Latest ArticlesApplying the molten salt heat storage technology to condensing thermal power units can enhance the flexibility of peak shaving. A bypass two-stage heat-storage peak shaving scheme was proposed and applied in an ultra-supercritical 660 MW unit. Taking 30% THA as the design condition for the thermal storage system, four heat storage capacity schemes were designed. The EBSILON software was used to analyze the peak regulation range, heat storage and release duration, peak regulation cost, and the change of net profit from heat-storage peak-shaving under different market rules. The results show that during off-design operations, there exist feasible domains for both heat charging and discharging. During the charging process, when the initial operating condition is between 40%~50%, the decrease in minimum generation load ratio is most significant, with the high-temperature thermal storage system reaching its design capacity first. During the discharging process, when the initial operating condition is 94.6%, the load ratio equivalent to VWO condition can be achieved, with the high-temperature thermal storage system completing discharge first. Regardless of the implementation approach, peak-shaving through thermal storage achieves maximum electricity revenue growth while minimizing lifespan depreciation costs and overall system expenses. In the peak-shaving ancillary service market, net profit peaks at 40% heat-storage starting condition, whereas in the capacity market scenario, the maximum net profit occurs at 45% heat-storage starting condition. The scheme with the smallest heat storage capacity demonstrates the lowest total cost increase, with both market mechanisms yielding optimal net profits.
As the power supply structure gradually shifts toward a diversified pattern, the risk of regional renewable energy consumption increases. In order to maximize the utilization of the existing adjustment capacity of hydropower and thermal power and improve the consumption level of wind power and photovoltaic power, a multi-energy complementary energy base including wind power, photovoltaic power, hydropower, thermal power and energy storage is constructed, and the advantages of complementary power generation among wind power, photovoltaic power, hydropower, thermal power and energy storage compared to other power generation modes are analyzed. Based on 8 760 time series points throughout the year, a multi-objective power generation capacity planning model that takes into account system reliability, economic benefits, environmental benefits, and the consumption level of renewable energy is constructed, and multi-objective optimization is achieved through the linear weighting method of unit standardization of the objective function. Finally, the annual historical wind and solar data of a certain place are selected for simulation experiments. The results show that, compared with single-objective optimization, multi-objective optimization can effectively balance the equilibrium points among different optimization objectives, avoid the limitations of single-objective optimization, and by rationally adjusting the output of hydropower, thermal power and energy storage, the complementary coordination of wind power, photovoltaic power, hydropower, thermal power and energy storage on multi-time scales can be achieved.
With the growing integration of renewable energy sources into the power grid, frequency fluctuations have become a significant challenge. To address this, hybrid energy storage systems (HESS) are used to assist thermal power units in responding to automatic generation control (automatic generation control, AGC) commands. This paper proposes a novel hybrid power distribution strategy based on stochastic model predictive control (SMPC) to enhance the regulation performance of thermal power units in AGC applications, particularly under fluctuating power demands. The aim is to optimize the power allocation between the thermal power unit and the HESS to improve the accuracy, stability, and efficiency of the regulation process, ensuring a more reliable response to AGC signals.
The proposed strategy first constructs a power demand model for the HESS system, consisting of lithium titanate batteries for high-power storage and lithium iron phosphate batteries for energy storage, based on a Markov probability matrix, which simulates the response of the thermal power unit to AGC commands. An adaptive mechanism is introduced to dynamically adjust the state transition probabilities in real-time, enhancing the accuracy of power demand predictions during AGC fluctuations. Additionally, a scene tree generation method is proposed, which combines probability thresholds with stratified sampling to transform the probability distribution output by the adaptive Markov model into a finite set of scenarios for optimization. This method is designed to better handle the uncertainty of power demand predictions under multiple future scenarios, addressing the inherent variability of AGC command responses. Finally, the strategy integrates the above components into an SMPC controller, which optimizes power distribution between the thermal power unit and HESS in real-time, considering the stochastic nature of power demands and control parameters.
Simulation experiments demonstrate that the proposed strategy significantly outperforms traditional frequency regulation strategies, which do not incorporate power prediction, and static SMPC strategies that lack dynamic correction of state transition probabilities. The performance index Kp is improved by 14.1% and 7.5%, respectively, showing that the SMPC strategy with adaptive power demand forecasting can achieve more precise and stable regulation performance. Additionally, the model's ability to handle uncertainty in power demand prediction allows for more accurate and timely responses to AGC fluctuations, resulting in better coordination between the thermal power unit and the HESS.
The proposed strategy effectively enhances the collaborative regulation performance between the thermal power unit and HESS, offering strong application potential. Further optimization of the model can improve its robustness and adaptability in practical applications, advancing the implementation of this technology.
