Integration Of Renewable Energy Sources Research Articles

As climate change accelerates, nearly zero-energy buildings (NZEBs) are emerging as a cornerstone of sustainable development. Rising global temperatures and shifting weather patterns necessitate innovative approaches to balancing heating and cooling demands while maximizing the integration of renewable energy sources. The study explores the future of NZEBs in the context of a warming climate, assessing the impact of global and regional climate change on building energy needs and adaptation strategies. It highlights cutting-edge technologies such as dynamic insulation, smart building envelopes, advanced energy management systems, and integrated HVAC solutions. Special emphasis is placed on modern passive cooling techniques, energy storage innovations, and the utilization of local renewable energy sources, including photovoltaics and geothermal systems. Additionally, the study examines the evolution of energy policies and building standards that facilitate NZEB development in the face of rising temperatures. Key challenges, including the increasing demand for cooling, the urban heat island effect, and the role of smart building management systems (BEMS) in optimizing energy performance, are critically analyzed Findings indicate that a holistic approach to the design and operation of NZEBs enhances energy efficiency while maintaining indoor comfort under changing climatic conditions.

The integration of renewable energy sources in micro-grids introduces significant operational challenges due to their intermittent nature and the mismatch between generation and demand patterns. Effective demand response strategies are crucial for maintaining system stability and economic efficiency, particularly in micro-grids with high renewable penetration. The aim of the work is prediction of load demand for micro-grid using genetic algorithm, however, the objectives are to model and implement a demand response system of a 4kva 24v smart solar system using Genetic Algorithm, optimize energy consumption and maximize revenue. In addition, to evaluate the performance of the Demand Response System and quantify its impact on energy savings and minimize total cost. This paper presents a comprehensive (genetic algorithm) model for optimizing operations in a micro-grid with solar generation and (battery energy storage systems). The model incorporates load classification, dynamic price threshold, and multi-period coordination for optimal event scheduling. Analysis across four distinct operational scenarios demonstrates consistent peak load reduction of 68.6% while achieving energy cost savings ranging from 4.5% to 11.3%. The highest performance was observed in scenarios with high demand, where the model achieved 11.3% energy cost reduction through optimal coordination of renewable resources and actions. The results validate the model’s effectiveness in managing diverse operational challenges while maintaining system stability and economic efficiency. The research therefore recommends that not only should this proposed method (genetic algorithm) be included in the curriculum for higher programs, but also applied when solving modelling and optimization of demand response for micro-grids problems. Doing this will assist research students in accomplishing desired result(s), eliminate rigorous calculation processes and obtain optimally converging solutions. The financial evaluation should be enhanced by including certain factors such as potential loss of heat during generation, reduced maintenance on the operating device and the deferred replacement of machine components.

The present study focuses on wind power forecasting (WPF) models based on deep neural networks (DNNs), aiming to evaluate current approaches, identify gaps, and provide insights into their importance for the integration of Renewable Energy Sources (RESs). The systematic review was conducted following the methodology of Kitchenham and Charters, including peer-reviewed articles from 2020 to 2024 that focused on WPF using deep learning (DL) techniques. Searches were conducted in the ACM Digital Library, IEEE Xplore, ScienceDirect, Springer Link, and Wiley Online Library, with the last search updated in April 2024. After the first phase of screening and then filtering using inclusion and exclusion criteria, risk of bias was assessed using a Likert-scale evaluation of methodological quality, validity, and reporting. Data extraction was performed for 120 studies. The synthesis established that the state of the art is dominated by hybrid architectures (e.g., CNN-LSTM) integrated with signal decomposition techniques like VMD and optimization algorithms such as GWO and PSO, demonstrating high predictive accuracy for short-term horizons. Despite these advancements, limitations include the variability in datasets, the heterogeneity of model architectures, and a lack of standardization in performance metrics, which complicate direct comparisons across studies. Overall, WPF models based on DNNs demonstrate substantial promise for renewable energy integration, though future work should prioritize standardization and reproducibility. This review received no external funding and was not prospectively registered.

