基于K-L信息量法的安徽省工业用电量预测

针对区域工业用电量与经济指标的相关性,运用K-L信息量法,在月度尺度上筛选出能够指示区域工业用电量变化趋势的经济先行指标,并获得各先行指标的先行期数。以经济先行指标为自变量、区域工业用电量为被因变量建立多元回归模型,根据AIC准则和BIC准则选取最佳拟合方程,得到工业用电量预测模型。运用模型预测安徽省2014年5月-12月的月用电量,结果显示预测精度较高,预测方法可以用于工业用电量预测。     

土石坝渗流监测统计模型的变量分离方法

统计回归模型是土石坝渗流安全监测分析中最常用和相对成熟的数学模型,通过分离变量分析环境量对效应量的影响特性是进行土石坝渗流安全评价的重要途径之一。针对采用最小二乘法分离土石坝渗流统计回归模型各影响分量时存在的因子多重共线性问题,提出了基于主成分分析的变量分离方法,并将其应用于冶勒混凝土心墙堆石坝的变量分析中。实例应用结果表明,该方法能有效避免因模型因子间的多重共线性而造成的回归系数不合理和分量峰值错位的问题,提高了分量分离结果的力学规律一致性与结构特性变化的吻合度。     

基于随机森林与长短期记忆网络的电力负荷预测方法

电力负荷具有非线性和时序性的特点,为了深入研究各特征变量对于电力负荷预测的重要性,进而获得更高的电力负荷预测精度,提出了基于随机森林(random forest,RF)算法及长短期记忆网络(long short-term memory,LSTM)的混合负荷预测模型。首先根据时间日期因素及气候因素建立高维特征数据集作为随机森林模型的输入,通过随机森林算法筛选出重要特征量,并使其与历史负荷结合作为LSTM模型的输入,经过粒子群算法对LSTM模型进行参数寻优后得到RF-LSTM混合模型及负荷预测结果。使用该方法对河北电网某台区的电力负荷进行预测,结果表明该混合模型的预测精度比未经特征变量筛选的传统单一的随机森林算法、LSTM模型以及BP神经网络更为理想。     

基于特征正交分解的土坝变形安全监控模型

为解决性态变化剧烈的大坝施工期或固结期实测资料建模的非平稳、小样本和多重共线性等问题,以某均质土坝坝顶上下游水平位移为研究对象,采用基于特征正交分解的逐步回归分析方法,对土坝坝顶下游侧实测值进行建模,分析了模型的可预报时间尺度和精度。结果表明,对于性态变化比较剧烈的土坝,基于特征正交分解回归方法,可有效解决非平稳、小样本和多重共线性问题,但该新建模型的预测精度在接近建模时间尺度的1/3范围内有效,后期实测资料与预报模型预测的差异反映大坝性态已发生了变化。     

基于功效系数法的隧洞塌方风险等级评价

鉴于引水隧洞是水电站的关键工程,其安全性备受关注,基于功效系数法原理,建立了隧洞塌方风险评价模型。该模型涵盖了引起隧洞塌方风险的主要影响因子,然后将各指标划分为极大型变量、极小型变量、稳定型变量和区间型变量,并计算了各指标的单项功效系数值,基于博弈论原理确定指标的权重系数,从而计算得到隧洞塌方风险的总功效系数值。以某水电站的引水隧洞为例进行建模分析,验证了该方法是可行的。     

数学模型用于黄土高原地区煤、电运输综合规划的研究

中国特大型煤田开发及能源基地建设伴生出煤矿区和电力基地的开发规模与合理布局、煤炭和电力运输优化等问题。本文选择较成熟的线性规划模型对上述问题进行了深入的研究。模型构思:在全国,以黄土高源能源基地建设为全国商品煤炭供应的调节源,满足全国各省在各经济发展时期能源供需平衡的要求;在区内,以各矿区和电力基地为基本单元,根据各省区能源平衡的需要决定其开发规模,在此基础上寻求矿区、电力基地及煤、电运输的优化布局方案。本模型选用投资和运行费用最小两个指标为目标函数,煤炭和水资源、运输通路及环境承受能力等为约束条件。此外,本模型还可以能源基地煤炭开发总规模进行灵敏度分析,反映该地区煤、电布局及其运输的变化,模型的运行和分析结果能够为未来能源基地大规模开发服务。     

