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The integrated production of bioethanol and biogas makes it possible to optimise the production of carriers from renewable raw materials. The installation analysed in this experimental paper was a hybrid system, in which waste from the production of bioethanol was used in a biogas plant with a capacity of 1 MWe. The main objective of this study was to determine the energy potential of biomass used for the production of bioethanol and biogas. Based on the results obtained, the conversion rate of the biomass—maize, in this case—into bioethanol was determined as the efficiency of the process of bioethanol production. A biomass conversion study was conducted for 12 months, during which both maize grains and stillage were sampled once per quarter (QU-I, QU-II, QU-III, QU-IV; QU—quarter) for testing. Between 342 L (QU-II) and 370 L (QU-I) of ethanol was obtained from the organic matter subjected to alcoholic fermentation. The mass that did not undergo conversion to bioethanol ranged from 269.04 kg to 309.50 kg, which represented 32.07% to 36.95% of the organic matter that was subjected to the process of bioethanol production. On that basis, it was concluded that only two-thirds of the organic matter was converted into bioethanol. The remaining part—post-production waste in the form of stillage—became a valuable raw material for the production of biogas, containing one-third of the biodegradable fraction. Under laboratory conditions, between 30.5 m3 (QU-I) and 35.6 m3 (QU-II) of biogas per 1 Mg of FM (FM—fresh matter) was obtained, while under operating conditions, between 29.2 m3 (QU-I) and 33.2 m3 (QU-II) of biogas was acquired from 1 Mg of FM. The Biochemical Methane Potential Correction Coefficient (BMPCC), which was calculated based on the authors’ formula, ranged from 3.2% to 7.4% in the analysed biogas installation.

The aim of the study was to assess leaching of Cl−, SO42−, and PO43− from ashes formed in household furnaces. The ashes were obtained following the combustion of conventional fuels, namely wood and hard coal, and alternative fuels with various fractions of municipal waste in a household boiler. Aqueous leachates of the ashes were used to determine concentrations of chlorides by titration (Mohr’s method) (21.3–3049.6 mg/dm3), sulphates by the gravimetric method (12.2–244.1 mg/dm3), and phosphates by spectrophotometry (0.01–67.2 mg/dm3). It was found that co-combustion of municipal waste with plastic-coated paper cartons, diapers, or a mixed waste fraction leaves the greatest amount of ashes on the furnace grate. The highest amounts of Cl−, SO42−, and PO43− were leached from ashes generated from burning a mix of wood and coals, or wood alone (different species). The addition of municipal waste to the process of burning the conventional and alternative fuels studied did not significantly increase Cl−, SO42−, and PO43− content in aqueous extracts of ashes, the exception being diapers and plywood. In light of the study results, it was concluded that all the ashes could be reused (as an additive to concrete) except for the ash generated from the combustion of a mixed municipal waste fraction and coal (due to the content of Cl−) and diapers (due to the content of PO43−). It was demonstrated that Cl−, SO42−, and PO43− content in the entire set of samples and in individual ash groups is highly heterogeneous and variable.

Cattle biomass (CB or manure from cattle) is proposed as reburn fuel under slightly fuel-rich conditions. The CB includes wastes from cattle feedlots (cattle grown in feedlots to slaughter weights of 450–640 kg) termed as Feedlot Biomass (FB) and cattle wastes from dairy farms termed as Dairy Biomass (DB). NOx emissions from coal-fired power plants can be reduced by using pure CB and Coal:CB mixtures as reburn fuels (10~30% by heat) injected after the primary combustion region. Experiments with Coal:CB mixtures as reburn fuels were performed using the 30 kW burner facility. Part I deals with results from experiments using pure FB and Coal:FB blends as reburn fuels while Part II presents results on extent of NOx reduction using pure DB and Coal:DB blends as reburn fuels (RF). In the current work, results on NOx emission are presented with FB and Coal:FB blends as RF. The parametric studies include: equivalence ratio in reburn zone (ERRBZ), vitiated air, angle of reburn nozzles, presence and absence of heat exchangers (HEXs), and baseline NOx concentration. The optimum operating conditions for FB reburning were found to be conditions with vitiation at ERRBZ = 1.1 with 45° upward counter-current injection in the presence of HEXs. NOx emissions were reduced by as much as 96%.

Although replacing biomass, (e.g., wood chips and pellets), with thinning wood and herbaceous biomass is eco-friendly and economically advantageous, their direct utilization in plant boilers is associated with ash-related challenges, including slagging and fouling. The aim of this study is to determine the effects of ash removal treatment (ashless biomass (ALB)) in the context of solid fuel power plant boilers. Ash was removed via neutralization of metal ions and carboxylic acids contained in the biomass ash. The ash removal rate of K, Na, Cl was indicated by assessing the total biomass before and after ash removal treatment, via XRF analysis. Co-combustion with sub-bituminous coal and ALB-treated biomass was analyzed using a drop tube furnace and revealed that NOx and SOx values converged converge toward an approximate 10 ppm error, whereas the Unburned Carbon (UBC) data did not exhibit a specific trend. Factors associated with slagging and fouling, (capture efficiency (CE), and energy based growth rate (GRE)) were calculated. All biomass samples without pretreatment exhibited V-shaped variation. Conversely, for ashless biomass (ALB) samples, CE and GRE gradually decreased. Thus, the ALB technique may minimize slagging and fouling in a boiler, thus, reducing internal corrosion associated with ash deposition and enhancing the economic operation of boilers.

The cocombustion of coal and pinus sawdust waste is an economically viable and sustainable option for increasing the share of biomass in energy production. This technology also has the potential to reduce the emission of greenhouse gases from existing coal fired power plants. The thermal synergistic effects of cocombusting Hwange bituminous coal (HC) with Pinus sawdust (PS) were thus investigated using thermogravimetric analysis. Fuel blending mass ratios of 100HC, 90HC10PS, 80HC20PS, 70HC30PS, and 100PS under an oxidative atmosphere at three different heating rates of 5, 12.5, and 20 °C/min were used for the experimental setup. Zero to negative synergy was generally observed for the mass loss curves (TG) at different blending ratios. Generally positive synergy was observed with relation to rate of mass loss curves (DTG) for the 80HC20PS and 70HC30PS fuel blends only. The ignition index increased with blending ratio by an average of 42.86%, whilst the burnout index showed a maximum increase of 14.6% at 20 °C/min. However, the combustion index representative of stability showed a decreasing trend generally for all the heating rates. No combustion index produced a linear variation with temperature, though upon evaluation, an optimum mass ratio of 20% pinus sawdust was suggested. The chosen optimum blending ratio demonstrated increased ignition and burnout indexes whilst maintaining the stability of combustion at a reasonable range.

