The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)h...The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.展开更多
Virtual power plant(VPP)integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions,promote the consumption of renewable energy,and improve economic effici...Virtual power plant(VPP)integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions,promote the consumption of renewable energy,and improve economic efficiency.In this paper,aiming at the uncertainty of distributed wind power and photovoltaic output,considering the coupling relationship between power,carbon trading,and green cardmarket,the optimal operationmodel and bidding scheme of VPP in spot market,carbon trading market,and green card market are established.On this basis,through the Shapley value and independent risk contribution theory in cooperative game theory,the quantitative analysis of the total income and risk contribution of various distributed resources in the virtual power plant is realized.Moreover,the scheduling strategies of virtual power plants under different risk preferences are systematically compared,and the feasibility and accuracy of the combination of Shapley value and independent risk contribution theory in ensuring fair income distribution and reasonable risk assessment are emphasized.A comprehensive solution for virtual power plants in the multi-market environment is constructed,which integrates operation strategy,income distribution mechanism,and risk control system into a unified analysis framework.Through the simulation of multi-scenario examples,the CPLEXsolver inMATLAB software is used to optimize themodel.The proposed joint optimization scheme can increase the profit of VPP participating in carbon trading and green certificate market by 29%.The total revenue of distributed resources managed by VPP is 9%higher than that of individual participation.展开更多
This paper suggests a way to improve teamwork and reduce uncertainties in operations by using a game theory approach involving multiple virtual power plants(VPP).A generalized credibility-based fuzzy chance constraint...This paper suggests a way to improve teamwork and reduce uncertainties in operations by using a game theory approach involving multiple virtual power plants(VPP).A generalized credibility-based fuzzy chance constraint programming approach is adopted to address uncertainties stemming from renewable generation and load demand within individual VPPs,while robust optimization techniques manage electricity and thermal price volatilities.Building upon this foundation,a hierarchical Nash-Stackelberg game model is established across multiple VPPs.Within each VPP,a Stackelberg game resolves the strategic interaction between the operator and photovoltaic prosumers(PVP).Among VPPs,a cooperative Nash bargaining model coordinates alliance formation.The problem is decomposed into two subproblems:maximizing coalitional benefits,and allocating cooperative surpluses via payment bargaining,solved distributively using the alternating direction method of multipliers(ADMM).Case studies demonstrate that the proposed strategy significantly enhances the economic efficiency and uncertainty resilience of multi-VPP alliances.展开更多
To meet the demand for intelligent and unmanned development in thermal power plants,an intelligent inspection system has been designed.This system efficiently performs inspection tasks and monitors the operational par...To meet the demand for intelligent and unmanned development in thermal power plants,an intelligent inspection system has been designed.This system efficiently performs inspection tasks and monitors the operational parameters of key equipment in real-time.The collected data is uploaded to the monitoring center,allowing operation and maintenance personnel to access equipment information promptly.Data analysis is used to provide fault warning and diagnosis for critical equipment.The system employs the Pure Pursuit algorithm,which effectively avoids obstacles and ensures path continuity and stability.Simulation results show that the Pure Pursuit algorithm significantly improves the navigation accuracy and task efficiency of the inspection robot,ensuring the reliability of thermal power plant inspections.展开更多
Texas is the largest state by area in the US after Alaska,and one of the top states in the production and consumption of electricity with many coal-fired plants.Coal-fired power plants emit greater than 70% of polluta...Texas is the largest state by area in the US after Alaska,and one of the top states in the production and consumption of electricity with many coal-fired plants.Coal-fired power plants emit greater than 70% of pollutants in the energy sector.When coal is burned to produce electricity,nitrogen oxides(NO_(x))are released into the air,one of the main pollutants that threaten human health and lead to a large number of premature deaths.The key to effective air quality management is the strict compliance of all plants with emission standards.However,not all Texas coal plants have the environmental equipment to lower pollutant emissions.Nitrogen dioxide(NO2)observations from the TROPOspheric Monitoring Instrument(TROPOMI)were used to evaluate the emissions for Texas power plants.Data from both the Emissions and Generation Resource Integrated Database(EGRID)and the Emissions Database for Global Atmospheric Research(EDGAR)were used to examine emissions.It was found that NOx emissions for Texas power plants range from 1.53 kt/year to 10.99 kt/year,with the Martin Lake,Limestone and Fayette Power Project stations being the top emitters.WA Parish and Martin Lake stations have the strongest NOx fluxes,with both exhibiting significant seasonal variability.Comparisons of bottom-up inventories for EDGAR and EGRID show a high correlation(r=0.956)and a low root mean square error(0.766).A more reasonable control policy would lead to much reduced NOx emissions.展开更多
