Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consump...Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.展开更多
二氧化碳捕集和封存(carbon capture and storage,CCS)是实现电力低碳化发展的关键技术,具有光明的发展前景。作为未来重要的电源选择,碳捕集电厂的运行性能与调整能力将对电网运行的安全性与高效性产生重大影响。结合碳捕集技术的基本...二氧化碳捕集和封存(carbon capture and storage,CCS)是实现电力低碳化发展的关键技术,具有光明的发展前景。作为未来重要的电源选择,碳捕集电厂的运行性能与调整能力将对电网运行的安全性与高效性产生重大影响。结合碳捕集技术的基本原理,深入研究了碳捕集电厂内部的能量流,量化分析了碳捕集电厂的运行区间,并揭示了碳捕集电厂的调峰性能。在此基础上,提出基于"电力系统调峰成本曲线"的分析方法,以直观、简明的方式实现了电力系统的调峰优化决策;并以具有复杂电源结构的电力系统为例进行调峰效果分析,特别测算了碳捕集电厂对电力系统容纳大规模风电接入的贡献率,评估了碳捕集电厂在提高电网运行安全裕度与降低系统调峰成本上的显著效益。展开更多
文摘Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.
文摘二氧化碳捕集和封存(carbon capture and storage,CCS)是实现电力低碳化发展的关键技术,具有光明的发展前景。作为未来重要的电源选择,碳捕集电厂的运行性能与调整能力将对电网运行的安全性与高效性产生重大影响。结合碳捕集技术的基本原理,深入研究了碳捕集电厂内部的能量流,量化分析了碳捕集电厂的运行区间,并揭示了碳捕集电厂的调峰性能。在此基础上,提出基于"电力系统调峰成本曲线"的分析方法,以直观、简明的方式实现了电力系统的调峰优化决策;并以具有复杂电源结构的电力系统为例进行调峰效果分析,特别测算了碳捕集电厂对电力系统容纳大规模风电接入的贡献率,评估了碳捕集电厂在提高电网运行安全裕度与降低系统调峰成本上的显著效益。
