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.     

Nominees as Candidates for the Executive Board 1989–1991

ABSTRACT

Principles of sustainable development are inviting a change in the approach used for reservoir management. Instead of designing and planning new reservoirs, more emphasis will be placed on management of existing reservoirs. Reservoir storage reallocation and reassessment of reservoir operational rules are considered to be the two main problems to be addressed by reservoir research in the near future. Appropriate techniques for solving these two problems are necessary An attempt at developing a sound methodology for a reservoir reassessment has been presented here. A simulation approach, based on the four probability criteria, has been used in this context for storage analysis. Reservoir management strategies have been reassessed using an implicit stochastic optimization model. The methodology has been applied to Wonogiri Reservoir located in Central Java, Indonesia. Analysis of the storage capacity indicated that the current capacity is larger than that required for the irrigation of an area of 25,319 ha, corresponding to the Wonogiri reservoir final development phase. Thus, the present size of the reservoir allows for additional multipurpose use of the reservoir storage. Optimization of the reservoir yield demonstrated that the reservoir can provide for municipal and industrial water supply and hydropower generation in addition to the current use af reservoir storage for irrigation water supply.     

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。     

梯级水库三维环境流体动力学数值预测和水温分层与累积影响规律研究

该文应用美国三维水环境流体动力学模型(简称EFDC模型)对特大型水库进行了水流水温数值耦合预测计算,深入研究了水库的水热循环规律,并对分层型水库的水位、流速和水温分布规律进行剖析。在梯级水电站联合运行环境下,重点分析了高坝大库水温分层规律,探讨了建库前后的水温变化规律和梯级水库建设的累积影响,为调整梯级水电规划方案提供科学决策依据。     

Reservoir Optimization-Simulation with a Sediment Evacuation Model to Minimize Irrigation Deficits

Importance of existing reservoirs for supplying fresh water has increased significantly due to population increase and enhanced living standards, while the reduced development of new reservoirs in recent decades has made it even more pertinent that the current battery of reservoirs be operated in a sustainable and efficient manner. In order to move a step towards the goal of sustainability, sediment evacuation must be considered when optimizing a reservoir??s operations. The Reservoir Optimization-Simulation with Sediment Evacuation (ROSSE) model is a recently developed tool which internalizes sediment evacuation routines and the simulation module in a newly developed GA-based optimization module. This article applies the ROSSE model with the aim of minimizing irrigation shortages in the Tarbela Reservoir, Pakistan. The article also calculates the suitable values of various GA parameters required to run the model through a sensitivity analysis. Simulation results of three sets of rule curves??one existing and two optimized sets??are compared with each other for parameters like irrigation shortage, power generation, sediment evacuation and flood dis-benefits (damages). It is found that the optimized rule curves of scenario 1 reduce the irrigation shortages by 39?% while the optimized rule curves of scenario 2 can reduce the irrigation shortages by 24?% of that of the shortages by existing rule curves. The optimized rule curves of scenario 2 also ensure the current level of hydropower generation and sediment evacuation for the Tarbela reservoir. The study recommends a change in the reservoir??s existing rule curves in order to reduce irrigation shortages. The incorporation of the sediment evacuation routine and availability of economic and hydro based objective functions in the optimization model will help achieving the goal of sustainability.     

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.     

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.     

A simulation model for the operation of multipurpose multireservoir system for River Narmada, India

This paper presents a simulation model for the operation of multipurpose multireservoir systems. The simulation model developed in this study has been applied to the Narmada River Reservoir System in India. The model has been formulated for the monthly operation of reservoirs to meet industrial, domestic, irrigation, and hydropower generation water requirements. The simulation model consists of four modules: inflow generation, minimum flow releases, simulation, and performance evaluation. The single-site Thomas–Fiering and multisite Lane's Applied Stochastic Techniques (LAST) inflow generation models have been used for the generation of a monthly inflow series. Five performance indicator indices have been formulated for evaluating the performance of the multipurpose multireservoir system: Volume Reliability Index (VRI), Time Reliability Index (TRI), Hydropower Production Index (HPI), Economic Benefits Index (ECBI), and Spill Prevention Index (SPPI). The trade-offs between the performance indicator indices have been obtained using the simulation model. The results from these trade-offs will assist decision makers in the operation of reservoirs, as well as with planning and comparing operation strategies.     

