基于能量平衡法的以热定电数学模型研究

为了缓解供热季节电网调峰压力,合理安排电量计划及最大限度吸纳风电,基于供热机组以热定电在线监测系统,提出了能量平衡法。通过进入汽轮机和排出汽轮机的能量进行平衡计算,确定不同采暖抽汽流量和工业抽汽流量下机组发电负荷的关系,可以准确计算出机组在不同的采暖抽汽流量和工业抽汽流量情况下最大可增出力和可减出力。     

基于年发电上网率的光伏发电项目并网经济性研究

在"自发自用、余电上网"的运营模式下,光伏发电项目的发电上网率是测算全寿命周期内上网发电量的基础,对准确评估经济性具有重要意义。为此,提出了建立发电上网率模型,以机组可输出功率、气候条件作为基础拟合机组典型日机组出力,运用最短距离聚类与关联度分析方法选取典型日负荷曲线。通过各季节典型日逐时光伏出力和用户负荷的动态对比,测算年发电上网率,填补了定量计算年上网发电量的空白。在此基础上,构建了光伏发电项目全寿命周期内的经济评价系统模型,并以光伏大棚项目为实际应用案例分析光伏发电项目的并网经济性。     

含风光电站的电力系统动态经济调度

随着电力系统中风电和光伏发电的接入比例不断增长,其输出功率的随机性给系统经济调度带来了不确定因素。通过将满足一定置信概率的风电、光伏发电的功率区间预测信息纳入发电计划中,同时引入了可中断负荷作为旋转备用,建立了基于功率区问预测的考虑机组组合的系统动态经济调度模型。求解模型时利用改进离散粒子群算法（discreteparticleswarlnoptimization,DPSO）来解决机组启停问题,采用连续粒子群算法来实现负荷的经济分配,并采用启发式调整规则,提高算法的效率和搜索性能。最后通过10机系统仿真算例验证了模型和算法的有效性。     

汽轮机变工况各级热力参数计算方法研究

针对汽轮机启停和变负荷运行过程中转子热应力计算所需的蒸汽热力参数很难准确确定的问题,对"低负荷应用比例法,高负荷应用变工况"的算法进行深入研究并对其完善。对该算法计算结果出现的参数波动问题进行修正处理,通过与回热系统计算的各级蒸汽参数误差分析比对,确定出最佳的变工况热力计算起点负荷和比例法计算的基准负荷。通过将修正算法下的计算结果与回热系统分析计算结果相比较,计算精度得到极大提高。提出的修正算法所需计算参数较少,在已知启动曲线的前提下,就可以得到各级特征面的热力参数,更加便于大型火电机组汽轮机变负荷下的热应力计算、寿命管理和运行经济性分析。     

基于安全低碳的发电权交易优化

为提高发电权交易系统的环保性和安全性,提出包含碳排放量和负荷裕度的发电权交易双目标优化模型,采用NBI法对模型进行求解,绘制Pareto曲线;计算碳排放量与负荷裕度的置换度指标,直观地反映两者之间的置换关系,为决策者提供判断依据,并利用IEEE30、IEEE118系统验证了模型的有效性及正确性。结果表明,该模型可为决策者确定系统的交易方案及机组的发电计划提供依据。     

含光热-光伏联合发电的电力系统随机生产模拟研究

针对光伏发电出力的随机性和时序性等不确定因素,提出了基于稀疏卷积递推间隔-频率曲线法的电力系统随机生产模拟方法。将间隔-频率曲线法和稀疏卷积递推法相结合,评估光伏发电接入对电力系统经济性和可靠性的影响。首先,利用间隔-频率曲线法对原始负荷和光伏出力的时变特性进行分析,并通过计算获得短时间内的净负荷曲线以及净负荷的累计概率、频率分布;然后,采用稀疏卷积递推法将净负荷曲线与发电机组的故障转移过程相结合,依次控制发电机组的运行,完成相应的卷积运算,并结合计算结果求得光热-光伏联合发电接入后电力系统的可靠性指标和生产成本;最后,基于EPRI 36发电系统的仿真结果,发现光热-光伏联合发电对电力系统有一定的容量可信度,因此可替代一定的常规机组,提高电力系统的可靠性和经济效益,这也验证了文章所提方法的正确性和有效性。     

求解计及失负荷概率约束机组组合问题的快速启发式算法

为了解决电力系统机组组合中的备用配置问题,提出了一种求解计及失负荷概率约束机组组合问题的快速启发式算法。该算法通过对计及备用约束机组组合问题与运行可靠性评估问题的迭代求解,将机组组合计划对应的失负荷概率指标控制在给定水平;通过对迭代中备用更新步长的直接估计,提高了备用更新效率,达到了减少迭代次数、提高整体计算速度的目的。相较于已有算法,该算法提高了计算的快速性及计算结果的鲁棒性。通过对单区域及多区域RTS-96系统的测试计算,验证了算法的有效性。     

基于ANN和遗传算法的联合循环电厂机组负荷经济分配

提出了适用于F级单轴联合循环机组负荷经济分配的数学模型.针对传统的热耗曲线难以真实反映联合循环机组热耗特性的问题,本文采用基于前馈型ANN的机组变工况模型来实现对机组热耗的精确计算.提出了一种改进型的遗传算法用于模型的求解.对一个含有3台(套)该类型机组发电厂的计算分析表明了该算法的可行性,其负荷分配结果发电成本更低,经济性更好.     