Most data centers currently tap into existing power grids to draw the immense amount of electricity they need to operate.But many of the data centers that Google(Mountain View,CA,USA)plans to open in the next few year...Most data centers currently tap into existing power grids to draw the immense amount of electricity they need to operate.But many of the data centers that Google(Mountain View,CA,USA)plans to open in the next few years will boast their own power plants,an arrangement known as colocation[1].Under an agreement announced in December 2024,the company will site data centers in industrial parks where its partner Intersect Power of Houston,TX,USA,has installed clean power facilities[1,2].The first of these complexes is scheduled to come online in 2026[1].展开更多
Virtual Power Plants(VPPs)are integral to modern energy systems,providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data.Traditional anomaly detection methodolo...Virtual Power Plants(VPPs)are integral to modern energy systems,providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data.Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations.We introduce the Memory-Enhanced Autoencoder with Adversarial Training(MemAAE)model to overcome these limitations,designed explicitly for robust anomaly detection in VPP environments.The MemAAE model integrates three principal components:an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors,an adversarial training module that enhances system resilience across diverse operational scenarios,and a prediction module that aids the autoencoder during the reconstruction process,thereby facilitating precise anomaly identification.Furthermore,MemAAE features a memory mechanism that stores critical pattern information,mitigating overfitting,alongside a dynamic threshold adjustment mechanism that adapts detection thresholds in response to evolving operational conditions.Our empirical evaluation of the MemAAE model using real-world solar power data shows that the model outperforms other comparative models on both datasets.On the Sopan-Finder dataset,MemAAE has an accuracy of 99.17%and an F1-score of 95.79%,while on the Sunalab Faro PV 2017 dataset,it has an accuracy of 97.67%and an F1-score of 93.27%.Significant performance advantages have been achieved on both datasets.These results show that MemAAE model is an effective method for real-time anomaly detection in virtual power plants(VPPs),which can enhance robustness and adaptability to inherent variables in solar power generation.展开更多
The heat transfer coefficient of the water surface is an important parameter in the design of thermal discharge in nuclear power plant engineering.In this study,in situ observations were performed in the northwestern ...The heat transfer coefficient of the water surface is an important parameter in the design of thermal discharge in nuclear power plant engineering.In this study,in situ observations were performed in the northwestern South China Sea near a coastal nuclear power plant to evaluate the applicability of heat transfer coefficient calculation algorithms commonly used in marine thermal discharge engineering in China.The results show that the Regulation for Hydraulic and Thermal Model in Cooling Water Projects(SL 160-2012)is not applicable in calculating the heat transfer coefficient in offshore areas.SL 160-2012 significantly overestimates the heat loss at the sea surface.However,Code for Design of Cooling for Industrial Recirculating Water(GB/T 50102-2014)performs well,and its estimation coefficient is roughly consistent with the estimations of the COARE 3.6 bulk algorithm,which is extensively used in physical oceanography for calculating air-sea heat fluxes,and the Gunneberg formula.In a 3-day observation,the average heat transfer coefficients estimated using these three algorithms were 50.4,48.5,and 48.8 W m^(-2)℃^(-1),respectively,with a deviation of less than 4% among them,whereas that estimated using SL 160-2012 was as high as 176.3 W m^(-2)℃^(-1).The abnormally large value of SL 160-2012 is due to its additional cooling term,which is artificially increased by 100 times because of the incorrect unit conversion used when developing the regulation.If this error is corrected,the value will decrease to 50.5 W m^(-2)℃^(-1),which is very close to the estimation of GB/T 50102-2014.展开更多
Given the power system balancing challenges induced by high-penetration renewable energy integration,this study systematically reviews international balancing mechanism practices and conducts an in-depth deconstructio...Given the power system balancing challenges induced by high-penetration renewable energy integration,this study systematically reviews international balancing mechanism practices and conducts an in-depth deconstruction of Germany’s balancing group mechanism(BGM).Building on this foundation,this research pioneers the integration of virtual power plants(VPPs)with the BGM in the Chinese context to overcome the limitations of traditional single-entity regulation models in flexibility provision and economic efficiency.A balancing responsibility framework centered on VPPs is innovatively proposed and a regional multi-entity collaboration and bi-level responsibility transfer architecture is constructed.This architecture enables cross-layer coordinated optimization of regional system costs and VPP revenues.The upper layer minimizes regional operational costs,whereas the lower layer enhances the operational revenues of VPPs through dynamic gaming between deviation regulation service income and penalty costs.Compared with traditional centralized regulation models,the proposed method reduces system operational costs by 29.1%in typical regional cases and increases VPP revenues by 24.9%.These results validate its dual optimization of system economics and participant incentives through market mechanisms,providing a replicable theoretical paradigm and practical pathway for designing balancing mechanisms in new power systems.展开更多
