Cleaner Energy Systems最新文献

Recently, time series forecasting has acquired considerable academic and industrial interests in various areas for different applications. Machine learning (ML) algorithms are known for their ability to capture the chaotic temporal non-linear relations in time series data. In this paper, various ML-based algorithms are employed and analyzed for time series forecasting of “regional wind power” in Ontario, Canada. To this end, the meteorological and spatial parameters with seasonal and temporal features are filtered and selected by a proposed deep feature selection approach. Then, multiple ML algorithms, including artificial neural network (ANN), deep neural network (DNN), long short-term memory (LSTM), bagging tree (BT), and support vector machine/regression (SVM/SVR), are used for training one-step ahead forecasting models. Finally, a comprehensive assessment of the constructed models is conducted based on different error criteria metrics. By evaluating and analyzing the performance of the models using testing data, it is observed that SVR/SVM is one of the most promising robust ML-based forecasting models. This technique results in reliable generic models that perform well with new data, where the testing MAPE % reaches a value of 13 %. Almost a similar MAPE is obtained from the ensemble modeling approach, which means combining process of the generated ML-based models does not significantly improve the predictions, in comparison with the developed SVR/SVM model. On the other hand, when constructing the multi-step ahead forecasting models, the predictions obtained from the multi-input multi-output (MIMO) LSTM approach are more reliable with higher accuracies. In other words, it is shown that the performance of the MIMO multi-step strategy is superior to the direct multi-step forecasting method, while employing algorithms with recursive properties.

Energy generated by fossil fuels is the main source of GHG emissions in the world. To mitigate global warming, Paris Agreement is an initiative that aims to limit global GHG emissions. Substitution of fossil fuels by renewable sources is considered a solution to reducing GHG emissions. Solar energy is particularly emphasized due to its high availability and low emissions. Although Brazil has excellent conditions for the generation of photovoltaic solar energy, its energy matrix is still composed of a large amount of fossil sources. There is a lack of studies on the change in GHG emissions by replacing these fossil sources with photovoltaic energy and the investment required for this change. This article aims to investigate GHG emissions of projected energy matrix of Brazil in 2030 and investment needs in photovoltaic energy for replacement of fossil sources, seeking an energy matrix within the goals Paris Agreement. Projections for substituting fossil fuels with photovoltaic energy in the year 2030 were made and GHG emissions in CO2eq were calculated using the IPCC method for Global Warming Potential - 100 years. The annual investment was estimated using data on capacity factor of photovoltaic generation for Brazil and value of installation costs provided by International Renewable Energy Agency. Results revealed that with the projected substitution of fossil fuels by photovoltaic in matrix energy 2030 emissions can be reduced by 36,9% (from 0.484GtCO2eq to 0,305GtCO2eq). The investment required for this replacement is estimated at U$S 376,5 billion. Despite the photovoltaic energy promising type of energy for Brazil, it is still unfeasible for the country to achieve goals in Paris Agreement (0,187 GtCO2e for 2030). In addition, a SWOT analysis provides an overview for decision-makers on strengths, weaknesses, opportunities and threats of photovoltaic energy in mitigation climate change context.

The stationary Compound Parabolic Concentrators (CPC) with a low concentration ratio (C<2) are suitable for pre-heating processes in industrial applications. The reported literature does not analyze the radiation available or the solar angles for whole year towards design of CPC which could provide maximum annual energy. This paper optimizes the design factors of CPC, such as acceptance half-angle (θ_a) and truncation ratio (TR), to yield maximum energy around the year, maximize optical efficiency and minimize reflector material. Annual irradiation data for the location - Tiruchirappalli, Tamilnadu, India (ϕ- 10°45′36″ N) is considered for the present study. The raw irradiation data is processed to get monthly average daily data, which is used as input for the TracePro v21.1. The experimental design (DOE) is made in response surface methodology (RSM) by considering the factors of θ_a - 18.5 to 28.5° and TR - 0.5 to 1.0. Nine Geometric models were created using Solidworks based on the combination of parameters given by RSM and simulated using TracePro to estimate the annual energy collection and average optical efficiency. Using the TracePro analysis month wise energy collection is calculated and reported. Further, a model relating θ_a and TR to the annual energy collected was obtained. According to the results obtained from the multi-objective optimization, the optimum concentration ratio is 2 with the θ_a of 28.5° without truncation. The results also indicate that the deviation from the average energy collected by the optimized design over the year is less when compared to the other designs.