中国各省可再生能源电力消纳量对碳排放的影响

以我国可再生能源电力消纳保障机制政策的出台为背景,研究各省（自治区、直辖市）CO2排放与可再生能源电力消纳量之间的关系。研究基于2016年和2017年我国各省的能源、经济、人口等统计数据,测算各省的CO2排放量;通过引入可再生能源电力消纳情况及全社会用电量等因素,对标准STIRPAT模型进行扩展,探究CO2排放量如何受到上述因素以及人口规模、经济水平、产业结构等变量的影响。研究针对2016年和2017年的数据分别进行回归分析,结果表明：① 各省可再生能源电力消纳量的增加使当地的CO2排放量显著降低;② 各省全社会用电量的增长显著提高了当地的CO2排放量;③ CO2排放量与地区生产总值之间存在倒U形关系,即随着经济增长,CO2排放量呈现先增后减的趋势;④ 各省的人口增长与当地碳排放具有显著的正相关关系。在两个年份的模型中,解释变量与各被解释变量之间的关系基本保持一致,部分变量对碳排放的影响程度有略微的不同,但所有自变量系数的显著性和正负均无明显差异。在回归模型的基础上,研究进一步测算了2018年和2019年各省可再生能源电力消纳的变化对当地碳排放可能产生的影响。研究结论证实了我国可再生能源电力消纳保障机制政策在环境改善方面的可行性与有效性,揭示了促进清洁能源电力的利用、提升可再生能源电力在能源消费结构中的占比对绿色发展的必要性与重要性。     

大坝安全监测数据中共线性问题的主成分分析法

针对建模因子间存在多重共线性影响模型精度问题,提出了优化措施,建模前对因子采用主成分分析,提取的主成分作为模型输入因子,消除或削弱了上述影响.实例分析结果表明,经优化后的模型更合理、计算效率更高.     

基于综合发电收益的分布式光伏系统规划研究

为实现分布式光伏系统综合发电收益最优,首先建立了光伏DG系统综合收益模型,并结合工程实践,归纳出组成光伏DG系统的四种典型拓扑结构,通过分析拓扑结构中各节点特点,提出直流系统电压等级、系统容量配比、系统集成简化、系统功率平衡四种影响分布式光伏综合发电收益的关键指标;其次分析各关键指标的经济变量,得出光伏DG系统总体优化成本变量曲线。研究结果表明,随装机容量的增加,系统总体成本相应增加,度电收益基本稳定;综合收益模型优化后成本降低,度电收益提高。研究结果为降低工程造价和单位度电成本提供借鉴。     

基于系统动力学分析的电力资源替代研究

为探索人类永续发展能力与电力资源短缺矛盾的解决路径,在阐释广义电力资源四层次含义的基础上,研究了环境、经济和电力构成的复杂系统协调发展问题,并运用系统动力学方法分析了由环境系统、经济系统和电力系统组成的需求系统的均衡发展问题。研究发现,人类需求系统是一个受多因素影响的复杂大系统,要实现人类的永续发展就需理清需求系统中各因素的因果关系,把握制约电力资源供应的主要变量,区别其极性,诱导正反馈变量,约束负反馈变量,从而实施电力资源替代战略。     

A flexible fuzzy regression algorithm for forecasting oil consumption estimation

Oil consumption plays a vital role in socio-economic development of most countries. This study presents a flexible fuzzy regression algorithm for forecasting oil consumption based on standard economic indicators. The standard indicators are annual population, cost of crude oil import, gross domestic production (GDP) and annual oil production in the last period. The proposed algorithm uses analysis of variance (ANOVA) to select either fuzzy regression or conventional regression for future demand estimation. The significance of the proposed algorithm is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and minimum absolute percentage error (MAPE), whereas previous studies consider the best fitted fuzzy regression model based on MAPE or other relative error results. Second, the proposed model may identify conventional regression as the best model for future oil consumption forecasting because of its dynamic structure, whereas previous studies assume that fuzzy regression always provide the best solutions and estimation. Third, it utilizes the most standard independent variables for the regression models. To show the applicability and superiority of the proposed flexible fuzzy regression algorithm the data for oil consumption in Canada, United States, Japan and Australia from 1990 to 2005 are used. The results show that the flexible algorithm provides accurate solution for oil consumption estimation problem. The algorithm may be used by policy makers to accurately foresee the behavior of oil consumption in various regions.     

