https://www.mdu.se/

This paper presents a grid-connected double storage system (DSS) consisting of pumped-storage hydropower (PSH) and battery. The system is supplied by photovoltaics and wind turbines. In the proposed hybrid system, batteries absorb excess renewable energy that cannot be stored in PSH and they cover loads that cannot be supplied from the water turbine. To improve the system performance, a novel energy management strategy for the DSS is proposed. The strategy is based on an optimized factor that governs the charging process of the DSS. The problem of the optimal system design is solved by a non-dominated sorting genetic algorithm (NSGA-II). The multi-objective function considers simultaneously the minimal investment cost and minimal CO2 emissions. A comparative study of photovoltaic/wind/pumped-storage hydropower and photovoltaic/wind/double storage system is performed to show the effectiveness of the proposed strategy in terms of system economic and environmental performance. The considered location of the PSH station is on Attaqa Mountain at Suez (Egypt). The results indicate the effectiveness of the proposed energy management strategy for the storage system from economic and environmental perspectives. Coupling the battery with the PSH reduces the electricity cost by 22.2% and results in minimal energy exchange with the national grid (5% of the annual demand). A sensitivity analysis shows the largest variation of the electricity cost with changing the capital cost of the solar and wind generators. Also, it is observed that when the load increases, the optimal size of the system components increases, but it isn't proportional with the demand increase as could be expected. (C) 2019 Elsevier Ltd. All rights reserved.

The idea behind this chapter is to provide teachers and students with material that can be used while studying renewable energy sources with special attention paid to their complementary characteristics. The questions and exercises included below refer to chapters presented in the book. In case of any questions, we provide the readers with contact details to chapters corresponding authors who would be happy in assisting you in case of any queries.

The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SGi) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications.

CFD is widely used in indoor air quality, air flow pattern, indoor pollutant distribution and thermal comfort as a cost effective and powerful tool and it can be used to predict, estimate and visualize the indoor radon level. The intention of this article is to use computational fluid dynamics (CFD), as a standalone tool to simulate indoor radon distribution and ventilation effects. This technique can be used to predict and visualize radon content and indoor air quality throughout a one family detached house in Stockholm. In this study a mechanical balanced ventilation system and a continuous radon monitor (CRM) were also used to measure the indoor ventilation rate and radon levels. In numerical approach the FLUENT, CFD package was used to simulate radon entry into the building and ventilation effects. Results of numerical study indicated that indoor pressure made by means of ventilation systems and infiltration through door or window has significant effects on indoor radon content. It is observed that the location of vents can affect the indoor radon level, particularly in breathing (seating) zone. The analytic solution is used to validate numeric results at 3 distinct air change rates. The comparison amongst analytical, numerical and measurement results shows close agreement.

Heat recovery ventilation systems, because of reducing ventilation loss through recovered exhaust air, can play a good role in the effectiveness of ventilation to reduce energy use. In this paper, the impact of a heat recovery ventilator (HRV) on the energy use and indoor radon in residential buildings is investigated. This paper describes the effects of a heat recovery ventilation system on energy consumption in a detached house in Stockholm, Sweden. The performance of the heat recovery ventilation system is examined with respect to radon mitigation and energy saving by measuring the radon concentration and analyzing the life cycle cost of a heat exchanger unit. In this study, a multizone model of a detached house is developed in IDA Indoor Climate and Energy (IDA ICE 4.0). The model is validated using measurements regarding use of energy for heating, ventilation and whole energy use. The results of the measurements and dynamic simulation showed that heat recovery ventilation system 74% energy savings of the ventilation loss, amounted about 30 kWh.m-2 per year. Life cycle cost analysis used for assessing total costs and the result showed that using this system is quite cost-effective and investment would payback during 12 years.

Purpose: This paper aims to investigate the impact of heat recovery ventilators (HRVs) on the energy use and indoor radon in a one family detached house. Heat recovery ventilation systems, because of reducing ventilation loss through recovered exhaust air, can play a good role in the effectiveness of ventilation to reduce energy use. In addition HRVs can maintain pressure balance and outdoor ventilation rate at a required level to mitigate indoor radon level. Design/methodology/approach: In this study, a multizone model of a detached house is developed in IDA Indoor Climate and Energy (IDA ICE 4.0). The model is validated using measurements regarding use of energy for heating, ventilation and whole energy use. The performance of the heat recovery ventilation system is examined with respect to radon mitigation and energy saving by measuring the radon concentration and analyzing the life cycle cost of a heat exchanger unit. Findings: The results of the measurements and dynamic simulation showed that the heat recovery ventilation system could lead to 74 per cent energy savings of the ventilation loss, amounting to about 30 kWh m-2 per year. Life cycle cost analysis used for assessing total costs and the result showed that using this system is quite cost-effective and investment would payback during 12 years. Research limitations/implications: Limitations of this study generally refer to radon measurement and simulation because of radon complex behavior and its high fluctuations even during short periods of time. Practical implications: Heat recovery ventilation systems with reducing radon concentration improve indoor air quality and decrease environmental problems with energy savings. Social implications: Using balanced heat recovery ventilation can have benefits from the viewpoint of environmental impacts and household economy. Originality/value: Employment of a heat recovery unit to control indoor radon level is a new usage of this technology which along with energy savings can improve sustainable development.

Balanced ventilation with heat recovery has strong effects on radon mitigation and energy saving in residential buildings. This new technology enables improvement of both indoor air quality and energy efficiency without sacrificing either. Reducing radon by means of forced ventilation requires an increase in outdoor supplied air (i.e. ventilation rate), which in turn can increase energy use. Energy losses in ventilation systems are inevitable, but new technologies such as heat recovery systems make it possible to recover most of this ventilation heat loss. Heat recovery ventilation systems, which recover energy from exhaust air, can significantly reduce ventilation losses, and balancing the indoor air pressure plays a positive role in the effectiveness of ventilation to reduce and mitigate radon levels and control indoor air quality. This paper describes a case study which considers the effects of a heat recovery ventilation system on the radon concentration and energy consumption in a detached house in Stockholm, Sweden. The performance of the heat recovery ventilation system is examined with respect to radon mitigation and energy saving by measuring the radon concentration and analyzing the life cycle cost in winter. The results of the measurements and dynamic simulation showed that a heat recovery ventilation system was able to reduce the radon level from around 600 Bq.m-3 to below 100 Bq.m-3 and reduce energy loss from ventilation by 80%, equivalent to around 3500 kWh per year. The results of life cycle cost analysis used to assess total costs showed that this system is cost-effective and investment would pay for itself in 12 years. It should be noted that this saving is a representative sample, and that actual savings would be influenced by a large number of factors. IDA 4.0 Indoor Climate and Energy software was used to perform the dynamic simulations.

Cooling loads are a substantial part of the total electricity demands of countries in the Middle East and North Africa (MENA). Fortunately, because of its warm and sunny climate, the MENA region is naturally suited to solar cooling technologies. This article summarizes the most recent research and developments in solar thermal cooling technologies. The working principles and a general literature survey of solar thermal cooling technologies including absorption, adsorption, and desiccant is presented. This is followed by a summary of the literature specific to the MENA region, along with a survey of the prototypes and commercial installation of solar cooling across the MENA region. Based on this review, pilot solar cooling projects in the region are around 1180 kWc, which are split between space conditioning and industrial refrigeration applications. Most of the pilot projects are of absorption cooling type using an ammonia–water binary cycle and parabolic trough solar collectors. However, a few adsorption cooling systems are employed with a water–silica working pair and flat plate collectors. Finally, desiccant cooling systems are still in their infancy, as research and experimental systems in educational institutes.