This paper highlights the importance of regionally tailored decarbonization strategies to reach emissions intensity targets. The presented Ideal Grid model was used to compare and contrast decarbonization strategies for 9 regions of the continental US. For each of these regions, techno-economic analysis (TEA) and life-cycle assessment (LCA) are completed to track emissions intensity and electricity cost based on system installations. Ten technologies are included in this analysis: nuclear, wind, solar, natural gas (3 types), coal (3 types), and energy storage (lithium-ion batteries). The impact of carbon ceilings and carbon taxes are explored. It is shown that a carbon tax can linearly incentivize decarbonization in certain regions and exponentially incentivize decarbonization in other regions. It is shown that wind capacity factors can be used to indicate decarbonization strategies due to a strong correlation that is explored. At deep decarbonization levels (25 gCO₂/kWh), regions have a varying reliance on nuclear. Regions source anywhere from 27-72% of their electricity from nuclear, with electricity costs ranging from $112/MWh to $137/MWh. At lenient decarbonization targets (100 gCO₂/kWh), electricity costs range from $93/MWh to $112/MWh.

This paper studies whether feasible and plausible pathways exist for Türkiye to meet its growing electricity demand while reducing its emissions, by relying more on renewables, instead of increasing the use of its local coal resources. Our quantitative analysis proceeds in two stages. In the first stage, we determine the future electricity demand of Türkiye from 2020 to 2040 with the use of a dynamic panel data model. A 41-country balanced panel data set that comprises of five-year interval data, between 1990 and 2015. In the second stage, we develop linear programming models to generate realistic and reasonable scenarios representing three probable future pathways to meet the econometrically estimated electricity demand. The scenarios we have designed are business-as-usual (BAU), which includes the nuclear power plant, minimize GHGs (minGHG), and maximize local resources (MaxLocal). The latter scenarios omit the possible completion of the nuclear power plant in the next ten to twelve years. The generated scenarios are compared in terms of investment requirements and CO₂e emissions. The model results of minGHG and MaxLocal both show that the share of renewable generation should reach around 65% to satisfy the projected demand by 2040. However, the difference in CO₂e emissions (Mton/TWh) between the two cases is enormous: 0.408 for MaxLocal vs. 0.180 for minGHG! Moreover, required annual investment under minGHG is USD 0.19 billion cheaper on average (per year), which corresponds to a 21-year cumulative difference of USD 3.99 billion in current dollars. Therefore, a secure low-carbon pathway with a lower investment requirement is possible for Türkiye without nuclear power or new coal plants, while also phasing-out the existing coal plants under a moderate transition plan with a minimum amount of stranded assets.

To cope with present energy and climate crises, maximization of energy use becomes essential. Maritime transport is the core of international trade and the majority of vessels are equipped with marine engines for propulsion and power generation. This paper provides an exhaustive state of the art review on enhancing efficiency technologies based in waste heat recovery and applicable to marine engines. A bibliometric analysis followed by a systematic review based on the PRISMA 2020 approach is presented in order to identify current used systems, not implemented but available technologies and non-explored heat sources. From a wide query on Scopus and Web of Science databases, 576 results were obtained for the bibliometric analysis. Further selection of the most relevant journal articles gave a total of 35 studies, 30 original articles and 5 reviews, for the in-depth analysis. As a result, the organic Rankine cycle was identified as the most common technique for waste heat recovery. Cold energy recovery was found to be an innovative strategy but limited to vessels with LNG facilities. Despite the low representation in scientific literature, thermoelectric generators appeared to be a promising direction for future research. The recovery of low-grade waste heat was identified as a promising gap on the knowledge.

With the adoption of the Paris Agreement at the 21st Conference of the Parties organized under the United Nations Framework Convention on Climate Change in 2015, many countries agreed to increase their efforts to reduce greenhouse gas (GHG) emissions. The introduction of renewable energy is expected to reduce emissions in the electricity and heat use sectors, where emissions are particularly high. Among renewable energies, woody biomass energy is attracting attention as a stable, largescale source of power; however, this energy source must be used sustainably because it originates from forests, which also absorb CO₂. In Japan, the quantity of power generated using woody biomass energy is increasing each year, but only a small percentage of the woody feedstock is derived from domestic sources. In this study, we estimated the unutilized woody biomass energy potential in Hokkaido, Japan, then modeled the GHG emissions and costs of using woody biomass in a large-scale woody biomass power plant. Both costs and GHG emissions increased as increasing proportions of unutilized wood were used as feedstock. Compared with the cost per unit of electricity generated for a woody biomass power plant in Japan (29.8 JPY·kWh^-1), the cost of using only unutilized wood was estimated to be 38.6–127 JPY·kWh^-1. Assuming that woody biomass energy is not carbon-neutral, the GHG emissions generated by wood combustion were 1.30–1.41 kg-CO₂ eq·kWh^-1, which were larger than the GHG emissions from coal-fired power plants. For unutilized thinned lumber and forest residues, the costs per unit of heat value and the GHG emissions associated with the use of tractors for lumber collection were higher than the costs and GHG emissions associated with other machinery; these costs and emissions increased along with increases in the amount of wood used. The costs per unit of heat and GHG emissions from the trucks used for transporting prunings were higher than the costs and GHG emissions of other machines. These costs increased as the collection area expanded because of the increase in the amount of woody biomass energy used.