The Virtual Power Plant(VPP),as an innovative power management architecture,achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources.However,due to significant ...The Virtual Power Plant(VPP),as an innovative power management architecture,achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources.However,due to significant differences in operational costs and flexibility of various types of generation resources,as well as the volatility and uncertainty of renewable energy sources(such as wind and solar power)and the complex variability of load demand,the scheduling optimization of virtual power plants has become a critical issue that needs to be addressed.To solve this,this paper proposes an intelligent scheduling method for virtual power plants based on Deep Reinforcement Learning(DRL),utilizing Deep Q-Networks(DQN)for real-time optimization scheduling of dynamic peaking unit(DPU)and stable baseload unit(SBU)in the virtual power plant.By modeling the scheduling problem as a Markov Decision Process(MDP)and designing an optimization objective function that integrates both performance and cost,the scheduling efficiency and economic performance of the virtual power plant are significantly improved.Simulation results show that,compared with traditional scheduling methods and other deep reinforcement learning algorithms,the proposed method demonstrates significant advantages in key performance indicators:response time is shortened by up to 34%,task success rate is increased by up to 46%,and costs are reduced by approximately 26%.Experimental results verify the efficiency and scalability of the method under complex load environments and the volatility of renewable energy,providing strong technical support for the intelligent scheduling of virtual power plants.展开更多
Hardfacing of valve sealings in power plants with Inconel 625 alloy has been reviewed in this paper.The overlaying processes,over-lay microstructures,and weldability issues during the hardfacing process have been anal...Hardfacing of valve sealings in power plants with Inconel 625 alloy has been reviewed in this paper.The overlaying processes,over-lay microstructures,and weldability issues during the hardfacing process have been analyzed.The results indicate that almost all melting welding processes can be used for hardfacing of Inconel 625 alloy.During hardfacing,it is necessary to strictly control the penetration,reduce the overlay dilution rate,so as to prevent the formation of partially mixed zone(PMZ)and solidification crack.From the perspective of controlling the penetration,reducing the overlay dilution rate,and automated hardfacing,the most suitable process for hardfacing Inconel 625 alloy on the valve sealings in power plants is cold metal transfer(CMT)welding process.展开更多
With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heter...With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.展开更多
As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power pla...As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.展开更多
Some types of renewable energy have been experiencing rapid evolution in recent decades, notably among the energies associated with the oceans, such as wave and current energies. The development of new energy conversi...Some types of renewable energy have been experiencing rapid evolution in recent decades, notably among the energies associated with the oceans, such as wave and current energies. The development of new energy conversion technologies for these two forms of energy has been offering a large number of equipment configurations and plant geometries for energy conversion. This process can be implemented aiming at the result of feasibility studies in places with energy potentials, establishing minimum feasibility limits to be reached. This work aims to contribute in this sense with a feasibility study of a system with ocean wave power plants and with socio-current power plants to be operated on the southern coast of Brazil. This study evaluates a hybrid system with contributions from energy supplies obtained from wave plants and current plants, connected to the grid and supplying the demand of the municipalities in the North Coast region of the State of Rio Grande do Sul, the southernmost state of Brazil. The study was carried out with simulations with the Homer Legacy software, with some adaptations for the simulation of ocean wave plants and ocean current plants. The results indicate that the ocean wave power plants were viable in the vast majority of simulated scenarios, while the ocean current power plants were viable in the scenarios with more intense average ocean current speeds and with more expensive energy acquired from the interconnected system.展开更多
Coal combustion and mercury pollution are closely linked, and this relationship is particularly relevant in China, the world＇s largest coal consumer. This paper begins with a summary of recent China-specific studies ...Coal combustion and mercury pollution are closely linked, and this relationship is particularly relevant in China, the world＇s largest coal consumer. This paper begins with a summary of recent China-specific studies on mercury removal by air pollution control technologies and then provides an economic analysis of mercury abatement from these emission control technologies at coal-fired power plants in China. This includes a cost-effectiveness analysis at the enterprise and sector level in China using 2010 as a baseline and projecting out to 2020 and2030. Of the control technologies evaluated, the most cost-effective is a fabric filter installed upstream of the wet flue gas desulfurization system（FF ＋ WFGD）. Halogen injection（HI） is also a cost-effective mercury-specific control strategy, although it has not yet reached commercial maturity. The sector-level analysis shows that 193 tons of mercury was removed in 2010 in China＇s coal-fired power sector, with annualized mercury emission control costs of 2.7 billion Chinese Yuan. Under a projected 2030 Emission Control（EC） scenario with stringent mercury limits compared to Business As Usual（BAU） scenario, the increase of selective catalytic reduction systems（SCR） and the use of HI could contribute to 39 tons of mercury removal at a cost of 3.8 billion CNY. The economic analysis presented in this paper offers insights on air pollution control technologies and practices for enhancing atmospheric mercury control that can aid decision-making in policy design and private-sector investments.展开更多
