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.

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.

A method for correcting characteristic X-ray net peak count from drifted shadow peak

To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse sequences.The trained model was validated using simulated pulses.The relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM models.As a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping algorithm.For slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping algorithm.The model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,respectively.Ten measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.

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.

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。

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。

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。

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。

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。

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。

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。

用于工业供汽的大型储蒸汽罐技术及结构分析

为满足供汽单位在常规火电快速变负荷及深度调峰时工业供汽的需求,针对直接存储蒸汽的设备进行研究,以保证供汽的快速性、稳定性及连续性。以某项目实际需求为依据,进行大型储蒸汽罐的方案设计,就无法采用常规计算且易发生失效主要结构,即罐壁与罐底连接结构进行数值模拟计算,通过计算结果分析,验证了该设备结构的可靠性,以及大型储蒸汽罐用于工业供汽方案的可行性。

基于混沌多目标粒子群算法的综合能源调度

目的针对当前综合能源系统中资源协同优化效率不足、微网运行经济性和环保性差的问题,提出了一种计及风电储能及不稳定因素的微网优化调度方法。方法该方法在微网负荷侧需求响应对新能源消纳影响的基础上,以消纳新能源和削峰填谷为目的,提出了优化负荷曲线的方案;然后,考虑微网调度侧风电出力的不稳定性以及微网内部设备的耦合,进行优化调度以降低微网运行成本、减少环境惩罚费用并提高风电消纳平稳性;最后,采用混沌多目标粒子群算法对优化问题进行求解,并在风电不稳定度占比0%、5%、10%和15%时进行了算例仿真分析。结果当风电不稳定度为10%和加入风电储能,系统运行成本和环境治理费用最少,比方案1和无风电储能少6919.4元,风电平稳量也提高38 kWh。在电热冷网中,负荷侧加入需求响应后,系统得到稳定运行和能源合理利用,可以很好地满足负荷侧用能需求。从算法对比中,混沌多目标粒子群算法加入自适应权重和变异率后,具有较强的全局搜索能力和更好的准确性。结论该方法通过合理设置风电不稳定度能够有效降低运行成本和环境惩罚费用,提高风电稳定性,其次,负荷侧的需求响应可以一定程度地削峰填谷和消纳新能源。

光储式5G通信基站集群灵活性聚合与协同调度策略

5G通信基站通常配备光储,数量庞大、功耗可调,是一种优质的电力灵活性调节资源。提出了多类型光储式5G基站集群灵活性资源聚合方法以及参与电网调峰的协同调度策略。首先,分析休眠机制下多类型基站功耗可调特性与计及基站备用电量的储能调节能力。基于极限场景思想,构建了光储式5G基站的灵活性空间量化模型。在此基础上,利用闵可夫斯基和法刻画异构基站柔性资源的时空耦合能量轨迹,得到海量基站集群的灵活性资源聚合可调域。其次,建立了基站集群聚合资源参与电能量市场和辅助服务市场的协同调度优化模型,提出了基于交替方向乘子法(alternating direction method of multipliers, ADMM)的分层分布式基站集群协同优化调度策略,将大规模基站集群调度问题降维分解为统一协同调峰功率响应、聚合功率自治调度和基站集群功率分配3个子问题进行求解。通过算例对比分析可知,所提策略可降低通信基站69.86%的用能成本,为提升通信资源利用率和电力系统灵活调节能力提供了有效手段。

600 MW机组燃煤锅炉30%深度调峰下现场试验研究

随着新能源发电机组装机容量不断增加,需要提高电网的新能源消纳能力,因此对火电机组的调峰要求越来越高。为了研究燃煤机组深度调峰的能力及影响,基于广东省内某600 MW机组燃煤锅炉,开展低负荷稳燃现场试验研究。试验结果表明:该锅炉可以在30%额定负荷(180 MW)条件下稳定运行,且整个过程中现有的各系统经过调控后基本能正常工作;在降负荷过程和低负荷稳定运行过程中,机组环保参数无超标,各污染物排放指标符合环保要求。研究结果可作为机组深度调峰依据,为燃煤锅炉低负荷运行控制提供参考。

乌东德水电站日调峰对下游裂腹鱼类栖息地的影响

日调峰是乌东德水电站日常运行的主要出力约束和调度目标。为了探讨电站调度引起的出库流量大幅涨落对下游生态环境的不利影响,采用河道内流量增量法,通过构建二维水动力模型,研究了乌东德水电站日调峰引起的下游河段水文水动力条件波动对裂腹鱼类繁殖期栖息地适宜性的影响,并分析了2023年电站基荷发电生态调度试验效果。结果表明,乌东德电站下泄流量低于1160 m^(3)/s,坝下江段裂腹鱼类潜在产卵场面积相对较大;流量高于2000 m^(3)/s,裂腹鱼类潜在的产卵场面积显著减少。电站日调峰结束时,出库流量降幅越大,裂腹鱼类潜在产卵场脱水面积及鱼卵脱水风险越大。乌东德电站基荷发电生态调度期间,坝下江段裂腹鱼类潜在产卵场的加权面积有所减少,但潜在产卵场和鱼卵早期的脱水风险显著降低。为提高繁殖期裂腹鱼类生境适宜性和持续性,有必要进一步减少电站日调峰频率,降低出库流量至适宜区间。

基于熔盐储热的燃煤机组调峰可行性分析

随着新能源发电装机量增加导致电网调峰压力增大,火电机组要承担更多调峰任务,但目前火电机组的灵活性和调峰能力都较差。针对某亚临界300 MW燃煤机组,进行熔盐储能改造,提出了6种储热策略和2种放热策略,分析了3种工况下储-放热过程对机组调峰能力及热力性能的影响,并用净现值法进行了技术经济分析。结果表明:抽再热蒸汽储热可行性高,调峰深度能到58.9%,但会增加煤耗;放热时,加热给水产生蒸汽做功的发电增量最大,能到额定发电量的11.3%,但对熔盐的温度水平要求高;高温熔盐代替低压加热器预热给水可行性高,但发电增量低;储-放热全过程中,循环电效率最高可达0.987;进行了储热改造经济性分析,计算得动态投资回收期为11.65年,净现值为4911.8万元,改造方案可行。

“光火储”一体化发电系统的季节适应性分析

为探索基于压缩空气储能的“光火储”一体化发电系统的季节适应性,选取一年内4个典型日分别代表春夏秋冬4个季节,研究季节特性对一体化发电系统5种运行模式的影响。结果表明:季节变化时,一体化发电系统性能变化较大,其中模式2、3性能受季节变化影响最大,模式4不受季节影响但性能较差,模式5季节适应性最好。此外,一体化发电系统可依据实际运行条件灵活选择运行模式,避免能源浪费。