Residential rooftop PV power generation to support cooling loads and national targets in Saudi Arabia

Saudi Arabia (SA) has a high per capita electricity consumption, predominantly supplied from fossil fuels. The residential sector accounts for about 50% of total electricity consumption with approximately 70% of which is used for air-conditioning (AC) loads. This research investigates the role of rooftop photovoltaic (PV) systems to displace cooling loads, hence reducing residential electricity demand. Daily and annual electrical demands were monitored in a villa in Jeddah, and a range of PV systems were modelled to determine their ability to support AC and other household loads. Seasonal performance data of such systems were compared to monitored load variations to understand variability and yields. The monitored electrical demand of the villa was in the range 66-167 kWh/day which was used to estimate the required PV systems’ capacities. The results indicate that PV systems in the range 2-10 kWp present significant shortfall to support the full demand. However, a 15kWp system was found to meet the daytime total loads. These results indicate that appropriately sized rooftop PV-systems can shave-off peak air-conditioning loads. The paper discusses the importance of utilising building integrated PV in such applications in SA, and highlights the need for dissemination at scale through country wide policy framework.

Integration of Ensemble GoogLeNet and Modified Deep Residual Networks for Short-Term Load Forecasting

Due to the strong volatility of the electrical load and the defect of a time-consuming problem, in addition to overfitting existing in published forecasting methods, short-term electrical demand is difficult to forecast accurately and robustly. Given the excellent capability of weight sharing and feature extraction for convolution, a novel hybrid method based on ensemble GoogLeNet and modified deep residual networks for short-term load forecasting (STLF) is proposed to address these issues. Specifically, an ensemble GoogLeNet with dense block structure is used to strengthen feature extraction ability and generalization capability. Meanwhile, a group normalization technique is used to normalize outputs of the previous layer. Furthermore, a modified deep residual network is introduced to alleviate a vanishing gradient problem in order to improve the forecasting results. The proposed model is also adopted to conduct probabilistic load forecasting with Monte Carlo Dropout. Two acknowledged public datasets are used to evaluate the performance of the proposed methodology. Multiple experiments and comparisons with existing state-of-the-art models show that this method achieves accurate prediction results, strong generalization capability, and satisfactory coverages for different prediction intervals, along with reducing operation times.

Solar Energy System for Brunei Residence

Brunei Darussalam is a country that receives high amounts of solar irradiation annually as it is located near the equator. With the abundance of oil & natural gas resources, the country has one of the cheapest electricity costs in the world. This would in turn make solar power underutilized. The purpose of this project is to design a solar system for Brunei’s medium sized residence to meet the daily energy demands. A comprehensive analysis was conducted on the solar photovoltaic system for determining the optimum sized parts and components. The design process was divided into detailed sections so that the values are calculated using PVSyst simulation software. The simulation also predicted the specific energy production, performance evaluation, and the losses. Cost analysis was also conducted to find the efficiency and the feasibility of the system. The designed solar energy system has a capacity of 60 kWp, producing 75 MWh of usable energy annually. This system uses 66% of the energy available from the sun to generate electricity which covers the electrical demand of Brunei’s residences.

Modeling public charging infrastructure considering points of interest and parking potentials

Considering climate change, it is essential to reduce CO2 emissions. The provision of charging infrastructure in public spaces for electromobility – along with the substitution of conventional power generation by renewable energies – can contribute to the energy transition in the transport sector. Scenarios for the spatial distribution of this charging infrastructure can help to exemplify the need for charging points and their impact, for example on power grids. We model two kinds of demand for public charging infrastructure. First, we model the demand for public charging points to compensate for the lack of home charging points, which is derived from a previously developed and published model addressing electric-vehicle ownership (with and without home charging options) in households. Second, and in the focus of the work presented here, is the demand for public charging infrastructure at points of interest (POIs). Their locations are derived from OpenStreetMap (OSM) data and weighted based on an evaluation of movement profiles from the Mobilität in Deutschland survey (MiD, German for “Mobility in Germany”). We combine those two demands with the available parking spaces and generate distributions for possible future charging points. We use a raster-based approach in which all vector data are rasterized and computations are performed on a municipality's full grid. The presented application area is Wiesbaden, and the methodology is generally applicable to municipalities in Germany. The model is compared with three other models or model variants in a correlation comparison in order to determine the influence of certain model assumptions and input data. The identification of potential charging points in public spaces plays an important role in modeling the future energy system – especially the power grid – as the rapid adoption of electric vehicles will shift locations of electrical demand. With our investigation, we would like to present a new method to simulate future public charging point locations and show the influences of different modeling methods.

Techno Economic Feasibility Analysis of Solar PV System in Jammu: A Case Study

Renewable sources of energy and related technologies are essential to the generation of energy worldwide. The photovoltaic (PV) is one of the renewable power technologies that support household electricity use. No prior research has studied the sustainability of the off-grid energy generation system in Jammu, India despite the potential of solar photovoltaics and significant amounts of global sun radiation in an area. The present work shown in the chapter is to calculate the residential load of the Patyari Kaltan situated in district Samba of Jammu by energy auditing. The NASA Surface Meteorology is used for the solar resource informationof selected village. The primary sources of electricity generation are fossil fuels. Recently, the energy demand and availability deficit has worsened due to the huge population and fossil fuels cannot fulfill huge energy requirement. Meanwhile they have negative impacts on the environment as well. Therefore, renewable energy offers suitable energy way out to the residents living in remote areas and in the areas near to Borders. In this paper the main aim is to examine the feasibility of solar-battery hybrid energy system to fulfill electrical demand of a residential area in a rural region in Jammu. The research shows that the cost of construction of the project can be repaid or recovered within 1 year 6 months. To accomplish the target, 214 solar panels of 325 watt are estimated to satisfy the demand 100 percent at all times. The findings of this modeling reveal that the off-grid PV system is both technical and economically viable for power generation; they may serve as a model for the successful development of the system for practical use. Furthermore, the model can promote assistance mechanisms for players in the renewable industry to introduce a PV system in residential buildings.