China’s energy dependents on coal due to the abundance and low cost of coal.Coal provides a secure and stable energy source in China.Over-dependence on coal results in the emission of Hazardous Trace Elements(HTEs)in...China’s energy dependents on coal due to the abundance and low cost of coal.Coal provides a secure and stable energy source in China.Over-dependence on coal results in the emission of Hazardous Trace Elements(HTEs)including selenium(Se),mercury(Hg),lead(Pb),arsenic(As),etc.,from Coal-Fired Power Plants(CFPPs),which are the major toxic air pollutants causing widespread concern.For this reason,it is essential to provide a succinct analysis of the main HTEs emission control techniques while concurrently identifying the research prospects framework and specifying future research directions.The study herein reviews various techniques applied in China for the selected HTEs emission control,including the technical,institutional,policy,and regulatory aspects.The specific areas covered in this study include health effects,future coal production and consumption,the current situation of HTEs in Chinese coal,the chemistry of selected HTEs,control techniques,policies,and action plans safeguarding the emission control.The review emphasizes the fact that China must establish and promote efficient and clean ways to utilize coal in order to realize sustainable development.The principal conclusion is that cleaning coal technologies and fuel substitution should be great potential HTEs control technologies in China.Future research should focus on the simultaneous removal of HTEs,PM,SOx,and NOx in the complex flue gas.展开更多
In efforts to overcome an foreseeable energy crisis predicated on limited oil and gas supplies, reserves; economic variations facing the world, and of course the environmental side effects of fossil fuels, an urgent n...In efforts to overcome an foreseeable energy crisis predicated on limited oil and gas supplies, reserves; economic variations facing the world, and of course the environmental side effects of fossil fuels, an urgent need for energy sources that provide sustainable, safe and economic supplies for the world is imperative. The current fossil fuel energy system must be improved to ensure a better and cleaner transportation future for the world. Despite the fact that the marine transportation sector consumes only 5% of global petroleum production; it is responsible for 15% of the world NOx and SOx emissions. These figures must be the engine that powers the scientific research worldwide to develop new solutions for a very old energy problem. In this paper, the most effective types of marine power plants were discussed. The history of the development of each type was presented first and the technical aspects were discussed second. Also, the fuel ceils as a new type of power plants used in marine sector were briefed to give a complete overview of the past, present and future of the marine power plants development. Based on the increased worldwide concerns regarding harmful emissions, many researchers have introduced solutions to this problem, including the adoption of new cleaner fuels. This paper was guided using the same trend and by implementing the hydrogen as fuel for marine internal combustion engine, gas turbines, and fuel cells.展开更多
Coal is the backbone of the Indian power sector. The coal-fired power plants remain the largest emitters of carbon dioxide, sulfur dioxide and substantial amounts of nitrogen oxides, which are associated with climate ...Coal is the backbone of the Indian power sector. The coal-fired power plants remain the largest emitters of carbon dioxide, sulfur dioxide and substantial amounts of nitrogen oxides, which are associated with climate and health impacts. Various CO2 mitigation technologies （carbon capture and storage--CCS） and SO2/NOx mitigation technologies （flue gas desulfurization and selective catalytic reduction） have been employed to reduce the environmental impacts of the coal-fired power plants. Therefore, it is imperative to understand the feasibility of various mitigation technologies employed. This paper attempts to perform environmental life cycle assessment （LCA） of Indian coal-fired power plant with and without CO2, SO2 and NOx mitigation controls. The study develops new normalization factors for India in various damage categories, using the Indian emissions and energy consumption data, coupled with the emissions and particulate emission to come up with a final environmental impact of coal-fired electricity. The results show a large degree of dependence on the perspective of assessment used. The impact of sensitivities of individual substances and the effect of plant efficiency on the final LCA results is also studied.展开更多
Based on the target analysis of the operation optimization for power plants, a novel system scheme called operation optimization decision support system (OODSS) is brought forward. According to the structure and desig...Based on the target analysis of the operation optimization for power plants, a novel system scheme called operation optimization decision support system (OODSS) is brought forward. According to the structure and design thinking of decision support system (DSS), the overall structure of the OODSS is studied, and the scheme of the sub systems in the OODSS such as the user interface system, the problem processing system, the database system, the model base system, the expert system (ES) and the data mining sy...展开更多
The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challeng...The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.展开更多
文摘The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.
