Search Results (100)

Aiming at the economic problems of industrial users with wind power, photovoltaic, and small hydropower resources in clean energy consumption and trading with superior power grids, this paper proposes a distributed pumped storage capacity optimization configuration method considering clean energy systems. First, considering the maximization of the investment benefit of distributed pumped storage as the upper goal, a configuration scheme of the installed capacity is formulated. Second, under the two-part electricity price mechanism, combined with the basin hydraulic coupling relationship model, the operation strategy optimization of distributed pumped storage power stations and small hydropower stations is carried out with the minimum operation cost of the clean energy system as the lower optimization objective. Finally, the bi-level optimization model is solved by combining the alternating direction multiplier method and CPLEX solver. This study demonstrates that distributed pumped storage implementation enhances seasonal operational performance, improving clean energy utilization while reducing industrial electricity costs. A post-implementation analysis revealed monthly operating cost reductions of 2.36, 1.72, and 2.13 million RMB for wet, dry, and normal periods, respectively. Coordinated dispatch strategies significantly decreased hydropower station water wastage by 82,000, 28,000, and 52,000 cubic meters during corresponding periods, confirming simultaneous economic and resource efficiency improvements. Full article

The global energy transition is accelerating, bringing new challenges to power systems. A high penetration of renewable energy increases grid volatility. Virtual power plants (VPPs) address this by dynamically responding to market signals. They integrate renewables, energy storage, and flexible loads. Additionally, they participate in multi-tier markets, including energy, ancillary services, and capacity trading. This study proposes a load factor-based VPP pre-dispatch model for optimal resource allocation. It incorporates the coupling effects of electricity–carbon markets. A Nash negotiation strategy is developed for multi-VPP cooperation. The model uses an accelerated adaptive alternating-direction multiplier method (AA-ADMM) for efficient demand response. The approach balances computational efficiency with privacy protection. Revenue is allocated fairly based on individual contributions. The study uses data from a VPP dispatch center in Shanxi Province. Shanxi has abundant wind and solar resources, necessitating advanced scheduling methods. Cooperative operation boosts profits for three VPPs by CNY 1101, 260, and 823, respectively. The alliance’s total profit rises by CNY 2184. Carbon emissions drop by 31.3% to 8.113 tons, with a CNY 926 gain over independent operation. Post-cooperation, VPP1 and VPP2 see slight emission increases, while VPP3 achieves major reductions. This leads to significant low-carbon benefits. This method proves effective in cutting costs and emissions. It also balances economic and environmental gains while ensuring fair profit distribution. Full article

A total of 108 maximum Kolmogorov entropy (S_K) values, calculated by means of chaos theory, are obtained from 108 time series (TSs) (each consisting of 28,463 hourly data points). The total TSs are divided into 54 urban meteorological (temperature (T), relative humidity (RH) and wind speed magnitude (WS)) and 54 pollutants (PM₁₀, PM_2.5 and CO). The measurement locations (6) are located at different heights and the data recording was carried out in three periods, 2010–2013, 2017–2020 and 2019–2022, which determines a total of 3,074,004 data points. For each location, the sum of the maximum entropies of urban meteorology and the sum of maximum entropies of pollutants, S_{K, MV} and S_{K, P}, are calculated and plotted against h, generating six different curves for each of the three data-recording periods. The tangent of each figure is determined and multiplied by the average temperature value of each location according to the period, obtaining, in a first approximation, the magnitude of the entropic forces associated with urban meteorology (F_{K, MV}) and pollutants (F_{K, P}), respectively. It is verified that all the time series have a fractal dimension, and that the fractal dimension of the pollutants shows growth towards the most recent period. The entropic dynamics of pollutants is more dominant with respect to the dynamics of urban meteorology. It is found that this greater influence favors subdiffusion processes (α < 1), which is consistent with a geographic basin with lower atmospheric resilience. By applying a heavy-tailed probability density analysis, it is shown that atmospheric pollution states are more likely, generating an extreme environment that favors the growth of respiratory diseases and low relative humidity, makes heat islands more stable over time, and strengthens heat waves. Full article

By combining a coupled inductor with voltage multipliers, the voltage gain of a boost converter can be improved significantly. This method has good application prospects in renewable energy generation and in DC microgrids. A coupled inductor is the core component of the high step-up DC/DC converter and has serious impact on its performance. However, shortage in the methods used to design the coupled inductor have limited the applications of such converters. By analyzing the operating modes of the high step-up DC/DC converter with a three-winding coupled inductor combined with two voltage multipliers, accurate and simplified models of currents in the three windings are established. Furthermore, a design methodology for a multi-winding coupled inductor is put forward, in which a method of calculating the boost inductance and product areas (AP) and a method for selecting the magnetic core are established. The influence of winding arrangements and loss evaluations of the coupled inductor are also investigated. Finally, a 200 W prototype converter with an input of 20 V and output of 200 V is prepared and tested. The correctness of the current models of and design methods used on coupled inductor are verified. More important, the method proposed to design multi-winding coupled inductors can be applied to design high step-up DC/DC converters with different topologies. Full article