A model for hydrogen production and storage during deep peak-shaving (less than 30% of rated capacity) was established by coupling a gas turbine combined cycle (GTCC) unit with a solid oxide electrolysis cell (SOEC), demonstrating the feasibility of efficiently matching electrothermal resources within the system to accommodate renewable energy. Machine learning was used to predict GTCC variable-load power output, heat recovery boiler models were used to calculate steam parameters, and SOEC thermochemical models were applied to determine hydrogen production electricity and heat consumption. Results show that SOEC electrolysis voltage and hydrogen production can quickly respond to changes in input electrical energy, with the thermal inertia temperature difference of hydrogen production stabilizing at 25~27 ℃. When the peak shaving depth (the ratio of accommodated renewable energy to rated capacity) increased from 50% to 100%, the overall efficiency of the energy storage peak shaving system rose from 47.8% to 55.3%. Using GTCC-SOEC to accommodate renewable energy reduced the total energy consumption of hydrogen production from 6.7 kJ/m³ to 5.8 kJ/m³, with a reduction of 13.4%. SOEC hydrogen production heat consumption is about 80% of the electricity consumption, and the efficiency of the coupled hydrogen production system is only approximately 3.15%~3.34% lower than the efficiency of GTCC standalone peak-shaving power generation. For every 1% increase in peak shaving depth, hydrogen production increases by about 0.1 t/h, and CO2 emissions from natural gas combustion decrease by 0.64 t/h. Considering storage costs and weather impacts, when the unit operates for 8 hours per day, as the peak periods for wind and solar generation increase from 0 to 1.5 hours, the hydrogen blending volume ratio increases to 30%, and the average efficiency of the hydrogen storage-release cycle increases from the baseline 56.7% to 62.5%; When the daily online duration of renewable energy reaches 2.5 hours, the average efficiency within the cycle can reach 67.1%. When the ratio of peak-shaving subsidy to electricity price is less than 0.2, the cost-to-output ratio initially decreases and then increases with the ratio of stored to grid electricity. When the subsidy-to-electricity price ratio is greater than 0.2, the cost-to-output ratio continuously increases with the stored-to-grid electricity ratio.
An efficient and feasible primary-side control method is proposed for the hybrid energy storage system (HESS) and wireless power transfer (WPT) system. This method can enable the system to dynamically maintain maximum power transfer under different charging states of the hybrid energy storage unit and keep the switching transistors of the Class E power amplifier (Eop) in a soft-switching operating state, ensuring stable and efficient wireless energy supplementation for the hybrid energy storage system. The system topology consists of an Eop, a series-series (S-S) type WPT circuit, and HESS. Based on the analysis of WPT transmission characteristics and Eop soft switching characteristics under different charging modes, the Eop optimal frequency tracking control and Eop soft switching duty cycle control strategies for HESS wireless charging are proposed. The key advantage is that all control mechanisms are located on the transmitting side, which can effectively reduce the volume of additional circuits in the energy storage unit on the receiving side and improve the volumetric energy density of the system. Finally, an experimental prototype is built based on the ZYNQ controller, and the experimental results show that the system can achieve efficient and stable wireless power transfer under different charging states and coupling conditions of the hybrid energy storage.
To address the demand for flexibility and economy in power grids with high penetration of renewable energy, this paper proposes a coordinated dispatch optimization model and scheduling strategy for multi-type energy storage systems considering peak-valley electricity prices and renewable energy fluctuations.
Based on mixed integer linear programming (MILP) and the non-dominated sorting genetic algorithm II (NSGA-II), a dual-objective optimization dispatch model is constructed, aiming to minimize system costs and maximize renewable energy utilization rate. The model is validated using typical daily load and wind-solar power output data from different seasons in Hebei region to evaluate its economic performance and renewable energy accommodation effectiveness.
The results show that the model demonstrates good robustness and can adapt to fluctuations in wind-solar power and load. The optimal energy storage output is 3 000 MW in spring and 2 000 MW in summer, autumn, and winter. The renewable energy utilization rate remains above 85% in all seasons, and the total system cost is controlled at around 15 000 yuan.
By integrating multi-season scenarios, multi-type energy storage, and multi-objective optimization, this study achieves coordinated improvement in both economic performance and renewable energy accommodation, effectively enhancing grid flexibility and reducing the curtailment rate of wind and photovoltaic power.