基金funded by the Department of Education of Liaoning Province and was supported by the Basic Scientific Research Project of the Department of Education of Liaoning Province(Grant No.LJ222411632051)and(Grant No.LJKQZ2021085)Natural Science Foundation Project of Liaoning Province(Grant No.2022-BS-222).
文摘Virtual power plant(VPP)integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions,promote the consumption of renewable energy,and improve economic efficiency.In this paper,aiming at the uncertainty of distributed wind power and photovoltaic output,considering the coupling relationship between power,carbon trading,and green cardmarket,the optimal operationmodel and bidding scheme of VPP in spot market,carbon trading market,and green card market are established.On this basis,through the Shapley value and independent risk contribution theory in cooperative game theory,the quantitative analysis of the total income and risk contribution of various distributed resources in the virtual power plant is realized.Moreover,the scheduling strategies of virtual power plants under different risk preferences are systematically compared,and the feasibility and accuracy of the combination of Shapley value and independent risk contribution theory in ensuring fair income distribution and reasonable risk assessment are emphasized.A comprehensive solution for virtual power plants in the multi-market environment is constructed,which integrates operation strategy,income distribution mechanism,and risk control system into a unified analysis framework.Through the simulation of multi-scenario examples,the CPLEXsolver inMATLAB software is used to optimize themodel.The proposed joint optimization scheme can increase the profit of VPP participating in carbon trading and green certificate market by 29%.The total revenue of distributed resources managed by VPP is 9%higher than that of individual participation.
基金supported by Science and Technology Project of SGCC(Research on Distributed Cooperative Control of Virtual Power Plants Based on Hybrid Game)(5700-202418337A-2-1-ZX).
文摘This paper suggests a way to improve teamwork and reduce uncertainties in operations by using a game theory approach involving multiple virtual power plants(VPP).A generalized credibility-based fuzzy chance constraint programming approach is adopted to address uncertainties stemming from renewable generation and load demand within individual VPPs,while robust optimization techniques manage electricity and thermal price volatilities.Building upon this foundation,a hierarchical Nash-Stackelberg game model is established across multiple VPPs.Within each VPP,a Stackelberg game resolves the strategic interaction between the operator and photovoltaic prosumers(PVP).Among VPPs,a cooperative Nash bargaining model coordinates alliance formation.The problem is decomposed into two subproblems:maximizing coalitional benefits,and allocating cooperative surpluses via payment bargaining,solved distributively using the alternating direction method of multipliers(ADMM).Case studies demonstrate that the proposed strategy significantly enhances the economic efficiency and uncertainty resilience of multi-VPP alliances.
文摘To meet the demand for intelligent and unmanned development in thermal power plants,an intelligent inspection system has been designed.This system efficiently performs inspection tasks and monitors the operational parameters of key equipment in real-time.The collected data is uploaded to the monitoring center,allowing operation and maintenance personnel to access equipment information promptly.Data analysis is used to provide fault warning and diagnosis for critical equipment.The system employs the Pure Pursuit algorithm,which effectively avoids obstacles and ensures path continuity and stability.Simulation results show that the Pure Pursuit algorithm significantly improves the navigation accuracy and task efficiency of the inspection robot,ensuring the reliability of thermal power plant inspections.
基金This work was supported by the Basic Research Top Talent Plan of Lanzhou Jiaotong University(2022JC05).
文摘Texas is the largest state by area in the US after Alaska,and one of the top states in the production and consumption of electricity with many coal-fired plants.Coal-fired power plants emit greater than 70% of pollutants in the energy sector.When coal is burned to produce electricity,nitrogen oxides(NO_(x))are released into the air,one of the main pollutants that threaten human health and lead to a large number of premature deaths.The key to effective air quality management is the strict compliance of all plants with emission standards.However,not all Texas coal plants have the environmental equipment to lower pollutant emissions.Nitrogen dioxide(NO2)observations from the TROPOspheric Monitoring Instrument(TROPOMI)were used to evaluate the emissions for Texas power plants.Data from both the Emissions and Generation Resource Integrated Database(EGRID)and the Emissions Database for Global Atmospheric Research(EDGAR)were used to examine emissions.It was found that NOx emissions for Texas power plants range from 1.53 kt/year to 10.99 kt/year,with the Martin Lake,Limestone and Fayette Power Project stations being the top emitters.WA Parish and Martin Lake stations have the strongest NOx fluxes,with both exhibiting significant seasonal variability.Comparisons of bottom-up inventories for EDGAR and EGRID show a high correlation(r=0.956)and a low root mean square error(0.766).A more reasonable control policy would lead to much reduced NOx emissions.