The steelmaking industry requires coke as a reducing agent, as an energy source, and for its ability to hold slag in a blast furnace. Coking coal as raw coke material is very limited. Studying the use of biomass as a mixture of coking coal in the synthesis of biocoke is necessary to reduce greenhouse gas coal emissions. This research focuses on biomass and heating temperature through the coal blending method to produce biocoke with optimal mechanical properties for the blast-furnace standard. The heating temperature of biomass to biochar was evaluated at 400, 500, and 600 °C. The blending of coking coal with biochar was in the compositions of 95:5, 85:15, and 75:25 wt.%. A compacting force of 20 MPa was employed to produce biocoke that was 50 mm in diameter and 27 mm thick using a hot cylinder dye. The green sample was heated at 1100 °C for 4 h, followed by quenching with a water medium, resulting in dense samples. Increasing heating temperature is generally directly proportional to an increase in fixed carbon and calorific value. Biocoke that meets several blast-furnace criteria is a coal mixture with coconut-shell charcoal of 85:15 wt.%. Carbonization at 500 °C, yielding fixed carbon, calorific value, and compressive strength, was achieved at 89.02 ± 0.11%; 29.681 ± 0.46 MJ/kg, and 6.53 ± 0.4 MPa, respectively. This product meets several criteria for blast-furnace applications, with CRI 29.8 and CSR 55.1.

Biomass is one of the most important sources of renewable energy. It is expected that in the coming decades, biomass will play a major role in replacing fossil fuels. The most commonly used biofuels include wood pellet, which is a cost-effective, uniform and easy-to-use material. In view of the growing interest in this type of resource, novel methods are being investigated to improve the quality of pellet. This article presents the results of a laboratory study focusing on wood pellets refined with waste sunflower cooking oil applied by spraying. In this work, authors attempted to modify the energy parameters of wood pellets with the use of waste cooking oil. Addition of waste cooking oil, applied at the rates of 2%, 4%, 6%, 8%, 10% and 12% relative to the weight of pellets, increased the calorific value of the pellets without decreasing their durability. The highest dose of the modifier (12%) on average led to a 12–16% increase in calorific value. In each case, the addition of sunflower oil resulted in decreased contents of ash in the pellets; on average a decrease of 16–38% was observed in the samples treated with the highest dose of the modifier. The treatment led to a higher content of elements affecting the heating value, i.e., carbon and hydrogen, which on average increased by 7.5–12%, and 7.0–10.0%, respectively. The presented method seems to be a promising way of increasing the calorific value of pellets. Further research on refining the method and the possibility of using it in industry is necessary.

Microwave torrefaction of oat hull was conducted to enhance its physicochemical properties. A bench-top reactor with an internal stirrer was used for oat hull pretreatment at temperatures of 225 °C, 255 °C, and 285 °C, and residence times of 3, 6, and 9 min, respectively. Results showed that a high temperature level at 3 min residence time or severe torrefaction increased calorific values by up to 35% of its original value, while decreasing mass yield down to 60.77%. Severe torrefaction further decreased moisture absorption, moisture content, and grinding energy consumption but decreased energy yield and bulk density. Residence time had no significant effect on biomass physicochemical changes; however, production cost may be significantly increased by longer residence times. It was also concluded that increased microwave power levels from 400 to 650 W decreased energy consumption by shortening processing times, resulting in a positive economic impact of the process. Moderate and severe torrefaction significantly enhanced biomass fuel properties, and short residence times are recommended in order to decrease electricity consumption. In addition, microwave pretreatment enhances biomass in a similar way to conventional torrefaction, but at a faster processing time. Moreover, the liquid fraction as a by-product may represent a valuable product for the food industry.

The digestate is a prospective biofertilizer and potential source of income for many biogas plants worldwide. However, its actual impact on the soil properties and biomass yield is still unexploited. The different digestates from eight agricultural biogas plants were researched in terms of their chemical composition and the fertilizing potential. The results obtained from digestate chemical analysis indicate that the digestate biomass had large amount of nitrogen (up to 73 g kg−1 fresh mass) and potassium (up to 25 g kg−1 fresh mass). The value of the digestate was estimated in the range of 2.88–7.89 EUR Mg−1 for liquid digestate and 7.62–13.61 EUR Mg−1 for solid digestate based on the commercial fertilizer market price of nitrogen, potassium phosphorus, organic carbon, Cu, Zn, Fe and Mg. The digestate produced at the 1 MW biogas plant is worth EUR 941–2095 per day in addition to energy sales income. The application of digestate on low-fertility land in areas close to the biogas plant allows the production of up to three-fold more biomass suitable for biogas production. The digestate’s application on semi-natural grass biomass production in the low-fertility soils near the biogas plants could be an alternative strategy for the biogas plant feedstock portfolio diversification.

Microalgae are natural resources of intracellular compounds with a wide spectrum of applications in, e.g., the food industry, pharmacy, and biofuel production. The extracellular polymeric substances (EPS) released by microalgal cells are a valuable bioproduct. Polysaccharides, protein, lipids, and DNA are the main constituents of EPS. This review presents the recent advances in the field of the determinants of the synthesis of extracellular polymeric substances by microalgal cells and the EPS structure. Physical and chemical culture conditions have been analyzed to achieve useful insights into the development of a strategy optimizing EPS production by microalgal cells. The application of microalgal EPS for flocculation and mechanisms involved in this process are also discussed in terms of biomass harvesting. Additionally, the ability of EPS to remove toxic heavy metals has been analyzed. With their flocculation and sorption properties, microalgal EPS are a promising bioproduct that can potentially be used in harvesting algal biomass and wastewater management.

The properties, types, and physical and chemical aspects of pig slurry used in the fermentation process were presented. Characterization of the pig slurry microflora for a controlled biogas production process was performed. A pilot biogas treatment installation was presented on the example of a farm with 1100 Dan Bred fatteners kept in a grate system. The research was carried out to measure the biogas flow rate resulting from the reference pressure in the fermentor. An independent assessment of the amount of biogas and the pressure drop in the skeletal deposit was carried out. The basis for assessing the hydrodynamics of gas flow through the adhesive bed is the flow characteristic, which results from the pressure that forces this flow. In each case, the determination of this characteristic consists in determining the influence of the biogas stream on the value of this overpressure, equivalent to the pressure drop (it is tantamount to determining the total biogas flow resistance through the adhesive bed). The results of the measurements indicate the practical application of pig slurry-a substrate in a polydisperse system for the production of agricultural biogas in the context of renewable energies. The article indicates that the ferment was periodically mixed during the day, together with the fermentation of the ferment with fresh substrate. The tests were conducted for 49 days, thus demonstrating that it is more advantageous to mix the ferment hydrodynamically, obtaining a CH4 level of about 80%.

