基于降雨预报信息的梯级水电站不确定优化调度研究I：聚合分解降维

基于降雨预报信息的水库群预报优化调度有利于提高水库群水电站发电效益。本文首先采用聚合分解思想将梯级水库群来水量和库容聚合等效为单库,从而简化水库群径流过程的描述和降低高维计算空间,使随机动态规划模型（SDP）在梯级水库群的应用中可以考虑更多的信息来提高模型效率;然后在径流预报中考虑美国全球预报系统（GFS）发布的未来10d降雨预报信息,来提高中期径流预报精度;最后在考虑径流预报不确定性的基础上建立了聚合分解贝叶斯随机动态规划模型（AD-BSDP）。同时与传统调度图、聚合来水量的随机动态规划模型（AF-SDP）和聚合来水量、库容的聚合分解随机动态规划模型（AD-SDP）进行对比分析,其结果表明,考虑预报信息不确定性的AD-BSDP模型比其他模型具有更高的效率和稳定性。     

基于降雨预报信息的梯级水电站不确定优化调度研究Ⅱ：耦合短、中期预报信息

基于聚合分解思想将梯级水库群聚合为“虚拟单库”,降低了随机动态规划（SDP）模型的计算量,从而使SDP模型可以考虑更多预报信息。为了更近一步充分利用预报信息和降低预报信息不确定性对调度的影响,将降雨预报和径流预报的不确定性考虑为随预见期延长而不断增加的过程。首先将浑江流域的10d预报径流划分为前5d和后5d,并将前5d预报径流视为较准确的部分,而后5d预报径流视为不确定性较大部分;然后建立短、中期径流预报信息相套接的分段聚合分解贝叶斯随机动态规划（Two-Step-BSDP,TS-BSDP）,为浑江梯级水库群制定前5d、后5d和10d的预报调度图;最后以5d为调度时段分别决策未来5d发电和10d发电计划。模拟调度结果表明,该模型中决策长度为5d的滚动决策具有更好的效果,该决策方式充分地利用了预报信息并有效地提高了发电效益。     

基于短、中期径流预报信息套接的水库群发电优化调度模型研究

在水库发电调度决策中，考虑径流预报信息是提高水库群发电效益减少弃水的有效措施。目前通过数值气象预报信息已可获得流域未来10天预报降雨量，但降雨预报信息和径流预报模型随预见期延长不确定性增加，即直接采用10天径流预报信息将会给水库发电调度决策带来较大的不确定性，而只采用预报较准确的短期预报信息又会缩短调度的预见期。因此本文首先以美国全球预报系统（GFS）发布的浑江流域未来10天预报降雨信息来预测未来前5天和后5天径流量，并将前5天预报径流视为较准确的部分，而后5天预报径流视为不确定性较大部分，然后采用聚合分解贝叶斯随机动态规划模型为浑江梯级水库群制定前5天和10天的预报调度图，最后以5天为调度时段向后滚动决策未来5天发电和10天发电计划。模拟调度结果表明该模型充分利用了预报信息，并有效提高了发电效益。     

长江上游水库群调蓄对下游电站发电能力影响分析

随着长江上游水利工程的陆续兴建投运，干流年来水量和径流年内分配过程发生显著变化，势必对下游水库群调度运行造成较大影响。以长江上游干支流水库群为研究对象，建立上游干支流水库群运行模拟模型以及金沙江下游-三峡梯级水库群联合发电优化调度模型，重点分析中长期尺度上游干支流水库群调蓄对金沙江下游-三峡梯级电站发电能力的影响。研究结果表明：上游干支流水库群调蓄极大地改变了溪洛渡及三峡水库来水年内分配，提高了枯期来水占比，进一步提高了梯级电站发电能力；丰水年条件下，梯级总发电量增加了1.22%,总弃水量减少了14.11%;枯水年条件下，梯级总发电量增加了3.16%,总弃水量减少了15.67%。     

径流贝叶斯概率预报在水库发电优化调度中的应用

为了充分考虑降雨、径流预报的不确定性和降低水库发电调度模型的复杂性,采用贝叶斯概率水文预报系统(BFS)耦合降雨预报的不确定性和径流预报模型本身的不确定性来定量描述径流预报的不确定性,发布径流确定性预报、概率预报和概率预报期望值;结合随机动态规划(SDP)模型和贝叶斯随机动态规划(BSDP)模型来制定发电调度图;以浑江桓仁水库流域为背景,采用美国国家天气局的全球预报系统(GFS)发布的10d降雨预报信息作为预报模型输入,模拟桓仁水库的发电调度过程。模拟结果表明基于径流贝叶斯概率预报的水库发电调度能有效提高水库的发电效益和保证率。     

