Search Results (18)

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however, uncertainty remains about optimal grid architectures to minimize cost, including how and when to incorporate long-duration energy storage. This study implements a novel, multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue, which has already successfully deployed solar photovoltaics, wind turbines, and battery storage systems. Using real community load, resource, and generation data, we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation, considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs, but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase, but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue, we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability, sustainability, and security. Full article

Renewable energy systems (RES) are strongly affected by many sources of uncertainty and variability. Nevertheless, traditional technical and economic evaluation methods often neglect uncertainty by deterministically assuming average nominal values, using simple sensitivity analysis to explore effects of changing conditions, or limiting to a few sources of uncertainty. Furthermore, long-term variability and changing scenarios during the life of the system are not considered. This leads to inaccurate estimation of inherent investment risk. To address this gap, this work proposes a framework for the economic evaluation of offshore wind farms, considering the effects of both epistemic and aleatory uncertainty. Uncertainty of correlations used to model the system, the variability of resources and energy prices, as well as the use of a financial hedging tool to cope with market risk, the impact of failures and disruptive events, the changing of long-term scenarios during the system’s life, and the wake effect due to wind direction variability are all considered. As demonstrated through an example of an application, this methodology will be useful to practitioners and academics to achieve a more realistic assessment of the profitability of the investment based on a more comprehensive propagation of uncertainty. Full article

In response to the urgent threat of climate change and the drivers of high greenhouse gas emissions, countries worldwide are adopting policies to reduce their carbon emissions, with net-zero emissions targets. These targets vary by region, with Canada aiming to achieve net-zero emissions by 2050. In response to the Independent Electricity System Operator’s (IESO’s) “Pathways to Decarbonization” report, which evaluates a proposed moratorium on new natural gas generating stations, this study presents a methodology to support energy transitions in Ontario by using a modified Dynamic Integrated Climate-Economy (DICE) model, which focuses on replacing fossil fuel power plants (FFPPs) with clean energy sources, including nuclear, solar, wind, and hydro. This research expands on our prior work that used the DICE model to evaluate the potential for replacing FFPPs with Small Modular Reactors (SMRs) on a global scale. This study includes solar, wind, hydro, and SMRs to provide a diversified clean energy portfolio and integrates fuzzy logic to optimize construction rates and address uncertainties. The study uses Ontario as a case study, aligning with IESO’s objectives for Ontario’s energy transition. The IESO’s projections for net zero by 2050 are applied. The study is extended to 2100 to assess the longer-term implications of sustained energy transition efforts beyond the immediate goals set by the IESO. This approach is scalable to other regions and countries with similar energy transition challenges. The study results indicate that to meet Ontario’s 2050 net-zero target, approximately 183 SMR units, 1527 solar units, 289 wind units, and 449 hydro units need to be constructed. For the 2100 target, the required number of units is slightly higher due to the longer time frame, reflecting a gradual ramp-up in construction. The optimization of construction rates using fuzzy logic shows that the pace of deployment is influenced by critical factors such as resource availability, policy support, and public acceptance. This underscores the need for accelerated clean energy deployment to meet long-term emissions reduction goals. The findings highlight the complexities of transitioning to a low-carbon energy system and the importance of addressing uncertainties in planning. Policymakers are urged to integrate these insights into strategic energy planning to ensure the successful deployment of clean energy technologies. This study provides valuable recommendations for optimizing energy transitions through a robust, flexible framework that accounts for both technological and socio-economic challenges. Full article