Blood from livestock slaughtering imposes a high organic pollution load and risks. If it is discharged untreated to sewer systems, it increases the organic pollution load on wastewater treatment plants by 35–50%. This paper reviews blood anaerobic digestion. It analyzes the quantities, composition, methane potential reported, microbiology, biochemical pathways of blood protein degradation, environmental and health issues, and strategies suggested to manage them during livestock blood anaerobic digestion. Although challenging, anaerobic digestion of blood as a mono-substrate is possible if the culture-reactor system is controlled based on a complete characterization and understanding of the microbial community and its metabolic activities. Co-digestion of blood and other feedstock proceeds well if the mixtures are well designed. Generally, the specific methane yield from digesting blood alone ranges between zero and 0.45 m3 kg−1 protein, whereas for co-digesting blood and other substrates, the yield varies between 0.1 and 0.7 m3 kg−1 volatile solids. More research is required for microbiology and kinetics, the role of adsorbents, reactor configuration, and culture adaptation during anaerobic digestion of blood to better control the process.

Biodiesel is a renewable energy source which is gaining prominence as an alternative fuel over fossil diesel for different applications. Due to their higher viscosity and lower volatility, biodiesels are blended with diesel in various proportions. B20 blends are viable and sustainable solutions in diesel engines with acceptable engine performance as they can replace 20% fossil fuel usage. Biodiesel blends are slightly viscous as compared with diesel and can be used in common rail direct injection (CRDI) engines which provide high pressure injection using an electronic control unit (ECU) with fuel flexibility. In view of this, B20 blends of three biodiesels derived from cashew nutshell (CHNOB (B20)), jackfruit seed (JACKSOB (B20)), and Jamun seed (JAMNSOB (B20)) oils are used in a modified single-cylinder high-pressure-assisted CRDI diesel engine. At a BP of 5.2 kW, for JAMNSOB (B20) operation, BTE, NOx, and PP increased 4.04%, 0.56%, and 5.4%, respectively, and smoke, HC, CO, ID, and CD decreased 5.12%, 6.25%, 2.75%, 5.15%, and 6.25%, respectively, as compared with jackfruit B20 operation.

The growing interest in processes that involve biomass conversion to renewable energy, such as anaerobic digestion, has stimulated research in this field in order to assess the optimum conditions for biogas production from abundant feedstocks, like agro-industrial wastes. Anaerobic digestion is an attractive process for the decomposition of organic wastes via a complex microbial consortium and subsequent conversion of metabolic intermediates to hydrogen and methane. The present study focused on the exploitation of liquid cow manure (LCM) and cheese whey (CW) as noneasily and easily biodegradable sources, respectively, using continuous stirred-tank reactors for biogas production, and a comparison was presented between single- and two-stage anaerobic digestion systems. No significant differences were found concerning LCM treatment, in a two-stage system compared to a single one, concluding that LCM can be treated by implementing a single-stage process, as a recalcitrant substrate, with the greatest methane production rate of 0.67 L CH4/(LR·d) at an HRT of 16 d. On the other hand, using the easily biodegradable CW as a monosubstrate, the two-stage process was considered a better treatment system compared to a single one. During the single-stage process, operational problems were observed due to the limited buffering capacity of CW. However, the two-stage anaerobic digestion of CW produced a stable methane production rate of 0.68 L CH4/(LR·d) or 13.7 L CH4/Lfeed, while the total COD was removed by 76%.

Iron-based industries are one of the main contributors to greenhouse gas (GHG) emissions. Partial substitution of fossil carbon with renewable biocarbon (biomass) into the blast furnace (BF) process can be a sustainable approach to mitigating GHG emissions from the ironmaking process. However, the main barriers of using biomass for this purpose are the inherent high alkaline and phosphorous contents in ash, resulting in fouling, slagging, and scaling on the BF surface. Furthermore, the carbon content of the biomass is considerably lower than coal. To address these barriers, this research proposed an innovative approach of combining two thermochemical conversion methods, namely hydrothermal carbonization (HTC) and slow pyrolysis, for converting biomass into suitable biocarbon for the ironmaking process. Miscanthus, which is one of the most abundant herbaceous biomass sources, was first treated by HTC to obtain the lowest possible ash content mainly due to reduction in alkali matter and phosphorous contents, and then subjected to slow pyrolysis to increase the carbon content. Design expert 11 was used to plan the number of the required experiments and to find the optimal condition for HTC and pyrolysis steps. It was found that the biocarbon obtained from HTC at 199 °C for 28 min and consecutively pyrolyzed at 400 °C for 30 min showed similar properties to pulverized coal injection (PCI) which is currently used in BFs due to its low ash content (0.19%) and high carbon content (79.67%).

Countries globally are focusing on alternative fuels to reduce the environmental pollution. An example is biodiesel fuel, which is leading the way to other technologies. In this research, the methyl esters of castor oil were prepared using a two-step transesterification process. The respective properties of the castor oil (Ricinus Communis) biodiesel were estimated using ASTM standards. The effect of performance and emission on diesel engines was noted for four various engine loads (25, 50, 75, and 100%), with two different blends (B5 and B20) and at two different engine speeds (1500 and 2000 rpm). The study determined that B5 and B20 samples at 1500 rpm engine speed obtained the same power, but diesel fuel generated greater control. The power increased at 2000 rpm for B5 samples, but B20 samples, as well as diesel, were almost the same values. In the 40–80% range, load and load values were entirely parallel for each load observed from the engine performance of the brake power in all samples.

The use of renewable energy sources increases the energy self-sustainability of cities, enabling citizens to reduce energy costs, which results in an increase in their standard of living. However, solar energy penetration in Bosnia and Herzegovina, and its capital Sarajevo, is not in line with the possibilities. Furthermore, the Sarajevo Canton is extremely polluted during the winter months because of the use of unacceptable heating fuel. The aim of this paper is to introduce photovoltaic power systems use in heating electrification system. In this paper AQI is calculated based on historical data and the hybrid model EMD-SARIMA for air pollution and a solar production forecast is presented. The methodology was tested in the Sarajevo Canton, taking into account 35,000 households. In order to ensure clean air, renewable electric energy use for household heating should be implemented. The widespread use of inefficient individual heating systems characterized by inefficient and expensive use of firewood and the use of coal in individual furnaces in populated areas are the main problems of internal and urban air pollution in Sarajevo Canton. In order to reduce energy poverty in Sarajevo Canton, the use of a floating photovoltaic power plant located on Lake Jablanica with a capacity of 30 MW and the solar prosumers with capacity of 115 MW to provide the 196 GWh necessary for heating electrification of 35,000 households is implemented in this paper. Finally, based on correlation between AQI forecast and solar production it was calculated that the values of the AQI, considering the application of solar energy during 150 days (five months) in one heating season, have significantly decreased. Also renewable energy sources have a very important role in reducing carbon dioxide (CO2) emissions into the atmosphere and reducing urban pollution. With this approach, households would be heated by renewable electricity, which would make Sarajevo a cleaner, smarter city.

