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1.
根据马尔柯夫决策规划原理及随机动态规划方法,对单库建立优化调度数学模型.通过模型上机计算,可获得水库优化调度方案.根据优化方案,结合小水电特点,编制方案的实施措施.  相似文献   

2.
根据马尔柯夫决策规划原理及随机动态规划方法,对单库建立优化调度数学模型,通过模型上机计算,可获得水库优化调度方案。根据优化方案,结合小水电特点,编制方案的实施措施。  相似文献   

3.
针对随机动态规划在解决多个水库联合优化调度时存在“维数灾”问题,尝试基于模糊集理论来解决该优化调度问题。以4个串联供水水库系统为例,目标为各供水片区最小的缺水率最大,将水库的入流过程视为模糊集,而需水过程视为确定性的,建立了模糊规划模型,并引入可靠度和满意度对优化调度结果进行评价。实例分析表明,该模型既可以刻画入流的不确定性,又可以简化问题,具有一定的实用性。  相似文献   

4.
多维随机动态规划的参数迭代法及在库群调度中的应用   总被引:1,自引:0,他引:1  
本文提出一种基于对余留期效益函数进行函数逼近的参数迭代法,使内存需要大大节省,计算机时也显著缩短,是克服随机动态规划“维数灾”的一种有效方法。文中列举了该法求解一个两库电力系统优化调度问题的实际算例,所得策略的效益还略优于常规的离散动态规划方法,结果令人满意。  相似文献   

5.
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。  相似文献   

6.
根据马尔柯夫决策规划理论建立的水电站群长期优化调度模型,依照逐次渐近法和离散微分动态规划的基本思想,本文构造了一种年内逆推动态规划、年间逐次逼近法、结合时段内廊道法寻优的改进算法、使运算时间大大缩短,并且易于在微机上实现,利于推广和应用。实例计算证明了该算法的有效性、科学性。  相似文献   

7.
双状态动态规划算法(BSDP)及其在水库群补偿调节中的应用   总被引:1,自引:0,他引:1  
应用动态规划原理(DP)求解水库群优化调度是一种有效的计算方法,但是随着库群的增多,存在着众所周知的“维数灾”问题。本文在总结以往方法的基础上,提出了一种新的计算方法——双状态动态规划法,有效地解决了十个以下水库群优化调度的“维数灾”问题,且计算简便,收敛速度快。通过华中地区8个库群联调,获得了令人满意的效果。  相似文献   

8.
粒子群算法在水电站日优化调度中的应用   总被引:16,自引:10,他引:6  
针对传统的动态规划方法求解水库优化调度问题存在的“维数灾”问题,给出一种全局随机优化算法[1]——粒子群优化算法并应用于水库日优化调度问题中。相对于动态规划,该算法原理简单,易编程,占用计算机内存少,能以较快的速度收敛到全局最优解,从而为分时电价环境下的水电站日优化调度问题提供了一种有效的解决办法。  相似文献   

9.
针对传统的动态规划方法求解水库优化调度阋题存在的"维数灾"问题,提出一种全局随机优化算法[1]--SAPSO算法及其在水库日优化调度问题中的应用.相对于动态规划,该算法原理简单,易编程实现,占用计算机内存少,能以较快的速度收敛到全局最优解,从而为分时电价环境下的水电站日优化调度问题提供了一种有效的解决办法.  相似文献   

10.
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10天降雨预报信息,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)进行了对比分析来验证模型的有效性,分析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。  相似文献   

11.
Limited by inflow forecasting methods, the forecasting results are so unreliable that we have to take their uncertainty and risk into account when incorporating stochastic inflow into reservoir operation. Especially in the electricity market, punishment often happens when the hydropower station does not perform as planned. Therefore, focusing on the risk of power generation, a benefit and risk balance optimization model (BRM) which takes stochastic inflow as the major risk factor is proposed for stochastic hydropower scheduling. The mean-variance theory is firstly introduced into the optimal dispatching of hydropower station, and a variational risk coefficient is employed to give service to managers’ subjective preferences. Then, the multi-period stochastic inflow is simulated by multi-layer scenario tree. Moreover, a specific scenario reduction and reconstruction method is put forward to reduce branches and computing time accordingly. Finally, the proposed model is applied to the Three Gorges Reservoir (TGR) in China for constructing a weekly generation scheduling in falling stage. Compared to deterministic dynamic programming (DDP) and stochastic dynamic programming (SDP), BRM achieves more satisfactory performance. Moreover, the tradeoffs for risk-averse decision makers are discussed, and an efficient curve about benefit and risk is formed to help make decision.  相似文献   

12.

