Life-cycle assessment of batteries for peak demand reduction

At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in power systems to reduce peak demands for deferring or avoiding augmentation in the network and power generation.As the battery cost is still very high at present,a comprehensive assessment is necessary to determine the optimum ESS capacity so that the maximum financial gain is achievable at the end of the batteries’lifespan.Therefore,an effective life-cycle assessment is proposed in this paper to show how the optimum ESS capacity can be determined such that the maximum net financial gain is achievable at the end of the batteries’lifespan when ESS is used to perform peak demand reductions for the customer or utility companies.The findings reveal the positive financial viability of ESS on the power grid,otherwise the projection of the financial viability is often seemingly poor due to the high battery cost with a short battery lifespan.An improved battery degradation model is used in this assessment,which can simulate the battery degradation accurately in a situation whereby the charging current,discharging current,and temperature of the batteries are intermittent on a site during peak demand reductions.This assessment is crucial to determine the maximum financial benefits brought by ESS.

Peak Electricity Demand Management and Energy Efficiency among Large Steel Manufacturing Firms in Nairobi Region, Kenya

To reduce peak electricity demand and hence reduce capacity costs due to added investment of generating additional power to meet short intervals of peak demand, can enhance energy efficiency. Where it is possible to adjust timing and the quantity of electricity consumption and at the same time achieve the same useful effect, the value of the energy service itself remains unchanged. Peak demand management is viewed as the balance between demand and generation of energy hence an important requirement for stabilized operation of power system. Therefore, the purpose of this study was to establish the correlation between peak electricity demand management strategies and energy efficiency among large steel manufacturing firms in Nairobi, Kenya. The strategies investigated were demand scheduling, Peak shrinking and Peak shaving. Demand scheduling involves shifting predetermined loads to low peak periods thereby flattening the demand curve. Peak shrinking on the other hand involves installation of energy efficient equipment thereby shifting the overall demand curve downwards. Peak shaving is the deployment of secondary generation on site to temporarily power some loads during peak hours thereby reducing demand during the peak periods of the plant. The specific objectives were to test the relationship between demand scheduling and energy efficiency among large steel manufacturing firms in Nairobi Region;to test the correlation between peak shrinking and energy efficiency among large steel manufacturing firms in Nairobi Region;and to test the association between peak shaving and energy efficiency among large steel manufacturing firms in Nairobi Region. The study adopted a descriptive research design to determine the relationship between each independent variable namely demand scheduling, peak shrinking, peak shaving and the dependent variable, the energy efficiency. The target population was large steel manufacturing firms in Nairobi Region, Kenya. The study used both primary and secondary data. The primary data was from structured questionnaires while secondary data was from historical electricity consumption data for the firms under study. The results revealed that both peak shrinking and peak shaving were statistically significant in influencing energy efficiency among the steel manufacturing firms in Nairobi Region, each with Pearson correlation coefficient of 0.903, thus a strong linear relationship between the investigated strategy and the dependent variable, energy efficiency. The obtained results are significant at probability value of 0.005 (p 0.05). The conclusion is that peak shrinking and peak shaving have an impact on energy efficiency in the population under study, and if properly implemented, may lead to efficient utilization of the available energy. The study further recommended that peak demand management practices need to be implemented efficiently as a way of improving the overall plant load factor and energy efficiency.

Tail Quantile Estimation of Heteroskedastic Intraday Increases in Peak Electricity Demand

Modelling of intraday increases in peak electricity demand using an autoregressive moving average-exponential generalized autoregressive conditional heteroskedastic-generalized single Pareto (ARMA-EGARCH-GSP) approach is discussed in this paper. The developed model is then used for extreme tail quantile estimation using daily peak electricity demand data from South Africa for the period, years 2000 to 2011. The advantage of this modelling approach lies in its ability to capture conditional heteroskedasticity in the data through the EGARCH framework, while at the same time estimating the extreme tail quantiles through the GSP modelling framework. Empirical results show that the ARMA-EGARCH-GSP model produces more accurate estimates of extreme tails than a pure ARMA-EGARCH model.