This work proposes a novel yet practical dragonfly optimization algorithm that addresses four competing objectives simultaneously. The proposed algorithm is applied to a hybrid system driven by the solid oxide fuel cell (SOFC) integrated with waste heat recovery units. A function-fitting neural network is developed to combine the thermodynamic model of the system with the dragonfly algorithm to mitigate the calculation time. According to the optimization outcomes, the optimum parameters create significantly more power and have a greater exergy efficiency and reduced product costs and CO2 emissions compared to the design condition. The sensitivity analysis reveals that while the turbine inlet temperatures of power cycles are ineffective, the fuel utilization factor and the current density significantly impact performance indicators. The scatter distribution indicates that the fuel cell temperature and steam-to-carbon ratio should be kept at their lowest bound. The Sankey graph shows that the fuel cell and afterburner are the main sources of irreversibility. According to the chord diagram, the SOFC unit with a cost rate of 13.2 $/h accounts for more than 29% of the overall cost. Finally, under ideal conditions, the flue gas condensation process produces an additional 94.22 kW of power and 760,056 L/day of drinkable water. 

In recent years, there has been an increasing interest in renewable energies in view of the fact that fossil fuels are the leading cause of catastrophic environmental consequences. Ocean wave energy is a renewable energy source that is particularly prevalent in coastal areas. Since many countries have tremendous potential to extract this type of energy, a number of researchers have sought to determine certain effective factors on wave converters’ performance, with a primary emphasis on ambient factors. In this study, we used metaheuristic optimization methods to investigate the effects of geometric factors on the performance of an Oscillating Surge Wave Energy Converter (OSWEC), in addition to the effects of hydrodynamic parameters. To do so, we used CATIA software to model different geometries which were then inserted into a numerical model developed in Flow3D software. A Ribed-surface design of the converter's flap is also introduced in this study to maximize wave-converter interaction. Besides, a Bi-level Hill Climbing Multi-Verse Optimization (HCMVO) method was also developed for this application. The results showed that the converter performs better with greater wave heights, flap freeboard heights, and shorter wave periods. Additionally, the added ribs led to more wave-converter interaction and better performance, while the distance between the flap and flume bed negatively impacted the performance. Finally, tracking the changes in the five-dimensional objective function revealed the optimum value for each parameter in all scenarios. This is achieved by the newly developed optimization algorithm, which is much faster than other existing cutting-edge metaheuristic approaches. 

Treatment of sinusitis by surgical procedures is recommended only when medication therapies fail to relieve sinusitis symptoms. In this study, a realistic 3D model of the human upper airway system was constructed based on CT images of an adult male and three different virtual functional endoscopic sinus surgeries (FESS), including only uncinectomy and uncinectomy with two different sizes of Middle Meatal Antrostomy (MMA) performed on that model. Airflow and deposition of micro-particles in the range of 1-30 mu m were numerically simulated in the postoperative cases for rest and moderate activity breathing conditions. The results showed that the uncinate process alone protects the maxillary sinus well against the entry of micro-particles, and its removal by uncinectomy allows particles to deposit on the sinus wall easily. Generally, uncinectomy with a degree of MMA increases the number of deposited particles in the maxillary sinuses compared to uncinectomy surgery alone. In the studied models, the highest particle deposition in the maxillary sinuses occurred among particles with a diameter of 10-20 mu m. Also, if a person inhales particles during rest breathing conditions at a low respiratory rate, the number of particles deposited in the sinuses increases.

Purpose: This study aims to offer a mid-range theory conceptualization of factors central to understanding and facilitating business actor engagement (BAE). Reports on a study of real estate companies and their sustainable development goal (SDG) driven business initiatives. The aim is to identify the factors that need to be in place to facilitate positive engagement amongst actors in business-to-business (B2B) settings. Design/methodology/approach: A case study of real estate companies (landlords of business premises) and their business customers (tenants of offices and warehouses) – comprising interviews and workshops – offer insights related to the factors that need to be in place to facilitate BAE types and outcomes. Findings: The identified central factors of BAE – needed to understand and facilitate positive engagement to unfold – are the actors’ perception of: willingness (to act), resourcefulness (to contribute and solve issues) and influence (to affect decisions) regarding solutions related to the business initiative at hand. Failing to facilitate these factors may result in negative outcomes of BAE where “engagement” merely constitutes perceived obligations and responsibilities. Research limitations/implications: The study offers theoretical and managerial insights on how to manage the factors needed for BAE. It also sheds light on how actors can use SDG-driven business initiatives to achieve sustainability goals. Originality/value: It contributes to the concept of BAE, by emphasizing the dynamics of engagement, from the motivational and behavioral dimensions specific to B2B settings. It offers insights how to managerially cogovern rather than control BAE. It presents central factors needed to include and capacitate customers, facilitating successful implementations of SDG-driven business initiatives to reduce absent or negative outcomes. 

This paper aims to evaluate the energetic, economic, and environmental performances of small-scale concentrated photovoltaics (CPV) power systems under 107 African climatic and financial zones with different energy mixes. The proposed concept focuses on small-scale installations for small- or medium-scale industrial premises as such devices are involved in the international strategy about micro-grids. Yearly average electric productions, capacity factors, economic and environmental considerations, and sensitivity were all analysed. The mathematical methodology for calculating the power of a concentrated triple-junction solar cell, the annual energy conversion of a CPV plant, the costs, and the CO2 mitigation were assessed. The parametric study shows that the capacity factor becomes highest for a cell area of 5.5 cm2 or if the concentration ratio reaches the value of 2400. Furthermore, LCOE is lowest for Errachidia in Morocco with 15.88 c$/kWh followed by Fada in Chad with 16.82 c$/kWh, while it is highest in Wad Hajm in Sudan as 5.23 × 1016 c$/kWh. Moreover, South Africa allows the highest reduction of indirect CO2 emissions. Furthermore, energy produced is greatest in Errachidia in Morocco (606.27 GWh), Tiaret in Tunisia (601.11 GWh), and Upington in South Africa (598.11 GWh). Results are shown on innovative GIS maps of Africa. © 2022 Elsevier Ltd

Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed of 240 km/h. In this regard, Earth Observation (EO) Satellite Remote Sensing (SRS) images can effectively highlight oil spills in marine areas as a “fast and no-cost” technique. However, clouds and the sea surface spectral signature complicate the interpretation of oil spill areas in the optical images. In this study, Principal Component Analysis (PCA) has been applied of Landsat-8 and Sentinel-2 SRS images to improve information from the optical sensor bands. The PCA produces an output unrelated to the main bands, making it easier to distinguish oil spills from clouds and seawater due to the spectral diversity between oil, clouds, and the seawater surface. Then, an additional step has been applied to highlight the oil spill area using PCAs with different band combinations. Furthermore, Sentinel-1 (SAR), Sentinel-2 (optical), and Landsat-8 (optical) SRS images have been analyzed with cross-sections to suppress the “look-alike” effect of marine oil spill areas. Finally, mean and high-pass filters were used for Land Surface Temperature (LST) SRS images estimated from the Landsat thermal band. The results show that the seawater value is about −17.5 db and the oil spill area shows a value between −22.5 db and −25 db; the Landsat 8 satellites thermal band 10, depicting contrast at some areas for oil spill, can be determined by the 3 × 3 and 5 × 5 Kernel High pass and the 3 × 3 Mean filter. The results demonstrate that the SRS images should be used together to improve oil spill detection studies results.