漫湾水电站水库水温分布观测与数学模型计算研究

近年来建设的高坝大库越来越多,下泄低温水的影响日趋严重,对水库下泄低温水的影响和减缓措施的研究应给予关注和不断加强。本文是对已投入运行的漫湾水电站库区的水温结构进行研究。通过2004年2月对漫湾水库进行水温分布现场观测,并辅以三维数值模型计算,获得了较详细的水温分布成果。研究结果表明,漫湾水库的水温结构既不属于典型分层型又不属于完全混合型,应属于局部分层型或过渡型。该成果对于流域梯级水电开发对水环境的影响研究,具有一定的代表性,可用于类比分析研究与漫湾水库条件类似的其它水电站。     

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.     

Improving Optimization Efficiency for Reservoir Operation Using a Search Space Reduction Method

Reservoir operation problems are challenging to efficiently optimize because of their high-dimensionality, stochasticity, and non-linearity. To alleviate the computational burden involved in large-scale and stringent constraint reservoir operation problems, we propose a novel search space reduction method (SSRM) that considers the available equality (e.g., water balance equation) and inequality (e.g., firm output) constraints. The SSRM can effectively narrow down the feasible search space of the decision variables prior to the main optimization process, thus improving the computational efficiency. Based on a hydropower reservoir operation model, we formulate the SSRM for a single reservoir and a multi-reservoir system, respectively. To validate the efficiency of the proposed SSRM, it is individually integrated into two representative optimization techniques: discrete dynamic programming (DDP) and the cuckoo search (CS) algorithm. We use these coupled methods to optimize two real-world operation problems of the Shuibuya reservoir and the Shuibuya-Geheyan-Gaobazhou cascade reservoirs in China. Our results show that: (1) the average computational time of SSRM-DDP is 1.81, 2.50, and 3.07 times less than that of DDP when decision variables are discretized into 50, 100, and 500 intervals, respectively; and (2) SSRM-CS outperforms CS in terms of its capability of finding near-optimal solutions, convergence speed, and stability of optimization results. The SSRM significantly improves the search efficiency of the optimization techniques and can be integrated into almost any optimization or simulation method. Therefore, the proposed method is useful when dealing with large-scale and complex reservoir operation problems in water resources planning and management.     

天生桥梯级水电站联合调度决策支持系统建设经验

决策支持系统是水库优化调度实用化的发展方向，对于提高水电站生产效益和水库调度管理水平具有重要意义。结合国电公司重点科技项目“天生桥梯级水电站联合调度决策支持系统”的开发，阐述了开发水电调度决策支持系统实现水库调度实用化的经验:正确反映用户需求、增强用户对系统的控制、用模拟仿真弥补优化调度的不足、用数据库技术扩展水库优化调度实用化功能。     

Structural and Non-Structural Climate Change Adaptation Strategies for the Péribonka Water Resource System

This paper discusses the possibility for a privately managed hydro-power system to adapt to a projected increase in water flow in their central-Québec watersheds by adding power generation potential. Runoffs simulated by a lumped rainfall-runoff model were fed into a stochastic dynamic programming (SDP) routine to generate reservoir operating rules. These rules were optimized for maximum power generation under maximal and minimal reservoir level constraints. With these optimized rules, a power generation simulator was used to predict the amount of generated hydropower. The same steps, excluding calibration, were performed on 60 climate projections (from 23 general circulation models and 3 greenhouse gas emission scenarios) for future horizons 2036–2065 and 2071–2100. Reservoir operation rules were optimized for every climate change projection for the 3 power plants in the system. From these simulations, it was possible to determine hydropower numbers for both horizons. The same steps were performed under a modified system in which an additional turbine was added to each power plant. Results show that both the non-structural (optimizing reservoir rules) and structural (adding turbines) adaptation measures allow for increased power production, but that adapting operating rules is sufficient to reap the most of the benefits of increased water availability.     

Benefit and Risk Balance Optimization for Stochastic Hydropower Scheduling

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.     

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。     

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.     

水电站群发电能力的优化方法及应用分析

针对水电站群中长期发电计划编制的调度期末控制水位决策需求,建立以发电能力最大为目标的水电站群联合优化调度模型,并采用改进的逐次逼近与离散微分动态规划组合算法进行求解。以华电贵州区域乌江和北盘江两流域水电站群为例,采用2013年实际来水过程计算,通过与发电量最大、补偿效益最大等传统优化目标对比分析。结果表明:本文模型可根据各电站发电效率变化情况自动确定水库蓄放策略,实现水电站群调度期末控制水位及对应发电计划过程的同步优化,准确体现水电站群调度期内总发电量和调度期末总蓄能量的动态博弈关系。     

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。     

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.