作为能源技术与互联网信息技术相融合的产物,能源互联网的构建离不开大型数据服务中心互联的支撑。然而,当前互联网数据中心(Internet Data Center,IDC)巨大能耗所带来的成本和环境压力,突显出对IDC能耗管理的重要性。文中基于实时电价...作为能源技术与互联网信息技术相融合的产物,能源互联网的构建离不开大型数据服务中心互联的支撑。然而,当前互联网数据中心(Internet Data Center,IDC)巨大能耗所带来的成本和环境压力,突显出对IDC能耗管理的重要性。文中基于实时电价和多电力市场构成的能源互联网市场环境,在考虑IDC散热成本、碳排放成本以及服务延迟约束的基础上,以IDC负荷周期内总的能耗成本最小化为目标,建立了IDC数据负荷在多时空尺度下的优化调度模型,并采用反馈分支定界算法对模型求解。最后,通过算例仿真验证所提方案的正确性,仿真结果表明该技术可以显著降低IDC的能耗成本。展开更多
With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to eva...With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to evaluate the volatility of wind power only consider its overall characteristics, such as the standard deviation of wind power, the average of power variables, etc., while ignoring the detailed volatility of wind power, that is, the features of the frequency distribution of power variables. However, how to accurately describe the detailed volatility of wind power is the key foundation to reduce its adverse influences. To address this, a quantitative method for evaluating the detailed volatility of wind power at multiple temporal-spatial scales is proposed. First, the volatility indexes which can evaluate the detailed fluctuation characteristics of wind power are presented, including the upper confidence limit, lower confidence limit and confidence interval of power variables under the certain confidence level. Then, the actual wind power data from a location in northern China is used to illustrate the application of the proposed indexes at multiple temporal(year–season–month–day) and spatial scales(wind turbine–wind turbines–wind farm–wind farms) using the calculation time windows of 10 min, 30 min, 1 h, and 4 h. Finally, the relationships between wind power forecasting accuracy and its corresponding detailed volatility are analyzed to further verify the effectiveness of the proposed indexes. The results show that the proposed volatility indexes can effectively characterize the detailed fluctuations of wind power at multiple temporal-spatial scales. It is anticipated that the results of this study will serve as an important reference for the reserve capacity planning and optimization dispatch in the electric power system which with a high proportion of renewable energy.展开更多
文摘作为能源技术与互联网信息技术相融合的产物,能源互联网的构建离不开大型数据服务中心互联的支撑。然而,当前互联网数据中心(Internet Data Center,IDC)巨大能耗所带来的成本和环境压力,突显出对IDC能耗管理的重要性。文中基于实时电价和多电力市场构成的能源互联网市场环境,在考虑IDC散热成本、碳排放成本以及服务延迟约束的基础上,以IDC负荷周期内总的能耗成本最小化为目标,建立了IDC数据负荷在多时空尺度下的优化调度模型,并采用反馈分支定界算法对模型求解。最后,通过算例仿真验证所提方案的正确性,仿真结果表明该技术可以显著降低IDC的能耗成本。
基金supported in part by the National Key R&D Program of China (No.2017YFE0109000)the project of China Datang Corporation Ltd
文摘With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to evaluate the volatility of wind power only consider its overall characteristics, such as the standard deviation of wind power, the average of power variables, etc., while ignoring the detailed volatility of wind power, that is, the features of the frequency distribution of power variables. However, how to accurately describe the detailed volatility of wind power is the key foundation to reduce its adverse influences. To address this, a quantitative method for evaluating the detailed volatility of wind power at multiple temporal-spatial scales is proposed. First, the volatility indexes which can evaluate the detailed fluctuation characteristics of wind power are presented, including the upper confidence limit, lower confidence limit and confidence interval of power variables under the certain confidence level. Then, the actual wind power data from a location in northern China is used to illustrate the application of the proposed indexes at multiple temporal(year–season–month–day) and spatial scales(wind turbine–wind turbines–wind farm–wind farms) using the calculation time windows of 10 min, 30 min, 1 h, and 4 h. Finally, the relationships between wind power forecasting accuracy and its corresponding detailed volatility are analyzed to further verify the effectiveness of the proposed indexes. The results show that the proposed volatility indexes can effectively characterize the detailed fluctuations of wind power at multiple temporal-spatial scales. It is anticipated that the results of this study will serve as an important reference for the reserve capacity planning and optimization dispatch in the electric power system which with a high proportion of renewable energy.