The vibrations of bearings holding the high-speed shaft of a steam turbine are critically controlled for the safe and reliable power generation at the power plants. In this paper, two artificial intelligence (AI) process models, i.e., artificial neural network (ANN) and support vector machine (SVM) based relative vibration modeling of a steam turbine shaft bearing of a 660 MW supercritical steam turbine system is presented. After extensive data processing and machine learning based visualization tests performed on the raw operational data, ANN and SVM models are trained, validated and compared by external validation tests. ANN has outperformed SVM in terms of better prediction capability and is, therefore, deployed for simulating the constructed operating scenarios. ANN process model is tested for the complete load range of power plant, i.e., from 353 MW to 662 MW and 4.07% reduction in the relative vibration of the bearing is predicted by the network. Further, various vibration reduction operating strategies are developed and tested on the validated and robust ANN process model. A selected operating strategy which has predicted a promising reduction in the relative vibration of bearing is selected. In order to confirm the effectiveness of the prediction of the ANN process model, the selected operating strategy is implemented on the actual operation of the power plant. The resulting reduction in the relative vibrations of the turbine's bearing, which is less than the alarm limit, are confirmed. This cements the role of ANN process model to be used as an operational excellence tool resulting in vibration reduction of high-speed rotating equipment. (c) 2021 THE AUTHORS. Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

In energy production the characterization of the fuel is a key aspect for modelling and optimizing the operation of a power plant. Near-infrared spectroscopy is a wellestablished method for characterization of different fuels and is widely used both in laboratory environments and in power plants for real-time results. It can provide a fast and accurate estimate of key parameters of the fuel, which for the case of biomass can include moisture content, heating value, and ash content. These instruments provide a chemical fingerprint of the samples and require a calibration model to relate that to the parameters of interest.

A near-infrared spectrometer can provide point data whereas a hyperspectral imaging camera allows the simultaneous acquisition of spatial and spectral information from an object. As a result, an installation above a conveyor belt can provide a distribution of the spectral data on a plane. This results in a large amount of data that is difficult to handle with traditional statistical analysis. Furthermore, storage of the data becomes a key issue, therefore a model to predict the parameters of interest should be able to be updated continuously in an automated way. This makes hyperspectral imaging data a prime candidate for the application of machine learning techniques. This paper discusses the modelling approach for hyperspectral imaging, focusing on data analysis and assessment of machine learning approaches for the development of calibration models.

Electrification poses a promising low-carbon or even zero-carbon transportation solution, serving as a strategic approach to reducing carbon emissions and promoting carbon neutrality in the transportation sector. Along the transportation electrification pathway, the goal of carbon neutrality can be further accelerated with an increasing amount of electricity being generated from renewable energies. The past decade observed the rapid development of battery technologies and deployment of electricity infrastructure worldwide, fostering transportation electrification to expand from railways to light and then heavy vehicles on roadways. In China, a massive number of electric buses have been employed and operated in dozens of metropolises. An important daily operations issue with these urban electric buses is how to coordinate their charging activities in a cost-effective manner, considering various physical, financial, institutional, and managerial constraints. This paper addresses a general charging scheduling problem for an electric bus fleet operated across multiple bus lines and charging depots and terminals, aiming at finding an optimal set of charging location and time decisions given the available charging windows. The charging windows for each bus are predetermined in terms of its layovers at depots and terminals and each of them is discretized into a number of charging slots with the same time duration. A mixed linear integer programming model with binary charging slot choice and continuous state-of-charge (SOC) variables is constructed for minimizing the total charging cost of the bus fleet subject to individual electricity consumption rates, electricity charging rates, time-based charging windows, battery SOC bounds, time-of-use (TOU) charging tariffs, and station-specific electricity load capacities. A Lagrangian relaxation framework is employed to decouple the joint charging schedule of a bus fleet into a number of independent single-bus charging schedules, which can be efficiently addressed by a bi-criterion dynamic programming algorithm. A real-world regional electric bus fleet of 122 buses in Shanghai, China is selected for validating the effectiveness and practicability of the proposed charging scheduling model and algorithm. The optimization results numerically reveal the impacts of TOU tariffs, station load capacities, charging infrastructure configurations, and battery capacities on the bus system performance as well as individual recharging behaviors, and justify the superior solution efficiency of our algorithm against a state-of-the-art commercial solver. 

Informatics and Communication Technology (ICT) provides relevant agricultural information, which is essential in agricultural development attempts, in timely fashion. Aiming to find out if it is able to improve the efficiency of agricultural counseling agents, this study examined agricultural counseling agents in Luwu Regency, South Sulawesi, Indonesia. As explanatory quantitative research, simple random sampling was applied to respondents of google form questionnaire and the data were analyzed as per Structural Equation Model (SEM) and supported by smart PLS application. The result came out with R2 value of 0.868, representing the agricultural counseling agent's proficiency and ICT application signified agricultural counseling agent's efficiency at 86.8 %. It is therefore conclusive that agricultural counseling agent's efficiency in extension activities relies on both their proficiency and ICT application.

Nitrogen recovery from wastewater treatment for fertilizers is a research topic that exists at the intersection of multiple topics important to the future of sustainable society. First, nitrogen recovery from wastewater implies a departure from the current methods of nitrogen mitigation, which involve nitrogen removal by conversion of various aqueous species to inert nitrogen gas. Secondly, by recovering nitrogen from wastewater specifically, there is the opportunity to begin a circular economy where value added products can be obtained from material that has historically been seen as a “waste”. Current wastewater treatment involves nitrogen removal through the biological transformation of aqueous nitrogen species to inert nitrogen gas. This process is energy intensive and risks the production of air pollutants such as N2O as intermediates in the biological transformation. If this nitrogen can be captured in a form that can be reused, a valuable product can be achieved with the potential reduction of both the energy required at the wastewater treatment plant as well as the carbon footprint. Finally, by recovering nitrogen in a form that can be used in agriculture as a fertilizer, additional environmental benefits can be realized by reducing reliance on Haber-Bosch based ammonia production, which is also energy intensive and contributes harmful emissions to the atmosphere.

The work described in the following licentiate aims to consider the current status of nitrogen recovery from wastewater for fertilizers as a research topic. Literature was analytically examined to compare different techniques in terms of energy requirements, cost for fertilizer production, market for final fertilizer product, and technological readiness. The most interesting findings from this review were that there seems to be a disconnect between the fertilizer product produced by nitrogen recovery techniques and the market, which will become a challenge if these techniques are implemented at a large scale. The attitude of the farmers with regards to fertilizers from waste was overall positive, with their concerns mainly focused on the performance ability and cost of the product. Additionally, many techniques such as microbial fuel cells and microbial electrolysis cells have been unable to move past the laboratory phase despite being researched for many years. This indicates there are cost and technological barriers that are preventing the further scale up and implementation of these techniques. Energy and cost analyses will be crucial to motivate investment into these processes, and these are missing for many of the techniques found around this topic.

To contribute to this field, experimental work was also included to assess the potential for ammonium adsorption from concentrated wastewater for fertilizer production. The experimental work focused on the utilization of the solid product of pyrolysis of sewage sludge (biochar) for adsorption and explored the potential enhancement of the char with various chemical treatments. The char with the best ammonium adsorption performance was found to be using a treatment of HNO3 followed by and NaOH, with an adsorption capacity of 4 mg NH4/g biochar. This char was compared with commercially activated carbon and clinoptilolite for full scale applications. It was found that even with this increased adsorption capacity, the use of chemically enhanced sewage sludge biochar for full scale applications is not realistic. The amount of raw material required for the complete recovery of ammonium from reject water at the municipal wastewater treatment plants exceeds the total amount of sewage sludge generated. Therefore it is recommended that the goal of incorporating sewage sludge biochar with wastewater treatment is to produce a solid fertilizer product loaded with ammonium (which would provide ammonium-N and phosphorus for plant growth, as well as carbon and other minerals for soil amendment) rather than having the goal be complete ammonium recovery from the wastewater stream.

