Peak Shaving Strategy of Concentrating Solar Power Generation Based on Multi-Time-Scale and Considering Demand Response

According to the multi-time-scale characteristics of power generation and demand-side response(DR)resources,as well as the improvement of prediction accuracy along with the approaching operating point,a rolling peak shaving optimization model consisting of three different time scales has been proposed.The proposed peak shaving optimization model considers not only the generation resources of two different response speeds but also the two different DR resources and determines each unit combination,generation power,and demand response strategy on different time scales so as to participate in the peaking of the power system by taking full advantage of the fast response characteristics of the concentrating solar power(CSP).At the same time,in order to improve the accuracy of the scheduling results,the combination of the day-ahead peak shaving phase with scenario-based stochastic programming can further reduce the influence of wind power prediction errors on scheduling results.The testing results have shown that by optimizing the allocation of scheduling resources in each phase,it can effectively reduce the number of starts and stops of thermal power units and improve the economic efficiency of system operation.The spinning reserve capacity is reduced,and the effectiveness of the peak shaving strategy is verified.

Combined hybrid energy storage system and transmission grid model for peak shaving based on time series operation simulation

This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.

Peak shaving operation optimization of high proportion new energypower generation considering wind-solar complementationand source-load coupling

To optimize peaking operation when high proportion new energy accesses to power grid,evaluation indexes are proposed which simultaneously consider wind-solar complementation and source-load coupling.A typical wind-solar power output scene model based on peaking demand is established which has anti-peaking characteristic.This model uses balancing scenes and key scenes with probability distribution based on improved Latin hypercube sampling(LHS)algorithm and scene reduction technology to illustrate the influence of wind-solar on peaking demand.Based on this,a peak shaving operation optimization model of high proportion new energy power generation is established.The various operating indexes after optimization in multi-scene peaking are calculated,and the ability of power grid peaking operation is compared whth that considering wind-solar complementation and source-load coupling.Finally,a case of high proportion new energy verifies the feasibility and validity of the proposed operation strategy.

Collaborative optimization model of renewable energy development considering peak shaving costs of various flexibility resources

China has set carbon emission goals for 2030 and 2060.Renewable energy sources,primarily wind and photovoltaic power,are being considered as the future of power generation.The major limitation to the development of new energies is the limited flexibility of regulations on power system resources,resulting in insufficient consumption capacity.Thus,the flexible resource costs for peak shaving as well as the reasonable coordinated development and operation optimization of regional renewable energy need to be considered.In this study,a renewable energy development layout configuration analysis method was established by considering the composite cost of a power system,comprehensively analyzing the potential of various flexibility regulation resources for the power system and its composite peak shaving cost,and combining renewable energy output characteristics,load forecasting,grid development,and other factors.For the optimization of various flexible resource utilization methods,a peak shaving cost estimation method from the perspective of the entire power system was established by combining the on-grid electricity prices and operating costs of different power sources.A collaborative optimization model of power system operation that aims at the lowest peak shaving cost and satisfies the constraints of operation,safety,and environmental protection was proposed.Finally,a certain area of Gansu Province was used as an example to perform detailed analysis and calculation,which demonstrated that the model has an optimal effect.This model can provide an analysis method for regional renewable energy development layout configurations and system optimization operations.