Characterization of Peaks and Valleys of Electricity Demand. Application to the Spanish Mainland System in the Period 2000-2020

Energy planning must anticipate the development and strengthening of power grids, power plants construction times, and the provision of energy resources with the aim of increasing security of supply and its quality. This work presents a methodology for predicting power peaks in mainland Spain’s system in the decade 2011-2020. Forecasts of total electricity demand of Spanish energy authorities set the boundary conditions. The accuracy of the results has successfully been compared with records of demand (2000-2010) and with various predictions published. Three patterns have been observed: 1) efficiency in the winter peak;2) increasing trend in the summer peak;3) increasing trend in the annual valley of demand. By 2020, 58.1 GW and 53.0 GW are expected, respectively, as winter and summer peaks in a business-as-usual scenario. If the observed tendencies continue, former values can go down to 55.5 GW in winter and go up to 54.7 GW in summer. The annual minimum valley of demand will raise 5.5 GW, up to 23.4 GW. These detailed predictions can be very useful to identify the types of power plants needed to have an optimum structure in the electricity industry.

Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.

人口特征对中国经济社会需水影响及峰值预测

人口规模结构是决定水资源需求的关键因子,在人口总量负增长的重大变化背景下,中国经济社会需水如何演变直接关系到水资源管理宏观决策。本文通过分析中国人口城乡结构、年龄结构对经济社会需水的影响规律,构建了基于人口特征的需水变化曲线,并在此基础上预测了经济社会需水演变态势及峰值时点。研究结果表明:中国经济社会需水峰值可能出现在2042年,达峰规模约为7300亿m^(3);2050年之前,城镇地区需水总量将会持续增加,农村地区需水峰值将于2030年左右出现。经济社会需水规模是决定区域用水特征的边界条件,研究认为在中国经济社会需水还未到达拐点、国际贸易环境动荡加剧的背景下,未来中国供水保障还应该保持一定的弹性和韧性。

计及需求响应的光热电站参与深度调峰的分层优化调度策略

从源、荷两侧挖掘系统调峰潜力,建立计及需求响应的光热电站参与深度调峰的分层优化调度模型。上层从负荷侧出发,提出一种基于负荷分类的价格需求响应模型,可有效缓解系统调峰压力;中层从电源侧出发,利用光热电站灵活的调节特性在深度调峰时段协调火电机组参与辅助调峰,构建以运行总成本最小为目标函数的日前调度模型;下层提出一种基于模型预测控制的日内动态调整模型,在滚动优化的同时,通过状态反馈环节实时调整光热电站储热装置充放热修正日前调度计划。仿真结果表明,所提调度策略在降低系统调峰成本的同时能有效抑制风光以及负荷的短时功率波动,在保证系统安全稳定运行的前提下提升风光消纳率。

关于中国水达峰几个问题的讨论

用水演变态势与需求峰值事关经济社会发展、生态环境保护大局,直接影响国家水网规划布局和重大水利工程建设。本文在水达峰内涵解析的基础上,总结了水达峰的自然社会特征和内涵,阐明了水达峰与需水、用水、缺水之间的关系,深入分析了中国统计用水量出现拐点的现象以及造成这种现象的6种可能原因,指出目前尚不能得出中国用水需求已达峰的结论。通过与国际上发达国家水达峰时人均GDP、产业结构、城镇化率等主要指标对比分析,认为中国现阶段也不具备水达峰的经济社会基础。最后指出,目前中国水资源保障安全的压力仍然较大,未来用水演变态势存在一定的不确定性,中国用水何时达峰、达峰水量是多少等结论需要进一步深入研究。需要做好国家水网工程的顶层设计,提升不同尺度的水资源优化配置能力,为国家高质量发展提供水资源保障。

全球石油消费超疫情前水平我国成品油市场格局加速演变——2023年国内外石油市场发展形势及2024年展望

2023年,全球石油需求快速恢复,地缘溢价有所消退及美联储持续加息带来国际油价回落。中国石油需求创历史新高,成品油需求恢复接近2019年的水平;原油供应由国内国际两个市场共同保障。从中长期看,交通电动化已对油品需求形成不可逆转的冲击,需求格局加速被迭代,达峰趋势不变,尤其汽油峰值被拉近至2024年,柴油再次回归下降区间,而煤油和化工用油是未来石油消费的增长点,国内成品油市场需求格局加速演变。此外,行业监管措施进一步落实,市场整治力度持续发力,行业格局将不断升级。