This article summarizes and performs a systematic analysis using experimental results from recent research on ammonium recovery from aqueous sources using biochar. Numerous studies have focused on using different materials to produce biochar adsorbents, and many have attempted to draw conclusions about the physical or chemical characteristics that dominate the adsorption to infer the mechanism. However, to date, there has not been statistical analysis performed on a large set of adsorption data and physical/chemical characteristics of chars to be able to draw conclusions about ammonium adsorption mechanisms. From this analysis, it was found that consistency in experimental methods and characteristic measurement reporting is lacking, and therefore it is difficult to perform metadata analysis and draw conclusions about ammonium adsorption on biochar. Among the important factors influencing ammonia recovery proposed in literature, the meta-analysis only strongly supports the effect of BET surface area and NH4+ concentration, with weaker support for the importance of cation exchange capacity and pyrolysis temperature. This suggests that standard procedures for biochar production, experiments and analysis of physical and chemical characteristics are needed to usefully compare results across different studies. Examples of the present difficulty in identifying trends across studies are shown by comparing clusters in the data identified by the analysis. The ability to make such comparisons would provide clearer direction in how best to further improve the adsorption capacity of biochars.

The present study proposes and thoroughly examines a novel approach for the effective hybridization of solar and wind sources based on hydrogen storage to increase grid stability and lower peak load. The parabolic trough collector, vanadium chloride thermochemical cycle, hydrogen storage tank, alkaline fuel cells, thermal energy storage, and absorption chiller make up the suggested smart system. Additionally, the proposed system includes a wind turbine to power the electrolyzer unit and minimize the size of the solar system. A rule-based control technique establishes an intelligent two-way connection with energy networks to compensate for the energy expenses throughout the year. The transient system simulation (TRNSYS) tool and the engineering equation solver program are used to conduct a comprehensive techno-economic-environmental assessment of a Swedish residential building. A four-objective optimization utilizing MATLAB based on the grey wolf algorithm coupled with an artificial neural network is used to determine the best trade-off between the indicators. According to the results, the primary energy saving, carbon dioxide reduction rate, overall cost, and purchased energy are 80.6 %, 219 %, 14.8 $/h, and 24.9 MWh at optimal conditions. From the scatter distribution, it can be concluded that fuel cell voltage and collector length should be maintained at their lowest domain and the electrode area is an ineffective parameter. The suggested renewable-driven smart system can provide for the building's needs for 70 % of the year and sell excess production to the local energy network, making it a feasible alternative. Solar energy is far less effective in storing hydrogen over the winter than wind energy, demonstrating the benefits of combining renewable energy sources to fulfill demand. By lowering CO2 emissions by 61,758 kg, it is predicted that the recommended smart renewable system might save 7719 $ in environmental costs, equivalent to 6.9 ha of new reforestation. 

This article aims to support the targeted worldwide green transition process by introducing and thoroughly analyzing a low-temperature heating and high-temperature cooling, smart building system. This concept allows for greater use of renewable energy while utilizing less input energy than conventional heating and cooling techniques. The proposed system consists of a reversible water-to-water heat pump driven by low-temperature geothermal energy. A rule-based control strategy is developed to establish an intelligent connection with the regional energy grids for peak shaving and compensating for the building's energy costs over the year. The dynamic simulation is carried out for a multi-family building complex in Stockholm, Sweden, using TRNSYS. The most favorable operating condition is determined via an artificial neural network-assisted tri-objective optimizer based on the grey wolf algorithm in MATLAB. The comparison of the proposed smart model with the conventional system in Sweden results in 332%, 203%, and 190% primary energy reduction, cost saving, and carbon dioxide emission mitigation, respectively. As indicated by the parametric results, the conflicting fluctuation between desirable and unfavorable indicators highlights the importance of multi-objective optimization. The grey wolf optimizer obtains 12% higher efficiency, 1.2 MWh lower annual bought energy, 24 $/MWh lower unit cost, and 5.1 MWh more yearly sold energy than the design condition. The scattered distribution reveals that tank volume and subcooling degree are sensitive parameters. According to the transient results, the suggested smart system can independently satisfy the building's heating, cooling, and electricity demands for more than 81% of the year, thanks to the two-way connection with the electricity and heating networks via the rule-based controller. 

The present research introduces an innovative zero-energy building complex equipped with a rule-based control approach for higher integration of renewable resources in the local energy network while bringing down energy costs. The idea centers on establishing several smart controllers to achieve a bidirectional interaction with the heating/electricity network for peak demand shaving and mitigate energy costs. The proposed system comprises Alkaline fuel cells integrated with a hydrogen storage tank driven by either a vanadium chloride cycle or an electrolyzer unit. The system also has an absorption chiller and smart thermal energy storage to supply the heating and cooling demands. TRNSYS-MATLAB developed code is applied to assess the system's indicators from techno-economic standpoints for a residential building complex in the Scandinavian climate. Also, the parametric investigation and time-dependent analysis are carried out to examine the impact of decision parameters and the ambient condition. According to the results, the solar system's physical appearance is very important since it significantly affects performance efficiency and total cost. The results further reveal that picking up the cells' current from 300 A to 500 A improves the performance efficiency by around 12% while lowering the total cost, illustrating the importance of optimization. The results highlight the importance of smart controllers by showing that over 70% of the year's net energy values are positive, indicating that the proposed system may meet demand and sell excess electricity+heating productions to regional networks. The results further demonstrate that since the net energy values are positive for the majority of days in the spring and summer, the system might operate more independently from the local energy networks on warmer days. Eventually, the higher share of solar in summer and wind energy in colder days for hydrogen production shows that the renewable resources combination results in a secure energy supply to obtain the highest independence from the local grid throughout the year.

The present paper introduces a new smart building system driven by photovoltaic thermal panels. The concept is to improve the contribution of renewable energy in the local matrix for peak load shaving by having a two-way connection with the local electricity network via a rule-based energy monitoring control design. Besides, the feasibility of removing the electrical storage unit with high investment cost is studied by establishing a dynamic interaction between the energy production and usage components to reduce the energy costs over the year. The system has intelligent thermal energy storage integrated with an electrically-driven coil, heat exchanger, pumps, and several smart valves and control units. The transient system simulation (TRNSYS) package is implemented to assess the practicality of the suggested intelligent model for a building complex in Malmo, Sweden. According to the parametric outcomes, by raising the panel area, while the generated electricity increases, the solar utilization factor falls, indicating conflictive changes among performance metrics. The results also show that the renewable resource covers the building's heating and electricity demands for the majority of the year and that a significant amount of energy is sold to the neighbourhood electricity grid, demonstrating the viability of the introduced intelligent model.