Potential of Rooftop PV Systems on Weekly Peak Load Shaving in Saudi Arabia

In recent years, high annual increasing load demand in Saudi Arabia has led to large investments in the construction of conventional power plants, which use oil or gas as the main fuel. The government is considering a large deployment of renewable energy for its 2030 vision plan. The Kingdom of Saudi Arabia is one of the best potential candidates for harvesting solar energy because of the country’s geographical location, clear sky, and vast land area. A recent energy policy announced by the government involves harvesting solar photovoltaic (PV) energy to reduce the country’s reliance on fossil fuel and greenhouse gas emissions. Using rooftop PV systems can help to shave the peak load and lead to a significant savings in the power sector through the reduction of annual installation of conventional power plants and standby generators. Employing solar PV at the end user level helps to reduce the overloading of transmission and distribution lines as well as decreases power losses. This paper will provide ratings for different rooftop PV systems that are being considered for installation for customers with various needs. The distribution of PV installation among the customers is as follows: 5% residential, 10% commercial, and 20% government. The effect of PV output power on weekly peak demand has been evaluated. The paper has also investigated the impact of the temperature on PV output power, especially during the summer. The PV power contribution is analyzed based on the assumption that weekly peak power production of solar PV coincides with weekly peak load demand. The PV model is implemented in Matlab to simulate and analyze the PV power.

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.

可再生能源电制氢合成氨系统的并/离网运行方式与经济性分析

可再生能源电制氢合成氨(renewablepowerto ammonia,Re Pt A)是风力发电、光伏发电等可再生能源的大规模消纳方式之一,也是化工行业实现低碳清洁发展的重要技术路线之一。利用风电光伏电解水制氢合成氨系统模型,分析电网调峰型系统、电网友好型系统、工艺离网型系统的绿氨成本,并对电化学储能设备规模、储氢设备规模、电解水制氢设备规模、电解水制氢设备效率等影响因素进行敏感性分析。基于3种绿氨系统的设备配置方案和运行方式评估不同类型系统所需的电网调峰电量,提出3种系统所适合的应用场景。研究结论是近期应该发展电网友好型RePtA系统,远期应该发展工艺离网RePtA系统。

适应锅炉调峰运行的水冷壁高温腐蚀预测模型

随着可再生能源大规模并网,传统火电机组作为调配能源的地位显著增强,这对锅炉灵活调峰运行的安全稳定性提出了更高要求。本文耦合烟气侧与工质侧传热,结合管壁温计算,形成水冷壁壁温分布耦合计算方法。同时,建立了用于确定还原性气氛下的燃料硫释放以及含硫组分的相互转化过程的SO_(x)生成模型。综合炉膛数值模拟、水冷壁壁温耦合计算以及包含时间维度的管壁高温腐蚀模型,提出了一种适应锅炉调峰运行的水冷壁高温腐蚀预测模型并基于Matlab GUI开发了对应软件。选取一台超临界600MW四角切圆燃煤锅炉为研究对象,结果表明:采用壁温耦合计算模型和SO_(x)生成模型得到水冷壁的壁温分布和近壁面H_(2)S浓度分布准确度高,为水冷壁高温腐蚀的准确预测提供了良好基础。不同负荷下水冷壁高温腐蚀特征存在差异,壁面腐蚀程度整体上随负荷降低而降低。100%BMCR与75%THA负荷下前墙水冷壁燃烧器与SOFA之间的区域腐蚀最为严重,最大年腐蚀深度分别为276μm和233μm;50%THA与35%BMCR负荷下高温腐蚀深度在燃烧器区域的上部迅速增加至最大值,分别为224μm和256μm。多工况运行水冷壁高温腐蚀状态表现为各工况腐蚀状态的时空叠加。运用水冷壁高温腐蚀预测模型可实现通过锅炉运行参数和运行时间预测多工况下水冷壁高温腐蚀状态程度的时空分布。

机器学习驱动锅炉燃烧优化技术的现状与展望

伴随可再生能源发电装机容量快速增加,深度调峰过程中负荷多变、燃烧失稳等不稳定工况对火电机组的燃烧优化控制提出了更高要求,快速发展的人工智能技术与深度学习算法为锅炉参数预测建模及优化提供了重要手段。在机器学习算法方面,总结了特征筛选与建模算法的研究现状,提出了传统统计学方法与线性降维方法的科学解释性较差且不能很好地辨识高维数据,结合深度学习算法的特征筛选方法在处理复杂的火电机组数据时优势更明显;对比了多种神经网络在NO_(x)排放浓度建模中的优缺点,其中长短期记忆神经网络与卷积神经网络在处理时序数据时效果更好、集成模型通过组合不同学习器的优势可提高整个模型的泛化能力和鲁棒性。在预测模型的应用方面,通过对SCR脱硝系统建立预测模型可以方便运行人员模拟并修正可调参数,同时作为软测量手段监测燃烧系统运行状态;引入NO_(x)排放浓度预测模型的前馈控制和模型预测控制等先进控制手段可有效改善火电机组传统PID控制效果较差的问题;在多目标优化中NO_(x)脱除效率通常与锅炉效率或脱硝成本共同作为优化目标,以期实现经济效益与社会效益的和谐统一。

