Modeling of Transition from Natural Gas to Hybrid Renewable Energy Heating System

Abstract Global energy demand is increased due to industrial development. Currently, fossil fuels, with more than 85%, are the most prominent source of energy in Iran, and their consumption has been raised, but it has destructive impacts on the environment and human health. This study aims to model and techno-economically assess renewable energy heating for replacing natural gas in Qazvin city.  The natural gas domestic demand was quantified, followed by consumption forecasting for 15 years. Six different scenarios were investigated to assess renewables’ potential to meet the city heat demand for the next 15years. The study uncovers that the best practice scenario can reduce natural gas consumption and increase renewable energies share. Finally, the proposed scenario was analyzed economically and environmentally. Results revealed that the return on investment would occur in 3 years by exporting the saved natural gas. Also, Iran can reduce CO2 emissions by about 1 million tons by the year 2029

A Spatial-Based Integration Model for Regional Scale Solar Energy Technical Potential

One of the main objectives of human society in the present century is to achieve clean and sustainable energy through utilization of renewable energy sources (RESs). In this paper, the main purpose is to identify the locations that are suitable for solar energy in the Kurdistan province of Iran. Initially, solar-related data are collected, and suitable criterion and assessment methods are chosen according to the available data. Then, the theoretical potential of solar energy is assessed and the solar radiation map is prepared. Moreover, the technical potential of various solar technologies is evaluated in that study area. These technologies include concentrating solar power (CSP) and photovoltaic (PV) in power plant applications, and rooftop PV panels and solar water heaters in general applications. The results show that the Kurdistan province has the potential capacity for 691 MW of solar photovoltaic power plants and 645 MW of CSP plants. In the case of using solar water heaters, 283 million cubic meters of natural gas and 1.2 million liters of gasoline could be saved in fuel consumption. The savings in the application of domestic PV will be 10.2 MW in power generation

Theoretical and technical potential evaluation of solar power generation in Iran

Nowadays, utilizing solar energy for power production at high efficiency and in a cost-effective status is a challenging issue for power plant engineers. This challenge would be answered by considering several affecting parameters such as technical, economic, and environmental criteria. In this investigation, in order to provide an assessment for implementing solar power plants in the southeast of Iran, Sistan and Baluchistan province, a multi-criteria decision making (MCDM) approach is linked to a geographic information system (GIS). The MCDM approach is used to appraise the effective criteria for implementing solar power plants. The environment, orography, economic and climate are selected as the important criteria. Each criterion is assessed for the defined location of the investigation (Sistan and Baluchistan province) and in addition, GIS is employed to provide a geographical-graphical valuation to determine the most appropriate place for installing a large-scale solar power production plant. The solar systems considered in this study are photovoltaic (PV) collectors and concentrated solar power (CSP) generation plants (e.g. solar trough collectors). Technical and theoretical valuations are made to specify the amount of solar power which can be harnessed in Sistan and Baluchistan. In overall, it is demonstrated that this specific location in the southeast of Iran has the technical potential to provide 7,419 TWh/y and 8,758 TWh/y of solar electricity by installing CSP and PV technologies, respectively

Numerical analysis of a small ducted wind turbine for performance improvement

In this study, Computational Fluid Dynamics simulations are carried out to assess the effect of duct geometry on power output and flow characteristics for a custom-designed wind turbine. The results show that the elevation of power output for a wind turbine is strongly reliant upon the shape of the duct. While before reaching a certain tip speed ratio, ducting would actually decrease the power output level. It is also argued that the velocity recovery index can also be considered dependent upon to the duct geometry. While the record power coefficient for the bare turbine is calculated to be 0.46, a power coefficient of 0.78 is shown to be reachable by adding a duct, which indicates an increase of approximately 70%. The near wake flow was analysed and a relation between the back-pressure level and the calculated power output was verified. The study also presents a framework for sizing the optimum duct

Toward comprehensive zero energy building definitions: a literature review and recommendations