ABSTRACT The increasing integration of renewable energy sources (RESs), such as wind turbines and photovoltaic (PV) arrays, introduces low-inertia characteristics that negatively affect the dynamic stability of modern power systems. To address this challenge, this study proposes an Adaptive Neuro-Fuzzy Damping Controller (ANFDC) based on a high-voltage direct current (HVDC) link to enhance system damping and stability. The controller employs a fuzzy linguistic rule to tune its parameters, converting dynamic input signals into linguistic variables during an offline training phase. A hybrid microgrid system combining offshore and onshore wind turbines, PV units, and small-scale synchronous generators (SSSGs) is used to train and validate the controller. During real-time operation, the ANFDC adaptively adjusts control signals to mitigate oscillations without relying on an explicit system model. The proposed method effectively integrates the strengths of neural networks and fuzzy logic to achieve fast, robust, and adaptive damping control. Simulation results on a grid-connected hybrid microgrid under various short-circuit faults demonstrate that the ANFDC significantly improves damping performance and maintains high stability margins compared with conventional control approaches. INDEX TERMS: Lowinertia resources (LIRs), high voltage direct current (HVDC), microgrid, wind turbines (WT), photovoltaic arrays (PV), small-scale synchronous generators (SSSGs), adaptive neuro-fuzzy-based damping controller (ANFDC).

The integration of renewable energy sources (RES) into power systems requires sophisticated control strategies to ensure stable operation. This study presents a comprehensive framework that combines Machine Learning (ML) techniques—specifically Artificial Neural Networks (ANNs) and Reinforcement Learning (RL)—with traditional Proportional-Integral (PI) controllers to enhance microgrid control performance. Traditional PI controllers, while essential for microgrid operation with RES technologies such as solar and wind systems, face challenges in parameter tuning. Suboptimal selection of proportional gain left({K}_{p}right) and integral gain left({K}_{i}right) values can result in system instability or degraded performance. Our proposed ML-enhanced framework dynamically adjusts {K}_{p} based on real-time operational data and historical performance metrics, addressing these limitations. We evaluate three control strategies—traditional PI, ANN-based PI, and RL-based PI controllers—through extensive simulations of a microgrid with distributed energy resources (DERs). The RL-based controller demonstrates superior performance by reducing voltage Total Harmonic Distortion (THD) to 0.43%, compared to 16.99% for traditional PI control. The ANN-based controller achieves a THD of 0.58%, representing a 96.6% improvement over conventional methods. Both ML-enhanced approaches exceed IEEE 1547 requirements while improving settling time by 75% and frequency stability by 93%. These results validate the effectiveness of ML and deep learning techniques in enhancing microgrid stability and reliability, providing practical solutions for advanced RES management in modern power systems.

In the face of increasing Polish and EU requirements for climate neutrality, the role of digital technologies in the design and operation of buildings is gaining strategic importance. This paper presents the integrated use of Building Information Modelling (BIM) and the Digital Twin concept in the context of selecting, modelling, and optimizing renewable energy sources (RES) in architectural structures. The analysis covers: (1) key tools and data exchange formats, (2) capabilities for dynamic simulations of energy consumption and production, and (3) the impact of digital twins on predictive management of PV installations, heat pumps, and micro wind turbines. A case study demonstrates the potential for a 32% reduction in CO2 emissions over five years through the integration of BIM and Digital Twin. The results indicate that the synergistic use of both technologies reduces the time required for RES scenario analysis by half and increases the accuracy of energy production forecasts by 15%. The paper concludes with recommendations for designers, investors, and researchers, emphasizing the need for tool interoperability and standardization of energy data.