2023年中国天然气调峰特性及2024年市场供需展望

为了助推天然气在中国新型能源体系建设中发挥更大的作用,回顾了2023年中国天然气市场调峰需求的主要特点,展望了2024年中国天然气市场的发展趋势。研究结果表明:(1)2023年,中国天然气供需总体宽松,天然气季节性调峰量为212×10^(8)m^(3),占该年全国天然气消费总量的5.4%,较2022年有所下滑;(2)2023年中国高月天然气不均匀系数为1.34,低月不均匀系数为0.85,月峰谷比为1.57,消费量月度波动幅度在年初市场异常和淡季需求量增长的影响下有所收窄;(3)2023年中国高峰天然气日消费量在历史性寒潮的刺激下直线攀升,峰值达到15.7×10^(8)m^(3)/d,同比增长18.9%;(4)城市燃气和发电用气成为2023年中国天然气调峰需求的主力,工业燃料和化工原料用气在一定程度上平抑了天然气消费量的季节性波动;(5)2023年中国储气设施顶峰能力再上新台阶,经营品种更加丰富;(6)在诸多有利条件叠加的情景下,预计2024年中国天然气需求量将有望达到4264×10^(8)m^(3),同比增长8.8%,新增需求量以工业燃料和发电用气为主;(7)2024年中国天然气调峰需求量预计为227×10^(8)m^(3),同比增长7.0%;(8)预计2024年中国天然气供应能力增长将超过需求量增长,整体上继续保持供应宽松的格局,并且在积极情景下LNG现货需求将保持旺盛;(9)建议国内天然气进口企业高度关注国际市场供应侧风险,销售企业提前做好冬季资源储备,城市燃气企业利用改革红利及时补齐相关短板,设施经营企业积极拓展市场化经营模式。

混合式抽水蓄能电站群容量计算研究Ⅰ:短期调峰特征与梯级开发增益

依托常规梯级水电站修建混合式抽水蓄能电站(简称混蓄电站)成为提升电力系统调峰能力的重要途径。混蓄电站即可抽水又可发电的典型运行方式增加了水电基地的灵活性调节能力,为此亟需研究其运行方式以及与原有梯级水电站运行方式间的互馈关系。研究以黄河上游待开发龙羊峡-拉西瓦、拉西瓦-尼那混蓄电站为对象,建立含混蓄电站的梯级水电站短期联合调峰优化模型,分析混蓄电站对原有梯级水电站运行方式的影响,基于理论推导与数值计算两种方法揭示混蓄电站调峰效果的变化特征。主要结论如下:(1)混蓄电站抽水水量经由原有梯级水电站机组下泄,使得非填谷时段原有水电站出力增加,水电站上旋备容量降低;(2)龙羊峡水电站满发流量相对较小、拉西瓦水库调节库容较小以及日综合利用供水流量过大是制约混蓄电站调峰效果的主要因素,发现了综合利用流量影响混蓄电站调峰效果的临界特性,并提出了临界综合利用流量的计算公式;(3)发现并量化了混蓄电站梯级开发模式较单一开发模式所带来的梯级抽水调峰增益现象,并揭示了梯级抽水调峰增益背后所蕴含的“河流梯级反向再造”内在机理。

