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This study combines convolutional neural network (CNN) recognition technology, Greenwich engineering software, and statistical yearbook methods to evaluate rural solar, wind, and biomass energy resources in pilot cities in China, respectively. The CNN method enables the rapid identification of the available roof area, and Greenwich software provides wind resource simulation with local terrain adaptability. The results show that the capacity of photovoltaic power generation reaches approximately 15.63 GW, the potential of wind power is 458.3 MW, and the equivalent of agricultural waste is 433,900 tons of standard coal. The city is rich in wind, solar, and biomass resources. By optimizing the hybrid power generation system through genetic algorithms, wind energy, solar energy, biomass energy, and coal power are combined to balance the annual electricity demand in rural areas. The energy trends under different demand growth rates were predicted through the LEAP model, revealing that in the clean coal scenario of carbon capture (WSBC-CCS), clean coal power and renewable energy will dominate by 2030. Carbon dioxide emissions will peak in 2024 and return to the 2020 level between 2028 and 2029. Under the scenario of pure renewable energy (H_WSB), SO2/NOx will be reduced by 23–25%, and carbon dioxide emissions will approach zero. This study evaluates the renewable energy potential, power system capacity optimization, and carbon emission characteristics of pilot cities at a macro scale. Future work should further analyze the impact mechanisms of data sensitivity on these assessment results.

The current stage of development of autonomous energy systems is characterized by a rapid increase in renewable energy sources’ installed capacity. Such growth is observed both in centralized and isolated energy systems. Renewable energy sources show high efficiency in the electrification of rural remote settlements around the world. The power of such power complexes varies from several kilowatts to tens of megawatts. When solving the problems of rural remote settlements electrification, the main issues of optimizing the composition of equipment and the structure of the energy systems play an extremely important role. Moreover, depending on the specifications of the problem being solved, criteria for evaluating efficiency are used, which are different. For example, the following are used as objective functions: minimization of the levelized cost of energy and fossil fuel consumption; maximizing the standard of people living and reliability indicators; the payback period of the project and other indicators. Various combinations of objective functions and the solution to the multi-criteria optimization problem are possible. Moreover, an important stage in the development of renewable energy in remote rural areas is the availability of new mechanisms to support an environmentally friendly generation. These mechanisms can be used in solving problems of optimizing the structure and composition of energy equipment in remote power systems. The main purpose of this article is to demonstrate the world practices of optimal design of isolated energy systems. The review includes both the main questions that arise when solving such problems, and specific problems that require a more detailed analysis of the object of study.

There is a global push to develop renewable energy to further a low-carbon society. However, the nature of variable renewable energy (VRE) sources such as wind power and solar photovoltaic (PV) systems may create problems because electricity grids require a stable power supply to match demand. To evaluate the potential capacity of VREs that may be installed, we develop an optimized model that balances power supply and demand and also considers grid balancing by battery storage and load frequency control. The model was applied to a case study of an isolated grid on a remote Japanese island. When set to optimize the grid in terms of lowest cost, the model suggested that, compared with the base case, the capacity of wind power should be increased by a factor of 1.7 and 15.8 for situations without and with battery storage, respectively. Since it was always considered to be more expensive than wind power, no change in solar PV capacity was observed. These approaches resulted in a decrease in the total power generation cost of 2% and 24%, respectively, while total CO2 emissions fell by 3% and 52%, primarily driven by decreased used of the existing fossil-fueled thermal plant.

The low-carbon transition of the power system is essential for China to achieve peak carbon and carbon neutrality. However, China could suffer power shortages due to radical policies in some extreme cases. The gap between power demand and supply from March 2021 to November 2021 ranged between 5.2 billion kW·h and 24.6 billion kW·h. The main reason for the power shortage was over-reliance on renewable energy and insufficient coal power supply for the power system. The low-carbon transformation path of the electric system needs to be explored with more flexibility for power security. This study applied a modified LEAP model and carried out a forecast analysis of thermal power generation and installed capacity in 2025 and 2030 under normal and extreme weather scenarios. The results suggested that: the installed capacity of thermal power will need to account for about 44.6–46.1% of power generation in 2025 and 37.4–39.3% in 2030, with the assumption of power shortages caused by the instability and uncertainty of renewable power. In the future, China needs to pursue the development of diversified energy sources and enhance the power supply security capability while strengthening the development and utilization of renewable energy.

This paper presents the results of laboratory tensile testing of segmentally-installed glue-in roof bolting. We studied roof bolting of the type Olkusz-16A (Boltech Sp. z o.o., ZGH Bolesław S.A., Bukowno, Poland), additionally equipped with a steel rod coil, which was mounted in steel cylinders filled with a concrete mixture using multi-part resin cartridges with a diameter of 0.024 m and length of 0.045 m. The mounting depths were 0.1 m and 0.2 m, respectively. Our main purpose was to determine the effect of the bolt hole diameter, which assumed the values 0.028 m, 0.032 m, 0.035 m, and 0.037 m, respectively, on the load-bearing capacity of the roof bolting in relation to the mounting depth. We found that the mounting depth of 0.2 m was sufficient for the roof bolting to exhibit its full load and displacement properties for all four diameters of the bolt hole. To determine whether the roof bolting was capable of transferring the load in situ, we presented the results of the predicted load on the roof bolting applied in a room and pillar mining method in an underground mine of zinc and lead ore deposits. Our objective was to determine the influence of the room and pillar mining method geometry on the range of the fault zone of rocks around pits. We designed the deposit excavation model using the Examine3D numerical modeling software, which is based on the boundary element method. We created three-dimensional models for three variants of working space opening widths: featuring two, three, and four rows of rooms. The geometry of rooms and pillars corresponded to the mine conditions; the width, height, and length parameters were all 5 m. We determined the strength, strain, and structural parameters of the rock mass on the basis of laboratory studies of the drill core and rock forms collected from the room longwall. We used the strength factor to specify the maximum range of the fault zone of rocks around pits. In the last stage of research, we compared the load value obtained based on numerical testing with the maximum load obtained in the tensile strength tests of the roof bolting and determined the safety factor of the segmentally-installed roof bolting.