This study presents a scenario analysis of Kazakhstan’s electricity market using the PyPSA-KZ model, with a focus on the integration of renewable energy sources (RES). As Kazakhstan transitions towards a low-carbon economy, this study evaluates the technical and economic implications of increasing RES penetration under various scenarios, ranging from 10% to 60% RES shares, with projections targeted for the year 2030. The study simulates system behavior across scenarios and analyzes key indicators, including total system cost, electricity tariff, generation mix, thermal ramping, and CO2 emissions. Results indicate that up to 30% RES integration is feasible without significant structural changes, delivering reduced system costs and emissions. However, scenarios beyond 30% reveal growing flexibility challenges, necessitating investment in grid modernization, energy storage, and flexible backup capacity. The model outcomes are benchmarked against the International Energy Agency’s 2030 carbon neutrality scenarios and show strong alignment, particularly at 45% RES share. Comparative insights are also drawn from international experiences in Denmark and China. This research demonstrates that the PyPSA-KZ model is a powerful tool for planning Kazakhstan’s energy transition and offers data-driven recommendations to support national energy security and climate goals.

In the context of harmonizing environmental protection and pollution control requirements, conventional power grids necessitate increased energy and resource consumption, which imposes higher demands on energy utilization. Consequently, flexible interconnection technologies for grid integration of new energy systems have become increasingly imperative. However, in the flexible interconnection new energy grid-connection structure system, the local control of the flexible interconnection device relies on rapid response logic, and the coordination between new energy and energy storage systems focuses on smoothing output fluctuations. Combined with long-term output prediction and optimized dispatching strategies, the power of new energy can be more stably integrated into the power grid. This paper conducts a comprehensive analysis of flexible interconnected power control strategies, categorizing application scenarios for hybrid distributed and centralized renewable energy systems. It adopts a hierarchical power allocation framework and an energy management strategy centered on optimizing energy utilization. While prioritizing the consumption of renewable energy generation and ensuring system stability, the study elucidates the synergistic logic among various components. This provides a practical approach for the efficient grid integration of diverse renewable energy sources, offering substantial significance for enhancing system stability and renewable energy utilization efficiency.

Efficient fault detection in modern hybrid power transmission systems is crucial due to increasing integration of renewable energy sources and the complexity of long-distance transmission networks. Identifying the faulty phase and terminal rapidly ensures reliable protection and improved power quality. This paper proposes a wavelet-based algorithm using Bior1.5 multi-resolution analysis is used to extract energy coefficients (CD) from post-fault current signals. The discrimination between faulty and healthy phases is achieved by comparing CD values against a threshold derived from simulation data. The method is validated on a 4-bus transmission system under both conventional and hybrid configurations, considering different fault impedances (50 Ω, 250 Ω), locations (4 km, 40 km), and fault inception angles (120°, 340°). Results demonstrate accurate fault phase and terminal identification within 25 ms for symmetrical and unsymmetrical faults, with improved accuracy compared to existing methods. The proposed approach offers robust and fast protection suitable for diverse operating conditions in hybrid power systems

Generation expansion planning is critical for the sustainable development of power systems, particularly with the increasing integration of renewable energy sources like wind power. This paper presents an innovative generation expansion model identifying the optimal strategy for constructing new wind power plants. The model determines the ideal size of wind power generation and strategically allocates wind resources across multi-area power systems to maximize their capacity credit. A novel fuzzy set approach addresses wind power’s inherent uncertainty and variability, which models wind data uncertainty through membership functions for each stochastic parameter. This method enhances the accuracy of capacity credit calculations by effectively capturing the unpredictable nature of wind power. The model uses the Effective Load Carrying Capability (ELCC) as the objective function to measure the additional load that can be reliably supported by wind generation. Additionally, integrating a compressed-air energy storage system (CAESS) is introduced as a novel solution to mitigate the intermittency of wind power, further boosting the wind power plants’ capacity credit. By incorporating an energy storage system (ESS), the model ensures greater resource availability and flexibility. The study evaluates a multi-area power network, where each area has distinct conventional generation capacity, reliability metrics, load profiles, and wind data. A three-interconnected power system case study demonstrates the model’s effectiveness in increasing the load carrying capability of intermittent renewable resources, improving system reliability, and enhancing resilience. This study provides new insights into optimizing renewable energy integration by leveraging advanced uncertainty modeling and energy storage, contributing to the long-term sustainability of power systems.

