Methodology for Assessing the Technical Potential of Solar Energy Based on Artificial Intelligence Technologies and Simulation-Modeling Tools

The use of renewable energy sources is becoming increasingly widespread around the world due to various factors, the most relevant of which is the high environmental friendliness of these types of energy resources. However, the large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different generation methods, each of which has its own specific features, and as a result, the data collection and processing necessary to optimize the operation of such energy systems become more relevant. Development of new technologies for the more optimal use of RES is one of the main tasks of modern research in the field of energy, where an important place is assigned to the use of technologies based on artificial intelligence, allowing researchers to significantly increase the efficiency of the use of all types of RES within energy systems. This paper proposes to consider the methodology of application of modern approaches to the assessment of the amount of energy obtained from renewable energy sources based on artificial intelligence technologies, approaches used for data processing and for optimization of the control processes for operating energy systems with the integration of renewable energy sources. The relevance of the work lies in the formation of a general approach applied to the evaluation of renewable energy sources such as solar and wind energy based on the use of artificial intelligence technologies. As a verification of the approach considered by the authors, a number of models for predicting the amount of solar power generation using photovoltaic panels have been implemented, for which modern machine-learning methods have been used. As a result of testing for quality and accuracy, the best results were obtained using a hybrid forecasting model, which combines the joint use of a random forest model applied at the stage of the normalization of the input data, exponential smoothing model, and LSTM model.

Analysis of nuclear-renewable hybrid energy system for marine ships

Electric vehicles (EVs), as facilitators of grid stability and flexibility, provide a critical solution to the energy infrastructure's evolving demands, underscored by the growing integration of renewable energy sources (RES) and the rapid increase in EV adoption worldwide. This trend is particularly evident in Europe which is experiencing dramatic increases in both the adoption of RES and EVs. Vehicle‐to‐grid (V2G) technology allows EVs to operate as a two‐way power flow to both draw and feed electricity into the grid. This multidisciplinary overview examines the role of V2G systems in enhancing grid performance, identifying corporate vehicle fleets as key flexibility providers, and integration with Smart Grid technologies as a key element for successful V2G implementation. In a scoping analysis of recent literature (2005–2024), we identify challenges such as privacy, security, and regulatory compliance as well as a critical gap in establishing economically sustainable models for aggregators, distribution system operators (DSOs), generation companies (GENCOs), and end‐users. Drawing from these insights, we then discuss the necessity for future research to develop models that ensure equitable benefits across stakeholders and the importance of models that can adapt to country‐specific mechanisms. The findings from our overview argue that the integration of EVs, V2G, and RES are essential components for developing future energy systems that are resilient, adaptable, decarbonized, and sustainable.This article is categorized under: Cities and Transportation > Electric Mobility Energy and Power Systems > Energy Infrastructure Energy and Power Systems > Energy Management

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.

To support the utilization of renewable energies, an optimized operation of energy systems is important. Often, the use of battery energy storage systems is stated as one of the most important measures to support the integration of intermittent renewable energy sources into the energy system. Additionally, the complexity of the energy system with its many interdependent entities as well as the economic efficiency call for an elaborate dimensioning and control of these storage systems. In this paper, we present an approach that combines the forward-looking nature of optimization and prediction with the feedback control of closed-loop controllers. An evolutionary algorithm is used to determine the parameters for a closed-loop controller that controls the charging and discharging control strategy of a battery in a smart building. The simulation and evaluation of a smart residential building scenario demonstrates the ability to improve the operation and control of a battery energy storage system. The optimization of the control strategy allows for the optimization with respect to variable tariffs while being conducive for the integration of renewable energy sources into the energy system.

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

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.

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 rapid integration of renewable energy sources and the increasing complexity of energy demands necessitate advanced strategies for optimizing multi-region energy systems. This study investigates the coordinated energy management of interconnected parks by incorporating wind power, demand response (DR) mechanisms, and energy storage systems. A comprehensive optimization framework is developed to enhance energy sharing among parks, leveraging demand-side flexibility and renewable energy integration. Simulation results demonstrate that the proposed approach significantly improves system efficiency by balancing supply-demand mismatches and reducing reliance on external power sources. Compared to conventional methods, the DR capabilities of industrial and commercial loads have increased by 8.08% and 6.69%, respectively, which is primarily due to enhanced utilization of wind power and optimized storage deployment. The inclusion of DR contributed to improved system flexibility, enabling a more resilient energy exchange framework. This study highlights the potential of collaborative energy management in multi-area systems and provides a pathway for future research to explore advanced control algorithms and the integration of additional renewable energy sources.