基于机组调峰能力的电力系统风电接纳能力研究

为准确评估区域电力系统风电接纳能力,针对目前风电规划问题,给出一种新的计算方法。以整个时间区间上系统接纳风电电量和系统火电机组发电电量为规划指标,改变了传统风电规划主要着眼于最严峻条件下系统最大风电接纳容量的规划方法。在实际计算过程中,将系统接纳风电电量计算分两步实现,简化了模型求解过程。通过遗传算法得到任意时间区间上的系统投运机组组合,从而分析系统机组调峰能力极限。此外,将系统负荷与系统机组出力视为能量供需双方,在每个时序点上达到供需平衡,结合风资源信息计算系统可接纳风电电量及火电机组发电电量。根据实际区域电网负荷数据以及发电机组数据,经仿真计算表明,该规划方法能准确评估区域电力系统风电接纳水平,为风电装机规划提供技术参考。     

基于混沌海鸥优化算法的含光伏发电系统负荷模型参数辨识

针对分布式光伏发电系统广泛接入配电网,导致电力系统潮流计算速度和精度较低的问题,提出一种基于混沌海鸥优化算法的含光伏发电系统负荷模型参数辨识模型。首先,在综合负荷模型的虚拟母线上接入等效光伏发电系统的负荷模型,从而建立配电网广义负荷模型;之后,提出一种将混沌优化与海鸥优化相结合的优化算法,基于该算法完成配电网的等值,并在此基础上进行含光伏发电的综合负荷模型参数辨识。最后,通过仿真表明该文提出的算法,相比于传统的粒子群算法和单一海鸥优化算法,在计算精度和收敛速度等方面具有优越性,并可应用于负荷模型的参数优化。     

基于深度学习的配变停电电量损失预测

停电电量损失预测可为电网调度及规划提供参考,有利于为用户提供可靠供电服务。针对当前配变停电过程中的电量损失问题,先基于模糊C均值聚类算法实现对配变负荷曲线的分类处理及精细化分析,挖掘配变负荷数据规律;在此基础上,运用皮尔逊相关系数算法提取选择输入特征,构建基于门控循环单元神经网络的预测模型,从而得到停电时间负荷值,进而分析预测负荷曲线得到损失电量;最后,基于停电管理工作分析,实现基于粒子群优化的台区用电行为停电优化问题求解。算例测试验证了所提方法的正确性和有效性。     

Optimal dynamic emergency reserve activation using spinning,hydro and demand-side reserves

This paper proposes an optimal dynamic reserve activation plan after the occurrence of an emergency situation (generator/transmission line outage, load increase or both). An optimal plan is developed to handle the emergency, using the coordinated action of fast and slow reserves, for secure operation with minimum overall cost. It considers the reserves supplied by the conventional thermal generators (spinning reserves), hydro power units and load demands (demand-side reserves). The optimal backing down of costly/fast reserves and bringing up of slow reserves in each sub-interval in an integrated manner is proposed. The proposed reserve activation approaches are solved using the genetic algorithm, and some of the simulation results are also compared using the Matlab optimization toolbox and the general algebraic modeling system (GAMS) software. The simulation studies are performed on the IEEE 30, 57 and 300 bus test systems. These results demonstrate the advantage of the proposed integrated/dynamic reserve activation plan over the conventional/sequential approach.     

储能对可再生能源并网发电的支撑作用

可再生能源发电并网对于电力系统的节能减排具有关键作用,选择合理的储能容量,并分析储能对可再生能源并网发电的支撑作用具有重要意义。从节能减排和供电可靠性角度建立具有储能装置的单母线电力系统和剩余电力系统的随机规划模型,并通过蒙特卡罗模拟算法对国外某地区的风力发电和储能联合运行系统进行模拟分析。结果表明,适度规模的储能容量不仅能有效解决可再生能源发电的功率波动问题、减小系统停电损失概率,而且能够降低系统的环境成本,但其减少程度与储能的全过程效率有关。     

Analysis method for non-schedulable generation in electric systems

The usefulness of alternative electric energy sources cannot be assessed without considering their operation within the context of the connected electric system. The value of wind and solar power is strongly dependent on the relationship of the times at which power is available to the times of peak load. A load-duration methodology is developed to assess the economic impact of alternative energy sources on the operation and expansion of the entire system. This methodology treats unschedulable generation as a negative load and obtains a modified load duration curve. On the other hand, schedulable or interruptible load can be treated as negative generation in meeting the load. Where the unit size for dispatching is a small fraction of the system, a continuous-function representation is used. Random factors in availability and load are treated statistically. Economic impacts are evaluated as perturbations on present operations.