As part of the transition to a sustainable future, energy-efficient buildings are needed to secure users' comfort and lower the built environment's energy footprint and associated emissions. This article presents a novel, realistic and affordable solution to minimize the footprint of smart building energy systems and enable higher renewable energy use in the building sector. For this, an intelligent system is being developed using a rule-based automation approach that considers thermal comfort, energy prices, meteorological data, and primary energy use. In order to lower the installation cost and part of the environmental footprint, batteries are not used, and the heat pump's size is decreased via component integration. Also, different renewable resources are effectively hybridized using photovoltaic thermal panels and an innovative biomass heater to increase the share of renewable energy, enhance reliability, and shave peak load. In order to secure feasibility, the suggested framework is assessed from the techno-economic and environmental standpoints for 100 residential apartments in Stockholm, Sweden. Our results show that 70.8 MWh of renewable electricity is transferred to the local grid, and the remaining 111.5 MWh is used to supply the building's needs and power the electrically-driven components. The biomass heater meets more than 65% of the space heating demand, mainly at low solar power and high electricity prices, illustrating the value of integration strategies to reduce the system's dependability on the local grid. The results further reveal that most energy purchases during the cloudy days and nights are repaid through the sale of excess renewable production during the warmer hours, with a bidirectional connection with the grid. The monthly energy cost is less than 140 $/MWh for most of the years. The cost can be held low due to the exclusion of batteries and minimizing the heat pump size. The proposed system has a low emission index of 11.9 kgCO2/MWh and can reduce carbon dioxide emissions by 70 TCO2/year compared to using the supply from the Swedish energy mix. 

This paper introduces an innovative and cost-effective multi-generation plant, driven by the central receiverbased concentrated solar systems, to facilitate the desired global green-transition process. The vanadium chlorine thermochemical cycle, which uses hydrogen instead of natural gas in the combustion chamber, is used as an innovative approach for reducing greenhouse gas emissions. The proposed system also includes a thermoelectric generator (TEG) for excess power generation and a multi-effect desalination (MED) unit to reduce exergy loss. The suggested system's technological, economic, and environmental metrics are analyzed and compared to a similar system that stores the created hydrogen rather than burning it in the combustion chamber. Furthermore, the viability of the studied model is investigated under the optimal operating condition, using the example of Sevilla in order to make the conclusions more reliable. According to the findings, the suggested novel configuration is a better alternative in terms of cost and environmental impact owing to decreased product energy costs and CO2 emissions. The outcomes further indicate that the substitution of the condenser with TEG leads to considerably higher power production. According to the optimization findings, the multi-objective grey wolf algorithm is the best optimization strategy compared to the non-dominated genetic and particle swarm approaches. At the best optimization point, 2.5% higher exergy efficiency, 1 $/GJ cheaper product energy cost, and 0.12 kg/kWh lower levelized CO2 emission are achieved compared to the operating condition. The Sankey diagram indicates that the solar heliostat system has the highest irreversibility. The exergy analysis results further reveal that the flue gas condensation process through the Rankine cycle and MED unit lead to a 53.2% reduction in exergy loss. Finally, considerable CO2 emission reductions show that the suggested new method is an effective solution for cleaner energy production in warmer climate countries.

Thermal energy storage (TES) is recognized as a well-established technology added to the smart energy systems to support the immediate increase in energy demand, flatten the rapid supply-side changes, and reduce energy costs through an efficient and sustainable integration. On the utilization side, low-temperature heating (LTH) and high-temperature cooling (HTC) systems have grown popular because of their excellent performance in terms of energy efficiency, cost-effectiveness, and ease of integration with renewable resources. This article presents the current state-of-the-art regarding the smart design of TES integrated with LTH and HTC systems. TES is first explained in basic concepts, classification, and design possibilities. Secondly, the literature on well-known existing control approaches, strategies, and optimization methods applied to thermal energy storage is reviewed. Thirdly, the specifications, types, benefits, and drawbacks of the LTH and HTC systems from the viewpoints of supply and demand sides are discussed. Fourthly, the smart design of TES integrated with the LTH and HTC systems based on the control approach/strategy, optimization method, building type, and energy supplier is investigated to find the newest technology, ideas, and features and detect the existing gaps. The present article will provide a realistically feasible solution for having a smart storage configuration with the maximum possible energy efficiency, reliability, and cost-effectiveness for the building owners and the energy suppliers.

This work presents an innovative, practical, and cost-effective solution for advancing state-of-the-art intelligent building energy systems and aiding the intended worldwide green transition with maximum renewable integration. The vanadium chloride cycle, electrolyzer unit, and Alkaline fuel cell are powered by the sun's and wind's energy to produce/store/use hydrogen. A rule-based control scheme is designed to provide a sophisticated interplay between the demand/supply sides, components, and local energy networks to reduce peak capacity, lower emissions, and save energy costs. TRNSYS is used to analyze and compare the techno-economic-environmental indicators of the conventional system and the suggested smart model for a multi-family building in Sweden. A grey wolf method is built in MATLAB with the help of machine learning to determine the optimum operating state with the maximum accuracy and the least amount of computational time. The results reveal that the suggested smart model considerably saves energy and money compared to the conventional system in Sweden while lowering CO2 emissions. According to the optimization results, the grey wolf optimizer and machine learning techniques enable greater total efficiency of 13 %, higher CO2 mitigation of 8 %, a larger cost saving of 38 %, and a reduced levelized energy cost of 41 $/MWh. The scatter distribution of important design parameters shows that altering the fuel cell current and electrode area considerably impacts the system's performance from all angles. The bidirectional connection of the proposed smart system with the heating and electrical networks through the rule-based controller demonstrates that it can supply the building's energy requirements for more than 300 days of the year. Eventually, the major contribution of the vanadium chloride cycle in the summer and the electrolyzer in the winter to the creation of hydrogen highlights the significance of renewable hybridization in reducing the dependence of buildings on energy networks.

This article proposes a cutting-edge smart building design that contributes to sustainable development objectives by fostering clean energy, facilitating sustainable cities and communities, and promoting responsible consumption and production. The main goal is to create a clever rule-based framework that will boost the penetration of renewable energy in local grids, reduce the size of the components and, consequently, investment costs, and promote the shift towards a more environmentally friendly future. The system is driven by photovoltaic thermal panels, a novel biomass heater scheme, and a scaled-down heat pump to supply the entire energy demands of multi-family houses. The grey wolf optimizer and a cascade forward neural network model achieve the most optimal condition. According to the results, the suggested smart model outperforms the conventional Swedish system, with an energy cost of 121.2 euro/MWh and a low emission index of 11.2 kg/MWh. The results show that knowing how biomass price changes affect the heat pump's operational mode is crucial to ensuring the system's economic viability. In comparison to the design condition, the optimized model increased efficiency by 3.8% while decreasing overall cost (2.1 euro/h), emission index (4.4 kg/MWh), and energy costs (29.9 $/MWh). The results further demonstrate that the heat pump meets the vast majority of the year's heating needs, but as electricity prices rise in December, the biomass heater becomes the principal energy provider. May is the month with the lowest average monthly cost, while December and July stand out as the most expensive months of the year due to a dramatic increase in demand. Eventually, the results show that the system runs without external energy sources through the designed optimal control framework and generates excess electricity for around half the year.

Augmented Reality (AR) has developed rapidly inrecent years and it is about to become a mainstream technology.We are witnessing how emerging technologies such as AR has been introduced and today widely applied in engineering education.The turmoil caused by the COVID-19 pandemic has in many ways highlighted the importance of AR technology for collaboration and remote assistance of frontline workers. Enabling experts to be much more productive in helping to debug problems and resolve production issues remotely. This kind of hands-on support and tutoring opportunities play well into the possibilities embedded in a more digitalized approach to engineering education. Still, both industries and universities are exploring ways to enhance the value-added credentials that come along with an integration and investment of AR. This paper set out to understand what type of assessment that are used to drive learning performance amongstudents in engineering education.