Ocean-going ships are one of the primary sources of Greenhouse Gas (GHG) emissions. Several actions are being taken to reduce the GHG emissions from maritime vessels, and integration of Renewable Energy Sources (RESs) is one of them. Ocean-going marine ships need a large amount of reliable energy to support the propulsive load. Intermittency is one of the drawbacks of RESs, and penetration of RESs in maritime vessels is limited by the cargo carrying capacity and usable area of that ship. Other types of reliable energy sources need to be incorporated in ships to overcome these shortcomings of RESs. Some researchers proposed to integrate fossil fuel-based generators like diesel generators and renewable energy in marine vessels to reduce GHG emissions. As the penetration of RESs in marine ships is limited, fossil fuel-based generators provide most of the energy. Therefore, renewable and fossil fuel-based hybrid energy systems in maritime vessels can not reduce GHG emissions to the desired level. Fossil fuel-based generators need to be replaced by emissions-free energy sources to make marine ships free from emissions. Nuclear energy is emissions-free energy, and small-scale nuclear reactors like Microreactors (MRs) are competent to replace fossil fuel-based generators. In this paper, the technical, environmental, and economic competitiveness of Nuclear-Renewable Hybrid Energy Systems (N-R HES) in marine ships are assessed. The lifecycle cost of MR, reliability of the proposed system, and limitations of integrating renewable energy in maritime vessels are considered in this study. The proposed N-R HES is compared with three different energy systems, namely ‘Standalone Fossil Fuel-based Energy Systems’, ‘Renewable and Fossil Fuel-based Hybrid Energy Systems’, and ‘Standalone Nuclear Energy System’. The cost modeling of each energy system is carried out in MATLAB simulator. Each energy system is optimized by using the Differential Evolution Algorithm (DEA), an artificial intelligence algorithm, to find out the optimal configuration of the system components in terms of Net Present Cost (NPC). The results determine that N-R HES has the lowest NPC compared to the other three energy systems. The performance of the DE algorithm is compared with another widely accepted artificial intelligence optimization technique called ‘Particle Swarm Optimization (PSO)’ to validate the findings of the DE algorithm. The impact of control parameters in the DE algorithm is assessed by employing the Adaptive Differential Evolution (ADE) algorithm. A sensitivity analysis is carried out to assess the impact of different system parameters on this study’s findings.

On the road to 100% renewable energy systems in isolated islands

Renewable energy sources (RES) are becoming more attractive due to the global demand for the utilization of clean energy and their easy accessibility. The integration of renewable energy technologies into power systems has been made easy, such that RES can be incorporated into small distribution systems or power grids. This integration of RES has negative impacts on power quality, system reliability, and network security. This could affect the RES performance and causes power quality-related problems such as harmonics, voltage flickers, swell, and sag. This paper presents a generation and transmission expansion planning model with the integration of large-scale RES considering harmonic emissions constraints. This method uses a weighted sum approach to combine the multi-objective optimization problems, thereby minimizing the total costs, active power loss, and harmonic power loss. An analytical technique was developed to estimate and quantify the harmonic emissions from the RES components. The proposed AC mixed-integer non-linear optimization problem was solved using algebraic modeling language. The proposed model is demonstrated on non-distorted Garver’s test bus system and 48-bus of Nigeria’s power system. The results obtained from the sensitivity analysis can be used as a decision-making tool to determine the best approach to minimize harmonic emissions from RES integration into the grid.

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.

During the last decades, an increasing number of studies have focused their attention on the development of energy system models in order to facilitate sustainable energy planning strategies and understand the technical challenges associated with the integration of renewable energy sources. However, these models usually require detailed and large amount of data as inputs. The data presented in this article provides key inputs and modelling assumptions adopted in the research paper titled “Large scale integration of renewable energy sources (RES) in the future Colombian energy system” [1]. These datasets can be used by researchers and policymakers in order to analyse different pathways oriented to the development of low carbon strategies for Colombia and countries with similar energy systems.

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