Energy poverty is high up on national and European Union policy agendas. A number of possible indicators to measure the issue have been identified in the literature, but comparable data with European coverage is scarce. The EU Commission thus proposes four independent indicators on the &ldquo, EU Energy Poverty Observatory&rdquo, based on self-reported items from the pan-European surveys on income and living conditions (SILC) and household budgets (HBS). It is of increasing public interest to analyse social impacts of energy policies, and quantify energy poverty indicators also from modelling. This paper first shortly outlines how the expenditure-based indicators using HBS micro data may be directly linked to existing macroeconomic models through their defining variables (energy expenditure and income). As endogenous modelling based on micro data is difficult, the link may be country-specific elasticities. The main contribution of the paper is a systematic in-depth sensitivity analysis of the two indicators to changes in income and energy expenditure following varying patterns in the underlying distributions of the micro data. The results may be used by future soft links to models. The results display sometimes counterintuitive effects. We find that whether these indicators increase/decrease after a change of income or energy expenditure largely depends on the specific country-wise income and energy expenditure distribution between households on a micro-level. Due to their definition, the examined indicators are especially sensitive, when income changes alter the indicator threshold values, which in these cases are the median values in underlying distributions. We discuss these findings and relate them to several indicator shortcomings and potential remedies through changes in indicator definition.

One of the key aspects of vehicle-to-grid technology (V2G) is the analysis of uncertainty in electric vehicle user behavior. Correct estimation of the amount of available energy from electric vehicles that are expected to provide ancillary services to the electricity system operator or to secure the end user’s demand is essential to design these services in an appropriate way. Therefore, it is necessary to analyze the probabilities of V2G service performance for different scenarios. This paper presents the author’s approach to determining the values of V2G service provision probabilities using survey data. It was found that estimating these values using simulation and forecasting tools makes sense when the coefficients resulting from survey responses are used as initial data. Thus, the paper also presents the results of the surveys that were conducted. As the results from the simulations show, the values of the probabilities of V2G services are not high, which should induce future operators of V2G services to offer a beneficial product for the customer.

New advances in small-scale generation and consumption technologies have shifted conventional buildings’ functionality towards energy-efficient active buildings (ABs). Such developments drew the attention of researchers all around the world, resulting in a variety of publications, including several review papers. This study conducts a systematic literature review so as to analyse the concepts/factors enabling active participation of buildings in the energy networks. To do so, a relatively large number of publications devoted to the subject are identified, introducing the taxonomy of control and optimisation methods for the ABs. Then, a study selection methodology is proposed to nominate potential literature that has investigated the role of ABs in the energy networks. The modelling approaches in enabling flexible ABs are identified, while the potential challenges have been highlighted. Furthermore, the citation network of included papers is illustrated by Gephi software and analysed using “ForceAtlas2” and “Yifan Hu Proportional” algorithms so as to analyse the insights and possibilities for future developments. The survey results provide a clear answer to the research question around the potential flexibility that can be offered by ABs to the energy grids, and highlights possible prospective research plans, serving as a guide to research and industry.

Global electricity markets are undergoing a rapid transformation in their energy mix to meet commitments towards sustainable electric grids. This change in energy mix engenders significant challenges, specifically concerning the management of non-dispatchable energy resources. System and market operators are required to meet power system security and reliability requirements whilst providing electricity at competitive prices. An overview of electricity markets is provided in this paper with a critical appraisal of each market’s ability to manage the large-scale energy mix transition. This paper provides a commentary on the distinct features of electricity market models implemented around the world and highlights the barriers within these market models that are hindering the energy mix transition. Various researchers and policymakers are proposing solutions and market reforms for the smooth transitioning of the energy mix. This paper presents a systematic review of the proposed solutions in the literature and critiques the effectiveness and ease of implementation of the reviewed solutions. Research gaps and future research directions are indicated to promote further exploration towards the effective integration of large-scale renewable energy technologies.

This paper proposes a 5.8 GHz highly efficient rectifier design using harmonic termination for wireless power transfer. The diode used to convert the received RF power to DC is a non-linear device, and a harmonic component is generated, which causes performance degradation. Therefore, in this paper, we designed a band stop filter for harmonic termination and proposed the N-stage harmonic terminated voltage multiplier (N-stage HTVM). The number of stages N can be designed differently to operate with high efficiency at various input powers for the proposed rectifier. In the proposed rectifier circuit, mathematical analysis of output DC voltage, power loss of the diode, and the power conversion efficiency (PCE) were evaluated through voltage/current waveform analysis of the diode. The design method of the filter for terminating harmonics is presented. Furthermore, the change of PCE according to the increase in the number of stages was analyzed using the equivalent model of the proposed circuit and verified through measurement. The maximum PCE of one-stage HTVM was 68% when 18 dBm of input power was applied. The DC output voltage was measured to 11.6 V. When the RF input power was 25 dBm and the load was 1500 Ω, the maximum PCE of the two-stage HTVM was 71% and the maximum DC output voltage was measured as 15.8 V. The measured performance of three-stage HTVM had a PCE of 67% and DC output voltage of 19.8 V when the input power was 30 dBm.

Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide use different methodologies to predict the demand, in order to keep the grid stable, but is there any difference between making a short time prediction of active power and reactive power into the grid? The current paper analyzes the most usual forecasting algorithms used in the electrical grids: ‘X of Y’, weighted average, comparable day, and regression. The subjects of the study were 36 different buildings in Terni, Italy. The data supplied for Terni buildings was split into active and reactive power demand to the grid. The presented approach gives the possibility to apply the forecasting algorithm in order to predict the active and reactive power and then compare the discrepancy (error) associated with forecasting methodologies. In this paper, we compare the forecasting methodologies using MAPE and CVRMSE. All the algorithms show clear differences between the reactive and active power baseline accuracy. ‘Addition X of Y middle’ and ‘Addition Weighted average’ better follow the pattern of the reactive power demand (the prediction CVRMSE error is between 12.56% and 13.19%) while ‘Multiplication X of Y high’ and ‘Multiplication X of Y middle’ better predict the active power demand (the prediction CVRMSE error is between 12.90% and 15.08%).

In this paper the idea of functioning of Building Management Systems and Object Management Systems in intelligent buildings is presented. New functionalities of intelligent buildings resulting from the introduction of microgeneration are described. Low-Power Wide-Area Networks (LPWAN) are characterized and compared. The selected Long-Range (LoRaWAN) technology is tested for its use for communication with energy meters and monitoring the power supply network in intelligent buildings. In the paper a new system for reading and monitoring the network is proposed, consisting of hardware, communication, and application layers. A key element of the system is a specially developed converter, which has been designed and tested in a real urban environment. Using our solution in practice could allow to change the architecture of a measurement data acquisition system to much more flexible and efficient.