Induction cooking technology offers enhanced energy efficiency and environmental benefits over traditional methods. However, its widespread adoption faces challenges such as power requirements, material selection, and integration with renewable energy sources. This systematic mapping study analyzes the current state of induction cooking technology, identifies key challenges and trends, and provides insights for future research and development. It conducted a systematic mapping study in five major databases to retrieve relevant studies published between 2013 and 2024. After applying inclusion and exclusion criteria, 58 primary sources focused on induction cooking systems, renewable energy integration, and related technologies were analyzed. Results show a positive trend in scientific publications related to induction cooking, with the half-bridge inverter and full-bridge topology being the most employed. Significant challenges include power requirements during prolonged use, topology, material selection, and integration of renewable energy sources. Emerging trends include the application of deep learning techniques, flexible induction stoves, and the use of gallium nitride technology. The review also highlights the need for standardized validation methodologies and material optimization for improved efficiency and user safety. This literature mapping provides a comprehensive overview of the current landscape of induction cooking technology and is a valuable resource for researchers, engineers, and policymakers.

The large-scale integration of renewable energy sources introduces significant uncertainty into modern power systems, posing new challenges for reliable and economical operation. Effective scheduling therefore requires accurate monitoring of uncertainty and efficient handling of nonlinear system dynamics. This paper proposes an optimization-based scheduling method that combines sensor-informed monitoring of photovoltaic (PV) uncertainty with advanced processing of nonlinear hydropower characteristics. A detailed hydropower model is incorporated into the framework to represent water balance, reservoir dynamics, and head–discharge–power relationships with improved accuracy. Nonlinear constraints and uncertainty are addressed through a unified approximation scheme that ensures computational tractability. Case studies on the modified IEEE −39 system show that the proposed method achieves effective multi-source coordination, reduces operating costs by up to 2.9%, and enhances renewable energy utilization across different uncertainty levels and PV penetration scenarios.

Global electric vehicle sales are growing exponentially, with the European Union actively promoting the adoption of electric vehicles to significantly reduce mobility-related emissions. Concurrently, research efforts are increasingly directed toward optimizing vehicle charging strategies for the effective integration of renewable energy sources. Nevertheless, despite extensive theoretical studies, few practical implementations have been carried out. In response, this paper presents a digital twin of a microgrid designed specifically for optimizing the charging schedules of an electric vehicle fleet, with the goal of maximizing photovoltaic self-consumption. Machine learning algorithms are utilized to forecast vehicle energy consumption, and various heuristic optimization methods are applied to determine optimal charging schedules. The system incorporates an interactive dashboard, enabling users to input specific preferences or delegate charging decisions to a real-time optimizer. Additionally, a user-centric decision support system was developed to provide recommendations on optimal vehicle connection timings and heat pump setpoints. Certain algorithms failed to converge on a feasible optimal solution, even after 340 s and over 500 generations, particularly within high-production scenarios. Conversely, using the GWO-WOA algorithm, optimal charging schedules are computed in less than 25 s, balancing photovoltaic power exports under varying weather conditions. Furthermore, K-Means was identified as the most effective clustering technique, achieving a Silhouette Score of up to 0.57 with four clusters. This configuration resulted in four distinct velocity ranges, within which energy consumption varied by up to 5.8 kWh/100 km, depending on the vehicle’s velocity. Finally, the facility managers positively assessed the usability of the DT dashboard and the effectiveness of the decision support system.

