Identifying Explicit Formulation of Operating Rules for Multi-Reservoir Systems Using Genetic Programming

Operating rules have been widely used to handle the inflows uncertainty for reservoir long-term operations. Such rules are often expressed in implicit formulations not easily used by other operators and/or reservoirs directly. This study presented genetic programming (GP) to derive the explicit nonlinear formulation of operating rules for multi-reservoir systems. Steps in the proposed method include: (1) determining the optimal operation trajectory of the multi-reservoir system using the dynamic programming to solve a deterministic long-term operation model, (2) selecting the input variables of operating rules using GP based on the optimal operation trajectory, (3) identifying the formulation of operating rules using GP again to fit the optimal operation trajectory, (4) refining the key parameters of operating rules using the parameterization-simulation-optimization method. The method was applied to multi-reservoir system in China that includes the Three Gorges cascade hydropower reservoirs (Three Gorges and Gezhouba reservoirs) and the Qing River cascade hydropower reservoirs (Shuibuya, Geheyan and Gaobazhou reservoirs). The inflow and storage energy terms were selected as input variables for total output of the aggregated reservoir and for decomposition. It was shown that power energy term could more effectively reflect the operating rules than water quantity for the hydropower systems; the derived operating rules were easier to implement for practical use and more efficient and reliable than the conventional operating rule curves and artificial neural network (ANN) rules, increasing both average annual hydropower generation and generation assurance rate, indicating that the proposed GP formulation had potential for improving the operating rules of multi-reservoir system.     

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。     

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。     

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.     

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。     

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.     

基于全微分法的多主体梯级水电站群联合调度增益归因及分配

公平、高效、合理的增益分配是开展多主体水库群联合优化调度的机制保障。以往基于水库库容、装机容量、发电量等单一指标或综合指标构建的按比例分摊方法,未能反映联合增益形成机制和径流变化对增益结果的影响。针对上述不足,本文将联合调度增益依照独立调度和联合调度情景下的效益差异进行逐项定量分解,提出基于全微分法的增益归因-分配模型:建立联合调度增益与时段弃水量、时段蓄量差、累积蓄量差的关系,采用全微分公式定量分解各水库增益贡献。实例结果表明:(1)全微分法可定量解构各水库各时段增益贡献及其物理成因机制,可得到满足增益分配基本原则的分配结果。(2)该方法综合考虑来水条件、水库参数、库群拓扑结构等因素对联合增益的综合影响,弥补了单一指标评价片面性的缺陷,避免了综合指标法中权重系数的确定问题及难以反映增益形成机理的问题。研究为多利益主体梯级水电站群增量效益分配问题提供了一种新的解决途径。     

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.     

Simulation-optimization model of reservoir operation based on target storage curves

This paper proposes a new storage allocation rule based on target storage curves. Joint operating rules are also proposed to solve the operation problems of a multi-reservoir system with joint demands and water transfer-supply projects. The joint operating rules include a water diversion rule to determine the amount of diverted water in a period, a hedging rule based on an aggregated reservoir to determine the total release from the system, and a storage allocation rule to specify the release from each reservoir. A simulation-optimization model was established to optimize the key points of the water diversion curves, the hedging rule curves, and the target storage curves using the improved particle swarm optimization （IPSO） algorithm. The multi-reservoir water supply system located in Liaoning Province, China, including a water transfer-supply project, was employed as a case study to verify the effectiveness of the proposed join operating rules and target storage curves. The results indicate that the proposed operating rules are suitable for the complex system. The storage allocation rule based on target storage curves shows an improved performance with regard to system storage distribution.     

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。     

乌江流域水电站群优化调控关键技术应用研究

根据乌江流域总体规划、长江三峡以上防洪要求、南方电网运行特点及乌江流域水文特性等．开展了乌江梯级水库优化调度和发电优化控制的基本理论研究，建立了乌江流域水电站群联合优化调控系统，其在系统中应用，实现了节水增发电的目的，取得了良好的社会经济效益。     

基于NSGA-Ⅱ方法的三峡水库汛末蓄水期多目标生态调度研究

为充分发挥三峡水库的经济效益和生态效益,建立了以三峡水电站发电量最大和下游河道适宜生态流量改变度最小为目标的水库优化调度模型,基于生态水文学法量化了下游河道适宜生态流量。为有效求解多目标优化问题,引入带有精英保留策略的非支配排序遗传算法(non-dominated sorting genetic algorithm,NSGA-II)作为调度模型的寻优算法。通过比较2015年调度期内三峡水库优化调度方案和现行调度方案的发电效益和生态效益表明:提高生态流量改变度0.63%可使发电量增加2.32%,降低0.58%的发电量将生态流量改变度降低8.86%。该算法避免了对多目标进行加权求解的盲目性,能获得多个兼顾两个目标且分布均匀、收敛性较好的优化调度方案,为决策者提供了多种可行的选择。研究成果为三峡水库蓄水优化调度提供理论依据和具体方案参考。     

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.     