水电站水库群防洪补偿联合调度模型研究及应用

针对具有下游防洪任务的水电站水库群，提出基于预报及库容补偿的水库群防洪补偿联合调度逐次渐进协调模型，推求水库汛期防洪库容动态控制方案。该模型运用了大系统分解协调理论及贝尔曼的逐步逼近思想，以各水库为独立的子系统建立3层递阶结构，针对库间水力、电力联系及防洪库容限制等复杂的约束条件进行不同层次的协调。模型经过多次迭代计算，得到最佳的水库群防洪库容协调方案。该模型应用于清江流域梯级水库，计算结果表明，在不降低水库及梯级原有的防洪标准前提下，能有效利用上游水布垭水库的防洪库容，分担隔河岩水库部分防洪任务，并显著提高梯级水库发电量。     

梯级水库群联合防洪预报调度方式风险分析

目前,梯级水库群防洪调度风险的研究存在仅考虑单一风险因子或缺乏考虑防护点风险等问题。针对上述问题,在识别梯级水库群联合预报调度方式风险源和风险的基础上,建立了考虑多种不确定性影响下的水库群自身及防护点的综合风险分析模型,采用蒙特卡洛随机模拟法由上游至下游计算其风险率。昭平台-白龟山梯级水库群的实例表明,联合预报调度方式的实施不会增加额外风险。     

一种考虑径流预报及其不确定性的水库优化调度模型

鉴于纯随机径流描述或确定性径流预报的水库(群)优化调度模型,未考虑径流预报及其不确定性,导致优化计算结果与水库实际运行情况存在较大差异.本文提出了一种利用后验的径流状态转移概率和径流预报的可预测性概率来描述径流预报及其不确定性的优化调度模型.依据二滩水电站径流及其预报的实际状况,考虑不同预见期的径流预报信息,建立了考虑...     

基于突变理论和相对风险的水库群联合调度风险补偿

梯级水库群联合调度风险补偿是均衡各水库风险水平、保障效益客观合理分配的重要措施。首先，引入相对风险模型，建立“风险源-风险事件-风险受体-评价终点”概念模型，分析梯级水库群联合调度中各因子的相互作用，进而考虑由水库建设规模和运行方式差异导致的不同风险承担水平差距；其次，借助突变理论多准则评价方法，客观推求概念模型各层的相对风险，量化各水库应承担的风险水平；最后，基于耦合相对风险模型和突变多准则评价的风险补偿模型，对补偿效益分摊方案进行相对风险折减，确定梯级水库群联合调度风险补偿方案。将所提风险补偿方法运用于溪洛渡、向家坝、三峡水库的联合调度，并以补偿效益分摊方案为基准进行对比分析。结果表明：（1）风险补偿方法克服了相对风险模型中系数量化的主观性，有效反映了梯级水库群联合调度效益与风险的互馈与均衡关系；（2）风险补偿方案与水库群实际运行方式相符；（3）风险补偿方法实现了多方合作共赢，有利于提高水库参与联合调度的积极性。通过探讨溪洛渡、向家坝和三峡水库联合调度风险补偿方案，可为梯级水库群联合调度风险分析、效益分配等提供参考。     

基于余留期效益函数的水光互补随机优化调度方法

针对多能互补模式下水库调度面临径流、新能源出力等多重不确定性叠加影响，以及如何协调近期效益和远期效益的最优余留库容等关键问题，提出了一种基于余留期效益函数的水光互补随机优化调度方法。结合数学期望模型提出了水光互补调度两阶段随机优化决策框架，基于水库调度的周期性马尔科夫特性，采用逐步迭代方法对多能互补系统的余留期效益函数进行近似逼近，以实现有限预报信息条件下水库长期余留库容动态决策。以锦屏一级水光互补系统为实例进行验证，结果表明：余留期效益函数受光伏出力影响较小，而与余留库容、径流呈正相关有关系；随着调度时段从枯期进入汛期，余留期效益函数逐渐从线性曲面变为凸曲面；相较于常规调度方法，提出的调度方法多年平均发电量在率定期、检验期分别增加了2.70亿、2.51亿kW·h,在径流、光伏出力预报信息相对较小的条件下，能有效指导水光互补系统的长期调度运行。     

A Bayesian Stochastic Optimization Model for a Multi-Reservoir Hydropower System

This paper presents the development of an operating policy model for a multi-reservoir system for hydropower generation by addressing forecast uncertainty along with inflow uncertainty. The stochastic optimization tool adopted is the Bayesian Stochastic Dynamic Programming (BSDP), which incorporates a Bayesian approach within the classical Stochastic Dynamic Programming (SDP) formulation. The BSDP model developed in this study considers, the storages of individual reservoirs at the beginning of period t, aggregate inflow to the system during period t and forecast for aggregate inflow to the system for the next time period t + 1, as state variables. The randomness of the inflow is addressed through a posterior flow transition probability, and the uncertainty in flow forecasts is addressed through both the posterior flow transition probability and the predictive probability of forecasts. The system performance measure used in the BSDP model is the square of the deviation of the total power generated from the total firm power committed and the objective function is to minimize the expected value of the system performance measure. The model application is demonstrated through a case study of the Kalinadi Hydroelectric Project (KHEP) Stage I, in Karnataka state, India.     