计及深度调峰的风-光-水-火-抽蓄多能源联网系统优化调度研究

多种能源联网运行逐步成为未来电力系统发展的方向,多能源联网系统的优化调度是应对规模化新能源并网消纳的重要手段。文中建立了计及火电机组深度调峰成本的风-光-水-火-抽蓄联网系统成本模型和考虑调峰主动性的风-光-水-火-抽蓄联网系统日前优化调度模型。针对多能源耦合系统结构复杂的情况,提出了分层优化调度的模型求解策略。上层模型以净负荷波动最小为优化目标,得到等效负荷曲线;下层模型根据上层模型的调度结果,以调峰运行的经济性最优和新能源弃电率最低为目标函数,计及调峰主动性,求解考虑火电机组深度调峰煤耗特性的含新能源系统的经济调度问题。仿真算例表明所建模型实现了多能源联网系统的互补运行,提升了火电机组的深度调峰能力,降低新能源弃电率和系统运行成本。

基于SOEC-OEC双工艺耦合的火电机组全容量长寿命调峰技术研究

现有火电灵活性改造方案难以消除频繁快速变负荷所带来的热力系统寿命折损与机组安全运行风险,为保障火电机组参与电网调峰的安全性、经济性与健康性,提出并构建了基于固体氧化物电解槽(SOEC)制氢技术与燃烧器局部富氧燃烧(OEC)技术相耦合的火电机组全容量长寿命调峰技术方案。以某超超临界1 000 MW二次再热机组为计算示例,对SOEC-OEC系统参与70%~100%的电网深度调峰进行了能效计算,并与常规碱性水电解制氢(ALK)系统进行了比较。计算结果表明,SOEC-OEC技术方案中抽汽电解制氢系统的能效高达49.86%,相比ALK系统提高约26.40%;富氧助燃系统最大可减少锅炉排烟量达23.7%,降低机组供电煤耗2.83 g/(kW·h),减少碳排放约2.82 t/h。此外,SOEC-OEC系统还可为机组带来超额的调峰补贴收益、氢气售卖收益、富氧节煤降碳收益以及设备延寿延保收益等,充分保障了火电调峰过程的经济高效、安全环保。

考虑高比例新能源消纳和火电机组调峰补偿的电力现货市场交易机制设计

针对新能源参与市场竞争和保障性消纳的矛盾,设计高比例新能源电力系统现货市场交易机制。首先,考虑到新能源的市场性消纳,对电力现货市场现有出清机制进行改进,在新能源和火电机组共同参与市场竞价的交易环境下建立出现弃风弃光时触发的发电替代模型,调动火电机组调峰能力,由新能源替代火电进行发电;其次,提出激励相容的结算机制,由新增消纳空间的新能源给予火电机组调峰补偿;最后,对不同调度场景进行仿真计算,结果表明所提机制能够在电力现货市场中实现调峰资源的供需匹配,促进新能源市场化消纳的同时补偿火电机组调峰费用,保证市场主体竞争公平、收益合理。

风光对水电调峰能力的影响初探:基于调峰幅度的角度

水风光互补是当前能源行业的研究潮流和方向,但风光出力的不稳定性和不可控性势必会影响水电站调峰能力的发挥。本研究旨在从调峰幅度角度为水电调峰能力评估及风光并入后对其的影响量化提供一种参考思路。首先提出了周期调节幅度、最大时段调节幅度、时段累计调节幅度以及水电站综合调峰幅度4个指标来评估水电站调峰状况,其次构建了水风光互补运行模型,并通过有无风光工况下4个指标的变化率来量化风光对水电调峰幅度的影响。通过某电网调峰梯级水电及其周边风光资源的多场景模拟计算得出,相比无风光场景,风电和光伏对单个指标的影响趋势不同,但无论是风电还是光伏,接入系统后,梯级水电都须发挥更大的综合调峰幅度才能调节消纳风光。结果表明,风光接入会给水电调峰带来不利影响,并且该影响具有累积效应。