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.     

三峡梯级日发电优化调度

三峡和葛洲坝构成的梯级水电厂根据不同的要求需要构造不同的优化调度模型。讨论在给定葛洲坝一天下泄流量过程的情况下三峡梯级电站发电站发电的情况。使用增量动态规划算法建立日运行模型并用VC程序实现,并用实例计算验证算法的可靠性和高效性。     

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。     

金沙江下游至三峡-葛洲坝梯级水库群 发电联合调度增益机制分析

金沙江下游溪洛渡、向家坝至三峡、葛洲坝四库梯级为长江流域水能资源调控重点枢纽工程,开展四库发电联合优化调度效益显著。研究建立四库系统独立发电、联合两种模式下优化调度模型,采用长系列径流系列计算发电效益增益,统计增益的时间、年型、空间分布特征。提出基于全微分法的增益占比析因方法辨识发电增益受发电水量、水头影响贡献占比,归纳增益产生机制。结果表明:(1)消落期发电增益集中产生在5月份;向家坝、葛洲坝为主要受益水库。(2)联合增益的产生原因在于联合调度过程中溪洛渡、三峡均化了5月、6月放水过程,降低向家坝、葛洲坝的弃水量,抬升两库水头,增加发电效益。(3)向家坝的补偿增益受发电水量、水头贡献占比相当,葛洲坝的补偿增益主要以发电水头抬升的水头效益为主,溪洛渡、三峡的效益变化主要受联合调度过程中水头变化影响。     

金沙江下游与三峡梯级水库群协同消落方式研究

梯级水库群消落期协同运行方式是梯级水库群联合调度重要环节。以溪洛渡、向家坝、三峡与葛洲坝梯级水库群为背景,将消落期来水分成丰水年组、平水年组和枯水年组,以分组期望发电量最大为目标,建立了消落期随机联合优化调度模型,结合各库不同消落期初水位开展了模拟计算,得到不同水文年型下梯级水库群的期望协同消落方案。结果表明:金沙江下游与三峡梯级水库群协同消落方式能在一定程度上提高梯级期望总发电量;当来水较少且三峡消落期初水位较低时,金沙江下游与三峡梯级水库群协同消落方式可显著提高三峡和葛洲坝水库的供水效益。最后,总结归纳了不同情境下溪洛渡-三峡协同消落策略,以指导金沙江下游与三峡梯级水库群消落期实际运行。     

长江上游控制型梯级水库群联合补偿效益分析

介绍了长江上游水电系统的总体特征以及主要控制型梯级水库的拓扑关系,以发电量最大或保证出力最大为目标建立了优化模型,并分别按梯级和库群2个层次构建了4个长序列优化调度方案,最后根据各方案优化结果分析了主要控制型梯级水库的联合补偿规律及补偿效益。分析结果表明:以发电量最大为主要目标时,库群年均发电量可增加11.79%;以保证出力最大为主要目标时,库群总保证出力可提高29.53%;同时,年均发电量增加9.91%,各梯级互补能力突出,联合运行时电力电量补偿效益显著。     

Deriving the optimal refill rule for multi-purpose reservoir considering flood control risk

For most multi-purpose reservoirs, there is a conflict between the flood control and refill operations. Refill before the end of the flood season is a valuable and effective solution to the conflict. In this paper, we present a method to derive the optimal refill rule for multi-purpose reservoir considering flood control risk. The paper begins with an investigation of the temporal trends of historical reservoir inflow series during refill period by the methods of linear regression, Mann–Kendall and Spearman's rho test. Six refill rules are then proposed. A procedure to couple a flood control risk module with utilization benefits analysis module is then developed to derive the optimal refill rule. China's Three Gorges Reservoir (TGR) is selected as a case study. The application results show that the optimal refill rule is that refill begins on September 1 with storage level reaching 160 m on September 30 linearly. Compared with the original rule, the optimal refill rule can increase hydropower generation by 7.19%, decrease spilled water by 25.07%, and improve the fullness storage rate to 95.35%, without increasing flood control risk.