The third Huaiyin pumping station in the South-to-North Water Diversion Project aims to solve the problem of lack of water. In order to save on electricity while satisfying the required flow demand, the operation optimization problem of the third Huaiyin pumping station is investigated, with a mathematical model set up to simulate the optimal daily operation, in which the pump units can have variable speed. After analyzing the characteristics of the mathematical model, an improved dynamic programming algorithm is presented to decrease the dimensions of the operation optimization problem and save electricity cost, which enables us to perform a practical engineering application. After discretization of the optimization problem has been achieved, the number of operational schedule sequences could be reduced and optimal scheduling could be achieved to save electricity costs by power constraint, classified enumeration constraint, feasible combination constraint and flow demand constraint. Through research and analysis of the working of the third Huaiyin pumping station, optimal operational scheduling of multiple pump units with variable speed operation using variable frequency drive (VFD) can lessen the electricity tariff significantly compared with dynamic programming with the successive approximation method and decomposition/aggregation-dynamic programming method. The operational electricity tariff is reduced by 7.71% by the improved dynamic programming algorithm in comparison with the benchmark scheduling. Simulation results demonstrate that the cost efficiency comes from variable speed operation with VFD and flow demand transfer from the time periods when a high electricity tariff applies to time periods of a low electricity tariff based on the time-of-use electricity tariff.

  相似文献   

13.
为解决水库优化调度过程中时间尺度不同而导致的水库综合效益不高的问题,以防洪与兴利为目标,将水库长期优化调度与中长期优化调度进行嵌套,建立水库多目标优化调度嵌套模型。其中,多目标问题通过约束法将防洪目标转换为硬性约束后,再转化为求解发电量最大值的单目标问题。算法方面,综合对比后确定长期优化调度采用动态规划算法求解、中长期优化调度采用遗传算法求解。为验证嵌套模型的优化效果,以澄碧河水库为例,进行水库优化模拟调度。结果表明:在满足防洪目标的前提下,嵌套模型优化调度方案相较于长期优化调度方案和实际调度方案效益均更高,验证了嵌套模型在水库优化调度问题中的优越性。  相似文献   

14.
In this study, an interval-parameter two-stage stochastic semi-infinite programming (ITSSP) method was developed for water resources management under uncertainty. As a new extension of mathematical programming methods, the developed ITSSP approach has advantages in uncertainty reflection and policy analysis. In order to better account for uncertainties, the ITSSP approach is expressed with discrete intervals, functional intervals and probability density functions. The ITSSP method integrates the two-stage stochastic programming (TSP), interval programming (IP) and semi-infinite programming (SIP) within a general optimization framework. The ITSSP has an infinite number of constraints because it uses functional intervals with time (t) being an independent variable. The different t values within the range [0, 90] lead to different constraints. At same time, ITSSP also includes probability distribution information. The ITSSP method can incorporate pre-defined water resource management policies directly into its optimization process to analyze various policy scenarios having different economic penalties when the promised amounts are not delivered. The model is applied to a water resource management system with three users and four periods (corresponding to winter, spring, summer and fall, respectively). Solutions of the ITSSP model provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable interval solutions were generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of stream flow. The obtained solutions are useful for decision makers to obtain insight regarding the tradeoffs between environmental, economic and system reliability criteria.  相似文献   

15.
针对传统马尔可夫链及其改进的预测方法只能进行状态预测的局限,根据相依随机变量的特点,在以传统马尔可夫链预测方法求得各状态预测概率的基础上,进一步以状态预测概率为权重与状态平均值加权求和,实现了马尔可夫链预测方法从状态预测到数值预测的关键性改进。利用我国西南国际大河怒江干流道街坝水文站1957-2010年径流和1964-2010年悬移质输沙序列为分析期,2011-2015年径流和悬移质输沙为验证期,对所建立的复权马尔可夫链预测方法步骤进行验证表明,复权马尔可夫链预测方法具有较高的数值预测精度,能够满足随机时间序列短期数值预测的需要。  相似文献   