An Optimal Operation Model for Hydropower Stations Considering Inflow Forecasts with Different Lead-Times

To make full use of inflow forecasts with different lead times, a new reservoir operation model that considers the long-, medium- and short-term inflow forecasts (LMS-BSDP) for the real-time operation of hydropower stations is presented in this paper. First, a hybrid model, including a multiple linear regression model and the Xinanjiang model, is developed to obtain the 10-day inflow forecasts, and ANN models with the circulation indexes as inputs are developed to obtain the seasonal inflow forecasts. Then, the 10-day inflow forecast is divided into two segments, the first 5 days and the second 5 days, and the seasonal inflow forecast is deemed as the long-term forecast. Next, the three inflow forecasts are coupled using the Bayesian theory to develop LMS-BSDP model and the operation policies are obtained. Finally, the decision processes for the first 5 days and the entire 10 days are made according to their operation policies and the three inflow forecasts, respectively. The newly developed model is tested with the Huanren hydropower station located in China and compared with three other stochastic dynamic programming models. The simulation results demonstrate that LMS-BSDP performs best with higher power generation due to its employment of the long-term runoff forecast. The novelties of the present study lies in that it develops a new reservoir operation model that can use the long-, medium- and short-term inflow forecasts, which is a further study about the combined use of the inflow forecasts with different lead times based on the existed achievements.

     

Medium-Term Hydro Generation Scheduling (MTHGS) with Chance Constrained Model (CCM) and Dynamic Control Model (DCM)

Medium-Term Hydro Generation Scheduling (MTHGS) plays an important role in the operation of hydropower systems. In the first place, this paper presents a Chance Constrained Model for solving the optimal MTHGS problem. The model recognizes the impact of inflow uncertainty and the constraints involving hydrologic parameters subjected to uncertainty are described as probabilistic statements. It aims at providing a more practical technique compared to the traditional deterministic approaches used for MTHGS. The stochastic inflow is expressed as a simple discrete-time Markov chain and Stochastic Dynamic Programming is adopted to solve the model. Then in order to use the information of long-term inflow forecast to improve dispatching decisions, a Dynamic Control Model is developed. Short-term forecast results of the current period and long-term forecast results of the remaining period are treated as inputs of the model. Finally, the two methods are applied to MTHGS of Xiluodu hydro plant in China. The results are compared to those obtained from Deterministic Dynamic Programming with hindsight and advantages and disadvantages of the two methods are analyzed.     

Bayesian Stochastic Dynamic Programming for Hydropower Generation Operation Based on Copula Functions

Bayesian stochastic dynamic programming (BSDP) has been widely used in hydropower generation operation, as natural inflow and forecast uncertainties can be easily determined by transition probabilities. In this study, we propose a theoretical estimation method (TEM) based on copula functions to calculate the transition probability under conditions of limited historical inflow samples. The explicit expression of the conditional probability is derived using copula functions and then used to calculate prior and likelihood probabilities, and the prior probability can be revised to the posterior probability once new forecast information is available by Bayesian formulation. The performance of BSDP models in seven forecast scenarios and two extreme conditions considering no or perfect forecast information is evaluated and compared. The case study in the Ertan hydropower station in China shows that (1) TEM can avoid the shortcomings of empirical estimation method (EMM) in calculating the transition probability, so that the prior and likelihood probability matrices can be distributed more uniformly with less zeros, and the problem that the posterior probability cannot be calculated can be avoided; (2) there is a positive correlation between operating benefit and forecast accuracy; and (3) the operating policy considering reliable forecast information can improve hydropower generation. However, an incorrect decision may be made in the case of low forecast accuracy.