Macroscopic structures consisting of two or more materials are called composites. The decreasing reserves of the world’s oil reserve and the environmental pollution of existing energy and production resources made the use of recycling methods inevitable. There are mechanical, thermal, and chemical recycling methods for the recycling of thermosets among composite materials. The recycling of thermoset composite materials economically saves resources and energy in the production of reinforcement and matrix materials. Due to the superior properties such as hardness, strength, lightness, corrosion resistance, design width, and the flexibility of epoxy/vinylester/polyester fibre formation composite materials combined with thermoset resin at the macro level, environmentally friendly sustainable development is happening with the increasing use of composite materials in many fields such as the maritime sector, space technology, wind energy, the manufacturing of medical devices, robot technology, the chemical industry, electrical electronic technology, the construction and building sector, the automotive sector, the defence industry, the aviation sector, the food and agriculture sector, and sports equipment manufacturing. Bonded joint studies in composite materials have generally been investigated at the level of a single composite material and single joint. The uncertainty of the long-term effects of different composite materials and environmental factors in single-lap bonded joints is an important obstacle in applications. The aim of this study is to investigate the effects of single-lap bonded GFRP (glass fibre-reinforced polymer) and CFRP (carbon fibre-reinforced polymer) specimens on the material at the end of seawater exposure. In this study, 0/90 orientation twill weave seven-ply GFRP and eight-ply CFRP composite materials were used in dry conditions (without seawater soaking) and the hand lay-up method. Seawater was taken from the Aegean Sea, İzmir province (Selçuk/Pamucak), in September at 23.5 °C. This seawater was kept in different containers in seawater for 1 month (30 days), 2 months (60 days), and 3 months (90 days) separately for GFRP and CFRP composite samples. They were cut according to ASTM D5868-01 for single-lap joint connections. Moisture retention percentages and axial impact tests were performed. Three-point bending tests were then performed according to ASTM D790. Damage to the material was examined with a ZEISS GEMINESEM 560 scanning electron microscope (SEM). The SEM was used to observe the interface properties and microstructure of the fracture surfaces of the composite samples by scanning images with a focused electron beam. Damage analysis imaging was performed on CFRP and GFRP specimens after sputtering with a gold compound. Moisture retention rates (%), axial impact tests, and three-point bending test specimens were kept in seawater with a seawater salinity of 3.3–3.7% and a seawater temperature of 23.5 °C for 1, 2, and 3 months. Moisture retention rates (%) are 0.66%, 3.43%, and 4.16% for GFRP single-lap bonded joints in a dry environment and joints kept for 1, 2, and 3 months, respectively. In CFRP single-lap bonded joints, it is 0.57%, 0.86%, and 0.87%, respectively. As a result of axial impact tests, under a 30 J impact energy level, the fracture toughness of GFRP single-lap bonded joints kept in a dry environment and seawater for 1, 2, and 3 months are 4.6%, 9.1%, 14.7%, and 11.23%, respectively. At the 30 J impact energy level, the fracture toughness values of CFRP single-lap bonded joints in a dry environment and in seawater for 1, 2, and 3 months were 4.2%, 5.3%, 6.4%, and 6.1%, respectively. As a result of three-point bending tests, GFRP single-lap joints showed a 5.94%, 8.90%, and 12.98% decrease in Young’s modulus compared to dry joints kept in seawater for 1, 2, and 3 months, respectively. CFRP single-lap joints showed that Young’s modulus decreased by 1.28%, 3.39%, and 3.74% compared to dry joints kept in seawater for 1, 2, and 3 months, respectively. Comparing the GFRP and CFRP specimens formed by a single-lap bonded connection, the moisture retention percentages of GFRP specimens and the amount of energy absorbed in axial impact tests increased with the soaking time in seawater, while Young’s modulus was less in three-point bending tests, indicating that CFRP specimens have better mechanical properties. Full article

In order to consider the impact of source-load uncertainty on traditional power system planning methods, a medium- and long-term optimization planning method based on source-load uncertainty modeling and time-series production simulation is proposed. First, a new energy output probability model is developed using non-parametric kernel density estimation, and the spatial correlation of the new energy output is described using pair-copula theory to model the uncertainty analysis of the new energy output. Secondly, a large number of source-load scenarios are generated using the Markov chain Monte Carlo simulation method, and the optimal selection method for discrete state numbers is provided, and then the scenario reduction is carried out using the fast forward elimination technology. Finally, the typical time-series curves of the source-load uncertainty characteristics obtained are incorporated into the optimization planning method together with various flexible resources, such as the demand-side response and energy storage, and the rationality of the planning scheme is judged and optimized based on key indicators such as the cost, wind–light abandonment rate, and loss-of-load rate. Based on the above methods, this paper offers an example of the power supply planning scheme for a certain region in the next 30 years, providing effective guidance for the development of new energy in the region. Full article