Differences are found between intermittent energy sources which are correlated with the load, and sources which are random; and between short-term and long-term impacts.

The results indicate that the benefits per unit of the individual alternative-energy devices are greatest for small penetrations. However, at larger penetrations some additional benefits may be derived from the interaction of complementary technologies.

The load-duration methodology is shown to identify the same trends as hour-by-hour dispatch simulation. It is useful for screening potential applications of new technologies to utility systems, or to estimate future cost-of-service rates to co-generators and small power producers.     

A new method for stochastic production simulation in generationsystem with multiple hydro units

A novel method is presented for calculating loss of load probability, expected energy generation and production cost for units in a generating system with multiple hydro units. The method uses the equivalent load duration curve (ELDC) obtained by convolving the distributions of the original load and the forced outage power loss of all generators. Hydro units are scheduled on the ELDC according to their assigned energy and available capacity. Then the deconvolution procedure is performed to obtain a load duration curve for an equivalent system without hydro units. The expected energy of the thermal units is achieved by convolving the generating units in an economic merit order of loading     

基于蚁群算法的水电站AGC机组组合与负荷分配优化

针对水电站厂级自动发电控制(AGC)中的经济运行问题,采用蚁群算法求解,所建模型综合考虑了功率平衡、机组出力约束、旋转备用和机组开停机时间约束等多重安全运行限制条件,并根据等微增率原则导出经济负荷分配公式嵌入并行随机搜索模式中,减少负荷分配时的迭代次数。算例中的机组耗量特性曲线根据实际数据采用二次曲线拟合。仿真结果表明,该方法既带有智能算法搜索的随机性又具有传统算法的稳定性,优化速度快且精度较高。     

A stochastic method for battery sizing with uninterruptible-power and demand shift capabilities in PV (photovoltaic) systems

This paper presents a stochastic simulation using Monte Carlo technique to size a battery to meet dual objectives of demand shift at peak electricity cost times and outage protection in BIPV (building integrated photovoltaic) systems. Both functions require battery storage and the sizing of battery using numerical optimization is popularly used. However, the weather conditions, outage events and demand peaks are not deterministic in nature. Therefore, the sizing of battery storage capacity should also be based on a probabilistic approach. The Monte Carlo simulation is a rigorous method to sizing BIPV system as it takes into account a real building load profiles, the weather information and the local historical outage distribution. The simulation is split into seasonal basis for the analysis of demand shifting and outage events in order to match the seasonal weather conditions and load profiles. Five configurations of PV (photovoltaic) are assessed that cover different areas and orientations. The simulation output includes the predicted PV energy yield, the amount of energy required for demand management and outage event. Therefore, consumers can base sizing decisions on the historical data and local risk of outage statistics and the success rate of meeting the demand shift required. Finally, the economic evaluations together with the sensitivity analysis and the assessment of customers’ outage cost are discussed.     

Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs

Wind energy is currently one of the types of renewable energy with a large generation capacity. However, since the operation of wind power generation is challenging due to its intermittent characteristics, forecasting wind power generation efficiently is essential for economic operation. This paper proposes a new method of wind power and speed forecasting using a multi-layer feed-forward neural network (MFNN) to develop forecasting in time-scales that can vary from a few minutes to an hour. Inputs for the MFNN are modeled by fuzzy numbers because the measurement facilities provide maximum, average and minimum values. Then simultaneous perturbation stochastic approximation (SPSA) algorithm is employed to train the MFNN. Real wind power generation and wind speed data measured at a wind farm are used for simulation. Comparative studies between the proposed method and traditional methods are shown.     

风力-生物质能联合发电系统在微电网中的扩展规划

提出了基于改进遗传算法的风力-生物质能联合发电系统在微电网中的扩展规划模型,在寻求总成本最小的扩展方案的同时,使微电网可靠性更高,而且满足系统规划和运行的非线性约束条件。在规划总成本中,不但包含机组投资的建设费用和运行费用,而且把电力供给不足所导致的需求侧停电损失成本也考虑在内。在模型中采用了适应性权重和方法构造双目标函数,很好地协调了成本和可靠性的问题。计算表明,文章所提出的模型和算法是可行、有效的,能对智能电网和分布式发电的规划和设计提供一定的理论依据和技术支持。     

Modelling of hybrid energy system—Part III: Case study with simulation results

This paper presents the results of the application of model (developed in part I) and simulation algorithm (developed in part II) for determining the techno-economics of battery storage type hybrid energy system intended to supply the load of a rural remote area having a cluster of nine villages (grid isolated). The hour-by-hour simulation model is intended to simulate a typical one month period of system operation. For simulation purpose, hourly solar insolation data and load data have been generated and used as an input data. Demand side management (DSM) is used in this study to smooth out the daily peaks and fill valleys in the load curve to make the most efficient use of energy sources. The economic analysis has resulted in the calculation of optimized hourly, daily, and monthly system unit cost of proposed hybrid energy system. The obtained results represent also a helpful reference for energy planners in Uttarakhand state and justify the consideration of hybrid energy systems more seriously.