The widespread integration of renewable energy sources (RESs) into the grid through inertia-less power converters is reducing the overall system inertia leading to large frequency variations. To mitigate this issue, grid-forming (GFM) control strategies in bidirectional battery chargers have emerged as a promising solution, since the inertial response of synchronous generators (SGs) can be emulated by power converters. However, unlike SGs, which can withstand currents above their rated values, the output current of a power converter is limited to its nominal design value. Therefore, the estimation of the power delivered by the GFM power converter during frequency events, called Virtual Inertia (VI) support, is essential to prevent exceeding the rated current. This article analyzes the VI response of GFM power converters, classifying the dynamic behavior as underdamped, critically damped, or overdamped according to the selected inertia constant and damping coefficient, parameters of the GFM control strategy. Subsequently, the transient power response under step-shaped and ramp-shaped frequency deviations is quantified. The proposed analysis is validated using a 1.2 KW single-phase power converter. The simulation and experimental results confirm that the overdamped response under a ramp-shaped frequency event shows higher fidelity to the theorical model.

Abstract Rapid urbanization presents significant challenges for sustainable housing, often exposing the inadequacies of traditional planning methods. This study addresses this critical gap by proposing Model-Based Engineering (MBE) as a transformative solution. Employing a mixed-methods framework that integrates computational modeling (BIM, GIS), data analytics, and case studies such as NEOM, the research demonstrates that MBE can significantly enhance urban sustainability. Key findings indicate a 15% reduction in carbon emissions, a 20% decrease in energy consumption, and improved urban resilience under extreme conditions. These results surpass traditional approaches and highlight the integration of renewable energy sources and adaptive infrastructure. The study provides actionable strategies for policymakers and urban planners, presenting a replicable model that aligns with Saudi Arabia's Vision 2030 and supports sustainable urban development. Future research directions will focus on integrating AI for predictive modeling and exploring MBE’s applicability in diverse urban contexts worldwide. By harmonizing technology with sustainability, MBE emerges as a pivotal tool for creating resilient urban environments that ensure long-term environmental and economic benefits.

Abstract Electric vehicles (EVs) with managed charging and discharging schedules have the potential to reduce costs, enhance grid resilience, and facilitate integration of renewable energy sources. However, the heterogeneity of consumer travel patterns and the variability of renewable energy generation present significant challenges to existing control strategies, often resulting in issues such as the “curse of dimensionality.” This study proposes a mobility-aware deep reinforcement learning-based charging control strategy using the Deep Q-Network (DQN) algorithm to minimize charging costs and maximize photovoltaic (PV) energy utilization. Leveraging real-time electricity prices, real-world EV travel data, and actual PV generation profiles, the proposed framework achieves low charging costs, high solar energy utilization, and reduced carbon emissions—approaching the performance of an ideal offline optimization algorithm with perfect foresight, and substantially outperforming baseline strategies such as random charging, Charge-As-Soon-As-Possible (CASAP), and greedy charging. Specifically, the RL-based approach reduces charging costs by 55% and lowers carbon emission by 11.6% compared to random charging, and achieves a PV utilization rate of 95%. Furthermore, the value of information regarding EV’s travel time and the building’s electricity demand is 2.4CNY/vehicle/day and $0.7/vehicle/day, respectively, underscoring the importance of addressing uncertainty in EV charging management. These findings demonstrate the feasibility and effectiveness of reinforcement learning in optimizing EV operations within integrated vehicle-grid-building-PV systems.

