Deriving Optimal Operating Rules of a Multi-Reservoir System Considering Incremental Multi-Agent Benefit Allocation

Joint multi-reservoir operation is one of the most efficient measures to meet the demand for increasing economic benefits. Operating rules have been widely used in long-term reservoir operations. However, reservoirs belong to multiple agents in most cases, which imposes difficulties on benefit allocation. This motivated us to derive optimal operating rules for a multi-reservoir system, considering incremental benefit allocation among multiple agents. Fairness of incremental benefits for multiple agents is proposed as one of the objective functions, and then optimal joint operating rules with fairness are derived. The optimal joint operating rules with fairness are compared with conventional, optimal individual, and joint operating rules. The Three Gorges (Three Gorges and Gezhouba) and Qing River (Shuibuya, Geheyan and Gaobazhou) cascade reservoirs are selected for case study. The optimal joint operating rules with fairness not only encourage agents to participate in joint operation, but also increase average annual hydropower generation and the assurance rate of hydropower generation relative to those of the conventional operating rules. Furthermore, the proposed optimal operating rules with fairness are easier to implement in practice than the optimal joint rules. This indicates that the proposed method has potential for improving operating rules of a multi-reservoir system.     

Derivation of Aggregation-Based Joint Operating Rule Curves for Cascade Hydropower Reservoirs

Operating rule curves have been widely applied to reservoir operation, due to their ease of implementation. However, these curves are generally used for single reservoirs and have rarely been applied to cascade reservoirs. This study was conducted to derive joint operating rule curves for cascade hydropower reservoirs. Steps in the proposed methodology include: (1) determining the optimal release schedule using dynamic programming to solve a deterministic long-term operation model, (2) identifying the forms of operating rule curves suitable for cascade hydropower reservoirs based on the optimal release schedule, (3) constructing a simulation-based optimization model and then using the non-dominated sorting genetic algorithm-II (NSGA-II) to identify the key points of the operating rule curves, (4) testing and verifying the efficiency of the generated joint operating rule curves using synthetic inflow series. China’s Qing River cascade hydropower reservoirs (the Shuibuya, Geheyan and Gaobazhou reservoirs) were selected for a case study. When compared with the conventional operating rule curves, the annual power generation can be increased by 2.62% (from 7.27 to 7.46 billion kWh) using the observed inflow from 1951 to 2005, as well as by about 1.77% and 2.52% using the synthetic inflows generated from two alternative hydrologic simulation methods. Linear operating rules were also implemented to simulate coordinated operation of the Qing River cascade hydropower reservoirs. The joint operating rule curves were more efficient and reliable than conventional operating rule curves and linear operating rules, indicating that the proposed method can greatly improve hydropower generation and work stability.     

水库群系统发电调度的合作博弈研究

水资源的竞争性和非排他性导致水库管理者基于个体利益进行发电调度,使得水库在满足个体利益的同时往往忽略了系统的整体效益。为了在保证个体利益的基础上实现系统总效益的最大化,建立了梯级水库群发电调度合作博弈模型;采用改进后的水循环算法对模型进行分层求解。以金沙江两库与三峡梯级构成的梯级水库群为研究对象,选取典型年进行实例计算。计算结果表明：梯级水库群发电调度的合作博弈模型在获得系统最大效益的同时使得个体利益达到Pareto最优状态,实现水库群总效益和单库个体效益的双赢,既优于联合优化调度模型又优于单库优化调度模型。该合作博弈模型及其新解法可为水库群调度决策分析开创一种新思路。     

