Electricity demand forecasting is essential for utilities. For the consumer, predictability of demand is vital for efficient operation, installation, sizing and maintenance planning. Hospitals, which are among the institutions with high-energy consumption, provide uninterrupted service 24 h a day, 7 days a week. Every hospital building is unique, and many do not conform to a typical shape or floor plan. Depending on the services provided, each hospital can differ significantly in terms of energy demand. Therefore, demand forecasting is one of the most complex elements of hospital construction. Although there are many studies on energy optimization related to hospital buildings in the literature, there is a knowledge gap regarding the maximum power estimation of hospitals. In this study, the annual electrical energy use of 23 public hospitals with over 100 beds in Istanbul is measured, and after determining the monthly peak loads, two new forecasting models are generated using regression techniques for maximum demand forecasting. It is determined that the design criteria used in power calculations in hospitals was very high. A positive result was obtained from the linear regression technique, which is one of the basic regression techniques, and it was shown that the maximum power needs of the hospital can be estimated with great confidence by determining a new design factor in the light of the determined values. This study allows designers to set maximum demands and select transformer and generator sizes with a single formula.

Estimation of the power obtained from intermittent renewable energy sources (IRESs) is an important issue for the integration of these power plants into the power system. In this study, the expected power not served (EPNS) formula, a reliability criterion for power systems, is developed with a new method that takes into consideration the power generated from IRESs and the consumed power (CP) estimation errors. In the proposed method, CP, generated wind power (GWP), and generated solar power (GSP) predictions made with machine learning methods are included in the EPNS formulation. The most accurate prediction results were obtained with the Multi Layer Perceptron (MLP), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) algorithms used for prediction, and these results were compared. Using different forecasting methods, the relation between forecast accuracy, reserve requirement, and total cost was examined. Reliability, smart reserve planning (SRP), and total cost analysis for power systems were carried out with the CNN algorithm, which provides the most successful prediction result among the prediction algorithms used. The effect of increasing the limit EPNS value allowed by the power system operator, that is, reducing the system reliability, on the reserve requirement and total cost has been revealed. This study provides a useful proposal for the integration of IRESs, such as solar and wind power plants, into power systems.

Son zamanlarda, ülkedeki yüksek güneş enerji potansiyeli, ilerleyen ve gelişen fotovoltaik (PV) teknolojiler sayesinde, güneş enerjisi en cazip yenilenebilir enerji kaynağı olmaktadır. Her bir teknolojinin, verimde, fiyatta ve kullanılan malzemede yarattığı farklılıklardan dolayı çeşitli PV üreten sistemler vardır. Bu çalışmanın amacı Bursa’nın Osmangazi ilçesinde 1 MW şebekeye bağlı, mono-kristal, poli-kristal ve ince film üç fotovoltaik sistemin performans analizini sunmaktır. İlk olarak, fotovoltaik sistemin ayrıntılı gölge çalışması yapılarak, PV kurulumu için uygunluğu incelenmiştir. Gölge çalışması Google Sketchup ve PVsyst programları kullanılarak gerçekleştirilmiştir ve daha sonra üç PV teknolojisinin performansını simüle etmek, modellemek ve tahmin etmek için PVsyst ve PV*SOL programları kullanılmıştır. Performans değerlendirmesi için kullanılan parametreler dizi verimi, nihai verim, PV verimi ve performans oranı gibi faktörlerdir. Elde edilen performans analizi sonuçlarına göre, sırasıyla mono-kristal silikon, poli-kristal silikon ve ince film (CIS) sistemlerinin, yılda 1416 MWh 1202 MWh ve 1150 MWh elektrik üreteceği ön görülmüştür. Yıllık ortalama performans oranı, mono-kristal panel için %85, poli-kristal panel için %72.1 ve ince film için %70’dir. Sonuçlar, yıllık enerji üretimi poli-kristalden ve ince filmden daha yüksek olduğu için mono-kristalin performans oranı açısından daha iyi bir seçim olduğunu göstermektedir. Çalışmanın son aşamasında Türkıye’deki farklı iklim koşulları altında, mono-kristal fotovoltaik teknolojisi kullanılarak performans değerlendirilmesi yapılmıştır. Analizler sonucunda yüksek güneş radyasyon değerine sahip Antalya ili 1806 MWh PV enerji üretimiyle iyi performans gösterdiği anlaşılmıştır.

The increasing deployment of Renewable Energy Sources (RESs), particularly wind and solar power, plays a critical role in reducing carbon emissions and supporting sustainable energy transitions. However, the large-scale integration of RESs into smart grids introduces significant technical challenges related to frequency stability, voltage regulation, rotor angle stability, power quality, inertia reduction, harmonic distortion, reverse power flow, Sub-Synchronous Interactions (SSIs), and protection coordination. Although numerous review studies have examined renewable energy integration, most focus on high-level frameworks, bibliometric analyses, optimization techniques, or isolated applications of artificial intelligence (AI) while lacking a comprehensive synthesis that bridges AI-driven solutions with the physical dynamics, control mechanisms, and protection requirements of highly renewable power systems. To address this gap, this review provides a comprehensive technical assessment of wind generator topologies, solar inverter architectures, grid-forming and grid-following control strategies, virtual inertia and virtual Synchronous Generator (SG) technologies, adaptive load-frequency control, energy storage integration, protection coordination, and real-time stability enhancement techniques for high-RES smart grids. Furthermore, the review systematically examines the role of AI in frequency regulation, voltage control, harmonic mitigation, predictive operation, parameter optimization, and system resilience. Unlike previous reviews, this study integrates physical-layer perspectives by connecting AI-driven decision-making with practical grid control mechanisms, inverter dynamics, wide-area monitoring, microgrid operation, High Voltage Direct Current (HVDC) interconnections, EV/Vehicle-to-Grid (V2G) integration, and multi-resource energy management. The review identifies key research priorities, including the development of real-time AI-assisted frequency control, adaptive protection schemes for low-inertia systems, coordinated grid-forming inverter control, resilient autonomous grid operation, and scalable multi-energy management frameworks. The findings provide actionable guidance for researchers, utilities, policymakers, and industry stakeholders seeking to enhance stability, reliability, and operational flexibility in future smart grids with very highly renewable energy penetration.