Hybrid-electric propulsion is recognized as an enabling technology for reducing aviation’s environmental impact. In this work, a serial/parallel hybrid configuration of a 19-passenger commuter aircraft is investigated. Two underwing-mounted turboprop engines are connected to electrical branches via generators. One rear fuselage-mounted electrically driven ducted fan is coupled with an electric motor and respective electrical branch. A battery system completes the selected architecture. Consistency in modeling accuracy of propulsion systems is aimed for by development of an integrated framework. A multipoint synthesis scheme for the gas turbine and electric fan is combined with physics-based analytical modeling for electrical components. Influence of turbomachinery and electrical power system design points on the integrated power system is examined. An opposing trend between electrical and conventional powertrain mass is driven by electric fan design power. Power system efficiency improvements in the order of 2% favor high-power electric fan designs. A trade-off in electrical power system mass and performance arises from oversizing of electrical components for load manipulation. Branch efficiency improvements of up to 3% imply potential to achieve battery mass reduction due to fewer transmission losses. A threshold system voltage of 1 kV, yielding 32% mass reduction of electrical branches and performance improvements of 1–2%, is identified. This work sets the foundation for interpreting mission-level electrification outcomes that are driven by interactions on the integrated power system. Areas of conflicting interests and synergistic opportunities are highlighted for optimal conceptual design of hybrid powertrains.

Hybrid-electric propulsion is recognized as one of the enabling technologies for reducing aviation’s environmental impact. In this work a serial/parallel hybrid configuration of a 19-passenger commuter aircraft is investigated. Two underwing-mounted turboprop engines are connected to electrical branches via generators. One rear fuselage-mounted electrically driven ducted fan is coupled with an electric motor and respective electrical branch. A battery system completes the selected architecture. Consistency in modelling accuracy of propulsion systems is aimed for by development of an integrated framework. A multi-point synthesis scheme for the gas turbine and electric fan is combined with physics-based analytical modelling for electrical components. Influence of turbomachinery and electrical power system design points on the integrated power system is examined. An opposing trend between electrical and conventional powertrain mass is driven by electric fan design power. Power system efficiency improvements in the order of 2% favor high-power electric fan designs. A trade-off in electrical power system mass and performance arises from oversizing of electrical components for load manipulation. Branch efficiency improvements of up to 3% imply potential to achieve battery mass reduction due to fewer transmission losses in mission-significant segments. A threshold system voltage of 1kV, yielding 32% mass reduction of electrical branches and performance improvements of 1-2%, is defined. Above the indicated threshold, benefits are limited, and system design complexity increases unfavorably. This work sets the foundation for interpreting mission-level electrification outcomes that are driven by interactions on the integrated power system. Areas of conflicting interests and synergistic opportunities are highlighted for optimal conceptual design of hybrid powertrains.

The building sector accounts for approximately 40% of primary energy use within the European Union, therefore reductions in the energy use intensity of this sector are critical in decreasing total energy usage. Given that the majority of energy used within the built environment is for space conditioning and domestic hot water preparation, prudence would suggest that decreasing primary energy used for these end purposes would have the biggest overall environmental impact. A significant portion of the energy demands in buildings throughout the year could potentially be met using solar energy technology for both heating and cooling. Additionally, improving the efficiency of current heating and cooling appliances can reduce environmental impacts during the transition from non-renewable to renewable sources of energy. However, in spite of favourable energy saving prospects, major energy efficiency improvements as well as solar heating and cooling technology are still somewhat underutilised. This is typically due to higher initial costs, and lack of knowledge of system implementation and expected performance.

The central premise of this thesis is that modular thermally (i.e., sorption) driven heat pumps can be integrated into heating and cooling systems to provide energy cost savings. These sorption modules, by virtue of their design, could be integrated directly into a solar thermal collector. With the resulting sorption integrated collectors, cost-effective pre-engineered solar heating and cooling system kits can be developed. Sorption modules could also be employed to improve the efficiency of natural gas driven boilers. These modules would effectively transform standard condensing boilers into high efficiency gas-driven heat pumps that, similar to electric heat pumps, make use of air or ground-source heat.

Based on the studies carried, sorption modules are promising for integration into heating and cooling systems for the built environment generating appreciable energy and cost-savings. Simulations yielded an annual solar fraction of 42% and potential cost savings of €386 per annum for a sorption integrated solar heating and cooling installation versus a state-of-the-art heating and cooling system. Additionally, a sorption integrated gas-fired condensing boiler yielded annual energy savings of up to 14.4% and corresponding annual energy cost savings of up to €196 compared to a standard condensing boiler.

A further evaluation method for sorption modules, saw the use of an artificial neural network (ANN) to characterise and predict the performance of the sorption module under various operating conditions. This generic, application agnostic model, could characterise sorption module performance within a ± 8% margin of error. This study thus culminates in the proposal of an overall systematic evaluation method for sorption modules that could be employed for various applications based on the analytical, experimental and simulation methods developed.

Increasing the share of renewable sources of energy as well as the effective use of waste energy sources is critical to the reduction of primary energy use and its associated environmental impact in the built environment. Sorption heat pumps are employed in various heat-driven cooling and heat pumping applications. These heat pumps may be driven by solar energy, natural gas, biogas, geothermal energy or waste heat. Given that a plethora of heat sources and sorption materials can be exploited for different applications, various sorption heat pump modules have been developed. The sorption modules are pre-engineered sorption components for increased ease of sorption system development, improved cost effectiveness and reduced system complexity for various applications. However, in the design of sorption modules, component and system modelling and simulation are useful in the process of determining the optimal candidate of several possible sorption working couples for a given application. A test platform has been developed and a test strategy devised for the rapid characterisation of the transient behaviour of the sorption modules. In the present study, a modular sorption unit is evaluated experimentally in an automated test setup. Key performance indicators were derived, and the test data used as input to train a model based on artificial neural networks (ANN) in MATLAB. The study showed that the model could adequately predict the dynamic behaviour of the sorption module. Results showed that the dynamic behaviour of the module could be adequately mapped, with average relative errors between measured and simulation results of 3.7%, 4.2%, 0.4%, and 0.3% for heat transfer rates to and from the reactor, and the condenser-evaporator during charge and discharge respectively. Additionally, the ANN model, trained with data from test run sequence of 54 cycles, predicted both cooling and heating COPs within a reasonable margin of error (<± 8%) with the majority of predictions having an error of less than ± 4%.

In this research, biogas production potential from beach wrack collected in Riga Gulf (Ragaciems, Jaunķemeri, Bigauņciems) and in coastline of Sweden (Kalmar) was studied using an anaerobic digestion method. Selected beach wrack masses laying ashore and containing macroalgal biomass of common macroalgae types specific to the Baltic Sea were mixed for consolidated samples. Anoxic fermentation of untreated beach wrack was carried out in 16 bioreactors applying a single filling mode at 38 °C. The study revealed that by utilizing beach wrack accumulated ashore as a feedstock for anaerobic digestion methane can be utilized if pretreatment and conditioning of the samples are performed.

The study was continued for selected brown algae containing biomass tested with three dewatering pretreatment methods: a) keeping in tap water for 24 hours; b) washing with running fresh water for one hour, and c) drying to relatively constant weight. The resulting methane outcome was compared with the data corresponding to raw brown algae. The study confirmed that washing of macroalgal biomass applied as pretreatment prior to anaerobic fermentation avoids inhibition of salts and promotes biomethane production.