     

水电站长中短期嵌套预报调度耦合实时来水系统动力学建模方法研究及应用

水电站优化调度是水电能源系统优化运行与管理研究领域的关键科学问题,已有水电站优化调度研究较少考虑不同尺度调度模型间的衔接,长、中、短期优化调度相互孤立,彼此之间缺乏耦合机制,在指导水电站实际运行中存在不足。为此,研究建立了水电站长中短期嵌套预报调度模型,并引入系统动力学反馈机制,提出了一种水电站长中短期嵌套耦合实时来水系统动力学预报调度模型。长、中、短期调度模型分层嵌套,中长期预报调度结果作为短期调度的"期望指导过程线",根据水电站预报来水和面临日实时来水存在误差所导致的水位偏差进行实时反馈、动态修正调度决策,从而指导水电站做出更为合理的调度决策。为检验本文模型方法的有效性,研究选取了三峡水电站作为研究对象进行实例验证。实验结果表明:研究所提出的模型方法指导水电站运行相比随机动态规划和常规调度图方法全年发电量提升效果明显,在2018年来水条件下模拟调度结果发电量分别提高了2.73%和2.31%,显著提高了来水不确定性条件下水电站运行发电效益,为解决水电站随机来水条件下长中短期嵌套预报调度实际工程技术难题提供了一种可行的实践理论方法工具,具有十分重要的理论研究意义和工程应用价值。     

A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based on stochastic modeling or artificial intelligence (AI) techniques. In this article, an adaptive neuro-fuzzy inference system (ANFIS) model is proposed to forecast the inflow for the Nile River at Aswan High Dam (AHD) on monthly basis. A major advantage of the fuzzy system is its ability to deal with imprecision and vagueness in inflow database. The ANFIS model divides the input space into fuzzy sub-spaces and maps the output using a set of linear functions. A historical database of monthly inflows at AHD recorded over the past 130 years is used to train the ANFIS model and test its performance. The performance of the ANFIS model is compared to a recently developed artificial neural networks (ANN) model. The results show that the ANFIS model was capable of providing higher inflow forecasting accuracy specially at extreme inflow events compared with that of the ANN model. It is concluded that the ANFIS model can be quite beneficial in water management of Lake Nasser reservoir at AHD.     

Water Resources Allocation in Transboundary River Basins Based on a Game Model Considering Inflow Forecasting Errors

Dynamic transboundary water resources allocation based on inflow prediction results is an important task for water resources management in river basins. This paper takes the watershed management agency as the leader and the associated area as the follower, and proposes a two-level asymmetric Nash-Harsanyi Leader-Follower game model considering inflow forecasting errors. In the proposed model, the Monte Carlo method is used to analyze the uncertainty of various stakeholder allocation results and the response regularity to the total water resource uncertainty. The quantitative relationship between the allocation results of stakeholders and the mean and standard deviation of total water resource uncertainty is subsequently established. The Huaihe River basin in China is selected as a case study. The results show the following: (1) the water allocated to the watershed management agency and three provinces has a normal distribution when the inflow forecasting error obeys the normal distribution; (2) the sum of the mean of the water allocated to stakeholders equals the mean of the forecast water resource and the sum of the standard deviations of the water allocated to stakeholders equals the standard deviation of the forecast water resource; (3) the mean and standard deviation of the allocation results have a good linear relationship with the mean and standard deviation of forecast water resource; (4) the distribution parameters of the stakeholder allocation results can be directly derived from the distribution parameters of the forecast information, thus aiding the stakeholders in making decisions and improving the practical value of the method.

     

基于贝叶斯理论的水文多模型预报

水文预报对于防洪、抗旱以及水资源调度等具有重要意义。水文预报通常依靠水文模型来完成,由于受到不同流域特点、产汇流机制等的限制,每个水文模型都具有各自的特点及适用区域。单一模型具有非常大的水文预报不确定性,为了解决单一模型局限性的问题,多模型水文预报常作为降低水文预报不确定性有效方法之一。选用三种常见的水文模型:时变增益水文模型、新安江模型和萨克拉门托模型,在珠江飞来峡流域进行分布式建模,采用相同的输入与初始场,三个模型独立进行模拟,然后对比三个模型的结果,并进行贝叶斯多模型加权平均和简单平均得到多模型平均结果,研究结果表明,贝叶斯模型处理后的结果要比单个模型模拟结果和简单平均处理后的结果准确率高。     

云南省汛期月度电量交易计划分解方法

针对日负荷和来水预测不确定性带来的中长期电量交易计划分解难以达到预控的进度目标,提出了汛期月度电量交易计划分解方法。利用日尺度负荷和径流预测结果,动态更新当日至月末的负荷和入库流量预测信息,采用当日的月累实发电量完成度与系统计划完成度偏差最大值最小的目标函数,生成参与发电计划调整的电站集合,滚动修正后续日发电计划。以云南省澜沧江、金沙江、怒江、红河以及伊洛瓦底江等干流流域约56座省调平衡水电站为研究对象,实例结果表明：该方法可满足当前云南电力交易中心的中长期交易电量分解到日的需求,有效解决了目前中期电量交易计划分解与梯级上下游水量平衡衔接不够紧密的实际难题。