Coupled Operating Rules for Optimal Operation of Multi-Reservoir Systems

Due to the complexity of multi-reservoir system operation problems, researchers usually prefer to assume lumped demands located downstream of such systems. Consequently, distributed local demands through the system are neglected or supplied completely (e.g. using Standard operating policy, SOP), in order to simplify the problem. In this study, Coupled Operating Rules (COR) as a simple and suitable operating policy is applied for optimal operation of multi-reservoir systems with local demands. The applied policy includes two types of linear rules, which are defined to determine total releases and local water allocations in decision points. This policy is adopted within a simulation-optimization approach to optimally operate a three-reservoir system in the Karkheh river basin. Obtained results indicate that the proposed strategy reduces the intensity of demand deficits and distributes the occurred shortages throughout the system properly. Moreover, the system losses are managed appropriately and big unbalanced local shortages are prevented. Although COR strategy decreases the reliability of local demands compared to SOP, it is a reasonable operating policy for systems with distributed local demand sites. Moreover, in this study an effective Improved Melody Search (IMeS) algorithm is proposed to achieve the optimum values of operating rules’ parameters. The efficiency of the optimization method is compared to the results achieved by other selected well-known heuristic search methods. Based on the experimental results, it is revealed that the proposed algorithm is more effective in finding precise solutions over a long-term period, comparing with the other conventional algorithms.     

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.     

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.     

水库群联合供水方案模拟优化研究

对于由两个或两个以上水库构成的供水系统,将水库群等效为一个聚合水库,建立联合调度供水模式。通过制定合理的供水调度原则,采用优化水库调度图,根据各库实时可供水量实现供水任务合理分配,模拟水库群联合调度供水过程,得到最优供水方案。将该供水方案应用于某市水库群供水调度中,结果表明:各用水户的供水保证率均高于设计保证率,水库群基本满足该地区的用水需求,验证了所提出的水库群联合调度供水方案的可行性与优越性。     

Simulation and Optimization of Multi-Reservoir Operation in Inter-Basin Water Transfer System

Inter-basin water transfer projects are usually considered as one of the most effective facilities to balance the non-uniform temporal and spatial distribution of water resources and water demands by diverting water from surplus to deficient area. However, the operation of these projects are always daunting, especially for projects with multi-donor reservoirs but only one recipient reservoir. In this study, a set of water transfer rule curves are firstly proposed to determine when, where and how much water should be diverted from each donor reservoir. In addition, a simulation-optimization model with the objective to minimize both water shortage risk and vulnerability is established to derive the optimal operation rule curves. Following that, the new transfer rules are applied to provide guidelines for the operation of a water transfer-supply project with two donor reservoirs in central China. The effects of water diversion on each reservoir are evaluated under different scenarios including no diversion, diversion from the donor reservoir with relatively sufficient water, diversion from the donor reservoir with relatively limited water, and diversion from both donor reservoirs. The results show the advantages of improving the performance of whole water diversion system and demonstrate the feasibility of the proposed approach.     

A Bayesian Stochastic Optimization Model for a Multi-Reservoir Hydropower System

This paper presents the development of an operating policy model for a multi-reservoir system for hydropower generation by addressing forecast uncertainty along with inflow uncertainty. The stochastic optimization tool adopted is the Bayesian Stochastic Dynamic Programming (BSDP), which incorporates a Bayesian approach within the classical Stochastic Dynamic Programming (SDP) formulation. The BSDP model developed in this study considers, the storages of individual reservoirs at the beginning of period t, aggregate inflow to the system during period t and forecast for aggregate inflow to the system for the next time period t + 1, as state variables. The randomness of the inflow is addressed through a posterior flow transition probability, and the uncertainty in flow forecasts is addressed through both the posterior flow transition probability and the predictive probability of forecasts. The system performance measure used in the BSDP model is the square of the deviation of the total power generated from the total firm power committed and the objective function is to minimize the expected value of the system performance measure. The model application is demonstrated through a case study of the Kalinadi Hydroelectric Project (KHEP) Stage I, in Karnataka state, India.     

Sustainable Basin-Scale Water Allocation with Hydrologic State-Dependent Multi-Reservoir Operation Rules

This study extends the PSO-MODSIM model, integrating particle swarm optimization (PSO) algorithm and MODISM river basin decision support system (DSS) to determine optimal basin-scale water allocation, in two aspects. The first is deriving hydrologic state-dependent (conditional) operating rules to better account for drought and high-flow periods, and the second is direct, explicit consideration of sustainability criteria in the model’s formulation to have a better efficiency in basin-scale water allocation. Under conditional operating rules, the operational parameters of reservoir target storage levels and their priority rankings were conditioned on the hydrologic state of the system in a priority-based water allocation scheme. The role of conditional operating rules and policies were evaluated by comparing water shortages associated with objective function values under unconditional and conditional operating rules. Optimal basin-scale water allocation was then evaluated by incorporating reliability, vulnerability, reversibility and equity sustainability indices into the PSO objective function. The extended model was applied for water allocation in the Atrak River Basin, Iran. Results indicated improved distribution of water shortages by about 7.5% using conditional operating rules distinguishing dry, normal and wet hydrologic states. Alternative solutions with nearly identical objective function values were found with sustainability indices included in the model.     