The 2018 drought in Sweden prompted questions about climate-adaptation and -mitigation measures – especially in the agricultural sector, which suffered the most. This study applies a water–food–energy nexus modelling framework to evaluate drought impacts on irrigation and agriculture in Sweden using 2018 and 2019 as case studies. A previous water–food–energy nexus model was updated to facilitate an investigation of the benefits of data-driven irrigation scheduling as compared to existing irrigation guidelines. Moreover, the benefits of assimilating earth observation data in the crop model have been explored. The assimilation of leaf area index data from the Copernicus Global Land Service improves the crop yield estimation as compared to default crop model parameters. The results show that the irrigation water productivities of the proposed model are measurably improved compared to conventional and static irrigation guidelines for both 2018 and 2019. This is mostly due to the advantage of the proposed model in providing evapotranspiration in cultural condition (ETc)-driven guidelines by using spatially explicit data generated by mesoscale models from the Swedish Meteorological and Hydrological Institute. During the drought year 2018, the developed model showed no irrigation water savings as compared to irrigation scenarios based on conventional irrigation guidelines. Nevertheless, the crop yield increase from the proposed irrigation management system varied between 10% and 60% as compared to conventional irrigation scenarios. During a normal year, the proposed irrigation management system leads to significant water savings as compared to conventional irrigation guidelines. The modelling results show that temperature stress during the 2018 drought also played a key role in reducing crop yields, with yield reductions of up to 30%. From a water–food–energy nexus, this motivates the implementation of new technologies to reduce water and temperature stress to mitigate likely negative effects of climate change and extremes. By using an open-source package for Google Earth®, a demonstrator of cost-effective visualization platform is developed for helping farmers, and water- and energy-management agencies to better understand the connections between water and energy use, and food production. This can be significant, especially during the occurrence of extreme events, but also to adapt to the negative effects on agricultural production of climate changes.

The water-food-energy nexus approach was identified by the 2008 World Economic Forum as a key concept and methodology for studying and optimizing the important links among energy, water, and food. Energy, water, and food are basic human needs and are threatened by megatrends such as climate change and population growth. Renewable energies play an important role in the energy-water nexus because their water footprint, except for hydropower and bioenergy, is extremely low as compared to conventional fossil-based energy systems, especially for solar power and wind power conversion systems. Solar power and wind power systems reduce pressure on water resources by allowing for better water management, especially when it comes to conflicts between water for energy versus water for food. Renewable energies also represent a key pathway for combating climate change. This chapter introduces the concept of the water-food-energy nexus and its complex interrelationships and gives particular attention to renewable energies. Subsequently, several water-food-energy nexus aspects related to applications of renewable energies are investigated more deeply, with reference to practical examples. Particular attention will be given to floating photovoltaic systems, photovoltaic water-pumping systems, and agrivoltaics. The chapter concludes with the competition of land for energy versus land for food and on the role of the nexus in renewable-based wastewater systems.

Agriculture is one of the most water- and energy-intensive sectors of the economy, consuming about 70% of global freshwater withdrawals. Access to clean and affordable water for irrigation is an essential step towards guaranteeing water and food security, improving incomes and living standards, decarbonizing an energy-intensive sector and attaining the United Nations Sustainable Development Goals (SDGs), in particular SDGs 2 (Zero Hunger), 6 (Clean Water and Sanitation), 7 (Affordable and Clean Energy), and 13 (Climate Action). Ensuring access to water for irrigation, as well as for other agricultural (i.e., livestock watering), domestic, and industrial purposes is a global challenge, and it is more challenging in remote areas where the grid connection is often not available. Solar-powered pumping systems represent a renewable solution for the decarbonization of the irrigation sector worldwide. While solar water pumping systems were used in the past to supply water for irrigation, livestock, and domestic purposes only in remote locations without access to the electric grid, the drastic drop in photovoltaic (PV) modules prices has made the technology also competitive for on-grid applications. This chapter reviews the configurations of solar water pumping systems for irrigation, highlighting the water–food–energy nexus aspects and recent advances, reviewing case studies, and analyzing the economics and current and future challenges. 

Photovoltaic (PV) systems in Sweden have primarily been seen as an energy efficiency measure to reduce the amount of purchased electricity for buildings, both residential and commercial. Only recently utility-scale solar systems have begun to increase their share of the solar market to support national energy and emissions targets. Due to the economies of scale, conventional ground-mounted PV (CGMPV) installations represent the best solution for producing electricity at the lowest specific initial investment costs. This relatively new solar market segment, with large-scale ground-mounted solar farms on agricultural land, has faced several challenges with the permitting process. Agricultural land that is suitable for cultivation is of "national importance" according to the Swedish Environmental Code. Cultivable agricultural land may be exploited for other purposes on a permanent basis only if it is necessary to satisfy essential societal interests and there is no other possible land to use within the area in question. Traditionally, ground-mounted solar farms have increased competition for land resources for food production and drawn criticism in the so-called "food-versus-fuel (electricity)" debate over whether agricultural land should be used for electricity generation or food production. Agrivoltaic (APV) systems represent an intelligent solution to avoid land use competition by combining arable farming and electricity production on the same agricultural land. The main objective of this project was to study how APV systems perform from an energy, agricultural and economic perspective compared to CGMPV systems and agriculture production. The project aimed to highlight advantages and disadvantages of APV systems at northern latitudes with an energy-food-water perspective. The aim was pursued by establishing an APV test site, the first APV system in Sweden, monitoring its performance both from an energy and agricultural point of view, and developing new techno-economic models. Data from the APV test site were used to better understand how APV systems at northern latitudes affect: 1) the efficiency of the solar modules; 2) crop productivity, and 3) the financial return for ground-based solar PV systems. The first agrivoltaic system in Sweden has been built on a permanent ley grass field, at Kärrbo Prästgård, Västerås, and research activities have been carried out on the ley grass during 2021 and 2022. As in previous research studies in other countries, we defined three sub-fields: 1) a sub-field is covered only by the ley grass (reference area), 2) a sub-field is a CGMPV system 11.8 kWp solar PV system with two rows of solar modules with a 30° tilt and 3) the last subfield is a  22.8 kWp APV system with three rows of vertically mounted solar modules, with ley grass between the modules. This field set-up allowed for comparisons between practices (agriculture and electricity generation) and technologies (CGMPV systems versus APV systems). The calculated specific electricity production during a typical meteorological year for the APV system and the CGMPV system was 1,067 kWh/kWp/year and 1,116 kWh/kWp/year, respectively. Nevertheless, the APV system tends to have higher efficiency than the CGMPV systems due to the solar irradiation patterns on the solar cell surfaces and wind cooling of the PV modules. The main results of the project in terms of shadow effects on the ley grass showed that the APV system did not significantly affect the productivity of the forage grass in 2021-2022. There was no statistically significant difference between the yield of the samples taken in the APV system and the reference area. Even so, the yield per hectare is reduced by approximatively 10%, when the distance between the vertically mounted solar modules is 10 meters, due to the area under the solar modules that cannot be mechanically harvested. The measurements performed at the test site allowed us to validate the earlier developed model for both electricity production and the effects of shading on crop production. Having a model to assess crop yields under APV systems is of utmost importance to be able to pre-assess the system's effects on food production, which is one of the main goals of APV system regulations worldwide. From an economic perspective, APV systems cannot compete with CGMPV systems due to lower electricity production per hectare, lower density of the solar modules per hectare, and higher investment costs per hectare. Nevertheless, APV systems can be the solution to overcome the legal obstacles that prohibit or hinder the use of agricultural land for electricity generation with PV systems. 

This paper presents a mathematical model for estimating the optimal sizing and assessing a standalone hybrid power system's performance entirely based on variable renewable energy sources and coupled with a hybrid energy storage system. This study evaluates how different levels of the main components' capital cost and the loss of power supply probability would affect the cost of energy and the power system's optimal sizing. The case study selected for this study was Ometepe Island in Nicaragua, where the crater lake of an extinct volcano was considered a feasible upper reservoir of a pumped storage hydropower plant, reducing the investments associated with this component. The mathematical formulation considers energy storage losses and gains, and the Pareto efficient solutions of the multi-objective optimization model simultaneously increase reliability, reduce the cost of energy, and minimize curtailment energy. By employing time-series with an hourly resolution, the model allows assessing the impact of the interannual variability of renewable energy sources on the system's performance. As for the case study, the cost of energy obtained from the model results ranges between (sic)0.047/kWh and (sic)0.095/kWh, based on international reference values, and these values match the information available in the literature and other databases.