This paper addresses the challenge of minimizing training time for the control of Heating, Ventilation, and Air-conditioning (HVAC) systems with online Reinforcement Learning (RL). This is done by developing a novel approach to Multi-Agent Reinforcement Learning (MARL) to HVAC systems. In this paper, the environment formed by the HVAC system is formulated as a Markov Game (MG) in a general sum setting. The MARL algorithm is designed in a decentralized structure, where only relevant states are shared between agents, and actions are shared in a sequence, which are sensible from a system’s point of view. The simulation environment is a domestic house located in Denmark and designed to resemble an average house. The heat source in the house is an air-to-water heat pump, and the HVAC system is an Underfloor Heating system (UFH). The house is subjected to weather changes from a data set collected in Copenhagen in 2006, spanning the entire year except for June, July, and August, where heat is not required. It is shown that: (1) When comparing Single Agent Reinforcement Learning (SARL) and MARL, training time can be reduced by 70% for a four temperature-zone UFH system, (2) the agent can learn and generalize over seasons, (3) the cost of heating can be reduced by 19% or the equivalent to 750 kWh of electric energy per year for an average Danish domestic house compared to a traditional control method, and (4) oscillations in the room temperature can be reduced by 40% when comparing the RL control methods with a traditional control method.

This paper focuses on the methods that ensure the rotor angle stability of electric power systems, which is most frequently analyzed with small-signal models. Over the past several decades, power system stabilizers (PSSs) for conventional excitation systems were the main tools for improving the small-signal stability of electromechanical oscillatory modes. In the last decade, power oscillation damping (POD) control implemented in photovoltaic (PV) inverters has been considered an alternative to PSSs. As PV generation undergoes massive rollout due to policy directions and renewable energy source integration activities, it could potentially be used as a source of damping, which is crucial for sustaining the rotor angle stability of the remaining in-service synchronous generators. Several studies have already been dedicated to the development of different damping strategies. This paper contributes to the existing research in power system stability by providing a comprehensive review of the effects of PV generation on small-signal stability, as well as the recent evolution of POD control through PV inverters. The features and impacts of the various ways to realize POD controllers are assessed and summarized in this paper. Currently, detailed information and discussions on the practical application of PV inverter PODs are not available. This paper is, thus, intended to initiate a relevant discussion and propose possible implementation approaches concerning the topic under study.

In time attack races, aerodynamics plays a vital role in achieving short track times. These races are characterized by frequent braking and acceleration supported by aerodynamic downforce. Usually, typical cars are modified for these races by amateurs. Adjusting the aerodynamic solutions to work with bodies developed for other flow conditions is difficult. This paper presents the results of a numerical analysis of the effects of installing a straight wing in front of or above the body on the modified vehicle system’s aerodynamic characteristics, particularly on the front wheels’ aerodynamic downforce values. The paper presents the methodology and results of calculations of the aerodynamic characteristics of a car with an additional wing placed in various positions in relation to the body. The numerical results are presented (Cd, Cl, Cm, Clf, Clr), as well as exemplary pressure distributions, pathlines, and visualizations of vortex structures. Strong interactions between the wing operation and body streamline structure are shown. An interesting and unexpected result of the analysis is that the possibility of obtaining aerodynamic downforce of the front wheels is identified, without an increase in aerodynamic drag, by means of a wing placed in a proper position in front of the body. A successful attempt to balance the additional downforce coming from the front wing on the front axle is made using a larger spoiler. However, for large angles of attack, periodically unsteady flow is captured with frequency oscillations of ca. 6–12 Hz at a car speed of 40 m/s, which may interfere with the sports car’s natural suspension frequency.

Due to the constant trend of building wind turbine sets and photovoltaic cells, the so-called renewable energy sources, it is important to develop a mathematical model that will allow us to analyse the economic viability of their operation. By using this model, multivariate calculations should be carried out and developed in the form of universal nomograms that will allow us to estimate this viability almost on an immediate basis. In this paper, such a model for the unit cost of electricity production from renewable energy sources is presented. It is based on a continuous-time Net Present Value notation. The multi-variant calculations performed with it take into account different subsidy levels and operating time of the renewable energy sources. The paper presents values of unit costs of electricity production from renewable energy sources and values of maximum subsidies for this production. It turns out that subsidies are necessary for renewable energy sources to exist on the energy market. An additional disadvantage is the fact that renewable energy sources are not able to provide a stable electricity supply throughout the year.

Considering loads when optimizing wind-farm layouts or designing farm-control strategies is important, but the computational cost of using high-fidelity wake models in the loop can be prohibitively high. Using simpler models that consider only the spatial variation of turbulence statistics is a tempting alternative, but the accuracy of these models with respect to the aeroelastic response is not well understood. This paper therefore highlights the effect of replacing wake meandering with spatially varying statistics (“profile functions”) in the inflow to a downstream turbine. Profile functions at different downstream and lateral locations are extracted from a large-eddy simulation with an upstream turbine and compared with two lower-fidelity models: one that prescribes both the mean and standard deviation of the turbulence and one that prescribes only the mean. The aeroelastic response of an NREL 5 MW wind turbine is simulated with the three different wake-model fidelities, and various quantities of interest are compared. The mean values for the power and rotor speed for the medium-and low-fidelity model match well, but the accuracy of the fatigue loads varies greatly depending on the load channel. Prescribing the profile function for the standard deviation is only beneficial for the tower-base fore-aft moment; all other DELs had similar accuracies for both the medium- and low-fidelity models. The paper concludes that blade DELs can be estimated using these simple models with some accuracy, but care should be taken with the load channels related to the shaft torsion and tower-base fore-aft bending moment.

In response to the inherent challenges of generating cost-effective electricity consumption schedules for dynamic systems, this paper espouses the use of GBM or Gradient Boosting Machine-based models for electricity price forecasting. These models are applied to data streams from the Irish electricity market and achieve favorable results, relative to the current state-of-the-art. Presently, electricity prices are published 10 h in advance of the trade day of interest. Using the forecasting methodology outlined in this paper, an estimation of these prices can be made available one day in advance of the official price publication, thus extending the time available to plan electricity utilization from the grid to be as cost effectively as possible. Extreme Gradient Boosting Machine (XGBM) models achieved a Mean Absolute Error (MAE) of 9.93 for data from 30 September 2018 to 12 December 2019 which is an 11.4% improvement on the avant-garde. LGBM models achieve a MAE score 9.58 on more recent data: the full year of 2020.

International audience; This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution.This paper presents a detailed analysis of 1200 V Silicon Carbide (SiC) power MOSFETexhibiting different short-circuit failure mechanisms and improvement in reliability by VDS andVGS depolarization. The device robustness has undergone an incremental pulse under differentdensity decreasing; either drain-source voltage or gate-driver voltage. Unlike silicon device, the SiCMOSFET failure mechanism firstly displays specific gradual gate-cracks mechanism and progressivegate-damage accumulations greater than 4 µs/9 J·cm−2. Secondly, a classical drain-source thermalrunaway appears, as for silicon devices, in a time greater than 9 µs. Correlations with short-circuitenergy measurements and temperature simulations are investigated. It is shown that the firstmechanism is an incremental soft gate-failure-mode which can be easily used to detect and protectthe device by a direct feedback on the gate-driver. Furthermore, it is highlighted that this newmechanism can be sufficiently consolidated to avoid the second drain-source mechanism which is ahard-failure-mode. For this purpose, it is proposed to sufficiently depolarize the on-state gate-drivevoltage to reduce the chip heating-rate and thus to decouple the failure modes. The device is muchmore robust with a short-circuit withstand time higher than 10 µs, as in silicon, no risk of thermalrunaway and with an acceptable penalty on RDS-ON.

