Integration of Renewable Energy Sources to Achieve Sustainability and Resilience of Mines in Remote Areas

The increasing demand for sustainable energy solutions has driven the integration of multiple renewable energy sources to ensure reliable and efficient power generation. This research presents a hybrid energy system combining wind, solar photovoltaic (PV), biomass, and energy storage systems (ESS) to meet consumer demand while enhancing power quality. The proposed system leverages the complementary nature of these renewable sources, ensuring a stable and continuous energy supply. Wind and solar power serve as primary generation sources, with biomass providing a consistent backup, while ESS mitigates intermittency and supports grid stability. A comprehensive techno-economic analysis and system optimization have been conducted using HOMER Pro Software, evaluating the feasibility, cost-effectiveness, and environmental benefits of the hybrid system. The study considers various economic parameters, including net present cost (NPC), levelized cost of energy (LCOE), and return on investment (ROI), to determine the optimal system configuration. The integration of these renewable energy sources not only reduces dependency on fossil fuels but also improves voltage and frequency stability, ensuring high power quality for end users. The results demonstrate that the proposed hybrid system significantly enhances energy reliability, minimizes operational costs, and promotes sustainable energy utilization. This study provides valuable insights into the design and implementation of integrated renewable energy systems, contributing to the development of resilient and economically viable power solutions for future energy needs.

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.

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.

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.

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.

Designing standalone hybrid energy systems minimizing initial investment, life cycle cost and pollutant emission

Optimal design and sizing of renewable energies in microgrids based on financial considerations a case study of Biskra, Algeria

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.

<abstract> <p>This study proposes a centralized control system for an islanded multivariable minigrid to improve its performance, stability and resilience. The integration of renewable energy sources and distributed energy storage systems into microgrid networks is a growing trend, particularly in remote or islanded areas where centralized grid systems are not available. The proposed control system is designed to be implemented at two levels a high-level control system and a low-level control system. Hence, the high-level control system balances energy resources and demand, makes decisions for effective resource utilization and monitors energy transactions within the minigrid. Real-time data from various sources and advanced algorithms are used to optimize energy management and distribution enabling the integration of renewable energy sources and enhancing the resilience of the minigrid against power outages.</p> <p>Moreover, the low-level control system monitors energy parameters such as voltage, current, frequency and mechanical energy. The control system ensures these parameters remain within the specified range, maintaining system stability and ensuring efficient energy distribution. It also protects the minigrid against power outages improving system reliability and security. Finally, the proposed centralized control system offers a promising solution for improving the performance, stability and resilience of microgrid networks. The system provides real-time monitoring, efficient energy management and distribution, and the integration of renewable energy sources. These results have important implications for the development and deployment of microgrid networks in remote or islanded areas.</p> </abstract>

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

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.

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.

Evaluating a business model for vehicle-grid integration: Evidence from Germany

The increasing integration of IoT-enabled industrial power networks and renewable energy sources has created a critical demand for intelligent load forecasting and demand response optimization. Conventional forecasting models struggle to adapt to the dynamic nature of industrial energy consumption and the inherent variability of renewable generation. To address these challenges, this chapter explores the application of Neuro-Evolutionary Algorithms (NEAs) for real-time, adaptive load management. By combining the predictive capabilities of deep learning models with the optimization strength of evolutionary algorithms, NEA-driven frameworks enhance forecasting accuracy, optimize demand-side management, and ensure grid stability. The implementation of edge computing in industrial power systems further enables real-time data processing, reducing latency and enhancing responsiveness in energy distribution. The integration of cyber-physical systems (CPS) with AI-based forecasting mechanisms strengthens decision-making by dynamically adjusting to fluctuating industrial load patterns. Case studies demonstrate the effectiveness of NEA-driven load forecasting in improving energy efficiency, reducing operational costs, and optimizing the integration of renewable energy sources in industrial grids. The research highlights key challenges, including computational complexity, cybersecurity risks, and scalability constraints, while proposing future directions for the advancement of intelligent demand response strategies. By leveraging AI-driven optimization, industrial power networks can achieve enhanced sustainability, resilience, and cost-effective energy management.