参与电网削峰调节的电动重卡换电站调度策略

换电站因其快速、便捷的特点获得了迅猛的推广和发展。在电动重卡领域,换电站建设已初具规模并保持高速发展。如何对电动重卡换电站进行充电调度以实现电网友好,是一个重点研究方向。首先,针对电动重卡换电需求在时间上分布规律较弱的特点,使用统计分析法分析换电站历史运行数据,得到一天内各时段的换电需求预测值;其次,提出基于换电需求预测的日内调度策略和基于实际换电需求的日内实时修正策略,以确定电池充电数量和允许充电时间;然后,建立以电网削峰为目标的时间-功率模型,结合电网背景负荷曲线,使用差分算法对电池的充电功率和实际充电时间进行求解;最后,通过算例证明了调度策略和时间-功率模型的有效性。

计及用户参与和调峰需求的储能配置优化研究

为了对分布式储能潜力进行充分挖掘以提升电网调控水平,提出一种全面考虑储能用户行为及调峰需求的聚合商侧储能配置优化模型。首先,对储能用户参与聚合的自身参与度与从众行为进行研究,建立储能容量模型。其次,综合考虑聚合商参与调峰收益、储能聚合成本、储能成本、储能充电成本,构建聚合商侧储能优化配置模型,并采用白鲸优化(BWO)算法对模型进行求解。最后,以修改后的IEEE33节点系统为例,验证了配置优化模型的有效性和算法的优越性。该模型不仅能得到储能优化配置容量,还能够充分考虑储能用户综合不确定性与电网调峰需求。该模型在国家鼓励多主体参与电力现货交易的背景下,具有广泛的适用性。

考虑需求响应与火蓄调峰的优化调度模型研究

为了应对风电等可再生能源接入引起的系统调峰压力增大且影响系统经济运行的问题,进一步挖掘新型调节电源、传统调节电源和负荷侧协同调峰的潜力,综合利用负荷侧的需求响应和电源侧的抽水蓄能、火电三种调峰措施联合调峰,构建考虑需求响应和火蓄参与深度调峰的电力系统两阶段优化调度模型。第一阶段优化模型中引入负荷侧的需求响应,以最小化负荷与风电差值的平方之和为目标,优化负荷曲线,降低系统峰谷差;第二阶段优化模型利用电源侧的抽水蓄能和火电深度调峰,计及火电深度调峰电量损失成本、抽蓄调峰成本和弃风成本,以系统总运行成本最小为目标,在第一阶段模型的基础上优化各机组出力,增加风电上网空间。以改进的IEEE30节点系统进行算例分析和两阶段优化模型有效性验证,结果表明,所建模型可提高调峰能力,促进风电消纳,并降低系统总运行成本。火电机组进一步降低出力,积极参与深度调峰,能够有效促进系统风电消纳。三种调峰方式有机结合联合参与调峰,降低了负荷的峰谷差,不仅有效降低了弃风率,还减轻了系统的调峰压力,缩短了火电深调时段。

计及典型日选取与源荷灵活性调节的优化调度研究

针对电网面临的反调峰特性和新能源消纳问题,提出一种计及源荷侧灵活性调节资源的电力系统优化调度模型。首先,根据负荷特点采用改进SSE-PFCM聚类算法求取负荷典型日。其次,基于负荷用户对电价变化的不同响应行为,提取出具有需求响应潜力的两类负荷,并且分别为其构建计及乐观响应隶属度的模糊负荷转移率模型以及需求价格弹性模型。然后,计及需求响应不确定性模型以及火电机组深度调峰,以系统综合运行成本最小为目标构建优化调度模型,采用引入步长弹性系数的改进粒子群算法求解。最后,以改进IEEE 30节点系统为例进行多种场景的仿真计算分析,结果表明所提策略能够有效提升系统可再生能源的消纳能力和系统运行的经济性。

电氢市场主体参与电力市场的路径及发展展望

随着世界各国氢能发展战略的出台,电氢耦合成为备受关注的方向。电氢市场主体作为电力系统的灵活调节资源,能够助力实现双碳目标下新型电力系统的安全经济运行。中国电力现货市场建设稳步推进,致力于通过电价信号引导电力系统的资源配置,电氢市场主体能够通过参与电力市场降低其购电成本并获得额外收益,进而促进电氢产业的发展。为此,首先从技术可行性和商业模式的角度综述了已有电氢市场主体参与电力市场的相关研究;总结现有电力市场机制下电氢市场主体的参与方式;提出电氢负荷参与电能市场的投标模型和提供调频服务的机会成本计算模型并进行算例验证,在此基础上结合中国电力现货市场建设现状,为促进电氢市场主体参与电力市场提出了待解决的问题和建议。

