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In grounding fault of DC system, overvoltage will appear on equipment nodes in converter station. Due to different operation mode and equipment type, it is very different from overvoltage in AC system. Based on Shanghaimiao–Linyi​ ±800kV Ultra High Voltage Direct Current (UHVDC) transmission project, this paper studies the converter station overvoltage characteristics under the conditions of DC pole line grounding, DC bus grounding and converter transformer valve side grounding fault, then analyzes the influence of smoothing reactor, filter capacitor and grounding pole line on overvoltage. In grounding fault at DC pole line near the converter station, the maximum overvoltage occurs on the inductance of DC filter, which is 592.75kV; In grounding fault at the top of the high voltage converter valve, the maximum overvoltage occurs on the neutral bus, which is 272.17kV. Increasing the inductance or capacitance of the smoothing reactor and filter capacitor, shortening the length of grounding electrode wire can significantly reduce the amplitude and steepness of overvoltage. The calculation results provide a reference for the internal overvoltage suppression of DC converter station with layered access at the inverter side.

Wind turbine gearboxes are required to operate under adverse operational conditions over a long service lifetime. Unfortunately, gearbox designers are yet to achieve the reliability anticipated by wind turbine manufacturers and operators. The poor understanding of variable loading conditions has resulted in the majority of wind turbine gearboxes being unable to reach their expected service lifetime of 20-25 years. This has led to an increasing need to investigate the fundamental issues associated with the degradation of wind turbine gearbox materials during operation in order to improve existing designs and optimise future ones. This paper investigates the various challenges that need to be addressed in order to achieve a noteworthy increase in the operational service lifetime of large-scale industrial wind turbine gearboxes.

The steady-state phase distribution and the structural parameters have been taken as the input for the nuclear physics calculation in the ADS windowless spallation target. The distribution of the extreme large power density of the heat load is imported back as the source term in the energy equation. Then temperature distribution is obtained based on the flow process and heat transfer. The preliminary results show that the temperature distribution reaches the steady-state and its shape is like the broken wings of the butterfly. This is very important for the further design and optimization of the ADS windowless spallation target. So the two-way coupling simulation of the heat transfer process is successfully performed between the computational fluid dynamics and the nuclear physics simulation.

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.

Abstract In this study, we develop indices for the overall technical efficiency (OTE) and energy-saving target ratio (ESTR) using data envelopment analysis (DEA) to calculate the relative efficiency and energy-saving potential of 30 provinces in China from 1997 to 2014. The results are as follows: (1) the OTE of China is 79.187%, indicating that there is 20.813% potential for improvement. The OTE exhibits decreasing efficiency values from the coastal areas to the inland areas and has clear geographical relationships. The average values of OTE in the east, midland and west are 0.932, 0.694 and 0.703. Theoretically, the total energy savings of CE, HE, ME and BE are 11080.60PJ, 5124.71PJ, 4729.24PJ and 6797.39PJ. (2) Regarding CE, HE, ME and BE, the provinces with the highest comprehensive ranks are Henan, Shanxi, Shaanxi, and Gansu, which simultaneously have the greatest energy-saving potentials and energy-saving targets. (3) The HE has the largest average ESTR of 38.357% and the values for BE, CE, and ME are 25.759%, 23.874%, and 22.143%, respectively. The CE category is the greatest in total energy savings (40.171%), which is followed by BE (24.150%), HE (18.384%), and ME (17.293%).

Abstract Transportation de-carbonization is a complex problem involving the economy, population, technology and environment. Implementing the pathway simulation based on systematic methods will help to optimize the transportation sustainability plan. This study’s key motivation is that earlier research ignored the relationship between driving factors and the transmission process. To quantitatively identify the path and process of emission reduction, a hybrid system dynamics STIRPAT-SD model is proposed to explore the transportation optimization’s de-carbonization ability. This study fully considers the composition of elements and subsystems based on the STIRPAT theoretical model and visually shows the system’s feedback relationship. Transportation structural and technical optimization scenarios are set to identify the threshold reduction paths. It is found these optimization strategies have significant de-carbonization effects. And transportation structure policy has the highest de-carbonization efficiency, the emission intensity decreased by 9.1% under the TSS2 scenario (Transportation structure scenario). This study proposes a novelty model combining dynamic simulating processes with a significantly theoretical model to improve simulation and factor composition accuracy. And the joint scenario setting identifies the most effective de-carbonization pathway and clarifies the threshold of all possible pathways. Research findings can effectively track, test, predict the achievement of policy goals, and provide policy optimization references for the sustainable development related to the transportation system in practice.

As a relatively mature technology, biomass molded fuel (BMF) is widely used in distributed and centralized heating in China and has received considerable government attention. Although many BFM incentive policies have been developed, decreased domestic traditional fuel prices in China have caused BMF to lose its economic viability and new policy recommendations are needed to stimulate this industry. The present study built a regionalized net present value (NPV) model based on real production process simulation to test the impacts of each policy factor. The calculations showed that BMF production costs vary remarkably between regions, with the cost of agricultural briquette fuel (ABF) ranging from 86 US dollar per metric ton (USD/t) to 110 (USD/t), while that of woody pellet fuel (WPF) varies from 122 USD/t to 154 USD/t. The largest part of BMF’s cost composition is feedstock, which accounts for up 50%–60% of the total; accordingly a feedstock subsidy is the most effective policy factor, but in consideration of policy implementation, it would be better to use a production subsidy. For ABF, the optimal product subsidy varies from 26 USD/t to 57 USD/t among different regions of China, while for WPF, the range is 36 USD/t to 75 USD/t. Based on the data, a regional BMF development strategy is also proposed in this study.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

The wind power forecasting error restricts the benefit of the wind farm in the electricity market. Considering the cooperation of wind power bidding and energy storage system (ESS) operation with uncertainty, this paper proposes a coordinated bidding/operation model for the wind farm to improve its benefits in the electricity market. The maximum entropy based deep reinforcement learning (RL) algorithm, Soft Actor-Critic (SAC) is used to construct the model. The maximum entropy framework enables the designed agent to explore various optimal possibilities, which means the learned coordinated bidding/operation strategy is more stable considering the forecasting error. Particularly, penalty terms are introduced into the benefit function to relax the constraints and improve the convergency. The case study illustrates that the learned policy can effectively improve the wind farm benefit while ensuring robustness.