OPEC and demand response to crude oil prices

This paper investigates demand response to crude oil price movements before and after the recent global financial and economic crisis. It employs several market power indices to structurally estimate price elasticities. A newly developed market power index for crude oil markets is implemented. In this approach OPEC is the central player and acts as a dominant producer in the global oil market. We quantify how a change in market structure (such as changes in marginal cost of production) would contribute to market power exercise of OPEC and have an ultimate impact on price elasticity of demand for oil. Our price elasticity predictions fall in a range reported in the literature, however estimates for pre-crisis deviate from the post-crisis ones. In fact, demand response to crude oil prices has almost doubled during the crisis. This severe change in price response can be associated with record price levels caused by supply shortages and surge in alternative renewable energy resources. The key advantages of this methodology over the existing literature are that it is simple to use and estimates price elasticity using a competition framework without specifying demand/supply function(s), and utilizes commonly observable market variables that can be applied to any admissible data frequency.     

Increasing of fuel cell economic benefits by optimal participation strategy with energy storages and other distributed resources and considering uncertainties and various markets

The Participation of Distributed Energy Resources (DERs) such as Fuel Cell (FC) in Daily Market is one of the main Problems of these resources. Uncertain nature of market price and power system parameters can be affected to economic performance of DERs and is caused deviation penalties during market settlements. This article introduces an optimal combined sharing strategy of fuel cell/wind turbine/battery storage unit and demand response as microgrid to improve all of them profit in market participation. In order to adjust of contracted power and reduce the imbalanced energy, the Adjusted Market (AM) is considered 3–7 h before the delivered time, which the offered power could be updated in this time. Uncertainty of markets and DERs generations is considered using an optimal stochastic optimization approach. The probabilistic scenarios are generated using forecast error database in daily, adjusted and imbalance market as production of DERs. Uncertain variables are predicted by a neuro-fuzzy model. Finally, to demonstrate of the proposed joint model strategy, a test system contain fuel cell/wind turbine/battery storage unit and demand response is considered and the results is calculated during one week, month, and year period in joint and separated conditions.     

Oil price shocks and policy uncertainty: New evidence on the effects of US and non-US oil production

Important interaction has been established for US economic policy uncertainty with a number of economic and financial variables including oil prices. This paper examines the dynamic effects of US and non-US oil production shocks on economic policy uncertainty using a structural VAR model. Such an examination is motivated by the substantial increases in US oil production in recent years with implications for US political and economic security. Positive innovations in US oil production are associated with decreases in US economic policy uncertainty. The economic forecast interquartile ranges about the US CPI and about federal/state/local government expenditures are particularly sensitive to innovations in US oil supply shocks. Shocks to US oil supply disruption causes rises in the CPI forecast uncertainty and accounts for 21% of the overall variation of the CPI forecaster disagreement. Dis-aggregation of oil production shocks into US and non-US oil production yields novel results. Oil supply shocks identified by US and non-US origins explain as much of the variation in economic policy uncertainty as structural shocks on the demand side of the oil market.     

A model of carbon price interactions with macroeconomic and energy dynamics

This paper develops a model of carbon pricing by considering two fundamental drivers of European Union Allowances: economic activity and energy prices. On the one hand, economic activity is proxied by aggregated industrial production in the EU 27 (as it provides the best performance in a preliminary forecasting exercise vs. other indicators). On the other hand, brent, natural gas and coal prices are selected as being the main carbon price drivers (as highlighted by previous literature). The interactions between the macroeconomic and energy spheres are captured in a Markov-switching VAR model with two states that is able to reproduce the ‘boom–bust’ business cycle (Hamilton (1989)). First, industrial production is found to impact positively (negatively) the carbon market during periods of economic expansion (recession), thereby confirming the existence of a link between the macroeconomy and the price of carbon. Second, the brent price is confirmed to be the leader in price formation among energy markets (Bachmeier and Griffin (2006)), as it impacts other variables through the structure of the Markov-switching model. Taken together, these results uncover new interactions between the recently created EU emissions market and the pre-existing macroeconomic/energy environment.     

