. The proposed DC-DC converter consists of many identical low voltage submodules which consist of two controllable switches, one diode and a capacitor. The switches of the proposed DC-DC converter are configured to provide the converter an inherent fault tolerant feature which enables it to isolate itself during DC faults in the HVDC system. Zero current switching (ZCS) is also achieved for all the switches used in the converter. The paper focusses on the integration of renewable energy sources to HVDC transmission system using the proposed tapping scheme. The performance of the proposed scheme to evacuate power from renewable energy sources to HVDC transmission system has been demonstrated using simulation studies done in PSCAD/EMTDC. The performance of the proposed DC-DC converter is also verified with experimental results.

The integration of renewable energy sources and the increasing demand for reliable power have posed significant challenges in the design and operation of distribution networks under uncertain conditions. The inherent variability in renewable energy generation and fluctuating consumer load demand requires advanced strategies for Distributed Energy Resources (DERs) allocation and sizing to enhance grid resilience and operational efficiency. This article introduces an innovative framework for optimizing distribution network design under these uncertainties. The approach integrates deep learning‐assisted Distributionally Robust Optimization (DRO) with Generative Adversarial Networks (GANs) to dynamically model and manage the inherent variability in renewable sources and demand fluctuations. Employing a combination of nonlinear optimization techniques and advanced statistical methods, the framework robustly optimizes network configurations to minimize losses and improve voltage stability. The model's efficacy is rigorously tested on the IEEE 33‐bus system, achieving a 15% reduction in power distribution losses and a 20% improvement in voltage stability compared to traditional models. Utilizing open‐source computational tools, the method not only boosts operational reliability and efficiency but also adapts effectively to the increasing integration of volatile renewable energy sources. These results underscore the framework's potential as a scalable and robust solution for modern power network design challenges.