Abstract This paper presents a novel IGBT power module design that integrates phase change material (PCM) above the chips, coupled with an optimized metal frame, to enhance overcurrent (OC) capability during Low Voltage Ride-Through (LVRT) events. Transient thermal simulations using ANSYS Fluent were conducted to evaluate the module's performance under varying OC scenarios, with mesh independence verification and simulation validation performed to ensure accuracy. The results demonstrate that the proposed IGBT module design significantly outperforms existing methods by offering faster thermal response and substantially reducing the steady-state operating temperature. When initially operating at 90% of the rated current, it can sustain current of 1.5 p.u. for 7.05 seconds, 2.0 p.u. for 1.91 seconds, and 3.0 p.u. for 0.37 seconds–over eight times longer than the original commercial module's withstand time. The integration of PCM absorbs the majority of the upward-transferred thermal power, resulting in a 5°C to 7°C reduction in junction temperature compared to a solid copper block. The study also identifies the optimal PCM container height for different OC levels and highlights the latent heat of the PCM as a key factor in enhancing thermal management. The proposed design effectively enables IGBT modules to provide multiple times their rated current for reactive current injection during LVRT without compromising reliability, contributing to improved grid stability and supporting the increasing integration of renewable energy sources.

Accurate electricity load forecasting is crucial for power system planning, reliability, and sustainability, enabling more efficient markets and reduced greenhouse gas emissions. This study leverages deep learning algorithms, specifically bidirectional recurrent neural networks, to develop a unified model for predicting one day-ahead electricity demand for the entire year of 2023. The model's performance was evaluated on a monthly basis, allowing for a detailed assessment of its forecasting capabilities across different time periods. Four neural network algorithms were compared: Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU. The GRU model demonstrated superior performance, achieving an R-squared value of 0.8526 in October and a Mean Absolute Percentage Error (MAPE) of 2.34% in March. These results highlight the potential of the proposed model as an effective tool for electricity demand forecasting, supporting the integration of renewable energy sources and enhancing grid resilience.

The article examines the institutional and legal evolution of the European Network of Transmission System Operators for Electricity (ENTSO-E) within the broader framework of the European Union’s climate policy and its strategic vision of a climate-neutral Europe by 2050. It explores how key legislative instruments – including Regulation (EU) 2019/943, Directive (EU) 2019/944, the network codes, and the Third Energy Package – have progressively transformed ENTSO-E from a primarily technical coordination platform into a central institutional actor in the EU’s green energy transition. Particular attention is paid to the legal framework that governs the integration of renewable energy sources into the grid, the phase-out of fossil fuel-based generation, the development of advanced balancing mechanisms, and the expansion of regional coordination centers (RCCs). These developments reflect the EU’s commitment to a decarbonized, interconnected, and resilient energy system. The article further examines the impact of the legal principle of unbundling, which aims to ensure non-discriminatory access to energy networks and promote competitive electricity markets. In addition, the study addresses the external and geopolitical dimension of ENTSO-E’s role, notably the emergency synchronization of Ukraine and Moldova with the Continental European electricity grid in March 2022. This synchronization is presented as both a technical and political milestone that strengthened the EU’s energy resilience and reinforced its strategic neighborhood policy. Ultimately, the article argues that ENTSO-E plays a pivotal legal and operational role in implementing the European Green Deal and advancing the EU’s dual objectives of climate neutrality and energy security. It concludes by emphasizing the need for deeper legal harmonization, accelerated deployment of interconnectors, and more effective multilevel coordination to fully realize the ambitions of a climate- neutral and integrated European energy market.

In hot, arid regions like Jacobabad, Pakistan, military barracks must be sustainable and energy-efficient. This study investigates architectural techniques to improve energy efficiency while maintaining the comfort of military soldiers. The research is guided by a systematic process that includes experimental design, climate assessment, case study analysis, and energy performance modelling. Case studies and climate analyses are crucial elements in detecting environmental issues like excessive heat and sun radiation. The Hourly Analysis Program (HAP) and Building Information Modeling (BIM) are used in energy performance modelling to assess the effects of ventilation, shading, insulation, and orientation. Materials, window locations, and shading strategies are evaluated using a design of experiments (DOE) framework. The integration of renewable energy sources, especially solar photovoltaic panels, and passive cooling techniques are given top priority in this study. Practicality is ensured by validation and optimization, which results in a framework for military accommodations that use less energy and are more sustainable in harsh environments. Keywords: Barrack; Design strategy; Energy efficient buildings; Sustainable buildings