溪洛渡-向家坝-三峡梯级水库联合蓄水方案与多目标决策研究

为协调好梯级水库联合蓄水调度过程中防洪、发电、蓄水和航运等目标之间的矛盾,实现各水库蓄水时机与蓄水进程的协同优化,以溪洛渡-向家坝-三峡梯级水库为例,推求了可权衡防洪与兴利之间矛盾的梯级水库联合蓄水方案,主要研究内容主要包括以下3个部分:(1)风险分析,推求了汛末各分期内坝前最高安全水位约束和联合蓄水方案的防洪风险;(2)兴利效益分析,分析了联合蓄水方案的发电和蓄水等兴利效益;(3)多目标决策,评价了联合蓄水方案的防洪风险、发电和蓄水效益,得出了最优非劣质蓄水方案。研究结果表明:溪洛渡-向家坝-三峡梯级水库的较优联合蓄水方案,分别为9月5日起蓄的同步起蓄方案和9月1日-9月5日-9月10日起蓄的异步起蓄方案,较原设计蓄水方案,年均发电量可分别增加23.76和20.43亿k W·h,增幅分别为3.25%和2.78%;蓄水率可由96.73%分别提高至97.51%和97.57%,两者均可在不降低原防洪标准的前提下,提高梯级水库的综合效益,前者的发电效益较优,而后者的蓄水效益较优。     

Optimal Allocation of Flood Control Capacity for Multi-Reservoir Systems Using Multi-Objective Optimization Approach

Operation of multi-reservoir systems during flood periods is of great importance in the field of water resources management. This paper proposes a multi-objective optimization model with new formulation for optimal operation of multi-reservoir systems. In this model, the release rate and the flood control capacity of each reservoir is considered as decision variable and the resulting nonlinear non-convex multi-objective optimization problem is solved with ε-constraint method through the mixed integer linear programming (MILP). Objective functions of the model are minimizing the flood damage at downstream sites and the loss of hydropower generation. The developed model is used to determine optimal operating strategies for Karkheh multi-reservoir system in southwestern Iran. For this purpose, the model is executed in two scenarios based on “two-reservoir” and “six-reservoir” systems and for floods with return periods of 25 and 50 years. The results show that in two-reservoir system, flood damage is at least about 114 million dollars and cannot be mitigated any further no matter how hydropower generation is managed. But, in the case of developing all six reservoirs, optimal strategies of coordinated operation can mitigate and even fully prevent flood damage.     

Deriving Reservoir Refill Operating Rules by Using the Proposed DPNS Model

The dynamic programming neural-network simplex (DPNS) model, which is aimed at making some improvements to the dynamic programming neural-network (DPN) model, is proposed and used to derive refill operating rules in reservoir planning and management. The DPNS model consists of three stages. First, the training data set (reservoir optimal sequences of releases) is searched by using the dynamic programming (DP) model to solve the deterministic refill operation problem. Second, with the training data set obtained, the artificial neural network (ANN) model representing the operating rules is trained through back-propagation (BP) algorithm. These two stages construct the standard DPN model. The third stage of DPNS is proposed to refine the operating rules through simulation-based optimization. By choosing maximum the hydropower generation as objective function, a nonlinear programming technique, Simplex method, is used to refine the final output of the DPN model. Both the DPNS and DPN models are used to derive operating rules for the real time refill operation of the Three Gorges Reservoir (TGR) for the year of 2007. It is shown that the DPNS model can improve not only the probability of refill but also the mean hydropower generation when compare with that of the DPN model. It's recommended that the objective function of ANN approach for deriving refill operating rules should maximize the yield or minimize the loss, which can be computed from reservoir simulation during the refill period, rather than to fit the optimal data set as well as possible. And the derivation of optimal or near-optimal operating rules can be carried out effectively and efficiently using the proposed DPNS model.     

Optimization of Multi-Reservoir Operating Rules for a Water Supply System

To obtain the optimal releases of the multi-reservoir system, two sets of joint operating rules (JOR-I and JOR-II) are presented based on the aggregation-disaggregation approach and multi-reservoir approach respectively. In JOR-I, all reservoirs are aggregated to an equivalent reservoir, the operating rules of which, the release rule of the system is optimized following operating rule curves coupled with hedging rules. Then the system release is disaggregated into each reservoir according to water supply priorities and the dynamic demand partition approach. In JOR-II, a two-stage demand partition approach is applied to allocate the different demand priorities to determine the release from each reservoir. To assess the reliability and effectiveness of the joint operating rules, the proposed rules are applied to a multi-reservoir system in Liaoning province of China. Results demonstrate that JOR-I is suitable for high-dimensional multi-reservoir operation problems with large-scale inflow data, while JOR-II is suitable for low-dimensional multi-reservoir operation problems with small-scale inflow data, and JOR-II performs better than JOR-I but requires more computation time. The research provides guidelines for the management of multi-reservoir system.     

Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression

The reservoir optimal operation depends on not only specific characteristics of reservoirs and hydropower stations but also stochastic inflows. The key issue of actual hydropower operation is to make an approximate optimal decision triggered by limited inflow forecasts. To implement actual optimal operation of hydropower system with limited inflows forecast, this paper makes use of Support Vector Regression (SVR) to derive optimal operating rules. To improve the performance of SVR, parameters in SVR model are calibrated with grid search and cross validation techniques. The trained SVR model describes the complex nonlinear relationships between reservoir operation decisions and factors by considering both generalization and regression performance, which overcomes local optimization and over fitting deficits. Hybrid programming platform is further developed to implement system simulation. This SVR model along with simulation platform is applied to the largest hydropower base in China – Jinsha system. Three scenarios are developed for comparison: deterministic optimal operation, SVR based simulation with calibrated parameters, SVR based simulation with default parameters. Comprehensive evaluation indicates that, operating rules derived from SVR presents a reliable performance in system power generation and output processes with respect to ideal deterministic results, especially when the parameters are calibrated. Hybrid programming technique provides a feasible and compatible platform for future research.     

长江上游控制性水库群联合调度及水资源影响分析

针对长江上游控制性水库群联合调度问题,建立了大规模混联水库群联合优化调度模型,并提出离散微分动态规划(DDDP)和逐步优化算法(POA)相结合的混合方法,实现大规模混联梯级水库群联合优化调度问题的高效求解。在此基础上,结合流域长系列历史径流资料,进行了长江上游控制性梯级水库群调度模拟,分析了联合调度的发电效益;并在此基础上,结合相关研究成果,探究并分析了梯级水库群建成投运后,联合调度对流域水资源的影响。成果表明,梯级水库群的建成及联合调蓄对于长江中下游枯水期的流量补偿效益十分明显,供水、航运以及压咸补淡等综合效益十分显著。     

三峡梯级电站日优化算法及应用

三峡-葛洲坝梯级水电站须根据不同的调度要求构造各自不同的优化数学模型。当长江处于汛期和枯水期时，就三峡梯级水库的具体特点，给定了葛洲坝1 d的下泄流量，使用状态逐密动态规划算法建立了日优化运行模型，并用C语言编制了算法程序，得到了满意的计算结果。     

三峡梯级日优化运行模型及算法

三峡梯级水电站日运行中,给定三峡用水量制定梯级发电计划,给定梯级总负荷优化电站间负荷分配,是其两种最基本的运行方式。针对这两种基本运行方式,探讨了几种常用优化准则的数学模型。根据实际运行中梯级的各种约束条件,建立了梯级日优化运行的数学模型,给出了相应的计算方法。在计算中,考虑电站的水头变化和两坝间水流时滞的影响,对上述优化问题采用了综合改进动态规划的方法进行计算。仿真计算表明,该模型及算法能够很好地解决三峡梯级日优化运行问题,并能够满足实际运行的需要。同时,还就实时优化问题进行了探讨。     

基于多重迭代算法的梯级水库群调度图优化方法

基于梯级水库群中各水库的常规调度图,建立了以梯级水库群多年平均发电量最大为目标的梯级水库群调度图优化数学模型,提出了基于多重迭代实现高维模型降维的求解算法,即以轮库迭代法实现空间多水库降维,以轮线迭代法实现单一水库多调度线降维,以逐次优化算法实现调度线状态降维。万安溪-白沙梯级水库群的算例计算结果表明,该优化方法能获得形态合理的优化调度图,并能有效提高水库群发电量。     

水电站水库防洪优化调度的模型与方法

水库防洪调度是一种确保水库及下游安全的控制运行方法，通过建立水电站水库优化调度模型，利用动态规划方法，可以计算单一水库或者梯级水电站群的最优调度方案，从而确保水库工程安全，有效利用防洪库容拦蓄洪水，削减洪峰，减免洪水灾害，充分发挥水库的综合效益。通过建立水库防洪调度的数学模型，利用动态规划方法对单一水电站水库的防洪调度进行了研究。     

