Journal of Energy - Energija

PV Integration in LV Networks and Capacity Analysis

2025-02-10T17:49:33+01:00

The increasing integration of photovoltaic (PV) systems in low-voltage (LV) networks presents challenges in violation of permitted voltage changes in the LV network and conductor and transformer capacity, which are critical for maintaining grid reliability and operational efficiency. This paper analyzes PV integration, focusing on voltage control, conductor capacity, and the importance of day-ahead PV generation and consumption for proactive grid management. Using MATLAB, the LV network is modeled to assess voltage analysis and conductor capacity for PV capacities ranging from 3 kW to 8 kW per consumer. Predictions of day-ahead PV production are conducted using a feedforward neural network trained on meteorological data such as solar irradiance, temperature, and cloud cover. The predictive model enabled voltage drop simulations and capacity analysis under forecasted conditions. The results demonstrated that voltage levels remained within the permissible range (+5%, -10% of 400 V) for PV capacities up to 8 kW, ensuring operational reliability. The neural network-based predictions are closely aligned with modeled values, with minimal differences, validating the forecasting approach. Voltage variations increased with higher PV capacities, but conductor current levels consistently remained below thermal limits. Incremental PV capacity integration revealed the network's ability to support distributed generation effectively but with limitations at higher capacities. This research highlights the role of accurate forecasting and optimization in ensuring reliable renewable energy adoption.

Diesel Engine Performance and Emission Properties using Kariya Biodiesel

2025-01-28T21:50:21+01:00

This study evaluated diesel engine performance and emission properties when fueled with an already-produced kariya oil biodiesel (KOB) and KOB blends. This intends to explore KOB blends as a supplement in diesel engines. A hand-held exhaust gas analyzer was used to determine the gas emitted. In contrast, the brake power and exhaust gas temperature were determined using a Schenck W230 Eddy Current dynamometer and a thermometer respectively during the operation of the diesel engine fueled with KOB. In contrast with standard petroleum-based diesel, the findings show that using KOB in diesel engines reduces CO and HC but increases NOx emitted due to its oxygenating property that aids fuel combustion. Also, with an increase in biodiesel blends, the fuel consumption increased, while the brake power and exhaust gas temperature decreased due to lower calorific value, higher viscosity, higher volumetric fuel per engine stroke, and the oxygen content dominating over lower calorific value for better combustion. From the study, both KOB B10 and B30 blends were considered optimally appropriate fuel supplements in a diesel engine.

The Roles of Battery Energy Storage System in Different Energy Communities

2025-05-12T12:11:23+02:00

The role of battery energy storage system in the different energy community is described, and the goal of this paper is to analyze the role of battery storage in different types of energy communities. A mathematical model was described, which was used to examine the profitability of the battery storage in 3 energy communities with different consumption curves, and the results obtained by simulation were presented.

Regional Solar Irradiance Forecasting Using Multi-Camera Sky Imagery and Machine Learning Models

2025-04-22T18:14:52+02:00

With the increasing integration of photovoltaic (PV) systems into power grids, accurate short-term forecasting of solar irradiance is essential for efficient energy management. This paper presents a machine learning model developed using a synthetic dataset designed to analyze the potential of multi- camera sky imaging for regional solar irradiance forecasting. The dataset, generated in a controlled simulation environment, cap- tures cloud dynamics and solar irradiance at multiple locations within a given region. The proposed model utilizes sky images from multiple virtual cameras strategically positioned to provide spatially distributed observations. By combining image-based features with historical irradiance measurements, the model shows improved forecasting accuracy compared to single-camera approaches. The results indicate that multi-camera systems better capture the spatial variability of cloud cover and allow the model to predict solar irradiance for locations without installed cameras. This research highlights the potential of multi-camera configurations for regional forecasting and provides valuable insights for grid operators and energy planners. The results support the adoption of distributed sky imaging networks as a practical approach to improve solar irradiance predictions and ultimately contribute to the stability and reliability of solar-powered energy systems through improved forecast accuracy.