A robust optimization model for capacity configuration of PV/battery/hydrogen system considering multiple uncertainties

The capacity configuration optimization of photovoltaic (PV) hydrogen system with battery has been widely concerned, but many existing studies only take hydrogen as an energy storage mode of smooth power, rather than dedicated to hydrogen production to meet the needs of chemical plants for hydrogen, and it ignores the impact of the fluctuation of PV output and the uncertainty of hydrogen demand on the system capacity configuration optimization. For overcoming this disadvantage, a PV/battery/hydrogen system is proposed, which is specially used for hydrogen production, and for the sake of system economy, the excess power will be weighed whether to store it in the battery or sell it in the green certificate trading market, and a new trading model is formed. Moreover, a robust optimization model is established to deal with the impact of PV output, green power transaction price and hydrogen demand volatility on system capacity configuration. Finally, the characteristics of the model are tested by a real case of a chemical plant in Jiuquan City, Gansu Province. The results show that the capacity configuration scheme of the proposed model can effectively resist the demand uncertainty of hydrogen, the capacity of the electrolyzer and battery can still meet the needs of the chemical plant, even if the demand of the hydrogen increases by 40%.     

基于可再生能源经济调度时序模拟的绿色证书市场交易研究

梳理可再生能源发电商、常规能源发电商与售电公司等交易主体之间的电力、绿色证书供需关系,以各发电商的利益最大化为目标,建立考虑绿色证书的能源经济调度模型,求解消纳保障机制下发电商电力调度及证书交易策略;通过算例分析说明了绿色证书机制提高可再生能源发电商收益的有效性,消纳责任权重对于绿证价格变化、市场力抑制等市场良性运营具...     

基于主成分回归分析的气象因子对光伏发电量的影响

采用主成分分析方法降低气象维度,提取互不相关的综合性评价指标,并将提取出的主要成分作为回归模型的自变量建立多元线性回归模型。通过检验可知:主成分回归模型的拟合优度优于直接回归模型,提高了预测精度,预测结果也较为稳定。因此,主成分分析法可有效提高光伏发电量的预测精度。     

考虑可再生能源消纳的火电机组市场力评估

随着可再生能源并网比例的不断提升,实现其全额消纳会对火电机组的市场机会产生越来越大的冲击。针对这一现象,提出了火电机组综合市场力评估方法。考虑可再生能源发电并网下火电机组备用作用的强化,对传统市场力指标——Lerner指数进行修正,建立了反映火电机组在电量市场与备用市场综合市场力的改进Lerner指标;同时计及风电出力的不确定性,采用正态分布与拉普拉斯分布的联合概率密度函数更好地反映风电预测误差的尖峰厚尾特性,完善了备用需求的表达;以电量完全竞争作为假设前提,采用电量—备用联合优化模型模拟电力市场交易,进而完成对火电机组综合市场力的准确度量。以10机系统为例进行仿真计算,验证了改进指标的有效性并分析风电渗透率、风电预测精度等因素变化对火电机组综合市场力的影响。     

A game theoretic analysis of a closed-loop water-energy nexus: The effect of technology efficiency and market competition on the market equilibrium and social welfare

Water and energy are two scarce and concerning resources interconnected in the closed-loop water-energy nexus, exemplifying the co-dependence of water and energy production. In this article, we investigate the interaction between the industries of these two resources and model it as a simultaneous game. We consider a supply chain that consists of water suppliers (WSs), power suppliers (PSs), and consumers of these commodities. In the supply chain, WSs purchase power from the power market, and PSs purchase water from the water market. Other consumers can also buy these resources at the water and power markets. The prices of these commodities depend on their quantities supplied to the markets. Each firm tries to maximize its own profit, in doing so the suppliers of water and power decide their production quantities. In this research, the Nash equilibria of the firms are determined and a comparative statics is performed on various economic measures. When there are multiple equilibria, the analysis finds the Pareto optimal equilibrium. We find that when there is sufficient supply to meet the demand of both industries and consumers, improvement of production technology improves social welfare and other economic measures of the supply chain. We also find that higher competition in either industry improves all economic measures. However, when either water or power supply is solely consumed by the firms in the cross industry, improvement of technology and higher competition can have a negative effect on some measures.