The characteristics of solar and wind energy determine that the optimization of a standalone hybrid wind turbine (WT)/photovoltaic panel (PV) system depends on the natural resources of the installation location. In order to ensure system reliability and improve the resource utilization, a method for determining the installed capacity ratio of a hybrid renewable energy system is required. This study proposes a calculation method to optimize the installed capacity ratio, considering the system reliability to meet the needs of the hybrid system to adapt to different natural resources. In this paper, a standalone hybrid WT/PV system to provide electricity for rural areas is designed. Taking the power supply guarantee rate and electricity supply continuity as indicators, the system is simulated by using the Transient System Simulator solver. The results show that the recommended installed capacity ratio of the WT and PV is 5:1 when the total solar irradiation is less than 5040 MJ/(m2·a) and the annual average wind velocity is in the range of 3.0~3.5 m/s. When the annual average wind velocity is in the range of 2.0~3.0 m/s, the PV plays an increasingly significant role in the hybrid system and exceeds the WT if the total solar irradiation is greater than 6300 MJ/(m2·a). However, if the total solar irradiation and the annual average wind velocity are less than 5040 MJ/(m2·a) and 2.0 m/s, respectively, it is not recommended to use the standalone hybrid system because it cannot meet the power demand. These conclusions provide guidance for the distribution strategies of the standalone hybrid WT/PV system within different natural resources.

Because of the randomness of wind power and photovoltaic (PV) output of new energy bases, the problem of peak regulation capability and voltage stability of ultra-high voltage direct current (UHVDC) transmission lines, we proposed an optimum allocation method of installed capacity of the solar-thermal power station based on chance constrained programming in this work. Firstly, we established the uncertainty model of wind power and PV based on the chance constrained planning theory. Then we used the K-medoids clustering method to cluster the scenarios considering the actual operation scenarios throughout the year. Secondly, we established the optimal configuration model based on the objective function of the strongest transient voltage stability and the lowest overall cost of operation. Finally, by quantitative analysis of actual wind power and photovoltaic new energy base, this work verified the feasibility of the proposed method. As a result of the simulations, we found that using the optimal configuration method of solar-thermal power stations could ensure an accurate allocation of installed capacity. When the installed capacity of the solar-thermal power station is 1 × 106 kW, the transient voltage recovery index (TVRI) is 0.359, which has a strong voltage support capacity for the system. Based on the results of this work, the optimal configuration of the installed capacity of the solar-thermal power plant can improve peak shaving performance, transient voltage support capability, and new energy consumption while satisfying the Direct Current (DC) outgoing transmission premise.

Post-installed elevator projects have grown significantly in recent years in response to the problem of insufficient vertical traffic capacity in existing buildings, but research on the seismic performance of post-installed elevator structures has been relatively limited. This study takes a 26-story-frame shear wall structure as an example. The seismic response characteristics of this structure before and after the installation of elevators were analyzed. In order to optimize the design scheme of the post-installed elevator structure, this study further analyzed how factors such as the standard height of the elevator shaft frame, the elevator location, and the way the post-installed elevator is connected to the structure affect the seismic response of the elevator structure. The results show that the post-installed elevators have a small impact on the seismic performance of the existing building and can slightly reduce the seismic response of the structure. In addition, the stiffness of the elevator shaft will be reduced, and its seismic response will be slightly increased as the standard shaft height is increased, but the construction cost can be reduced. The installation location has a greater impact on the seismic response of the post-installed elevator. The seismic response of the post-installed elevator is minimal when it is arranged near the elevator shaft of the existing building.

In this study, an improved particle-swarm-optimisation (IPSO) method with dynamically changing inertia weight and acceleration coefficients is employed in determining the installed capacity selection of a hybrid energy generation system (HEGS). The studied HEGS, which includes wind power, photovoltaic (PV) and fuel cells, is used to suppress the penalty bill caused by exceeding the contract power capacity with the power company and to supply the backup power when needed. The objective is to achieve the optimal ratio of the installed capacity of the HEGS, so that each energy source can make the best contribution in the system, satisfy the load demand at a minimal installation cost and shorten the payback period. To realise this objective, the payback period is selected as the optimisation objective function by considering the installation cost and cost recovery. In the IPSO, the penalty technique is designed to solve the optimisation problem with equality and inequality constraints for updating the particle's position and its global best position. The proposed IPSO algorithm has been examined, tested and compared with other methods on the optimisation problem and proven to be more efficient in searching the global solution through numerical simulations of a real case.

The installed productive capacity of a healthcare center’s equipment limits the efficient use of its resources. This paper, therefore, analyzes the installed productive capacity of a hospital angiography room and how to optimize patient demand. For this purpose, a Discrete Event Simulation (DES) model based on historical variables from the current system was created using computer software. The authors analyzed 2044 procedures performed between 2014 and 2015 in a hospital in San José, Costa Rica. The model was statistically validated to determine that it does not significantly differ from the current system, considering the DMAIC stages for continuous process improvement. In the current scenario, resource utilization is 0.99, and the waiting list increases every month. The results showed that the current capacity of the service could be doubled, and that resource utilization could be reduced to 0.64 and waiting times by 94%. An increase in service efficiency could be achieved by shortening maximum waiting times from 6.75 days to 3.70 h. DES simulation, therefore, allows optimizing of the use of healthcare systems’ resources and hospital management.