文摘Most data centers currently tap into existing power grids to draw the immense amount of electricity they need to operate.But many of the data centers that Google(Mountain View,CA,USA)plans to open in the next few years will boast their own power plants,an arrangement known as colocation[1].Under an agreement announced in December 2024,the company will site data centers in industrial parks where its partner Intersect Power of Houston,TX,USA,has installed clean power facilities[1,2].The first of these complexes is scheduled to come online in 2026[1].
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-002)the Technology Development Program(RS-2023-00266141)funded by the Ministry of SMEs and Startups(MSS,Republic of Korea).
文摘Virtual Power Plants(VPPs)are integral to modern energy systems,providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data.Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations.We introduce the Memory-Enhanced Autoencoder with Adversarial Training(MemAAE)model to overcome these limitations,designed explicitly for robust anomaly detection in VPP environments.The MemAAE model integrates three principal components:an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors,an adversarial training module that enhances system resilience across diverse operational scenarios,and a prediction module that aids the autoencoder during the reconstruction process,thereby facilitating precise anomaly identification.Furthermore,MemAAE features a memory mechanism that stores critical pattern information,mitigating overfitting,alongside a dynamic threshold adjustment mechanism that adapts detection thresholds in response to evolving operational conditions.Our empirical evaluation of the MemAAE model using real-world solar power data shows that the model outperforms other comparative models on both datasets.On the Sopan-Finder dataset,MemAAE has an accuracy of 99.17%and an F1-score of 95.79%,while on the Sunalab Faro PV 2017 dataset,it has an accuracy of 97.67%and an F1-score of 93.27%.Significant performance advantages have been achieved on both datasets.These results show that MemAAE model is an effective method for real-time anomaly detection in virtual power plants(VPPs),which can enhance robustness and adaptability to inherent variables in solar power generation.
基金supported by the Laoshan Laboratory(No.LSKJ202201600)the National Natural Science Foundation of China(No.41821004)。
文摘The heat transfer coefficient of the water surface is an important parameter in the design of thermal discharge in nuclear power plant engineering.In this study,in situ observations were performed in the northwestern South China Sea near a coastal nuclear power plant to evaluate the applicability of heat transfer coefficient calculation algorithms commonly used in marine thermal discharge engineering in China.The results show that the Regulation for Hydraulic and Thermal Model in Cooling Water Projects(SL 160-2012)is not applicable in calculating the heat transfer coefficient in offshore areas.SL 160-2012 significantly overestimates the heat loss at the sea surface.However,Code for Design of Cooling for Industrial Recirculating Water(GB/T 50102-2014)performs well,and its estimation coefficient is roughly consistent with the estimations of the COARE 3.6 bulk algorithm,which is extensively used in physical oceanography for calculating air-sea heat fluxes,and the Gunneberg formula.In a 3-day observation,the average heat transfer coefficients estimated using these three algorithms were 50.4,48.5,and 48.8 W m^(-2)℃^(-1),respectively,with a deviation of less than 4% among them,whereas that estimated using SL 160-2012 was as high as 176.3 W m^(-2)℃^(-1).The abnormally large value of SL 160-2012 is due to its additional cooling term,which is artificially increased by 100 times because of the incorrect unit conversion used when developing the regulation.If this error is corrected,the value will decrease to 50.5 W m^(-2)℃^(-1),which is very close to the estimation of GB/T 50102-2014.
基金supported by the National Natural Science Foundation of China(no.72471087)Beijing Nova Program(no.20250484853)+1 种基金Beijing Natural Science Foundation(no.9242015)National Social Science Foundation of China(no.24&ZD111).
文摘Given the power system balancing challenges induced by high-penetration renewable energy integration,this study systematically reviews international balancing mechanism practices and conducts an in-depth deconstruction of Germany’s balancing group mechanism(BGM).Building on this foundation,this research pioneers the integration of virtual power plants(VPPs)with the BGM in the Chinese context to overcome the limitations of traditional single-entity regulation models in flexibility provision and economic efficiency.A balancing responsibility framework centered on VPPs is innovatively proposed and a regional multi-entity collaboration and bi-level responsibility transfer architecture is constructed.This architecture enables cross-layer coordinated optimization of regional system costs and VPP revenues.The upper layer minimizes regional operational costs,whereas the lower layer enhances the operational revenues of VPPs through dynamic gaming between deviation regulation service income and penalty costs.Compared with traditional centralized regulation models,the proposed method reduces system operational costs by 29.1%in typical regional cases and increases VPP revenues by 24.9%.These results validate its dual optimization of system economics and participant incentives through market mechanisms,providing a replicable theoretical paradigm and practical pathway for designing balancing mechanisms in new power systems.