Submarine pipelines have become integral for transporting resources and drinking water across large bodies. Therefore, ensuring the stability and reliability of these submarine pipelines is crucial. Incorporating climate change impacts into the design of marine structures is paramount to assure their lifetime safety and serviceability. Deterministic design methods may not fully consider the uncertainties and risks related to climate change compared to risk-based design models. The latter approach considers the future risks and uncertainties linked to climate and environmental changes, thus ensuring infrastructure sustainability. This study pioneers a Hybrid 3D Hydrodynamic Monte Carlo Simulation (HMCS) Model to improve the reliability-based design of submarine pipelines, incorporating the effects of climate change. Current design approaches may follow deterministic methods, which may not systematically account for climate change’s comprehensive uncertainties and risks. Similarly, traditional design codes often follow a deterministic approach, lacking in the comprehensive integration of dynamic environmental factors such as wind, waves, currents, and geotechnical conditions, and may not adequately handle the uncertainties, including the long-term effects of climate change. Nowadays, most countries are developing new design codes to modify the risk levels for climate change’s effects, such as sea-level rises, changes in precipitation, or changes in the frequency/intensity of winds/storms/waves in coastal and marine designs. Our model may help these efforts by integrating a comprehensive risk-based approach, utilizing a 3D hydrodynamic model to correlate diverse environmental factors through Monte Carlo Simulations (MCS). The hybrid model can promise the sustainability of marine infrastructure by adapting to future environmental changes and uncertainties. Including such advanced methodologies in the design, codes are encouraged to reinforce the resilience of maritime structures in the climate change era. The present design codes should inevitably be reviewed according to climate change effects, and the hybrid risk-based design model proposed in this research should be included in codes to ensure the reliability of maritime structures. The HMCS model represents a significant advancement over existing risk models by incorporating comprehensive environmental factors, utilizing advanced simulation techniques, and explicitly addressing the impacts of climate change. This innovative approach ensures the development of more resilient and sustainable maritime infrastructure capable of withstanding future environmental uncertainties. Full article

The renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS’s robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS’s robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power. Full article

Many countries around the world are rapidly advancing sustainable development (SD) through the exploitation of clean energy sources such as solar and wind energy, which are becoming the core of the sustainable energy transition. In recent years, the continuous advancement of Earth system models (ESMs) has facilitated numerous studies utilizing them to predict long-term and large-scale meteorological elements, consequently enabling forecasts of wind and solar energy. These forecasts provide critical guidance for formulating national renewable energy policies. Nevertheless, the current literature on ESMs predicting wind and solar energy lacks sufficient integration. Hence, to comprehend the focal points and future research prospects, we conducted this systematic review, employing four academic search tools to comprehensively analyze the relevant literature from the past five years. We summarized the general analytical process and compared the content and conclusions of the literature. The study reveals that future photovoltaic (PV) potential for electricity generation may increase in certain regions but decrease in others, while the global potential for concentrated solar power (CSP) may diminish, influenced by diverse factors and displaying significant regional disparities. In addition, wind resource trends vary in different regions, and forecasts exhibit considerable uncertainty. Therefore, many studies have corrected wind speeds prior to predicting wind energy. Subsequent research endeavors should concentrate on optimizing ESMs, investigating the impacts of technological innovation, and enhancing the prediction and analysis of extreme weather events. Full article