Large-scale penetration of renewable energy generation brings various challenges to the power system in terms of safety, reliability, economy and flexibility. The development of large-scale, high-security energy-storage technology can effectively address these challenges and improve the capabilities of power systems in power-supply guarantee and flexible adjustment. This paper proposes a novel distributed risk-averse optimization scheduling model of a hybrid wind–solar–storage system based on the adjustability of the storage system and the complementarity of renewable energy generation. The correlation of wind power and photovoltaic generation is quantified based on a Copula function. A risk-averse operation optimization model is proposed using conditional value at risk to quantify the uncertainty of renewable energy generation. A linear formulation of conditional value at risk under typical scenarios is developed by Gibbs sampling the joint distribution and Fuzzy C-Means clustering algorithm. A distributed solution algorithm based on an alternating-direction method of multipliers is developed to derive the optimal scheduling of hybrid wind–solar–storage system in a distributed manner. Numerical case studies based on IEEE 34-bus distribution network verify the effectiveness of the proposed model in reducing the uncertainty impact of renewable energy generation on an upstream grid (the overall amount of renewable energy generation sent back to the upstream grid has decreased about 80.6%) and ensuring the operational security of hybrid wind–solar–storage system (overall voltage deviation within 5.6%). Full article

To improve the operational efficiency of the Virtual Power Plant (VPP) and the effectiveness and reliability of scheduling boundary characterization, this paper proposes a time-decoupled distributed optimization algorithm. First, based on the Lyapunov optimization theory, time decoupling is implemented within the VPP, transforming long-term optimization problems into single-period optimization problems, thereby reducing optimization complexity and improving operational efficiency. Second, the Alternating Direction Method of Multipliers (ADMM) framework is used to decompose the optimization problem into multiple subproblems, combined with a hybrid strategy to improve the particle swarm optimization algorithm for solving the problem, thus achieving distributed optimization for the VPP. Finally, to facilitate intra-day interaction between the VPP and the distribution network, the remaining controllable capacity of the VPP’s devices is used as the spinning reserve to address renewable energy fluctuations. A dynamic scheduling boundary model is constructed by introducing wind and solar fluctuation factors. Based on time decoupling and algorithm improvement, the scheduling boundaries are solved and updated on a rolling basis. Simulation results show that, firstly, the time decoupling strategy based on Lyapunov optimization has an error of less than 3%, and the solving time is reduced by 86.11% after decoupling, significantly improving solving efficiency and validating the feasibility and effectiveness of the time decoupling strategy. Secondly, the hybrid strategy-improved particle swarm optimization algorithm achieves improvements in convergence speed and accuracy compared to other algorithms. Finally, the VPP scheduling boundary and scheduling cost characterization times are 115 s and 6.7 s, respectively, effectively meeting the timeliness of VPP and distribution network interaction while ensuring the safety and reliability of the scheduling boundaries. Full article

In three periods of 3.25 years each, and at the same six different heights of a basin geomorphology, measurements were made, in the form of a time series, of urban meteorological variables (MV) (temperature, relative humidity, wind speed magnitude) and pollutants (P) (PM₁₀, PM_2.5, and CO). It is verified that each time series has a fractal dimension, and the value of its maximum Kolmogorov entropy is determined. These values generate two entropic surfaces according to measurement periods: one for urban meteorology and another for pollutants. The calculation of the gradient to each entropic surface multiplied by the average temperature of the period according to the measurement location gives, approximately, the average entropic force for each location. Combining these results with an analysis of the ratio between urban meteorological entropies and pollutant entropies, it is shown that in a basin morphology the entropic forces associated with pollutants are dominant, a source of heat, and there is a high probability that they produce extreme events. This condition also favors anomalous subdiffusion. Full article

