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
Adequacy is a key concern of power system planning,which refers to the availability of sufficient facilities to meet demand.The capacity value(CV)of variable renewable energy(VRE)generation represents its equivalent c...Adequacy is a key concern of power system planning,which refers to the availability of sufficient facilities to meet demand.The capacity value(CV)of variable renewable energy(VRE)generation represents its equivalent contribution to system adequacy,in comparison to conventional generators.While VRE continues to grow and increasingly dominates the generation portfolio,its CV is becoming non-negligible,with the corresponding impact mechanisms becoming more complicated and nuanced.In this paper,the concept of CV is revisited by analyzing how VRE contributes to power system balancing at a high renewable energy penetration level.A generalized loss function is incorporated into the CV evaluation framework considering the adequacy of the power system.An analytical method for the CV evaluation of VRE is then derived using the statistical properties of both hourly load and VRE generation.Through the explicit CV expression,several critical impact factors,including the VRE generation variance,source-load correlation,and system adequacy level,are identified and discussed.Case studies demonstrate the accuracy and effectiveness of the proposed method in comparison to the traditional capacity factor-based methods and convolution-based methods.In the IEEERTS79 test system,the CV of a 2500 MW wind farm(with40%renewable energy penetration level)is found to be 6.8%of its nameplate capacity.Additionally,the sensitivity of CV to various impact factors in power systems with high renewable energy penetration is analyzed.展开更多
The objective of this paper is to assess an economic dispatch considering a power system portfolio, which includes predominant amount of hydro power and increasing quantities of intermittent renewables in relation to ...The objective of this paper is to assess an economic dispatch considering a power system portfolio, which includes predominant amount of hydro power and increasing quantities of intermittent renewables in relation to the total electric capacity. With growing importance of intermittent wind and solar generation taking part into power systems worldwide, there is need for greater chronological resolution to estimate the flexibility of the power system to offer firm capacity. In this way, a linear optimization model operating hourly is developed to calculate the minimum power system cost, while stablishing the capacity allocation to meet the projected load throughout one-year simulation, as an estimation of how the hourly economic dispatch impacts the scheduling of generators belonging to a power system with this portfolio composition. A central focus is how to operate the available hydro capacity to back up intermittent renewables, evaluating the physical hydro operating constraints, monthly energy balance and maximum power availability. A case study was simulated based on the Brazil’s power system configuration, showing that existing hydro capacity provide hourly flexibility to back-up intermittent renewables, potentially saving 1.2 Billion R$, about 3.6% of total system cost referred to 2019. It is worthwhile to realize that the developed methodology can be employed to other power systems with similar capacity portfolio structure for the purpose of calculating its optimum allocation for a specified region and target year.展开更多
As more variable renewable energy(VRE)such as wind and solar are integrated into electric power systems,technical challenges arise from the need to maintain the balance between load and generation at all timescales.Th...As more variable renewable energy(VRE)such as wind and solar are integrated into electric power systems,technical challenges arise from the need to maintain the balance between load and generation at all timescales.This paper examines the challenges with integrating ultrahigh levels of VRE into electric power system,reviews a range of solutions to these challenges,and provides a description of several examples of ultra-high VRE systems that are in operation today.展开更多
Wind and solar energy have seen significant decreases in the cost of these technologies over that last decade which has lead to increasing levels integrated into the grid.They also offer unique benefits such as no fue...Wind and solar energy have seen significant decreases in the cost of these technologies over that last decade which has lead to increasing levels integrated into the grid.They also offer unique benefits such as no fuel costs,quick installation,and no pollution.But as more variable renewable energy(VRE)such as wind and solar is integrated into electrical power systems.展开更多
The capability of shifting the electricity generation or consumption to proper time of the day,also defined as energy shift(ES),is the key factor to ensure the power balance,especially under high penetration of variab...The capability of shifting the electricity generation or consumption to proper time of the day,also defined as energy shift(ES),is the key factor to ensure the power balance,especially under high penetration of variable renewable energy(VRE).However,the ES is not characterized and traded as an independent product in current market mechanisms.In this letter,the marginal utility of an ES is assessed and leveraged to characterize the effective ES,while a novel market scheme is proposed considering the trading of both ES and energy level(EL).The proposed scheme can well integrate ES producers such as virtual power plants that cannot be rewarded sufficiently to actively participate in the current market because they are principally labeled as EL consumers.Finally,the novel concept and mechanism are illustrated by a numerical study and verified to outperform the existing price schemes on integrating the ES resources and VRE.展开更多
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.