Renewable energy sources have shown remarkable growth in recent times in terms of their contribution to sustainable societies. However, integrating them into the national power grids is usually hindered because of their weather-dependent nature and variability. The combination of different sources to profit from their beneficial complementarity has often been proposed as a partial solution to overcome these issues. Thus, efficient planning for optimizing the exploitation of these energy resources requires different types of decision support tools. A mathematical index for assessing energetic complementarity between multiple energy sources constitutes an important tool for this purpose, allowing a comparison of complementarity between existing facilities at different planning stages and also allowing a dynamic assessment of complementarity between variable energy sources throughout the operation, assisting in the dispatch of power supplies. This article presents a method for quantifying and spatially representing the total temporal energetic complementarity between three different variable renewable sources, through an index created from correlation coefficients and compromise programming. The method is employed to study the complementarity of wind speed, solar radiation and surface runoff on a monthly scale using continental Colombia as a case study during the year of 2015.•This paper describes a method for quantifying and spatially representing energetic complementarity between three renewable energy sources.•The method quantifies energetic complementarity by combining known metrics: correlations and compromise programming.•The proposed index for energetic complementarity assessment is sensitive to the time scale adopted. 

Buildings are currently controlled in a sub optimal way, using a WC controller that is dependent only on the external temperature. A rich amount of real-time data from installed sensors is available within the buildings and the network and can be used to counteract this. To better control the indoor temperature and the heat supply this degree-project develops a model and optimizer for control of the indoor temperature, where industry standard data streams are used as inputs. The model and optimizer can be implemented in a MPC which takes the future external temperature into consideration and enhances the ability to control the heat supply. There are two main reasons why enhanced control is interesting to look at, the economic aspects and the comfort of the occupancies. This degree project is focused on developing a general building model for the purpose of utilizing the building as an energy storage for peak-shaving. 

The finalized model is a dynamic grey-box model developed using data from a multifamily building, Building A, located in Västerås Sweden. The training period is set to 408 hours, and the prediction horizon is set to 48 hours as a result of the verification. To demonstrate the utilization possibilities of using the building as a heat storage, an optimizer is constructed to evaluate a peak shaving control strategy. The control objective (Q_supply) is controlled by manipulating the indoor temperature (T_in) within a set interval. By setting a fixed interval for the indoor temperature within the comfort interval, the comfort is still maintained. For the peak shaving different flexibilities within the indoor temperature have been examined with a range from 22 +/- 0.25 degrees Celcius to 22 +/- 2.00 degrees Celcius. 

The model is verified in 4 steps: prediction ability on the historic data, parametric verification on the time constant, simulation of heat supply separately from the historic data and model generality by implementing the model on a second multifamily building, Building B. The model has a RRMSE of 8% for Building A and 9% for Building B which is considered excellent.  Due to the lack of access to the real building, the developed model is not validated.

Based on peak shaving and energy consumption, the preferred solution is 22+/- 1.25 degrees Celcius. But based on surveys about occupancies attitude toward flexibility in the indoor temperature and economical aspects, an indoor temperature of 22 +/- 0.50 degrees Celcius is considered the best choice with the maximum peak in the heat supplied decreased by 35% and the energy consumption is decreased by 10% compared to the historical case. We suggest allowing the customers to choose their preferred flexibility to ensure comfort. 

Transforming the road public transport to run on renewable energy is vital solution to achieve carbon neutral and net zero goals. This paper evaluates the potential of using solar radiation-generated electricity as an auxiliary power supplementary for the battery of electric buses, based on a developed framework that using publicly street-view panoramas, GPS trajectory data and DEM data as input parameters of solar radiation model. A case study of Qingdao, China with 547 bus routes, 28,661 street-view panoramas shows that the solar-radiation electricity generated at noon during the operation accounts for about one-fifth, one-eighth of the total elec-tricity consumption of a bus traveling one kilometer in a sunny day and a cloudy day, respectively. Spatial variability shows significant solar-radiation power generation advantages in newly-launched areas and expressway. The solar power generated in a sunny day can make a bus half of passengers and with air conditioner off at least one extra trip in 2:1 replacement schedule, and nearly close to one extra trip in 4:3 replacement schedule. A correlated relation between the solar-radiation power generation benefit and the operation schedule of electric buses is observed, implying that the high cost of 2:1 replacement schedule for long-distance routes during summer or winter can be reduced. The proposed framework can help us evaluate and understand the feasibility of solar radiation-generated electricity energy of electric bus fleets covering the large-scale urban areas at different times, locations, and weather conditions, so as to support effective decisions at better planning of PV-integrated electric buses.

Firms need multi-stakeholder ecosystems to create successful second-life business models for electric vehicle (EV) batteries. However, there is a lack of guiding instrumentsto support the process of strategizing and managing the EV battery ecosystem for secondlife operations. The purpose of this study is to propose a guiding framework that could support firms in the EV battery ecosystem to establish and manage various configurations for second-life operations. The study developed a framework with four configuration phases—namely, firm-level initiation, ecosystem construction, firm-level optimization, and ecosystem orchestration. Based on these phases, the paper describes three configuration pathways to establish and manage second-life operations

Geothermal heat is considered a sustainable energy source with significant global potential. Together with heat distribution networks, it can provide clean thermal energy to individual and commercial consumers. However, peaks in heat demand can require additional peaking sources at times. In this paper, we investigated how wind turbines can act as a peak energy source for a geothermal district heating system. We studied a model consisting of a geothermal heat source, a heat storage and wind power generator using historical weather data of Warsaw (Poland) and showed that wind power could increase the renewable share to supply a considerable heat demand compared to a geothermal heat source alone. The results indicate that wind power can be a suitable complement for a geothermal heat source to provide energy for heating. It is shown that a theoretical geo-wind-thermal storage based district heating network supplying 1000 m(2), which requires 100 W/m(2) at an outdoor temperature of -20 degrees C should have the following parameters: 4.8 MWh of thermal energy storage capacity, 45 kW of geothermal capacity and 5 kW of wind capacity. Such a system would ensure minimal wind curtailment, high utilization of geothermal source and high reliability of supply.

The coupling of a pressurized solid oxide fuel cell (SOFC) and a gas turbine has been proven to result in extremely high efficiency and reduced emissions. The presence of the gas turbine can improve system durability compared to a standalone SOFC, because the turbomachinery can supply additional power as the fuel cell degrades to meet the power request. Since performance degradation is an obstacles to SOFC systems commercialization, the optimization of the hybrid system to mitigate SOFC degradation effects is of great interest. In this work, an optimization approach was used to innovatively study the effect of gas turbine size on system durability for a 400 kW fuel cell stack. A larger turbine allowed a bigger reduction in SOFC power before replacing the stack, but increased the initial capital investment and decreased the initial turbine efficiency. Thus, the power ratio between SOFC and gas turbine significantly influenced system economic results.

With consumer awareness increasing, the importance of the environmental impact of everyday products such as food becomes a major factor for the customer. This awareness was increased with the COVID-19 pandemic. This book introduces green and sustainable food technology and its use during a pandemic. In addition, it covers current topics such as future food processing, methods of maintaining industrial hygiene and microbiological quality and safety. Circular economy approach to food technology. Focus on green and innovative methods for food production. Investigates the handling of food production, food chain, deliveries and quality during a pandemic.