With the expansion and continuous development of the photovoltaic power generation industry, photovoltaic modules will face large-scale decommissioning in the coming years, and the recycling of modules has become an important issue for global energy sustainability and the circular economy system. Decommissioned photovoltaic modules are rich in high-value precious metal elements such as silicon, silver, and aluminum, but also contain toxic substances such as lead and fluorides, which can cause significant harm to the environment and human health if not properly handled. The current development status and decommissioning status of the photovoltaic power generation industry, as well as the structure and main components and values of photovoltaic modules, are summarized. The principles, advantages, and disadvantages of recycling technologies such as physical methods, thermal treatment methods, and chemical methods are analyzed and compared. Moreover, the progress of recycling technologies in China is investigated. Finally, the recycling of waste photovoltaic modules is systematically summarized and prospected, providing a scientific basis and decision-making support for the low-carbon development and resource recycling of the photovoltaic power generation industry.
As China’s installed capacity of wind power and photovoltaic systems firmly ranks first globally, the first batch of large-scale commissioned equipment is approaching centralized decommissioning. It is estimated that by 2030, the cumulative decommissioned photovoltaic modules will reach 1.5 million tons, and the decommissioned wind power equipment capacity will reach 2 million tons. Through a systematic analysis of the current status of resource utilization of decommissioned wind and photovoltaic equipment in China, and by integrating international typical experiences such as the EU’s WEEE Directive system, the technology innovation-driven model in the United States, and Japan’s forward-looking legislative practices, this paper, in view of the problems existing in China’s standardization development, industrial chain coordination, and regulatory systems, proposes to adhere to the principles of systematicness, coordination, adaptability, and operability, and constructs a four-tier standard framework comprising basic general standards, specialized technical standards, management specifications, and testing certification standards. Moreover, specific policy recommendations are presented from four aspects: standard development, policy coordination, regulatory mechanisms, and international cooperation.
The “retirement wave” of wind turbines is approaching, and the harmless treatment of a large number of decommissioned wind turbine blades is an important bottleneck for the wind power industry to achieve a complete green closed loop. Under the background of the country’s active implementation of energy conservation and carbon reduction renovations in coal-fired power plants, the co-processing of decommissioned wind turbine blades by coal-fired boilers can achieve the reduction, resource utilization and harmless treatment of solid waste. In response to the demand for large-scale continuous heat treatment of decommissioned wind turbine blades, a process route of coupling coal-fired boilers with pyrolysis-gasification of wind turbine blades is proposed. By extracting the flue gas from the boiler as the heat source for pyrolysis-gasification, the combustible oil and gas produced by pyrolysis-gasification are sent into the furnace for combustion, simplifying the processing procedure of wind turbine blades and reducing the initial investment. The process was simulated and verified by using Aspen Plus software. The results indicate that when the processing rate of wind turbine blades was 1 t/h, the proportion of flue gas extracted did not exceed 0.6%, thereby meeting the energy requirements for pyrolysis-gasification. A pilot-scale experimental study was conducted based on a 7 MW chain grate boiler, and it was found that when the oxygen volume fraction in the flue gas was 5%~10%, clean glass fibers could be obtained through “one-step pyrolysis-gasification”. Based on both simulation results and experimental data, a flue gas conditioning-based integrated treatment process was proposed: control the flue gas temperature at 600~700 ℃ and the oxygen volume fraction at 5%~10% by blending flue gas from the economizer outlet and adding air, so as to enhance the strength of the recovered fibers and further simplify the wind turbine blade treatment process, providing a reference for the large-scale treatment of wind turbine blades.
The rapid expansion of the cumulative installed capacity of wind power in recent years has driven the continuous growth of market demand for wind power operation and maintenance (O&M). Lubricating oil for wind turbine gearboxes is an indispensable and crucial component in O&M. The waste lubricating oil for wind turbine gearboxes is classified as hazardous waste due to its harmful components such as heavy metals and degraded additives. Its efficient recycling and harmless treatment are crucial for achieving clean development in the wind power industry. Firstly, based on the characteristics and composition of waste lubricating oil, the variation laws and underlying causes of typical performance indicators during operating are expounded, including kinematic viscosity, pour point, moisture content, particle contamination level, acid value, and elements (e.g. Fe, P, and S). These variations are primarily induced by factors such as external contamination, oxidation at high temperatures and pressures, and additive degradation. On this basis, the latest research progress in current waste mineral oil regeneration processes is systematically summarized, covering mainstream technical routes such as pretreatment, molecular distillation, solvent extraction, and hydrofining. Furthermore, the feasibility and limitations of the aforementioned methods in extending the service life of waste lubricating oil and realizing its recovery and regeneration are analyzed. The identified limitations include issues such as low treatment efficiency, insufficient processing depth, and restricted application. Finally, the future development directions of high-value waste oils represented by synthetic lubricating oil for wind turbine gearboxes are discussed from the perspectives of industrial development, technological advancement and life management. It emphasizes that establishing a classified recycling system, developing green and efficient regeneration processes, and implementing life management will be the key pathways to realizing a circular economy in the wind power industry.