In the transportation sector, electric battery bus (EBB) deployment is considered to be a potential solution to reduce global warming because no greenhouse gas (GHG) emissions are directly produced by EBBs. In addition to the required charging infrastructure, estimating the energy consumption of buses has become a crucial precondition for the deployment and planning of electric bus fleets. Policy and decision-makers may not have the specific tools needed to estimate the energy consumption of a particular bus network. Therefore, many state-of-the-art studies have proposed models to determine the energy demand of electric buses. However, these studies have not critically reviewed, classified and discussed the challenges of the approaches that are applied to estimate EBBs’ energy demands. Thus, this manuscript provides a detailed review of the forecasting models used to estimate the energy consumption of EBBs. Furthermore, this work fills the gap by classifying the models for estimating EBBs’ energy consumption into small-town depot and big-city depot networks. In brief, this review explains and discusses the models and formulations of networks associated with well-to-wheel (WTW) assessment, which can determine the total energy demand of a bus network. This work also reviews a survey of the most recent optimization methods that could be applied to achieve the optimal pattern parameters of EBB fleet systems, such as the bus battery capacity, charger rated power and the total number of installed chargers in the charging station. This paper highlights the issues and challenges, such as the impact of external factors, replicating real-world data, big data analytics, validity index, and bus routes’ topography, with recommendations on each issue. Also, the paper proposes a generic framework based on optimization algorithms, namely, artificial neural network (ANN) and particle swarm optimization (PSO), which will be significant for future development in implementing new energy consumption estimation approaches. Finally, the main findings of this manuscript further our understanding of the determinants that contribute to managing the energy demand of EBBs networks.

Solar still, a small equipment using evaporation and condensation processes to get clean water, is expected to be widely used for sea/brackish water desalination, water purification, and wastewater treatment because of its convenient carrying, friendly environment, and low energy consumption. In recent years, considerable progress has been made in improving the productivity of solar still. This paper will reclassify the methods to improve the solar still by elevating the evaporation rate and condensation rate. The main methods increasing evaporation rate are as follows: (i) adding heat storage materials; (ii) using nanoparticles; (iii) changing structure of the absorption plate; and (iv) using photothermal materials. The primary methods increasing the condensation rate are as follows: (i) cooling the condensing surface; (ii) increasing the condensation area; (iii) changing the wettability of the condensing surface; and (iv) using a separate condenser. The advantages and disadvantages of each method are compared. Furthermore, this paper includes an economic analysis of current solar stills and a forecast of future developments. The freshwater cost of solar still is in the range of about USD 0.0061–0.277/L, which provides reference and direction for future researching solar stills on their low cost and high productivity.

Organic Rankine cycle (ORC) is considered a promising heat-to-power technology to utilize waste heat and renewable energy, including solar, biomass and geothermal. However, since the thermodynamic, economic and environmental performance is usually conflict, the single objective design could no longer meet the requirements of the ORC system, putting forward urgent requirements for multi-objective optimization, which has attracted increasing attention with lots of papers published. However, due to these different decision variables, optimization objectives and approaches, existing research is significantly different from each other and is difficult to compare without a systematic summary. Therefore, this paper provides an overview of ORC multi-objective research from three perspectives: optimization objective, method and optimization parameters. Based on the classification of different objectives, this work summarizes the involved variables and provides a recommendation for selecting appropriate objectives in different scenarios. For the optimization method, this work compares different approaches and reveals their advantages and disadvantages. Finally, the decision variables are reviewed and classified into four levels. Then the integrated design approach considering “system-process-component-fluid” is proposed and recommended for further development.

This paper presents improved methods to detect cracks and thermal leakage in building envelopes using unmanned aerial vehicles (UAV) (i.e., drones) with video camcorders and/or infrared cameras. Three widely used contour detectors of Sobel, Laplacian, and Canny algorithms were compared to find a better solution with low computational overhead. Furthermore, a scheme using frame-based location identification was developed to effectively utilize the existing approach by finding the current location of the drone-assisted image frame. The results showed a simplified drone-assisted scheme along with automation, higher accuracy, and better speed while using lower battery energy. Furthermore, this paper found that the cost-effective drone with the attached equipment generated accurate results without using an expensive drone. The new scheme of this paper will contribute to automated anomaly detection, energy auditing, and commissioning for sustainably built environments.

This paper has tried to execute accurate demand forecasting by utilizing big data visualization and proposes a flexible and balanced electric power production big data virtualization based on a photovoltaic power plant. First of all, this paper has tried to align electricity demand and supply as much as possible using big data. Second, by using big data to predict the supply of new renewable energy, an attempt was made to incorporate new and renewable energy into the current power supply system and to recommend an efficient energy distribution method. The first presented problem that had to be solved was the improvement in the accuracy of the existing electricity demand for forecasting models. This was explained through the relationship between the power demand and the number of specific words in the paper that use crawling by utilizing big data. The next problem arose because the current electricity production and supply system stores the amount of new renewable energy by changing the form of energy that is produced through ESS or that is pumped through water power generation without taking the amount of new renewable energy that is generated from sources such as thermal power, nuclear power, and hydropower into consideration. This occurs due to the difficulty of predicting power production using new renewable energy and the absence of a prediction system, which is a problem due to the inefficiency of changing energy types. Therefore, using game theory, the theoretical foundation of a power demand forecasting model based on big data-based renewable energy production forecasting was prepared.

The links of low power wireless sensor networks are error prone and the transmission on a wireless link is determined probabilistically by the packet reception rate (PRR) of the link. On the other hand, there is a very strict requirement in the end-to-end reliability and delay of sensor data in industrial wireless sensor networks (IWSNs). The existing approaches to provide the end-to-end reliability in IWSNs is retransmitting the packet when failure occurs. These approaches transmit a packet multiple times in successive time slots to provide the required reliability. These approaches, however, can increase the average delay of packets and the number of packets buffered in a queue. This paper proposes a new scheme to estimate the probabilistic amount of packets, called queue level (QL), in the buffer of each node based on the PRRs of the wireless links. This paper also proposes a QL-based centralized scheduling algorithm to assign time slots efficiently in TDMA-based IWSNs. The proposed scheduling algorithm gives higher priority to the nodes with higher QL. By assigning time slots first to the node with the highest QL, we can reduce the average end-to-end delay of packets and reduce the amount of buffered packets in the queue while satisfying the required end-to-end reliability. The performance of the proposed scheduling algorithm have been evaluated through a simulation using the Cooja simulator and compared with the existing approach. In the simulation on an sample network with the target end-to-end reliability of 99%, all of the flows were shown to guarantee the target reliability in both algorithms: on average, 99.76% in the proposed algorithm and 99.85% in the existing approach. On the other hand, the proposed algorithm showed much better performance than the existing approach in terms of the average end-to-end delay of packets (about 47% less) and the number of maximally buffered packets in the queue of each node (maximally, more than 90% less).