16.
高堆石坝施工挡水风险的时变性和填筑进度的不确定性增加了坝体施工期度汛方案决策的难度。针对高堆石坝施工度汛过程动态变化的特点,随机模拟大坝挡水风险,并将其划分3个风险状态以判断度汛行动。以整个度汛施工期成本最小化为目标,考虑洪水来流、填筑进度和决策成本等关键因素,建立基于马尔科夫过程的高堆石坝施工度汛决策模型,分析逐月大坝挡水风险状态下的度汛策略、成本函数和风险状态转移概率。在检验其马尔科夫特性基础上,采用决策迭代算法求解每一决策时刻状态下的最优施工度汛方案及度汛过程的决策路径。工程实例分析表明,该决策模型对高堆石坝施工度汛计划策略的调控结果符合实际施工度汛高程变化情况,为指导快速准确制定大坝施工度汛方案提供了参考。  相似文献   

17.
Seasonal inflow variability, climate non-stationarity and climate change are matters of concern for water system planning and management. This study presents optimization methods for long-term planning of water systems in the context of a non-stationary climate with two levels of inflow variability: seasonal and inter-annual. Deterministic and stochastic optimization models with either one time-step (intra-annual) or two time-steps (intra-annual and inter-annual) were compared by using three water system optimization models. The first model used one time-step sampling stochastic dynamic programming (SSDP). The other models with two time-steps are long-term deterministic dynamic programming (LT-DDP) and long-term sampling stochastic dynamic programming (LT-SSDP). The study area is the Manicouagan water system located in Quebec, Canada. The results show that there will be an increase of inflow to hydropower plants in the future climate with an increase of inflow uncertainty. The stochastic optimization with two time-steps was the most suitable for handling climate non-stationarity. The LT-DDP performed better in terms of reservoir storage, release and system efficiency but with high uncertainty. The SSDP had the lowest performance. The SSDP was not able to deal with the non-stationary climate and seasonal variability at the same time. The LT-SSDP generated operating policies with smaller uncertainty compared to LT-DDP, and it was therefore a more appropriate approach for water system planning and management in a non-stationary climate characterized by high inflow variability.  相似文献   

18.
In this study, a continuous model of stochastic dynamic game for water allocation from a reservoir system was developed. The continuous random variable of inflow in the state transition function was replaced with a discrete approximant rather than using the mean of the random variable as is done in a continuous model of deterministic dynamic game. As a result, a new solution method was used to solve the stochastic model of game based on collocation method. The collocation method was introduced as an alternative to linear-quadratic (LQ) approximation methods to resolve a dynamic model of game. The collocation method is not limited to the first and second degree approximations, compared to LQ approximation, i.e. Ricatti equations. Furthermore, in spite of LQ related problems, consideration of the stochastic nature of game on the action variables in the collocation method would be possible. The proposed solution method was applied to the real case of reservoir operation, which typically requires considering the effect of uncertainty on decision variables. The results of the solution of the stochastic model of game are compared with the results of a deterministic solution of game, a classical stochastic dynamic programming model (e.g. Bayesian Stochastic Dynamic Programming model, BSDP), and a discrete stochastic dynamic game model (PSDNG). By comparing the results of alternative methods, it is shown that the proposed solution method of stochastic dynamic game is quite capable of providing appropriate reservoir operating policies.  相似文献   

19.
水库防洪预报调度的风险分析   总被引:18,自引:4,他引:14  
姜树海  范子武 《水利学报》2004,35(11):0102-0107
本文从水文预报误差的不确定性分析出发,将短期洪水预报精度评定指标转化为入库洪水过程的随机特征值,并引入水库调洪演算随机数学模型,从而实现水文预报风险向预报调度风险的转化,为定量考察预报调度风险率、合理选择动态的汛限水位提供了科学的依据。通过这一方法论证了水文预报精度对水库防洪预报调度风险率的影响,表明提高水文预报精度将有利于降低水库调洪风险率。  相似文献   

20.
This paper introduces an optimization method(SCE-SR) that combines shuffled complex evolution(SCE) and stochastic ranking(SR) to solve constrained reservoir scheduling problems,ranking individuals with both objectives and constrains considered.A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals.This method is suitable for handling multiple conflicting constraints,and is easy to implement,requiring little parameter tuning.The search properties of the method are ensured through the combination of deterministic and probabilistic approaches.The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system,and its performance is compared with that of two classical methods(the dynamic programming and genetic algorithm).The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly,with sufficient global convergence properties and robustness.The operation schedules obtained satisfy the basic scheduling requirements of reservoirs.  相似文献   

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