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.     

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.     

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。     

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.     

基于来水不确定性的梯级水电站负荷调整耦合模型——以锦官电源组梯级水电站为例

针对来水不确定性引起的实际调度要求与原定发电计划的偏差问题,一方面结合风险管理内涵和梯级水电站发电调度操作规程,建立了梯级水电站安全运行预警机制,对发电计划进行滚动监测与评估;另一方面,基于误差分析技术和水库调度理论,建立了梯级水电站负荷调整耦合模型,并以预警结果为时间控制节点,构建二者的嵌套应用模式,同时提出了一种计及电网安全约束的负荷调整逐层递进求解算法。以雅砻江流域锦官电源组梯级水电站为实例进行计算,结果表明,所建模型可有效解决来水不确定性(偏小或偏大)导致的电站被迫降低出力运行或水库弃水问题,制定的负荷调整方案满足电网安全稳定运行约束和水电站水库运行要求,验证了所建模型的合理性。     

雅砻江流域中长期径流预测方法研究

为科学合理安排流域梯级水电站水库运行方式并充分利用水量发电,以期为梯级电站发电预计划的编制提供重要依据,采用物理成因分析法和多种数理统计方法对雅砻江流域梯级水电站的年、月入库流量进行预测。年、月平均流量预测均采用定性和定量结合的方法,预测结果具有较高可信度。根据生产实际的需要和径流的实际变化情况,对月、旬径流分枯期、过渡期和丰水期单独建模进行预测,并基于丰枯形势分析结果给出了径流预测的推荐值。研究还表明,中长期径流预测要充分把握和降水密切相关的气象因子的变化,并以此来指导径流预测,提高径流预测精度。     

基于可行空间搜索遗传算法的梯级水库群调度规则研究

以梯级水库群系统多年平均发电量和旬出力保证率最大为目标函数,以梯级水库群内各水库拐点式调度图为决策变量,建立梯级水库群联合发电调度模型,并采用可行空间搜索遗传算法进行求解。为了避免模型求解过程中对不可行解的过多处理,有针对性地对可行解进行优化。最后,以汉江流域梯级水库群为例,对模型和算法的有效性进行了验证。     

湖北汉江梯级水库群联合优化调度研究

湖北汉江流域已形成大规模梯级水库群,为了充分发挥梯级水库群联合补偿调节的优势,实现水电站最优经济运行,本文对湖北汉江流域水电站群运行特性进行分析研究。考虑各电站之间的水力和电力联系,在常规调度图模拟及传统优化调度的基础上,采用基于GAMS平台的非线性规划法以及基于可行空间搜索遗传算法,制定复杂混联水库群的发电优化调度规则。优化效果显著,充分体现水库群优化调度作用,为湖北汉江梯级水电站水库群的实际调度提供最佳的指导和方案。     

Optimizing Irrigation Deficit of Multipurpose Cascade Reservoirs

Reservoirs play a strategic role in the rapid monetary growth of the world by providing numerous benefits. However, the reduction in appropriate sites along with environmental and social apprehensions has resulted in curtailment of new reservoirs around the world in twenty-first century. There is a potential of benefits available from existing reservoirs which can be best capitalized through their optimized operation. Reservoirs Operation Optimization considering Sediment Evacuation (RESOOSE), recently developed model which combines multiple reservoirs operation and sediment evacuation with Genetic Algorithm based optimization module, has been used in the study. The objective of the study was to optimize the irrigation deficit through cascade reservoirs with consideration to hydropower, sediment evacuation and flood damages reduction benefits. The RESOOSE model was applied to optimize the irrigation deficits of Tarbela and Diamer Basha Reservoirs in Pakistan using developed objective function. The article computed and compared the benefits of optimized and existing rule curves. The hydropower benefits of 36.92 Billion Kw, sediment evacuation benefits of 21.534 Million m³ and flood damages of 616.19 Million US$ due to existing rule curves were considered as minimum benefits for achieving the optimized rule curves to minimize irrigation deficits. The developed optimized rule curves reduced the irrigation shortages of case study reservoirs from 6.9 to 5.8 Billion m³ (16% enhancement) annually as compared to existing rule curves. The optimized rule curves minimized the irrigation deficits by maintaining the existing benefits and without lowering the minimum operating levels of case study reservoirs. The study suggests change in existing rule curves of Tarbela and Diamer Basha Reservoirs due to less irrigation shortages. The RESOOSE model can be applied to other cascade reservoirs for optimizing the rule curves.     

Real-Time Operation of Reservoir System by Genetic Programming

Reservoir operation policy depends on specific values of deterministic variables and predictable actions as well as stochastic variables, in which small differences affect water release and reservoir operation efficiency. Operational rule curves of reservoir are policies which relate water release to the deterministic and stochastic variables such as storage volume and inflow. To operate a reservoir system in real time, a prediction model may be coupled with rule curves to estimate inflow as a stochastic variable. Inappropriate selection of this prediction model increases calculations and impacts the reservoir operation efficiency. Thus, extraction of an operational policy simultaneously with inflow prediction helps the operator to make an appropriate decision to calculate how much water to release from the reservoir without employing a prediction model. This paper addresses the use of genetic programming (GP) to develop a reservoir operation policy simultaneously with inflow prediction. To determine a water release policy, two operational rule curves are considered in each period by using (1) inflow and storage volume at the beginning of each period and (2) inflow of the 1^st, 2^nd, 12^th previous periods and storage volume at the beginning of each period. The obtained objective functions of those rules have only 4.86 and 0.44?% difference in the training and testing data sets. These results indicate that the proposed rule based on deterministic variables is effective in determining optimal rule curves simultaneously with inflow prediction for reservoirs.     

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.     