The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in the cloud and then deployed on edge devices. Most IoT devices generate large amounts of unlabeled data, which are expensive and challenging to annotate. This paper introduces the self-learning autonomous edge learning and inferencing pipeline (AEP), deployable in a resource-constrained embedded system, which can be used for unsupervised local training and classification. AEP uses two complementary approaches: pseudo-label generation with a confidence measure using k-means clustering and periodic training of one of the supported classifiers, namely decision tree (DT) and k-nearest neighbor (k-NN), exploiting the pseudo-labels. We tested the proposed system on two IoT datasets. The AEP, running on the STM NUCLEO-H743ZI2 microcontroller, achieves comparable accuracy levels as same-type models trained on actual labels. The paper makes an in-depth performance analysis of the system, particularly addressing the limited memory footprint of embedded devices and the need to support remote training robustness.

The proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-DG units into the modern grids, security prediction using varying grid features is crucial. The computation burden concerns of conventional time-domain security assessment techniques make it unsuitable for real-time security prediction. This paper, therefore, proposes a fast security monitoring model that includes security prediction and load shedding for security control. The attributes considered in this paper include the load level, inertia constant, fault location, and power dispatched from the renewable energy sources generator. An incremental Naïve Bayes algorithm is applied on the training dataset developed from the responses of the grid to transient stability simulations. An additive Gaussian process regression (GPR) model is proposed to estimate the load shedding required for the predicted insecure states. Finally, an algorithm based on the nodes’ security margin is proposed to determine the optimal node (s) for the load shedding. The average security prediction and load shedding estimation model training times are 1.2 s and 3 s, respectively. The result shows that the proposed model can predict the security of the grid, estimate the amount of load shed required, and determine the specific node for load shedding operation.

The present paper deals with issues related to activities undertaken by local government. The article focuses on the identification and assessment of the significance of local government’s different bottom-up initiatives that support development of local entrepreneurship. The article also indirectly discusses the importance of activities referring to energy efficiency. Through the digitization process and by using digital tools, LGs realize projects, improve local actors’ awareness, and finally achieve new challenges such as a higher proportion of gross final energy consumption being from renewable sources. The aim of the paper is to present the relationship between the LGs’ entrepreneurial activities, which we call bottom-up initiatives, and the local entrepreneurship level, understood as the number of entrepreneurs active on the territory of a LG. This paper presents the results of research carried out on the bottom-up initiatives undertaken by Polish local governments to support entrepreneurship. The research was based on survey and statistical tools and stepwise regression analysis. In the paper, bottom-up initiatives undertaken by local governments (such as organizing events at the national or regional scale that enable networking among entrepreneurs or establishing departments for supporting local entrepreneurship that offer cooperation with local entrepreneurs) were found to influence the number of entrepreneurs active in local government territories. The paper concludes that local governments and their bottom-up activities are an important factor influencing local entrepreneurship development. The presented research results have implications for policy makers and may be useful for local governments in Poland and in other countries in the context of supporting entrepreneurship by undertaking bottom-up initiatives at the local level.

The aim of the study is to explore the intellectual structure of the field and fronts in research on energy efficiency in the context of cloud computing and thus to contribute to science mapping of the research field. The research process was driven by the following study questions: (1) what are the most influential publications in the research field? and (2) what are the research fronts in the research field? The method of direct citation analysis was employed in the research process. Data for analysis were obtained from the Scopus database and analyzed with the use of VOSviewer science mapping software. In response to the first question, we identified the most influential publications in the research field and analyzed their types (i.e., whether they are original research papers or rather the “context” papers e.g., survey or review papers, framework papers, challenges papers, and study papers). Moreover, a comparison analysis between the types of papers among the most cited “classical” publications and “emerging stars” was conducted. In response to the second research question, we identified five research fronts concentrated around the issues of: virtual machine management (“VM”); task-focus, concerning data replication, task consolidation, and task scheduling (“task”); energy efficiency (“energy”); modelling and optimization (“model”); and energy efficiency in the networking context (“network”).

This paper investigates the impact of parameter variation between parallel connected SiC MOSFETs on short circuit (SC) performance. SC tests are performed on parallel connected devices with different switching rates, junction temperatures and threshold voltages (VTH). The results show that VTH variation is the most critical factor affecting reduced robustness of parallel devices under SC. The SC current conducted per device is shown to increase under parallel connection compared to single device measurements. VTH shift from bias–temperature–instability (BTI) is known to occur in SiC MOSFETs, hence this paper combines BTI and SC tests. The results show that a positive VGS stress on the gate before the SC measurement reduces the peak SC current by a magnitude that is proportional to VGS stress time. Repeating the measurements at elevated temperatures reduces the time dependency of the VTH shift, thereby indicating thermal acceleration of negative charge trapping. VTH recovery is also observed using SC measurements. Similar measurements are performed on Si IGBTs with no observable impact of VGS stress on SC measurements. In conclusion, a test methodology for investigating the impact of BTI on SC characteristics is presented along with key results showing the electrothermal dynamics of parallel devices under SC conditions.

This paper presents a power control method for a hybrid electrical system, which enhances a conventional main AC grid by adding a DC sub-grid in parallel. In this system, each prosumer connects to the sub-grid via a couple of half-bridge inverters of a device called a power router. All of the power routers connected to the sub-grid are controlled such that they maintain the voltage of the sub-grid equally, which is an essential feature of peer-to-peer power accommodation without synchronization. This paper proposes an adaptive hysteresis current-control technique for controlling sub-grid-connected inverters that maintains the voltage of the sub-grid while accommodating a given power. The proposed control method also balances currents between positive and negative lines of the sub-grid simultaneously. The proposed control method is implemented on a high-speed FPGA-based digital controller and is evaluated on three prototypes of the power router. The experimental results show that the proposed method yields fast and stable responses and enables the power router to accommodate power with a high accuracy.

This paper addresses the problem of route planning for a fleet of electric vehicles departing from a depot and supplying customers with certain goods. This paper aims to present a permutation-based method of vehicle route coding adapted to the specificity of electric drive. The developed method integrated with an evolutionary algorithm allows for rapid generation of routes for multiple vehicles taking into account the necessity of supplying energy in available charging stations. The minimization of the route distance travelled by all vehicles was taken as a criterion. The performed testing indicated satisfactory computation speed. A real region with four charging stations and 33 customers was analysed. Different scenarios of demand were analysed, and factors affecting the results of the proposed calculation method were indicated. The limitations of the method were pointed out, mainly caused by assumptions that simplify the problem. In the future, it is planned for research and method development to include the lapse of time and for the set of factors influencing energy consumption by a moving vehicle to be extended.

