Wastewater treatment plants(WWTPs)are important and energy-intensive municipal infrastructures.High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutr...Wastewater treatment plants(WWTPs)are important and energy-intensive municipal infrastructures.High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality.However,water-energy nexus analysis and models for WWTPs have rarely been reported to date.In this study,a cloud-model-based energy consumption analysis(CMECA)of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters.The principal component analysis(PCA)and K-means clustering were applied to classify the influent condition using water quality and volume data.The energy consumption of the WWTP is divided into five standard evaluation levels,and its cloud digital characteristics(CDCs)were extracted according to bilateral constraints and golden ratio methods.Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity.The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption,represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption.The local WWTP has high energy consumption with 0.3613 kW·h·m^(-3)despite low influent concentration and volumes,across four consumption levels from low(I)to relatively high(IV),showing an unsatisfactory operation and management level.The average oxygenation capacity,internal reflux ratio,and external reflux ratio during high energy efficiency days recognized by further clustering were obtained(0.2924-0.3703 kg O_(2)·m^(-3),1.9576-2.4787,and 0.6603-0.8361,respectively),which could be used as a guide for the days with low energy efficiency.Consequently,this study offers a water-energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.展开更多
抽水蓄能作为电力系统中最为成熟的新能源储能技术,凭借其能调节电网负荷、平衡电力波动及提升系统稳定性的独特优势,已成为实现中国“双碳”目标的重要路径之一。因此,对抽水蓄能电站综合效益进行科学评估,是项目决策及政策制定中至关...抽水蓄能作为电力系统中最为成熟的新能源储能技术,凭借其能调节电网负荷、平衡电力波动及提升系统稳定性的独特优势,已成为实现中国“双碳”目标的重要路径之一。因此,对抽水蓄能电站综合效益进行科学评估,是项目决策及政策制定中至关重要的一环。为此,本文提出一种基于博弈论组合赋权‒云模型的综合效益评价模型。首先,运用社会网络分析法(SNA)筛选关键评价指标,构建包含财务评价、国民经济评价、技术效益、动态效益、静态效益、电网效益、综合可持续性效益和社会效益8个1级指标及其下属30个2级指标的评价指标体系。其次,采用序关系分析(G1)法和CRITIC(criteria importance through intercriteria correlation)法相结合的方式,对各评价指标进行主观与客观权重赋值。通过引入博弈论组合赋权方法,进一步优化各指标的权重分配。最终,基于云模型构建综合效益评价模型。利用博弈论组合赋权‒云模型对紫云山抽水蓄能电站进行实例分析,结果表明,该电站的综合效益评估等级为“好”,与实际情况相符,充分验证了所构建模型的有效性与准确性。该研究不仅为抽水蓄能电站的综合效益评估提供了科学的评估框架,并为类似项目的决策和实施提供了理论支持和实践依据。展开更多
为研究科学有效的锂电池储能电站风险评估方法,从法律、法规、规程、标准和文献中提取并确定锂电池储能电站风险评价指标集,包括电池组、外部刺激、保护系统、运行环境、人为因素和安全管理等6个一级指标,以及电池基本情况、电池使用工...为研究科学有效的锂电池储能电站风险评估方法,从法律、法规、规程、标准和文献中提取并确定锂电池储能电站风险评价指标集,包括电池组、外部刺激、保护系统、运行环境、人为因素和安全管理等6个一级指标,以及电池基本情况、电池使用工况、电池模组状态等27个二级指标。采用序关系分析(G1)法确定指标主观权重,采用人工鱼群算法(artificial fish swarm algorithm,AFSA)确定各评价指标客观权重,采用乘积法确定最终的组合权重,结合云模型理论建立锂电池储能电站安全风险评价模型。以某电站为例开展研究,研究结果表明:储能电站风险综合期望值为61.382,风险处于较高水平,需采取适当措施进行风险防控。模型能够有效识别关键风险路径,评价结论与现场风险表现具有较高一致性。研究结果可为锂电池储能电站风险预防和管控提供参考。展开更多
基金the financial support from the National Key Research and Development Program of China(2019YFD1100204)the National Natural Science Foundation of China(52091545)+2 种基金the State Key Laboratory of Urban Water Resource and Environment,Harbin Institute of Technology(2021TS03)The Important Projects in the Scientific Innovation of CECEP(cecep-zdkj-2020-009)the Open Project of Key Laboratory of Environmental Biotechnology,Chinese Academy of Sciences(kf2018002).