Accurate wind speed prediction is significantly important for the full utilization of wind energy resources and the improvement in the economic benefits of wind farms. Because the ensemble forecast takes into account the uncertainty of information about the atmospheric motion, domestic and foreign weather service forecast centers often choose to use the ensemble numerical forecast to achieve the fine forecast of wind speed. However, due to the unavoidable systematic errors of the ensemble numerical forecast model, it is necessary to correct the deviation in the ensemble numerical forecast wind speed. Considering the typical spatio-temporal characteristics of the grid prediction data of the wind field, based on Convolutional Long–Short Term Memory (ConvLSTM) units and attention mechanism, this paper takes the complex and representative North China region as the research area, aiming to reveal the shortcomings of existing deep learning integrated prediction correction models in extracting temporal features of grid prediction data. We propose a new ensemble prediction wind field correction model integrating multi-factor and spatio-temporal characteristics. This model uses reanalyzed land data provided by the European Center for Medium-Range Weather Forecasts as the real data to correct the deviation in the near-surface 10 m wind field data predicted by the regional ensemble numerical prediction model of the China Meteorological Administration. We used the reanalyzed land data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the live data to correct the deviation in the near-surface 10 m wind field data predicted by the regional ensemble numerical forecast model of the China Meteorological Administration (CMA). At the same time, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used as the scoring indicators, and the results of the China Meteorological Administration–Regional Ensemble Prediction System (CMA–REPS) ensemble average, multiple linear regression method correction, Long–Short Term Memory (LSTM) method correction, and U-net (UNET) method correction were compared. Compared with the UNET model method, the experimental results show that when processing the 10 m zonal wind data, 10 m meridional wind data, and 10 m average wind speed data of CMA–REPS 24 h forecasts, the correction results of our model can reduce the RMSE score index by 9.15%, 4.83%, and 7.79%. At the same time, when processing the 48 h and 72 h near-surface 10 m wind field data of the CMA–REPS forecast, our model can improve the prediction accuracy of CMA–REPS near-surface wind forecast data. Therefore, the correction effect of the proposed model in a complex terrain area is evidently better compared to other methods. Full article

The production of renewable energy is becoming one of the most important issues for communities due to the increasing energy demand. The purpose of this paper is to develop a systematized, sustainability-focused evaluation framework for determining the efficiency of wind farms in Turkey. The environmental impact and long-term viability of wind farms are evaluated using an evaluation framework centered on sustainability. The evaluation of their sustainability involves analyzing their energy production, environmental impacts and economic viability. In this study, DEA–Fuzzy COPRAS aims to evaluate the efficiency of 11 wind power plants located in Turkey in the Marmara Region. As inputs, the number of wind turbines, investment cost and distance from the grid are selected. As output, electricity is produced, and daily production time is considered. The proposed DEA–Fuzzy COPRAS aims to eliminate the disadvantages of the conventional methods and to be able to make better decisions regarding the weight value under uncertain conditions. The main advantages of the proposed DEA–Fuzzy COPRAS include a more accurate evaluation of efficiency and the ability to consider multiple criteria simultaneously. Additionally, the proposed DEA–Fuzzy COPRAS considers uncertainty in the inputs and outputs of wind energy production. The results of the proposed work are validated by comparing them with those obtained from a sensitivity analysis of the criteria. Therefore, decision makers can evaluate the efficiency of wind power plants accurately under an imprecise environment. Wind power plant managers or investors and other renewable energy projects can benefit from the proposed method’s implementation by allowing governments and stakeholders to save money and make better use of resources during the planning phase. Full article

In the process of building a new power system with new energy sources as the mainstay, wind power and photovoltaic energy enter the multiplication stage with randomness and uncertainty, and the foundation and support role of large-scale long-time energy storage is highlighted. Considering the advantages of hydrogen energy storage in large-scale, cross-seasonal and cross-regional aspects, the necessity, feasibility and economy of hydrogen energy participation in long-time energy storage under the new power system are discussed. Firstly, power supply and demand production simulations were carried out based on the characteristics of new energy generation in China. When the penetration of new energy sources in the new power system reaches 45%, long-term energy storage becomes an essential regulation tool. Secondly, by comparing the storage duration, storage scale and application scenarios of various energy storage technologies, it was determined that hydrogen storage is the most preferable choice to participate in large-scale and long-term energy storage. Three long-time hydrogen storage methods are screened out from numerous hydrogen storage technologies, including salt-cavern hydrogen storage, natural gas blending and solid-state hydrogen storage. Finally, by analyzing the development status and economy of the above three types of hydrogen storage technologies, and based on the geographical characteristics and resource endowment of China, it is pointed out that China will form a hydrogen storage system of “solid state hydrogen storage above ground and salt cavern storage underground” in the future. Full article