Buildings are one of the most important emitters of CO2, causing climate change. This fact, together with the finiteness of conventional energy, results in the Zero Energy Building (ZEB) being future buildings. Although ZEB is a simple concept, but there is no valid universal definition. This is one of the significant building's energy systems challenges, which need to be appropriately addressed. Thisreview paper is going to review and summarize existing definitions to address a comprehensive definition of ZEB. The published articles were reviewed, and the definitions of zero energy buildings were drawn out. Then the differences in the existing definitions were analysed. Finally, suggestions are presented on suitable definitions from four perspectives, including energy, carbon, exergy, and economics. This definition is used as a standard communication by energy planners and policymakers to facilitate their decision making on energy transition

A scenario-based approach for optimal operation of energy hub under different schemes and structures

Because of economic and environmental issues' importance in the energy field, moving toward smart energy systems and energy hubs (EHs) has accelerated. The impacts of uncertainties, e.g., stochastic behaviors of renewable distributed generations (DGs), on EHs are fundamental challenges that should be considered carefully. Although several studies have been done in the area of EHs, a knowledge gap exists about developing an approach considering uncertainties under different EHs' structures and topologies. This research purposes of responding to such a research gap. In this research, a scenario-based approach for EHs' optimal operation considering wind turbine (WT) and photovoltaic (PV) uncertainties is proposed. The proposed approach is applied to EH under different schemes. Using the k-means clustering algorithm decreases the computational burden, while the appropriate accuracy is achievable. The proposed stochastic optimization problem is solved using the genetic algorithm (GA). The comparative view is considered to investigate the impacts of cooling and heating components like the heat pump (HP), absorption chiller (AC), and heat storage (HS) on EH's optimal operation and energy cost. According to this research findings, the EH's daily energy cost under a scheme using the AC, HP, and HS is approximately 6.5% less than a scheme only using HP. Also, using the HS and HP alongside the AC leads to 5.6% and 6.4% cost-saving, respectively. But for a better comparison, the investment and operation and maintenance (O&M) cost are considered, in which case Structure 3 (AC þ HP) is more efficient both in terms of energy consumption and investment costs

Artificial intelligence and machine learning in energy systems: A bibliographic perspective

Economic development and the comfort-loving nature of human beings in recent years have resulted in increased energy demand. Since energy resources are scarce and should be preserved for future generations, optimizing energy systems is ideal. Still, due to the complexity of integrated energy systems, such a feat is by no means easy. Here is where computer-aided decision-making can be very game-changing in determining the optimum point for supply and demand. The concept of artificial intelligence (AI) and machine learning (ML) was born in the twentieth century to enable computers to simulate humans' learning and decision-making capabilities. Since then, data mining and artificial intelligence have become increasingly essential areas in many different research fields. Naturally, the energy section is one area where artificial intelligence and machine learning can be very beneficial. This paper uses the VOSviewer software to investigate and review the usage of artificial intelligence and machine learning in the energy field and proposes promising yet neglected or unexplored areas in which these concepts can be used. To achieve this, the 2000 most recent papers in addition to the 2000 most cited ones in different energy-related keywords were studied and their relationship to AI- and ML-related keywords was visualized. The results revealed different research trends in recent years from the basic to more cutting-edge topics and revealed many promising areas that are yet to be explored. Results also showed that from the commercial aspect, patents submitted for artificial intelligence and machine learning in energy-related areas had a sharp increase

Sustainable energy system modelling with a high renewable energy penetration rate for rich oil regions

The power supply and demand have been studied, modelled, analysed, and foresighted as one of the most important energy carriers. The Business as usual (BAU) scenario was compared based on continuing the status quo with seven other proposed possible scenarios up to the horizon 2050. Possible solutions such as demand management, increased productivity, upgrading power plant technology, and development of energy resources attempt to reduce electricity demand, as well as improving and promoting the share of renewable energies in power generation and reducing emissions. The cost–benefit technique has also been used to analyse the economic and environmental benefits. As a results, a Khuzestan electricity policy scenario that has a well-coordinated and cost-effective solution for both supply and demand. It is preferred scenario for flexibility and stability of grid with a $3,549 million profit with 35% renewable energy share