文摘Wastewater treatment plants(WWTPs)are important and energy-intensive municipal infrastructures.High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality.However,water-energy nexus analysis and models for WWTPs have rarely been reported to date.In this study,a cloud-model-based energy consumption analysis(CMECA)of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters.The principal component analysis(PCA)and K-means clustering were applied to classify the influent condition using water quality and volume data.The energy consumption of the WWTP is divided into five standard evaluation levels,and its cloud digital characteristics(CDCs)were extracted according to bilateral constraints and golden ratio methods.Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity.The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption,represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption.The local WWTP has high energy consumption with 0.3613 kW·h·m^(-3)despite low influent concentration and volumes,across four consumption levels from low(I)to relatively high(IV),showing an unsatisfactory operation and management level.The average oxygenation capacity,internal reflux ratio,and external reflux ratio during high energy efficiency days recognized by further clustering were obtained(0.2924-0.3703 kg O_(2)·m^(-3),1.9576-2.4787,and 0.6603-0.8361,respectively),which could be used as a guide for the days with low energy efficiency.Consequently,this study offers a water-energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.
文摘抽水蓄能作为电力系统中最为成熟的新能源储能技术,凭借其能调节电网负荷、平衡电力波动及提升系统稳定性的独特优势,已成为实现中国“双碳”目标的重要路径之一。因此,对抽水蓄能电站综合效益进行科学评估,是项目决策及政策制定中至关重要的一环。为此,本文提出一种基于博弈论组合赋权‒云模型的综合效益评价模型。首先,运用社会网络分析法(SNA)筛选关键评价指标,构建包含财务评价、国民经济评价、技术效益、动态效益、静态效益、电网效益、综合可持续性效益和社会效益8个1级指标及其下属30个2级指标的评价指标体系。其次,采用序关系分析(G1)法和CRITIC(criteria importance through intercriteria correlation)法相结合的方式,对各评价指标进行主观与客观权重赋值。通过引入博弈论组合赋权方法,进一步优化各指标的权重分配。最终,基于云模型构建综合效益评价模型。利用博弈论组合赋权‒云模型对紫云山抽水蓄能电站进行实例分析,结果表明,该电站的综合效益评估等级为“好”,与实际情况相符,充分验证了所构建模型的有效性与准确性。该研究不仅为抽水蓄能电站的综合效益评估提供了科学的评估框架,并为类似项目的决策和实施提供了理论支持和实践依据。
文摘为研究科学有效的锂电池储能电站风险评估方法,从法律、法规、规程、标准和文献中提取并确定锂电池储能电站风险评价指标集,包括电池组、外部刺激、保护系统、运行环境、人为因素和安全管理等6个一级指标,以及电池基本情况、电池使用工况、电池模组状态等27个二级指标。采用序关系分析(G1)法确定指标主观权重,采用人工鱼群算法(artificial fish swarm algorithm,AFSA)确定各评价指标客观权重,采用乘积法确定最终的组合权重,结合云模型理论建立锂电池储能电站安全风险评价模型。以某电站为例开展研究,研究结果表明:储能电站风险综合期望值为61.382,风险处于较高水平,需采取适当措施进行风险防控。模型能够有效识别关键风险路径,评价结论与现场风险表现具有较高一致性。研究结果可为锂电池储能电站风险预防和管控提供参考。