The wind turbine gearbox, used as a multiplier, is one of the main components directly related to a wind turbine’s efficiency and lifespan. Therefore, strict control of the gearbox and its manufacturing processes and even minor improvements in this component strongly and positively impact energy production/generation over time. Since only some papers in the literature analyze the mechanical aspect of wind turbines, focusing on some parts in depth, this paper fills the gap by offering an analysis of the gearbox component under the highest amount of stress, namely relating to the sun shaft, as well as a more holistic analysis of the main gear drives, its components, and the lubrification system. Thus, this work diagnoses the fracture mechanics of a 1600 kW gearbox to identify the main reason for the fracture and how the chain of events took place, leading to catastrophic failure. The diagnoses involved numerical simulation (finite element analysis—FEA) and further analysis of the lubrication system, bearings, planetary stage gears, helical stage gears, and the high-speed shaft. In conclusion, although the numerical simulation showed high contact stresses on the sun shaft teeth, the region with the unexpectedly nucleated crack was the tip of the tooth. The most likely factors that led to premature failure were the missed lubrication for the planetary bearings, a lack of cleanliness in regard to the raw materials of the gears (voids found), and problems with the sun shaft heat treatment. With the sun gear’s shaft, planet bearings, and planet gears broken into pieces, those small and large pieces dropped into the oil, between the gears, and into the tooth ring, causing the premature and catastrophic gearbox failure. Full article

Since private cars and vans accounted for more than 25% of global oil consumption and about 10% of energy-related CO₂ emissions in 2022, increasing the share of electric vehicle (EV) ownership is considered an important solution for reducing CO₂ emissions. At the same time, reducing emissions entails certain economic losses for those countries whose exports are largely covered by the oil trade. The explosive growth of the EV segment over the past 15 years has given rise to overly optimistic forecasts for global EV penetration by 2050. One of the major obstacles to such a development scenario is the limited availability of resources, especially critical materials. This paper proposes a mathematical model to predict the global EV fleet based on the limited availability of critical materials such as lithium, one of the key elements for battery production. The proposed model has three distinctive features. First, it shows that the classical logistic function, due to the specificity of its structure, cannot correctly describe market saturation in the case of using resources with limited serves. Second, even the use of a special multiplier that describes the market saturation process taking into account the depletion (finiteness) of the used resource does not obtain satisfactory economic results because of the “high speed” depletion of this resource. Third, the analytical solution of the final model indicates the point in time at which changes in saturation rate occur. The latter situation allows us to determine the tracking of market saturation, which is more similar to the process that is actually occurring. We believe that this model can also be validated to estimate the production of wind turbines that use rare earth elements such as neodymium and dysprosium (for the production of powerful and permanent magnets for wind turbines). These results also suggest the need for oil-exporting countries to technologically diversify their economies to minimize losses in the transition to a low-carbon economy. Full article

Energy storage is crucial for enhancing the economic efficiency of integrated energy systems. This paper addresses the need for flexible resources due to high renewable energy integration and the complexity of managing multiple resources. We propose a decentralized collaborative multi-stage distributionally robust scheduling method for electric-thermal systems, incorporating energy storage to mitigate renewable energy fluctuations. Firstly, we model the electric-thermal system with multiple flexible resources. Uncertain parameters of renewables are estimated using conditional generative adversarial networks (CGANs), assuming empirical probability distributions. Secondly, given the distinct operators of electric and thermal systems and information barriers, we develop a data-driven distributionally robust chance-constrained optimization model (DRCCO). This model ensures decentralized collaboration without compromising information security or fairness. Then, we introduce an Alternating Direction Method of Multipliers (ADMM) algorithm with parallel regularization to decouple the model. This approach facilitates rapid solution finding with minimal information exchange. Finally, numerical examples confirm the model’s effectiveness in enhancing system flexibility and ensuring wind power consumption. Full article

Sliding bearings are widely used in wind turbine gearboxes, and the accurate identification of coupling interface loads is critical for ensuring the reliability and performance of these systems. However, the space–time coupling nature of these loads makes them difficult to calculate and measure directly. An improved method utilizing the POD decomposition algorithm and polynomial selection technology is proposed in this paper to identify the sliding bearing coupling interface loads. By using the POD decomposition algorithm, the sliding bearing coupling interface loads can be decomposed into the form of a series of independent oil film time history and spatial distribution functions. Then, it can be converted into space–time independent sub-coupled interface load identification in which oil film time history can be transformed into the recognition of a certain order modal load and the corresponding oil film spatial distribution function can be fitted with a set of Chebyshev orthogonal polynomial. To address the ill-posedness caused by the weak correlation between the modal matrix and polynomial options during the identification process, this paper introduces polynomial structure selection technology. Firstly, displacement responses are collected, and a series of modal loads are identified using conventional concentrated load identification methods. Then, the polynomial structure selection technology is applied to select the effective modal shape matrix, using a specific mode load as the oil film time history function. The load ratios of other mode loads to this reference mode load are compared, and the effective Chebyshev orthogonal polynomials are selected based on the error reduction ratio. Finally, multiplying the identified oil film time histories by the corresponding oil film spatial distribution functions yields the coupling interface load. The results of the numerical examples verify the improved method’s rationality and effectiveness. Full article

