This paper proposes an innovative supervision method that can provide project supervisors with realtime supervision of engineering projects and contractor activity. To obtain real-time and comprehensive state of proje...This paper proposes an innovative supervision method that can provide project supervisors with realtime supervision of engineering projects and contractor activity. To obtain real-time and comprehensive state of project, we use grid management to divide the project supervision grid in three levels: stage, objective, and milestone. Then, a detailed supervision mechanism is designed to help supervisors measure the project situation in real time. This mechanism checks that if the project objectives(such as schedule, cost, quality, and safety) in every supervision grid cell are under the healthy limits, any project deviation can be identified as soon as possible.A schedule objective is selected as an example to illustrate the method used to calculate the healthy limit.展开更多
The electric power industry is undergoing profound transformations driven by big data,posing challenges to the traditional power grid marketing management model.These challenges include neglecting market demands,insuf...The electric power industry is undergoing profound transformations driven by big data,posing challenges to the traditional power grid marketing management model.These challenges include neglecting market demands,insufficient data support,and inadequate customer service.The application of big data technology offers innovative solutions for power grid marketing management,encompassing critical aspects such as data collection and integration,storage management,analysis,and mining.By leveraging these technologies,power grid enterprises can precisely understand customer needs,optimize marketing strategies,and enhance operational efficiency.This paper explores strategies for power grid marketing management based on big data,addressing areas such as customer segmentation and personalized services,as well as market demand forecasting and response.Furthermore,it proposes implementation pathways,including essential elements such as organizational structure and team building,data quality and governance systems,training,and cultural development.These efforts aim to ensure the effective application of big data technology and maximize its value.展开更多
We describe a specific approach to capacity man a ge ment for distribution grids. Based on simulations, it has been found that by curtailing a maximum of 5% of the yearly energy production on a per-generator basis, di...We describe a specific approach to capacity man a ge ment for distribution grids. Based on simulations, it has been found that by curtailing a maximum of 5% of the yearly energy production on a per-generator basis, distribution grid connection capacity can be doubled. We also present the setting and fi rst results of a fi eld test for validating the approach in a rural distribution grid in northern Germany.展开更多
The world’s energy industry is experiencing a significant transformation due to increased energy consumption, the rise in renewable energy usage, and the demand for sustainability. This review paper explores the pote...The world’s energy industry is experiencing a significant transformation due to increased energy consumption, the rise in renewable energy usage, and the demand for sustainability. This review paper explores the potential for transformation offered by Artificial Intelligence (AI) in improving energy infrastructure, specifically looking at how it can be used in managing smart grids, predicting maintenance needs, and integrating renewable energy sources. Machine learning (ML) and deep learning (DL) are crucial AI technologies that have become necessary for enhancing grid stability, reducing operational costs, and improving energy efficiency. AI-powered predictive maintenance has proven to lower unexpected downtime by 40%, while AI-based demand forecasting has reached prediction accuracy of 90%, allowing utilities to efficiently manage supply and demand. In addition, AI helps tackle the issues of fluctuating renewable energy by playing a key role in enhancing energy storage and distribution in nations like Denmark and the US. Moreover, cryptographic frameworks such as Elliptic Curve Cryptography (ECC) and Post-Quantum Cryptography (PQC) offer robust security measures to protect AI-driven energy systems. ECC provides lightweight, efficient encryption ideal for IoT-enabled grids, while PQC frameworks, like the SIKE algorithm, ensure long-term resilience against quantum computing threats, safeguarding critical infrastructure. Nevertheless, obstacles like limited data access, cybersecurity weaknesses, and financial limitations continue to hinder widespread AI implementation, especially in less developed areas. This review emphasizes the significance of adopting essential strategies such as smart grid development, public-private collaborations, strong regulatory frameworks, and standardized data-sharing protocols. It is essential to have strong implementation and monitoring systems, improved cybersecurity measures, and ongoing investment in AI research in order to fully harness AI’s ability to revolutionize energy systems. By tackling these obstacles, AI has the potential to significantly impact the development of a more enduring, productive, and flexible worldwide energy system, hastening the shift towards a renewable-focused energy landscape.展开更多