Offshore wind is expected to play a key role in future energy systems. Wind energy resource studies often call for long-term and spatially consistent datasets to assess the wind potential. Despite the vast amount of available data sources, no current means can provide relevant sub-daily information at a fine spatial scale (~1 km). Synthetic aperture radar (SAR) delivers wind field estimates over the ocean at fine spatial resolution but suffers from partial coverage and irregular revisit times. Physical model outputs, which are the basis of reanalysis products, can be queried at any time step but lack fine-scale spatial variability. To combine the advantages of both, we use the framework of multiple-point geostatistics to realistically reconstruct wind speed patterns at time instances for which satellite information is absent. Synthetic fine-resolution wind speed images are generated conditioned to coregistered regional reanalysis information at a coarser scale. Available simultaneous data sources are used as training data to generate the synthetic image time series. The latter are then evaluated via cross validation and statistical comparison against reference satellite data. Multiple realizations are also generated to assess the uncertainty associated with the simulation outputs. Results show that the proposed methodology can realistically reproduce fine-scale spatiotemporal variability while honoring the wind speed patterns at the coarse scale and thus filling the satellite information gaps in space and time. Full article

Lack of electricity in rural communities implies inequality of access to information and opportunities among the world’s population. Hybrid renewable energy systems (HRESs) represent a promising solution to address this situation given their portability and their potential contribution to avoiding carbon emissions. However, the sizing methodologies for these systems deal with some issues, such as the uncertainty of renewable resources. In this work, we propose a sizing methodology that includes long short-term memory (LSTM) cells to predict weather conditions in the long term, multivariate clustering to generate different weather scenarios, and a nonlinear mathematical formulation to find the optimal sizing of an HRES. Numerical experiments are performed using open-source data from a rural community in the Pacific Coast of Mexico as well as open-source programming frameworks to allow their reproducibility. We achieved an improvement of 0.1% in loss of load probability in comparison to the seasonal naive method, which is widely used in the literature for this purpose. Furthermore, the RNN training stage takes 118.42, 2103.35, and 726.71 s for GHI, wind, and temperature, respectively, which are acceptable given the planning nature of the problem. These results indicate that the proposed methodology is useful as a decision-making tool for this planning problem. Full article

In power system planning, the growth of renewable energy generation leads to several challenges including system reliability due to its intermittency and uncertainty. To quantify the relatively reliable capacity of this generation, capacity credit is usually adopted for long-term power system planning. This paper proposes an evaluation of the capacity credit of renewable energy generation using stochastic models for resource availability. Six renewable energy generation types including wind, solar PV, small hydro, biomass, biogas, and waste were considered. The proposed models are based on the stochastic process using the Wiener process and other probability distribution functions to explain the randomness of the intermittency. Moreover, for solar PV—the generation of which depends on two key random variables, namely irradiance and temperature—a copula function is used to model their joint probabilistic behavior. These proposed models are used to simulate power outputs of renewable energy generations and then determine the capacity credit which is defined as the capacity of conventional generation that can maintain a similar level of system reliability. The proposed method is tested with Thailand’s power system and the results show that the capacity credit depends on the time of day and the size of installed capacity of the considered renewable energy generation. Full article

One of the most critical challenges for modern power systems is to reliably supply electricity to its consumers during and in the aftermath of natural disasters. As our dependence on electrical power has increased over the years, long-term power outages can lead to devastating impacts on affected communities. Furthermore, power outages can halt the operation of water treatment plants, leading to shortages in clean water, which is essential during post-disaster recovery. One way to address this is to temporarily reconfigure power and water networks into localized networks, i.e., electric microgrids and water micro-nets, that utilize local resources to supply local demand independently of the main power grid and/or water network. Utilizing distributed energy resources such as wind and solar and treating wastewater locally for potable reuse can provide the operational flexibility for such systems to operate sustainably. However, due to uncertainties in both renewable energy generation and electric/water consumption, ensuring sustainable operation is a challenging task. In this paper, an optimal operational strategy is proposed for an islanded microgrid/micro-net, considering the stochastic nature of renewable energy resources, electric demand, and water demand. An energy storage system is modeled to address the uncertainty in power generation and demand, in conjunction with local water storage and wastewater treatment to accommodate variable water demands. A two-stage stochastic programming model is formulated and solved to determine an optimal operation strategy for the combined system. Full article