Using the Large Temperature Jump (LTJ) experimental technique, alongside a review of the literature, sodium bromide (NaBr) and manganese chloride (MnCl₂) have been identified as a suitable working pair with ammonia refrigerant for a proof-of-concept resorption heat pump system. LTJ tests using a tube-side and shell-side unit cell reactor (sorption heat exchanger), show that the experimentally obtained equilibrium lines for adsorption and desorption of sodium bromide are: ΔH_ADS = 30,102.5 J/mol; ΔS_ADS = 207.7 J/(mol·K); ΔH_DES = 30,216.4 J/mol; and ΔS_DES = 206.8 J/(mol·K). Using a semi-empirical model, the NaBr composite salt (salt impregnated in expanded natural graphite (ENG)) has been characterised for use as a low temperature salt in a resorption heat pump, with manganese chloride as the high-temperature salt. The model constants, A and n, for adsorption are 1 and 3, and for desorption are 5 and 4 respectively for NaBr. Manganese chloride data has been previously reported (Hinmers et al., 2022). With an appreciation of the reaction dynamics and behaviour of the NaBr and MnCl₂ composite salts, a proof-of-concept resorption system has been designed and manufactured. The reactor design, alongside the overall experimental rig design (including data acquisition system) is reported. Initial filling and flushing tests show the success of the data acquisition and control system, and thus the overall suitability of the proof of-concept system for investigations into the coupled nature of ammonia salt reactions for a resorption heat pump application.

Power electronic inverters are one of the most significant components for the integration of clean energy systems and the proper control of these power electronic interfaces helps to enhance power quality. This paper presents a robust step-by-step full-order sliding mode voltage control strategy for standalone single-phase inverters that can be interfaced with cleaner renewable energy sources. The proposed controller employs a cascaded method through outer- and inner-loops to facilitate tracking of the desired load voltage. In contrast to the most commonly used sliding mode techniques for interfacing these cleaner energy sources, which usually utilize the canonical-form of the system’s state-space model, the designed controller uses a block-controllable model that mitigates unwanted noises. To design the controller, the capacitor voltage and the inductor current of the LC filter are measured to be employed in the outer second-order sliding mode voltage control loop and the inner first-order sliding mode current control loop, respectively. The utilization of such full-order sliding surfaces in each step effectively reduces the chattering in the control signals as well as facilitates the transient and steady-state responses with less harmonic distortion and thereby, promoting the effective integration of renewable energy sources. To verify the performance of the proposed controller under various loading conditions as external disturbances, a 2.2 kW standalone single-phase inverter is simulated using MATLAB/Simulink platform along with a microcontroller-based processor-in-loop through the digital signal processing. In terms of internal disturbances, the proposed controller is also tested under different filter parameters’ variations over a wider range. The results indicate a better alignment for coupling with clean energy sources and the comparisons with other control techniques show better performance of the proposed controller.

Many off-grid communities in Canada are dependent on diesel generators to fulfill their utility and transportation needs, causing destructive environmental impact. This study aims to optimize and investigate the techno-economic feasibility of a hybrid renewable energy system to satisfy the 1.6 MWh/day electricity, 184.2 kWh/day thermal, and 428.38 kg/year hydrogen demand simultaneously, Trout Lake, a remote community of Northern Alberta. A novel hybrid energy system consisting of solar PV, wind turbine, electrolyzer, hydrogen tank, battery, fuel cell, hydrogen boiler, and thermal load controller has been proposed to generate electricity, heat, and hydrogen by renewables which reduce carbon emission utilizing the excess energy (EE). Five different scenarios were developed in HOMER Pro software, and the results were compared to identify the best combination of hybrid renewable energy systems. The results indicate that the fifth scenario is the optimal renewable energy system that provides a lower cost of energy (COE) at $0.675/kWh and can reduce 99.99% carbon emission compared to the diesel-based system. Additionally, the utilization of thermal load controller, battery, and fuel cell improved the system's reliability, increasing renewable fraction (RF) (93.5%) and reducing EE (58.3%) significantly. In comparison to the diesel-based systems, it is also discovered that battery energy storage is the most affordable option, while fuel cells are the more expensive choice for remote community. Sensitivity analyses are performed to measure the impact of different dominating factors on COE, EE, and RF.