This paper proposes a two-stage, three-layer stochastic robust model and its solution method for a multi-energy access system (MEAS) considering different weather scenarios which are described through scenario probabilities and output uncertainties. In the first stage, based on the principle of the master–slave game, the master–slave relationship between the grid dispatch department (GDD) and the MEAS is constructed and the master–slave game transaction mechanism is analyzed. The GDD establishes a stochastic pricing model that takes into account the uncertainty of wind power scenario probabilities. In the second stage, considering the impacts of wind power and photovoltaic scenario probability uncertainties and output uncertainties, a max–max–min three-layer structured stochastic robust model for the MEAS is established and its cooperation model is constructed based on the Nash bargaining principle. A variable alternating iteration algorithm combining Karush–Kuhn–Tucker conditions (KKT) is proposed to solve the stochastic robust model of the MEAS. The alternating direction method of multipliers (ADMM) is used to solve the cooperation model of the MEAS and a particle swarm algorithm (PSO) is employed to solve the non-convex two-stage model. Finally, the effectiveness of the proposed model and method is verified through case studies. Full article

Adaptation to climate change has remained a major socio-ecological issue in the Northern Region of Cameroon since 1973. Presently, this region is subject to the severe chaos of drought, floods, and ecosystem degradation, causing harm and disrupting climatic patterns. Climate change results in the drying of surface water and crops, threatening food security and the well-being of households. It has a serious impact on the entire agricultural production system at global scale. Here, it is suggested that successive adjustments to deeper systemic and transformational adaptations through efforts from NGOs, the Government, and donors, as well as innovations, are necessary to offset the negative impact of climate change on the agricultural value chain. Therefore, this research aimed to identify adaptation strategies and practices for rural communities and households, who suffer from limited access to these agricultural innovations, for a transformative adaptation. Through surveys and focus group discussions carried out in several villages in the Northern Cameroon Region, this study provides empirical data on emerging agricultural innovations in contrasting socio-economic, agricultural, and ecological contexts. Our findings demonstrate that agricultural innovations fostered at the village level have several characteristics that contribute to adaptation and mitigation of the impact of climate change. To begin with, conservation agriculture is very interesting, because crop residues left on the soil protect it from rainfall and dry winds, and gradually add humus to the top soil. In addition, agroforestry plays an important role for the household regarding ecosystem services, including food supply, soil fertility, protection from erosion, regulation of water regime, and sociocultural value. Generally, heads of households (83%) were more involved in innovative initiatives than other social strata, resulting in unequal access and proximity to agricultural innovations. Furthermore, the results highlight a significant lack of coordination and poor visibility of permanent structures supporting agricultural innovations at local level, weakening the sustainable transformation of adaptation. From a scientific perspective, this study could help build a conceptual relationship between agricultural innovation and sustainability transformation, i.e., a climate-smart agriculture. In practice, it provides levers that can be used to multiply and expedite agricultural innovation processes, water conservation, and livestock sustainability, thus contributing to the sustainability of the whole agricultural system in Cameroon and within the Sahel region of Africa. Full article

The mechanical multiplier gearbox is one of the most important parts for wind power generation efficiency. Modern epicyclic gearboxes are compact, lightweight builds capable of high power ratings with coaxial input and output shafts. To achieve this, it is very important to select the proper internal gearbox layout and other relevant parameters in the early design stages as the wrong choices will result in a suboptimal solution. Parametric optimization was applied to select the optimal gearbox solution for a wind turbine application, while taking into account both two-carrier and three-carrier solutions. The large number of possible solutions has resulted in the development of the 2-SPEED software to conduct systematic analysis and comparison. The best five two-carrier solutions and the one best three-carrier solution have been selected from the solution pool, with the selection being based on the criteria of maximum efficiency, minimum weight, and minimal greater-ring diameter size. One optimal two-carrier solution was then selected from the five and compared to the three-carrier solution. Recommendations for the selection of either two-carrier and three-carrier gear train solutions according to the application demands have been deducted and provided. This will result in lighter, more efficient designs with smaller radial dimensions. Full article

The “dual carbon” goal has made it a mainstream trend for new energy stations (NESs) and energy storage stations (ESSs) to jointly participate in market regulation. This paper proposes a multiple time scale distributed optimization method for NESs and ESSs based on the alternate direction multiplier method (ADMM). By first considering the uncertainty of new energy output and the volatility of electricity market prices, a multi time scale revenue model is constructed for day-ahead, intraday, and real-time markets. Then, the objective function is built by maximizing the comprehensive market revenues and is simplified using the synergistic effect of NESs and ESSs. Next, the simplified objective function is solved by the ADMM, and the revenues are maximized while each energy meets the relevant constraints. Lastly, the 33-node network topology is used to illustrate the feasibility of the proposed method. The simulation results show that after optimization, the output of NESs and ESSs can coordinate work in day-ahead, intraday, and real-time markets, while the abandonment power of wind and light is significantly improved. Full article