An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited i...An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.展开更多
“双碳”背景下,以煤电为主的大型城市能源系统如何低成本低碳转型成为重要研究课题。当前研究较少详细定量考虑未来高比例可再生能源情景下的高波动性风光出力带来的运营安全挑战,特别是长时储能、短时储能的部署及其成本效益。针对中...“双碳”背景下,以煤电为主的大型城市能源系统如何低成本低碳转型成为重要研究课题。当前研究较少详细定量考虑未来高比例可再生能源情景下的高波动性风光出力带来的运营安全挑战,特别是长时储能、短时储能的部署及其成本效益。针对中国北方某大型城市,构建2021—2060长期能源系统规划模型(long-range energy alternatives planning system,LEAP),基于下一代能源优化模块(next energy modeling system for optimization,NEMO)考虑每年8760 h的需求负荷和可再生能源出力,实现40年长期能源规划与小时级电力运营优化相统一。构建基准情景、电化学储能情景和氢储能情景,探究不同情景下能源系统的运营调度、成本和碳排放量。结果表明电化学储能和氢储能的成本下降和加速部署有助于支撑更高比例的可再生能源装机和发电,减少对化石燃料和调入电力的依赖,降低城市能源系统碳排放。短期内电化学储能和氢储能的部署会提高约15%电力系统成本,而在中长期会降低约39%的电力系统成本和约28%的总成本。展开更多
基金Project(51606225) supported by the National Natural Science Foundation of ChinaProject(2016JJ2144) supported by Hunan Provincial Natural Science Foundation of ChinaProject(502221703) supported by Graduate Independent Explorative Innovation Foundation of Central South University,China
文摘An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.
文摘“双碳”背景下,以煤电为主的大型城市能源系统如何低成本低碳转型成为重要研究课题。当前研究较少详细定量考虑未来高比例可再生能源情景下的高波动性风光出力带来的运营安全挑战,特别是长时储能、短时储能的部署及其成本效益。针对中国北方某大型城市,构建2021—2060长期能源系统规划模型(long-range energy alternatives planning system,LEAP),基于下一代能源优化模块(next energy modeling system for optimization,NEMO)考虑每年8760 h的需求负荷和可再生能源出力,实现40年长期能源规划与小时级电力运营优化相统一。构建基准情景、电化学储能情景和氢储能情景,探究不同情景下能源系统的运营调度、成本和碳排放量。结果表明电化学储能和氢储能的成本下降和加速部署有助于支撑更高比例的可再生能源装机和发电,减少对化石燃料和调入电力的依赖,降低城市能源系统碳排放。短期内电化学储能和氢储能的部署会提高约15%电力系统成本,而在中长期会降低约39%的电力系统成本和约28%的总成本。