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。     

Impacts of Inflow Variations on the Long Term Operation of a Multi-Hydropower-Reservoir System and a Strategy for Determining the Adaptable Operation Rule

Obvious inflow variations resulting from changing environments bring big challenges to the operations of hydropower reservoirs. This study reveals the impacts of average annual inflow volume (AAIV) variations on the long term operation of a multi-hydropower-reservoir (MHR) system, and presents a strategy for determining the adaptable operation rule. The strategy includes two parts. One part is making different inflow scenarios based on the change points of AAIVs. Another part is applying the principle of cross validation to select the adaptable rule from the formulated operation rules in various inflow scenarios. Specifically, the change points of AAIVs are identified by three statistical methods. An optimization operation model of an MHR system is built, and three evolutionary and meta-heuristic algorithms are applied to resolve the model in different inflow scenarios. Based on the optimal operation results, two machine learning algorithms are employed to formulate operation rules in each inflow scenario. The MHR system at the upstream of Yellow River basin is taken as a case study. The results show that (1) the long term operation of an MHR system is sensitive to the AAIV variations; and (2) the presented strategy is feasible in determining the adaptable operation rule for an MHR system under the AAIV variations. The findings of the study are helpful for the long term operation of an MHR system under the AAIV variations.

     

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。     

对水电效益影响最小的水光互补运行方式研究

为解决水光互补之后光伏消纳率低的问题,在水光日内互补调度规则的基础上,充分考虑水电和光伏的互补特性,提出了水光月内互补调度规则,利用梯级水库的可调节性能适应光伏出力曲线。采用水光月内互补调度规则对澜沧江上游西藏段梯级水电站进行了互补后梯级水电站出力过程和光伏消纳率计算,结果表明,水光互补之后,水电与光电的合计发电量从55.835亿kW·h上升到了59.189亿kW·h,互补后的发电增益量为33.54亿kW·h,光伏消纳率提高了20%。     

乌江梯级水电站联合优化调度效果分析——以应对20081106洪水为例

综合分析了应对20081106洪水的乌江梯级水电站联合调度效果及经验,并应用多元线性回归方法构建了乌江梯级水电站总发电量拟合模型,识别影响乌江梯级水电站发电量的关键因素及影响程度。结果表明,洪家渡和东风流域累计平均降雨在2008年10月和11月期间,分别比多年平均同期偏多40％～54％和71％～112％,经乌江梯级水电站联合优化调度后,梯级综合月末可用水量分别比2005～2007年同期偏高136％和162％,梯级综合发电量比2005～2007年同期分别偏多9％和141％,梯级水电站联合优化调度效果显著,且出库流量及龙头电站洪家渡的水位和月末可用水量是影响乌江梯级总发电量的决定性因素。研究结果对于从梯级水电站联合调度角度减缓极端天气事件对水电行业的影响具有重要的参考价值。     

乌江梯级水电站联合优化调度效果分析——以应对20081106洪水为例

综合分析了应对20081106洪水的乌江梯级水电站联合调度效果及经验,并应用多元线性回归方法构建了乌江梯级水电站总发电量拟合模型,识别影响乌江梯级水电站发电量的关键因素及影响程度。结果表明,洪家渡和东风流域累计平均降雨在2008年10月和11月期间,分别比多年平均同期偏多40%～54%和71%～112%,经乌江梯级水电站联合优化调度后,梯级综合月末可用水量分别比2005～2007年同期偏高136%和162%,梯级综合发电量比2005～2007年同期分别偏多9%和141%,梯级水电站联合优化调度效果显著,且出库流量及龙头电站洪家渡的水位和月末可用水量是影响乌江梯级总发电量的决定性因素。研究结果对于从梯级水电站联合调度角度减缓极端天气事件对水电行业的影响具有重要的参考价值。     

溪洛渡向家坝梯级水电站联合蓄放水规律分析

传统的梯级水电站联合运行蓄放水是下游梯级水库"先蓄后放",保持高水头运用的方式,但由于没有充分考虑汛期水库发电水头受阻、水头重叠等情况,其结果不一定符合实际。针对溪洛渡和向家坝梯级组合具有上游梯级库容大、水头变幅大、受阻更多、梯级水头重叠较多的特点,并考虑梯级电站的发电特性,采用溪洛渡-向家坝联合优化调度模型,对两电站的蓄放水规律进行了研究。结果表明,上游溪洛渡电站先蓄水后放水可减少汛期梯级电站发电受阻程度,提高总预想出力,有利于提高梯级电站的发电量。     

耦合出力破坏深度与弃电准则的梯级水风光互补调度规则研究

梯级水电调度规则是指导控制型水库发电蓄放水的重要依据。随机、波动、间歇性新能源的接入改变了梯级水电运行边界，增加了缺电、弃电风险，导致仅考虑径流季节性波动的调度规则不再适用。依托贵州某梯级水风光综合基地实际工程，剖析了导致缺电、弃电的因素，并从规则形式和模型构建角度给出解决方案。在标准调度规则的基础上，结合对冲规则、满蓄规则，并考虑输电通道容量限制与新能源消纳规则，提出梯级水风光六段式互补调度规则，以减少缺电、弃电;提出出力破坏深度指标度量缺电风险、提出弃电准则避免非必要弃电，并以规则参数为决策变量构建多目标参数模拟优化模型;采用目标优先级非支配排序遗传算法优化规则参数。设置多种新能源装机容量场景、输电通道容量场景和对比规则，从多角度验证本文方法的有效性。结果表明，本文方法能够显著降低梯级水风光综合基地缺电、弃电风险，提高发电量，其中出力破坏深度指标使缺电程度平均降低36.04%、弃电准则使弃电平均减少7.96%。最后，绘制更加简明、直观的梯级水风光互补调度图，以便于实际应用。     

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。