The development of smart grids marks a pivotal transformation in the global energy landscape.As traditional power grids face inefficiencies,high costs,and challenges related to renewable energy integration,smart grids...The development of smart grids marks a pivotal transformation in the global energy landscape.As traditional power grids face inefficiencies,high costs,and challenges related to renewable energy integration,smart grids offer a solution through the incorporation of advanced information and communication technologies(ICT),automation,and real-time data analytics.These technologies enhance the monitoring,control,and optimization of energy systems,enabling better integration of renewable sources,efficient energy distribution,and two-way communication between consumers and utilities.Despite the promising benefits,the widespread deployment of smart grids is hindered by technological,economic,regulatory,and social barriers.This paper explores the technological advancements,current applications,challenges,and future prospects of smart grids,emphasizing the need for global collaboration,innovation,and adaptable policies.The successful implementation of smart grids is essential for achieving a sustainable and resilient energy future,requiring concerted efforts across multiple sectors to overcome existing obstacles.展开更多
Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment fr...Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.展开更多
Besides grid-to-vehicle(G2 V) and vehicle-to-grid(V2 G) functions, the battery of an electric vehicle(EV) also has the specific feature of mobility. This means that EVs not only have the potential to utilize the stora...Besides grid-to-vehicle(G2 V) and vehicle-to-grid(V2 G) functions, the battery of an electric vehicle(EV) also has the specific feature of mobility. This means that EVs not only have the potential to utilize the storage of cheap electricity for use in high energy price periods, but can also transfer energy from one place to another place. Based on these special features of an EV battery, a new EV energy scheduling method has been developed and is described in this article. The approach is aimed at optimizing the utilization EV energy for EVs that are regularly used in multiple places. The objective is to minimize electricity costs from multiple meter points. This work applies real data in order to analyze the effectiveness of the method. The results show that by applying the control strategy presented in this paper at locations where the EVs are parked, the electricity cost can be reduced without shifting the demand and lowering customer's satisfaction. The effects of PV size and number of EVs on our model are also analyzed in this paper. This model has the potential to be used by energy system designers as a new perspective to determine optimal sizes of generators or storage devices in energy systems.展开更多
基金the National Natural Science Foundation of China(No.71271085)the Beijing "12th Five-Year Plan" Project of Philosophy and Social Sciences(No.12JGB044)
文摘This paper proposes an innovative supervision method that can provide project supervisors with realtime supervision of engineering projects and contractor activity. To obtain real-time and comprehensive state of project, we use grid management to divide the project supervision grid in three levels: stage, objective, and milestone. Then, a detailed supervision mechanism is designed to help supervisors measure the project situation in real time. This mechanism checks that if the project objectives(such as schedule, cost, quality, and safety) in every supervision grid cell are under the healthy limits, any project deviation can be identified as soon as possible.A schedule objective is selected as an example to illustrate the method used to calculate the healthy limit.
文摘The electric power industry is undergoing profound transformations driven by big data,posing challenges to the traditional power grid marketing management model.These challenges include neglecting market demands,insufficient data support,and inadequate customer service.The application of big data technology offers innovative solutions for power grid marketing management,encompassing critical aspects such as data collection and integration,storage management,analysis,and mining.By leveraging these technologies,power grid enterprises can precisely understand customer needs,optimize marketing strategies,and enhance operational efficiency.This paper explores strategies for power grid marketing management based on big data,addressing areas such as customer segmentation and personalized services,as well as market demand forecasting and response.Furthermore,it proposes implementation pathways,including essential elements such as organizational structure and team building,data quality and governance systems,training,and cultural development.These efforts aim to ensure the effective application of big data technology and maximize its value.
文摘We describe a specific approach to capacity man a ge ment for distribution grids. Based on simulations, it has been found that by curtailing a maximum of 5% of the yearly energy production on a per-generator basis, distribution grid connection capacity can be doubled. We also present the setting and fi rst results of a fi eld test for validating the approach in a rural distribution grid in northern Germany.