三峡梯级电厂日优化调度

梯级水电厂间的电力联系和水 力联系使得水电厂的短期经济运行非常复杂，其调度方式也灵活多变。文中针对三峡梯级水 电厂的3种可能调度方式，建立日优化运行模型，并用动态规划进行求解。计算结果表明， 动态规划方法对于求解梯级水电调度问题是行之有效的。     

基于分时电价的三峡梯级水电站联合优化调度研究

针对电力市场下水电站优化运行问题,建立了基于分时电价的三峡梯级水电站联合优化调度 模型。优化调度模型以系统发电效益最大为目标,考虑了分时电价因子和梯级水库间流量传播问题。 采用改进遗传算法求解模型,运用正切轮盘赌选择算子保持适应度函数非负;应用随个体适应值大小和 群体分散程度自适应控制的遗传参数,保持种群多样性。研究成果表明:在电力市场下,水库优化调度 的目标函数选取为发电效益最大有实用价值,对未来三峡梯级水电站日发电计划的编制有一定借鉴意 义和应用价值。     

Optimal Operation of Reservoir Systems using Simulated Annealing

A stochastic search technique, simulated annealing (SA), is used to optimize the operation of multiple reservoirs. Seminal application of annealing technique in general to multi-period, multiple-reservoir systems, along with problem representation and selection of different parameter values used in the annealing algorithm for specific cases is discussed. The search technique is improved with the help of heuristic rules, problem-specific information and concepts from the field of evolutionary algorithms. The technique is tested for application to a benchmark problem of four-reservoir system previously solved using a linear programming formulation and its ability to replicate the global optimum solution is examined. The technique is also applied to a system of four hydropower generating reservoirs in Manitoba, Canada, to derive optimal operating rules. A limited version of this problem is solved using a mixed integer nonlinear programming and results are compared with those obtained using SA. A better objective function value is obtained using simulated annealing than the value from a mixed integer non-linear programming model developed for the same problem. Results obtained from these applications suggest that simulated annealing can be used for obtaining near-optimal solutions for multi-period reservoir operation problems that are computationally intractable.     

Shuffled complex evolution coupled with stochastic ranking for reservoir scheduling problems

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.     

溪洛渡、向家坝水库与三峡水库联合蓄水调度研究

由于溪洛渡、向家坝水库与三峡水库蓄水时间上的同步性,使三峡水库蓄水难度进一步加大,进而影响其综合效益的发挥。为满足下游地区在蓄水期对上游梯级水库下泄流量的新要求,研究金沙江溪洛渡、向家坝水库与三峡水库联合蓄水调度方案,优化梯级水库蓄水过程。在综合分析防洪、泥沙、库区、发电及供水等指标基础上,推荐梯级水库蓄水调度方案。防洪、库区淹没及泥沙淤积的影响分析表明,所提方案可进一步缓解下游地区的供水压力,对金沙江梯级水库联合蓄水调度一定实践指导意义。     

Application of an Interior-Point Algorithm For Optimization of a Large-Scale Reservoir System

Optimization of a multi-reservoir system operation is challenging due to the non-linearity, stochasticity, and dimensionality involved in such a problem. In this research, a long-term planning model is presented for optimizing the operation of Iranian Karoon-Dez reservoir system using an interior-point algorithm. The system is the largest multi-purpose reservoir system in Iran with hydropower generation, water supply, and environmental objectives. The focus is on resolving the dimensionality of this problem while considering hydropower generation and water supply objectives. The weighting and constraints methods of multi-objective programming are used to assess the trade-off between water supply and hydropower objectives so as to find noninferior solutions. The computational efficiency of the proposed approach is demonstrated using historical data taken from Karoon-Dez reservoir system.     

三峡(围堰发电期)-葛洲坝梯级调度规程编制

2003年6月三峡工程开始蓄水至135 m,实现围堰发电及双线五级船闸通航,它标志着三峡工程从勘测设计、建设走向开始运用、逐步发挥效益的阶段.为规范和指导三峡(围堰发电期)-葛洲坝水利枢纽梯级的调度运用而编制了调度规程.阐述了规程编制的框架设计,调度的主要原则,规程编制中关于水库日运行水位,防洪非常调度,发电、航运调度,调度协调机制的建立等重大问题的处理等各方面,介绍了围堰发电期梯级调度规程编制的考虑与体会.