基金supported by the National Key Research and Development Program of China,Grant No.2020YFB0905900.
文摘The Virtual Power Plant(VPP),as an innovative power management architecture,achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources.However,due to significant differences in operational costs and flexibility of various types of generation resources,as well as the volatility and uncertainty of renewable energy sources(such as wind and solar power)and the complex variability of load demand,the scheduling optimization of virtual power plants has become a critical issue that needs to be addressed.To solve this,this paper proposes an intelligent scheduling method for virtual power plants based on Deep Reinforcement Learning(DRL),utilizing Deep Q-Networks(DQN)for real-time optimization scheduling of dynamic peaking unit(DPU)and stable baseload unit(SBU)in the virtual power plant.By modeling the scheduling problem as a Markov Decision Process(MDP)and designing an optimization objective function that integrates both performance and cost,the scheduling efficiency and economic performance of the virtual power plant are significantly improved.Simulation results show that,compared with traditional scheduling methods and other deep reinforcement learning algorithms,the proposed method demonstrates significant advantages in key performance indicators:response time is shortened by up to 34%,task success rate is increased by up to 46%,and costs are reduced by approximately 26%.Experimental results verify the efficiency and scalability of the method under complex load environments and the volatility of renewable energy,providing strong technical support for the intelligent scheduling of virtual power plants.
文摘Hardfacing of valve sealings in power plants with Inconel 625 alloy has been reviewed in this paper.The overlaying processes,over-lay microstructures,and weldability issues during the hardfacing process have been analyzed.The results indicate that almost all melting welding processes can be used for hardfacing of Inconel 625 alloy.During hardfacing,it is necessary to strictly control the penetration,reduce the overlay dilution rate,so as to prevent the formation of partially mixed zone(PMZ)and solidification crack.From the perspective of controlling the penetration,reducing the overlay dilution rate,and automated hardfacing,the most suitable process for hardfacing Inconel 625 alloy on the valve sealings in power plants is cold metal transfer(CMT)welding process.
文摘With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.
文摘As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.
文摘Some types of renewable energy have been experiencing rapid evolution in recent decades, notably among the energies associated with the oceans, such as wave and current energies. The development of new energy conversion technologies for these two forms of energy has been offering a large number of equipment configurations and plant geometries for energy conversion. This process can be implemented aiming at the result of feasibility studies in places with energy potentials, establishing minimum feasibility limits to be reached. This work aims to contribute in this sense with a feasibility study of a system with ocean wave power plants and with socio-current power plants to be operated on the southern coast of Brazil. This study evaluates a hybrid system with contributions from energy supplies obtained from wave plants and current plants, connected to the grid and supplying the demand of the municipalities in the North Coast region of the State of Rio Grande do Sul, the southernmost state of Brazil. The study was carried out with simulations with the Homer Legacy software, with some adaptations for the simulation of ocean wave plants and ocean current plants. The results indicate that the ocean wave power plants were viable in the vast majority of simulated scenarios, while the ocean current power plants were viable in the scenarios with more intense average ocean current speeds and with more expensive energy acquired from the interconnected system.
基金sponsored by the Major State Basic Research Development Program of China (973 Program) (No. 2013CB430001)the National Natural Science Foundation of China (No. 21307070)+1 种基金the MEP's Special Funds for Research on Public Welfares (201209015)the Sino-Norwegian cooperation project (SINOMER Ⅲ)
文摘Coal combustion and mercury pollution are closely linked, and this relationship is particularly relevant in China, the world＇s largest coal consumer. This paper begins with a summary of recent China-specific studies on mercury removal by air pollution control technologies and then provides an economic analysis of mercury abatement from these emission control technologies at coal-fired power plants in China. This includes a cost-effectiveness analysis at the enterprise and sector level in China using 2010 as a baseline and projecting out to 2020 and2030. Of the control technologies evaluated, the most cost-effective is a fabric filter installed upstream of the wet flue gas desulfurization system（FF ＋ WFGD）. Halogen injection（HI） is also a cost-effective mercury-specific control strategy, although it has not yet reached commercial maturity. The sector-level analysis shows that 193 tons of mercury was removed in 2010 in China＇s coal-fired power sector, with annualized mercury emission control costs of 2.7 billion Chinese Yuan. Under a projected 2030 Emission Control（EC） scenario with stringent mercury limits compared to Business As Usual（BAU） scenario, the increase of selective catalytic reduction systems（SCR） and the use of HI could contribute to 39 tons of mercury removal at a cost of 3.8 billion CNY. The economic analysis presented in this paper offers insights on air pollution control technologies and practices for enhancing atmospheric mercury control that can aid decision-making in policy design and private-sector investments.