The real-time application research on the Fuzzy Logic Systems (FLSs) and Artificial Neural Networks (ANN) is vast and, in this paper, a technique for a photovoltaic failure analysis using the type 2 FLS and ANN is proposed. The method is proposed to build T2 FLS with a guaranteed value equal to or lower than T2 and ANN. Several explanations are conducted to illustrate the effectiveness of the methodologies. It is found that both the type 2 Fuzzy and ANN can be configured for productive actions in applications for a PV fault analysis, and choice is typically applied. The methods discussed in this paper lay the groundwork for developing FLSs and ANNs with durable characteristics that will be extremely useful in many functional applications. The result demonstrates that specific fault categories can be detected using the fault identification method, such as damaged PV modules and partial PV unit shades. The average detection performance is similar in both ANN and fuzzy techniques. In comparison, both systems evaluated show approximately the same performance during experiments. The architecture of the type 2 fuzzy logic system and ANN with radial basic function, including the roles of the output port and the rules for identifying the type of defect in the PV structure is slightly different.

Bioenergy with carbon capture and storage (BECCS) can sequester atmospheric CO2, while producing electricity. The CO2 avoidance cost (CAC) is used to calculate the marginal cost of avoided CO2 emissions for BECCS as compared to other established energy technologies. A comparative analysis using four different reference-case power plants for CAC calculations is performed here to evaluate the CO2 avoidance cost of BECCS implementation. Results from this work demonstrate that BECCS can generate electricity at costs competitive with other neutral emissions technologies, while simultaneously removing CO2 from the atmosphere. Approximately 73% of current coal power plants are approaching retirement by the year 2035 in the U.S. After considering CO2 sequestered from the atmosphere and coal power plant CO2 emissions displaced by BECCS, CO2 emissions can be reduced by 1.4 billion tonnes per year in the U.S. alone at a cost of $88 to $116 per tonne of CO2 removed from the atmosphere, for 10% to 90% of available biomass used, respectively. CAC calculations in this paper indicate that BECCS can help the U.S. and other countries transition to a decarbonized electricity grid, as simulations presented in this paper predict that BECCS power plants operate at lower CACs than coal plants with CCS.

The paper deals with the exposition of the main barriers and drivers of renewable energy usage in rural communities. Climate change mitigation is causing governments, policymakers, and international organizations worldwide to embark on policies, leading to increased use of renewable energy sources and improvement of energy efficiency. Climate change mitigation actions, including the Green Deal strategy in the EU, require satisfying the expanding energy demand and complying with the environmental restrictions. At the same time, the prevailing market structure and infrastructure relevant to the energy systems are undergoing a crucial transformation. Specifically, there has been a shift from centralized to more decentralized and interactive energy systems that are accompanied by a low-carbon energy transition. Smart Grid technology and other innovations in the area of renewable energy microgeneration technologies have enabled changes in terms of the roles of energy users: they can act as prosumers that are producing and consuming energy at the same time. Renewable energy generation that is allowing for deeper involvement of the citizens may render higher social acceptance, which, in turn, fuels the low-carbon energy transition. The collective energy prosumption in the form of energy cooperatives has become a widespread form of renewable energy initiatives in rural communities. Even though renewable energy consumption provides a lot of benefits and opportunities for rural communities, the fast penetration of renewables and energy prosumption encounter several important barriers in the rural areas. This paper analyses the main barriers and drivers of renewable energy initiatives in rural areas and provides policy implications for the low-carbon energy transition in rural areas.

This paper frames itself in the realm of smart energy technologies that can be utilized to satisfy the electricity demand of consumers. In this environment, demand response programs and the intelligent management of energy consumption that are offered by utility providers will play a significant role in implementing smart energy. One of the approaches to implementing smart energy is to analyze consumption data and provide targeted contracts to consumers based on their individual consumption characteristics. To that end, the identification of individual consumption features is important for suppliers and utilities. Given the complexity of smart home load profiles, an appliance-based identification is nearly impossible. In this paper, we propose a different approach by grouping appliances based on their rooms, thus, we provide a room-based identification of energy consumption. To this end, this paper presents and tests an intelligent consumption identification methodology, that can be implemented in the form of an ensemble of artificial intelligence tools. The ensemble, which comprises four convolutional neural networks (CNNs) and four k-nearest neighbor (KNN) algorithms, is fed with smart submeter data and outputs the identified type of room in a given dwelling. Results obtained from real-world data exhibit the superiority of the ensemble, with respect to accuracy, as compared with individual CNN and KNN models.

In this paper, a distributed energy storage design within an electric vehicle for smarter mobility applications is introduced. Idea of body integrated super-capacitor technology, design concept and its implementation is proposed in the paper. Individual super-capacitor cells are connected in series or parallel to form a string connection of super-capacitors with the associated management unit to form a panel. These super-capacitor panels are shaped to fit the alternative concept of vehicle design, and it solves the design issues and prepares for configurable electric vehicles. Body integration of super-capacitors enhances the acceleration, and regenerative braking performances of the electric vehicle increases the operating life of the Li-ion battery and improves space utilization by giving more area for the main energy source, the Li-ion battery. Integrating super-capacitor into the car body involves special packaging technology to minimize space and promotes distributed energy storage within a vehicle. This pioneering design encourages future configurable electric vehicles. Model of both the Li-ion battery and the super-capacitor employed is studied with its series internal resistance determined at various C-rates. Loss and the efficiency analysis of the bi-directional converter, traits of body integrated super-capacitors system and control of the interleaved bi-directional converter to regulate the power-sharing in the hybrid energy storage system is presented.

Electromobility is one of the leading trends transforming public transport worldwide. Supported by international organizations, such as the European Union, and national cofounding, public transport operators and local authorities are taking strategic decisions on the direction and scope of the electrification of rolling stock. Most of the electric buses that are being put into operation replace the previously used conventional buses, and consequently, most of the electric buses are operating on existing bus lines. By applying a strategic approach to selecting bus routes for electrification, the advantages of electric vehicles can be maximized. Based on a case study of the Polish city of Gdynia, this paper explores the usefulness of the multi-criteria analysis for selecting the bus lines for electrification. Multi-criteria analysis methods help decision makers to consider and weigh diverse criteria that include, among others, economic, social, technological and environmental aspects. To fulfil the above purpose, the paper compares different methods for evaluating electromobility options at an early stage. The primary research methods include multi-criteria analysis, literature review and case study analysis. An example of using multi-criteria analysis in the decision-making process of in-motion charging trolleybuses to replace diesel buses on particular lines is discussed and concluded. It is found that the multi-criteria analysis method could be used at an early but important stage of the operational level when particular lines to be replaced are being discussed. Moreover, the case is made that the local context should always be considered, including features of the existing public transport systems, and that cost–benefit analysis should be conducted for the selected optimum scenario.