文摘The world’s energy industry is experiencing a significant transformation due to increased energy consumption, the rise in renewable energy usage, and the demand for sustainability. This review paper explores the potential for transformation offered by Artificial Intelligence (AI) in improving energy infrastructure, specifically looking at how it can be used in managing smart grids, predicting maintenance needs, and integrating renewable energy sources. Machine learning (ML) and deep learning (DL) are crucial AI technologies that have become necessary for enhancing grid stability, reducing operational costs, and improving energy efficiency. AI-powered predictive maintenance has proven to lower unexpected downtime by 40%, while AI-based demand forecasting has reached prediction accuracy of 90%, allowing utilities to efficiently manage supply and demand. In addition, AI helps tackle the issues of fluctuating renewable energy by playing a key role in enhancing energy storage and distribution in nations like Denmark and the US. Moreover, cryptographic frameworks such as Elliptic Curve Cryptography (ECC) and Post-Quantum Cryptography (PQC) offer robust security measures to protect AI-driven energy systems. ECC provides lightweight, efficient encryption ideal for IoT-enabled grids, while PQC frameworks, like the SIKE algorithm, ensure long-term resilience against quantum computing threats, safeguarding critical infrastructure. Nevertheless, obstacles like limited data access, cybersecurity weaknesses, and financial limitations continue to hinder widespread AI implementation, especially in less developed areas. This review emphasizes the significance of adopting essential strategies such as smart grid development, public-private collaborations, strong regulatory frameworks, and standardized data-sharing protocols. It is essential to have strong implementation and monitoring systems, improved cybersecurity measures, and ongoing investment in AI research in order to fully harness AI’s ability to revolutionize energy systems. By tackling these obstacles, AI has the potential to significantly impact the development of a more enduring, productive, and flexible worldwide energy system, hastening the shift towards a renewable-focused energy landscape.
文摘The development of smart grids marks a pivotal transformation in the global energy landscape.As traditional power grids face inefficiencies,high costs,and challenges related to renewable energy integration,smart grids offer a solution through the incorporation of advanced information and communication technologies(ICT),automation,and real-time data analytics.These technologies enhance the monitoring,control,and optimization of energy systems,enabling better integration of renewable sources,efficient energy distribution,and two-way communication between consumers and utilities.Despite the promising benefits,the widespread deployment of smart grids is hindered by technological,economic,regulatory,and social barriers.This paper explores the technological advancements,current applications,challenges,and future prospects of smart grids,emphasizing the need for global collaboration,innovation,and adaptable policies.The successful implementation of smart grids is essential for achieving a sustainable and resilient energy future,requiring concerted efforts across multiple sectors to overcome existing obstacles.
基金The authors would like to thank Dr.-Ing.Nils Bornhorst for the fruitful discussion.The publication and development of this work was funded by the Hessian Ministry of Higher Education,Research,Science and the Arts,Germany through the K-ES project under reference number:511/17.001.
文摘Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.
基金supported by the China Scholarship Council and Donghua University Graduate Student Degree Thesis Innovation Fund Project (Grant No. CUSF-DH-D-2013059)
文摘Besides grid-to-vehicle(G2 V) and vehicle-to-grid(V2 G) functions, the battery of an electric vehicle(EV) also has the specific feature of mobility. This means that EVs not only have the potential to utilize the storage of cheap electricity for use in high energy price periods, but can also transfer energy from one place to another place. Based on these special features of an EV battery, a new EV energy scheduling method has been developed and is described in this article. The approach is aimed at optimizing the utilization EV energy for EVs that are regularly used in multiple places. The objective is to minimize electricity costs from multiple meter points. This work applies real data in order to analyze the effectiveness of the method. The results show that by applying the control strategy presented in this paper at locations where the EVs are parked, the electricity cost can be reduced without shifting the demand and lowering customer's satisfaction. The effects of PV size and number of EVs on our model are also analyzed in this paper. This model has the potential to be used by energy system designers as a new perspective to determine optimal sizes of generators or storage devices in energy systems.