基金financial support of National Key Research&Development Project of China(2018YFB0605101)National Natural Science Foundation of China(No.201706050)+1 种基金Key Project Natural Science Foundation of Tianjin(18JCZDJC39800)The Science and Technology Key Project of Tianjin(18ZXSZSF00040,18KPXMSF00080,18PTZWHZ00010)。
文摘China’s energy dependents on coal due to the abundance and low cost of coal.Coal provides a secure and stable energy source in China.Over-dependence on coal results in the emission of Hazardous Trace Elements(HTEs)including selenium(Se),mercury(Hg),lead(Pb),arsenic(As),etc.,from Coal-Fired Power Plants(CFPPs),which are the major toxic air pollutants causing widespread concern.For this reason,it is essential to provide a succinct analysis of the main HTEs emission control techniques while concurrently identifying the research prospects framework and specifying future research directions.The study herein reviews various techniques applied in China for the selected HTEs emission control,including the technical,institutional,policy,and regulatory aspects.The specific areas covered in this study include health effects,future coal production and consumption,the current situation of HTEs in Chinese coal,the chemistry of selected HTEs,control techniques,policies,and action plans safeguarding the emission control.The review emphasizes the fact that China must establish and promote efficient and clean ways to utilize coal in order to realize sustainable development.The principal conclusion is that cleaning coal technologies and fuel substitution should be great potential HTEs control technologies in China.Future research should focus on the simultaneous removal of HTEs,PM,SOx,and NOx in the complex flue gas.
文摘In efforts to overcome an foreseeable energy crisis predicated on limited oil and gas supplies, reserves; economic variations facing the world, and of course the environmental side effects of fossil fuels, an urgent need for energy sources that provide sustainable, safe and economic supplies for the world is imperative. The current fossil fuel energy system must be improved to ensure a better and cleaner transportation future for the world. Despite the fact that the marine transportation sector consumes only 5% of global petroleum production; it is responsible for 15% of the world NOx and SOx emissions. These figures must be the engine that powers the scientific research worldwide to develop new solutions for a very old energy problem. In this paper, the most effective types of marine power plants were discussed. The history of the development of each type was presented first and the technical aspects were discussed second. Also, the fuel ceils as a new type of power plants used in marine sector were briefed to give a complete overview of the past, present and future of the marine power plants development. Based on the increased worldwide concerns regarding harmful emissions, many researchers have introduced solutions to this problem, including the adoption of new cleaner fuels. This paper was guided using the same trend and by implementing the hydrogen as fuel for marine internal combustion engine, gas turbines, and fuel cells.
文摘Coal is the backbone of the Indian power sector. The coal-fired power plants remain the largest emitters of carbon dioxide, sulfur dioxide and substantial amounts of nitrogen oxides, which are associated with climate and health impacts. Various CO2 mitigation technologies （carbon capture and storage--CCS） and SO2/NOx mitigation technologies （flue gas desulfurization and selective catalytic reduction） have been employed to reduce the environmental impacts of the coal-fired power plants. Therefore, it is imperative to understand the feasibility of various mitigation technologies employed. This paper attempts to perform environmental life cycle assessment （LCA） of Indian coal-fired power plant with and without CO2, SO2 and NOx mitigation controls. The study develops new normalization factors for India in various damage categories, using the Indian emissions and energy consumption data, coupled with the emissions and particulate emission to come up with a final environmental impact of coal-fired electricity. The results show a large degree of dependence on the perspective of assessment used. The impact of sensitivities of individual substances and the effect of plant efficiency on the final LCA results is also studied.
文摘Based on the target analysis of the operation optimization for power plants, a novel system scheme called operation optimization decision support system (OODSS) is brought forward. According to the structure and design thinking of decision support system (DSS), the overall structure of the OODSS is studied, and the scheme of the sub systems in the OODSS such as the user interface system, the problem processing system, the database system, the model base system, the expert system (ES) and the data mining sy...
基金Department of Navy Awards N00014-22-1-2001 and N00014-23-1-2124 issued by the Office of Naval Research。
文摘The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.
