This study focuses on the important issue of predicting electricity load for efficient energy management. To achieve this goal, different statistical methods were compared, and results over time were analyzed using various ratios and layers for training and testing. This study uses an artificial neural network (ANN) model with advanced prediction techniques such as genetic algorithms (GA) and adaptive neuro-fuzzy inference systems (ANFIS). This article stands out with a comprehensive compilation of many features and methodologies previously presented in other studies. This study uses a long-term pattern in the prediction process and achieves the lowest relative error values by using hourly divided annual data for testing and training. Data samples were applied to different algorithms, and we examined their effects on load predictions to understand the relationship between various factors and electrical load. This study shows that the ANN–GA model has good accuracy and low error rates for load predictions compared to other models, resulting in the best performance for our system.

In recent years, photovoltaic energy has become a popular alternative to traditional fossil fuels due to its renewable and sustainable nature, and the incentives provided by countries to encourage research and development in this field have accelerated the implementation of photovoltaic roofs. One important factor in maximizing the efficiency of solar panels is their orientation to harness maximum irradiation with minimum roof area. In this article, an optimizer-supported east–west-oriented solar power plant installation is experimentally presented to utilize more solar energy with less area compared to wide-area south orientation. Furthermore, an analysis of different angle placements is simulated by PVSOL and PVGIS to test the robustness of the presented approach, and the pros/cons of the features of the east–west and south orientation are discussed comparatively. The obtained results are promising for the ideal placement of solar systems along with the use of optimizers to further enhance their performance, and for the minimization of the economic return of such an investment.

Recently, harmful gases emitted into the atmosphere disrupt the balance of nature and cause climate changes. The tendency towards renewable energy is increasing in order to prevent climate change. The importance of the conventional power plants in the power system is maintained due to the intermittent nature of renewable energy sources. In this regard, nuclear power which is one of the conventional power plants takes attention. In this study, a new approach for optimal scheduling of a Pumped Hydro Energy Storage (PHES) integrated Nuclear Power Plant (NPP) system is proposed. It is expected that PHES provides significant economic contribution to the grid operator especially by ensuring energy supply security of the NPP. Two different market types named Day Ahead Market (DAM) and Balancing Market (BM) in Turkey are implemented in the trades by using real electricity prices obtained from Energy Exchange Istanbul (EXIST), which is Energy Market Operator in Turkey. In order to evaluate the effectiveness of the proposed model, seasonal impacts of four different months are also examined in several case studies. Furthermore, sustainability analyzes are conducted considering probability of interruption of the NPP during 8 hours. According to the results, it is observed that promising solutions can be obtained thanks to the integration of the PHES in the presence of the different market types even if outages are planned.

Son yıllarda, menzil ve şarj süresinde gözlemlenen olumlu geri dönüşler, elektrikli taşıtlara olan ilgiyi arttırmıştır. Mevcut çalışmanın amacı; artan bu ilginin, elektrik şebekeleri üzerindeki etkisinin gözlemlenmesi ve karşılaşılabilecek olası sorunlara yönelik çözüm önerilerinin sunulmasıdır. Bu amaçla; modelleme için, MATLAB ortamı seçilmiş, bir yıllık zaman periyodu esas alınmış ve Sancaktepe ilçesinde (Sancaktepe, İstanbul) yer alan şebeke bölgesi modellenmiştir. Modellemeyle uyumlu olarak MATLAB ortamında kurgulanan kod aracılığıyla, farklı koşullar altında, şebeke ve transformatör davranışları gözlemlenmiştir. Gözlemler doğrultusunda elektrikli taşıtların dağıtım şebekeleri üzerindeki olası olumsuz etkilerine yönelik çözüm teklifleri sunulmuştur. Çalışmamız şebeke kararlılığının sağlanmasında, üretim ve tüketim bazlı çözümleri bir araya getirmeyi amaçlamaktadır. Bu doğrultuda, ‘yük öteleme metodunun’ uygulanmasının ve ‘yenilenebilir kaynaklı dağıtılmış güç üretimi sistemlerinin’ şebekeye entegrasyonunun değişen koşullarda vereceği tepki incelenmiştir.