基于紧迫性需求的电动汽车协调充电优化策略

电动汽车(electric vehicles,EV)的随机充电会导致峰值负荷突变,进而增加了电网运行波动的风险。为此,提出了一种基于微电网的电动汽车协调充电调度策略,算例表明该策略可以降低负荷的峰值,减弱了随机充电行为对于电网波动的风险。首先,考虑充电紧急性指标(charging urgency index,CUI)来反映不同的充电需求,以及利用蒙特卡洛法模拟电动汽车充电时间行为的随机性,进一步保证电动汽车的协调充电。其次,以整体峰谷负荷差最小为目标,建立协调充电调度的优化模型,考虑了电动汽车慢速和快速充电以及微电网运行各种限制。仿真结果表明,在两种充电模式下,该方法较不协调充电方法在500辆电动汽车的情况下能够将峰值负荷分别降低54.71%、46.38%,能有效地解决电动汽车车主的充电选择模式以及降低微电网运行的风险,为微电网削峰填谷提供策略。

含风电并网的源荷双层机组优化组合模型

针对系统调峰能力不足,严重限制新能源的消纳问题,提出计及源荷两侧可调节资源的综合市场服务机制与需求响应的双层机组组合模型。上层对荷侧建立计及响应不确定性的以净负荷波动最小为目标函数的价格型需求响应模型;下层对源侧建立包含电力市场深度调峰辅助服务与碳市场奖惩阶梯式碳交易机制的综合市场交易机制,构建以综合运行成本最小为目标函数的机组组合模型;最后,将原问题转化为混合整数线性规划问题,通过设置不同场景验证该模型的有效性。

峰谷分时电价调整对夏季高温负荷影响分析

研究峰谷分时电价调整对湖北省夏季高温负荷的影响。在抽取分时电价调整前后的夏季典型高温日负荷曲线数据基础上,建立了分时电价对负荷影响分析测算模型,开展了湖北省2021年以来二次峰谷分时电价调整对总体用电、不同用电类别及工业重点行业用电负荷影响的定量分析,为峰谷分时电价的进一步调整优化提供了参考依据。

考虑温度再设的暖通空调电力需求响应策略研究

近年来,空调负荷的急剧增长已经成为季节性电力紧张的最主要原因,利用空调需求响应策略,调整空调负荷对于实现电网削峰填谷有重要意义。以风机盘管加新风形式的空调系统为对象,建立其TRNSYS仿真模型,基于分时电价政策,针对温度再设需求响应策略进行研究,采用再次设定制冷区域温度和热舒适指标两种策略,对比其在需求响应期间的峰值负荷削减量和整体系统运行的费用。结果表明基于温度再设的需求响应策略峰期负荷转移率约为30.3%,基于热舒适指标控制的需求响应策略的峰期负荷转移率约为22.7%,然而,这两种策略并不能节省电费,因此,除了分时电价的优惠政策外还需要给予相应的激励措施。

基于网格搜索的建筑集群电力调峰调度策略研究

针对需求响应技术中存在的不确定性,尤其建筑集群用户的负荷调节潜力及各用户用电如何分摊,匹配电网调峰需求缺少量化进行研究。基于20栋建筑集群的实际用能数据,分析不同年份空调、照明用电情况,得出建筑集群全年用电特性曲线。按照响应电网侧5%的峰值负荷为调控目标,建立距离模型计算照明、空调的调节参数,利用网格搜索寻优计算耦合调节系数。结果表明,建筑集群各用户为实现全年5%调峰指标,照明与空调用电应耦合调节,各建筑全年照明负荷应调节为各家峰值负荷的80%~99%,在冬季空调用电几乎不用调节,夏季空调应调节为各家峰值负荷的70%~83%,仅通过调高设定温度难以实现调峰,需采用其他节能手段或用可再生能源调峰。最后量化需求响应时间内各家在不同时刻应减少的负荷指标,指导建筑集群各用户进行负荷管理,响应需求侧调峰信号。