基金supported in part by the National Key R&D Program of China(No.2022YFB2403300)in part by the Scientific&Technical Project of State Grid Shanghai Electric Power Company(No.SGSHDK00DWJS2310470)in part by the Scientific&Technical Project of China Electric Power Planning&Engineering Institute(No.K202316)。
文摘Adequacy is a key concern of power system planning,which refers to the availability of sufficient facilities to meet demand.The capacity value(CV)of variable renewable energy(VRE)generation represents its equivalent contribution to system adequacy,in comparison to conventional generators.While VRE continues to grow and increasingly dominates the generation portfolio,its CV is becoming non-negligible,with the corresponding impact mechanisms becoming more complicated and nuanced.In this paper,the concept of CV is revisited by analyzing how VRE contributes to power system balancing at a high renewable energy penetration level.A generalized loss function is incorporated into the CV evaluation framework considering the adequacy of the power system.An analytical method for the CV evaluation of VRE is then derived using the statistical properties of both hourly load and VRE generation.Through the explicit CV expression,several critical impact factors,including the VRE generation variance,source-load correlation,and system adequacy level,are identified and discussed.Case studies demonstrate the accuracy and effectiveness of the proposed method in comparison to the traditional capacity factor-based methods and convolution-based methods.In the IEEERTS79 test system,the CV of a 2500 MW wind farm(with40%renewable energy penetration level)is found to be 6.8%of its nameplate capacity.Additionally,the sensitivity of CV to various impact factors in power systems with high renewable energy penetration is analyzed.
文摘The objective of this paper is to assess an economic dispatch considering a power system portfolio, which includes predominant amount of hydro power and increasing quantities of intermittent renewables in relation to the total electric capacity. With growing importance of intermittent wind and solar generation taking part into power systems worldwide, there is need for greater chronological resolution to estimate the flexibility of the power system to offer firm capacity. In this way, a linear optimization model operating hourly is developed to calculate the minimum power system cost, while stablishing the capacity allocation to meet the projected load throughout one-year simulation, as an estimation of how the hourly economic dispatch impacts the scheduling of generators belonging to a power system with this portfolio composition. A central focus is how to operate the available hydro capacity to back up intermittent renewables, evaluating the physical hydro operating constraints, monthly energy balance and maximum power availability. A case study was simulated based on the Brazil’s power system configuration, showing that existing hydro capacity provide hourly flexibility to back-up intermittent renewables, potentially saving 1.2 Billion R$, about 3.6% of total system cost referred to 2019. It is worthwhile to realize that the developed methodology can be employed to other power systems with similar capacity portfolio structure for the purpose of calculating its optimum allocation for a specified region and target year.
基金supported by the U.S.Department of Energy under Contract No.DE-AC36-08GO28308 with Alliance for Sustainable Energy,LLC,the Manager and Operator of the National Renewable Energy Laboratory.
文摘As more variable renewable energy(VRE)such as wind and solar are integrated into electric power systems,technical challenges arise from the need to maintain the balance between load and generation at all timescales.This paper examines the challenges with integrating ultrahigh levels of VRE into electric power system,reviews a range of solutions to these challenges,and provides a description of several examples of ultra-high VRE systems that are in operation today.
文摘Wind and solar energy have seen significant decreases in the cost of these technologies over that last decade which has lead to increasing levels integrated into the grid.They also offer unique benefits such as no fuel costs,quick installation,and no pollution.But as more variable renewable energy(VRE)such as wind and solar is integrated into electrical power systems.
基金supported by the National Key Research and Development Program of China(No.2019YFE0122600)the National Natural Science Foundation of China(No.51877134).
文摘The capability of shifting the electricity generation or consumption to proper time of the day,also defined as energy shift(ES),is the key factor to ensure the power balance,especially under high penetration of variable renewable energy(VRE).However,the ES is not characterized and traded as an independent product in current market mechanisms.In this letter,the marginal utility of an ES is assessed and leveraged to characterize the effective ES,while a novel market scheme is proposed considering the trading of both ES and energy level(EL).The proposed scheme can well integrate ES producers such as virtual power plants that cannot be rewarded sufficiently to actively participate in the current market because they are principally labeled as EL consumers.Finally,the novel concept and mechanism are illustrated by a numerical study and verified to outperform the existing price schemes on integrating the ES resources and VRE.