某660 MW超临界锅炉深度调峰过程中分隔屏超温计算分析及改造方案研究

针对660 MW超临界褐煤锅炉分隔屏实炉数据分析发现,在175 MW负荷下,分隔屏的超温报警主要发生在屏3、屏4靠近炉内的管子。为解决分隔屏超温报警问题,采用流动网络系统法,结合质量、能量和动量守恒方程建立了分隔屏水动力计算数学模型,提出添加节流圈以及升级管子材料两种改造方案。对分隔屏进行回路及管段划分,采用175 MW负荷实炉测量数据反推计算得到炉内实际热偏差,对低、中、高负荷下添加节流圈与升级材料为Super304H的分隔屏出口汽温进行计算。计算结果表明:添加节流圈后分隔屏出口汽温偏差变小,且屏3、屏4出口汽温降低至材料TP347H报警温度以下;升级材料为Super304H后分隔屏出口汽温均小于材料的报警温度,分隔屏可安全运行。对添加节流圈以及将材料升级为Super304H两种方案进行壁温计算及强度校核,结果显示两种改造方案在各个负荷下,壁温以及强度均是安全的。为给锅炉运行留出更大的安全裕度,建议在分隔屏最后一片屏上添加节流圈的同时将材料由TP347H升级为Super304H。

火电耦合压缩空气储能技术研究进展及展望

火电耦合压缩空气储能技术作为一种提升传统火电机组灵活性和经济性的有效手段,近年来受到了广泛关注。该文从分析火电耦合压缩空气储能技术在提升电网调峰能力、促进可再生能源整合等方面的重要意义出发。首先,介绍典型耦合系统的结构组成与工作原理;然后,探讨耦合系统中关键设备的技术现状和面临的挑战;之后,梳理火-储耦合系统的集成方案设计、调峰运行优化以及经济性评估的研究现状;最后,针对当前技术难点,展望未来火电耦合压缩空气储能技术的研究方向与重点。该文旨在为今后相关领域研究提供参考与借鉴,推动其持续发展和创新。

太阳能辅助热电联产机组供热、发电及调峰性能分析

针对新能源存在不稳定性导致电网调峰问题日益严重的现状,结合较为成熟的光煤互补发电技术及多热源联合供热调峰系统,设计光煤混合供热发电系统,使热电联产机组具有一定的调峰能力。以某300 MW热电联产机组为研究对象,利用Ebsilon Professional软件搭建发电、供热可灵活调节的太阳能辅助热电联产系统,基于供热机组实际双机运行工况,在保证供热负荷前提下,分析太阳能辅助双机热电联产机组耦合方式,比较耦合前后双机调峰性能。结果表明:凝汽器出口与太阳能集热系统换热器间的管道上设置动态节流阀,改变太阳能集热系统辅助供热机组的运行模式,能够实现发电、供热、调峰一体系统的灵活运行;其中,以太阳能集热系统仅用于补充供暖的运行方式调峰能力最强,太阳能辅助单机供热前后调峰容量比值为0.76,太阳能辅助双机供热前后调峰容量比值为0.55,太阳能辅助承担最大供热负荷的1号机组在调峰容量及调峰补偿上效果最佳。

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。

考虑不确定性和定价补偿的源荷储联合调峰优化

针对大规模新能源并网带来的系统调峰和消纳难题,文中在分析源荷储调峰能力及其互补性的基础上,挖掘系统的调峰能力,促进新能源消纳。首先,考虑火电、独立储能和电动汽车的调峰定价及辅助服务补偿,分析调峰费用分摊与补偿机制,建立深度调峰模型;其次,考虑风光出力的不确定性与相关性,基于核密度估计和Frank Copula函数生成典型风光出力序列,并建立阶梯型需求响应模型以实现响应量的分级补偿,提高需求侧用户的响应积极性;最后,以总运行成本最小为目标,构建考虑不确定性和定价补偿的源荷储联合调峰优化模型,并以改进的IEEE 30节点系统为例进行分析。结果表明,所提模型能够挖掘源荷储三侧资源的灵活调峰潜力,提升系统调峰能力和新能源消纳水平。