Multi-Objective Optimization of the Proposed Multi-Reservoir Operating Policy Using Improved NSPSO

Severe water shortage is unacceptable for water-supply reservoir operation. For avoiding single periods of catastrophic water shortage, this paper proposes a multi-reservoir operating policy for water supply by combining parametric rule with hedging rule. In this method, the roles of parametric rule and hedging rule can be played at the same time, which are reducing the number of decision variables and adopting an active reduction of water supply during droughts in advance. In order to maintain the diversity of the non-dominated solutions for multi-objective optimization problem and make them get closer to the optimal trade-off surfaces, the multi-population mechanism is incorporated into the non-dominated sorting particle swarm optimization (NSPSO) algorithm in this study to develop an improved NSPSO algorithm (I-NSPSO). The performance of the I-NSPSO on two benchmark test functions shows that it has a good ability in finding the Pareto optimal set. The water-supply multi-reservoir system located at Taize River basin in China is employed as a case study to verify the effect of the proposed operating policy and the efficiency of the I-NSPSO. The operation results indicate that the proposed operating policy is suitable to handle the multi-reservoir operation problem, especially for the periods of droughts. And the I-NSPSO also shows a good performance in multi-objective optimization of the proposed operating policy.     

A Novel Optimization Method for Multi-Reservoir Operation Policy Derivation in Complex Inter-Basin Water Transfer System

Joint operation of multiple reservoir system in inter-basin water transfer-supply project is a complex problem because of the complicated structure and cooperated operation policy. The combination of high-dimensional, multi-peak and multiple constraints makes it incredibly difficult to obtain the optimal rule curves for multi-reservoir operation. In view of this, we constructed a joint optimization operation model, considering both water supply and transfer, and proposed the concept of “shape constraints”. To obtain the solution of this high-dimensional optimization model, a novel progressive optimum seeking method, namely Progressive Reservoir Algorithm-Particle Swarm Optimization (PRA-PSO), is presented based on the nature of progressive optimization algorithm (POA) and standard particle swarm optimization (PSO). The water transfer project in northeast China, consisting of three routes eight reservoirs, is selected as a case study. The results show that (1) PRA-PSO is yielding much more promising results when compared with other optimization techniques; (2) shape constraints would narrow the scope of feasible solution area but increase the convergence of algorithm; (3) because of the strong interaction between water transfer and water supply action, the progressive setting of PRA-PSO should be in accordance with the order of reservoir water transfer. The case study indicates the novel optimization method could effectively increase the chance of jumping out of local optimal points, thereby searching for better solutions.     

Deriving Hedging Rules of Multi-Reservoir System by Online Evolving Neural Networks

This paper presents an online optimization scheme for combined use of Artificial Neural Networks (ANN), hedging policies and harmony search algorithm (HS) in developing optimum operating policies for Tehran water resources system. Past efforts in this area are concentrated on using an offline approach. In that approach, an optimization method is first used to derive a long-term set of optimum reservoir releases. These releases are then used as the target vector for training the ANN model. The online method simultaneously uses the optimization and ANN methods and can adopt objective functions other than minimizing the error indices. Therefore, it requires methods other than the backpropagation for training the ANN model. Hence, under the proposed online approach the application of a heuristic method, such as HS, is inevitable for training the network. This is accomplished by using an optimization-simulation procedure where different objective functions and system constraints could be easily handled. The proposed approach is a novel and efficient method for finding the parameters of hedging policies where earlier methods suffered from high computational costs and the curse of dimensionality. The results show the superiority of the proposed online scheme. Moreover, a surrogate model for the hedging policy is presented, which by adhering to the principle of parsimony is more efficient in large scale systems involving many decision variables.     

Lumped Approach to a Multi-Period–Multi-Reservoir Cyclic Storage System Optimization

Cyclic storage system (CSS) refers to the joint development and operation of surface impoundment and subsurface subsystems with natural and physical interaction and a prespecified operating rule which manages the inter-relation between different components of the system. This paper presents a lumped modeling approach to a generalized large scale cyclic storage system. The model is capable to optimally design and operate a cyclic system in an irrigable area. The excitation units with pronounced impact and a complexity of the semi-distributed model are replaced with approximated lumped functions, while maintaining a desirable level of accuracy in the system’s performance in a long-term planning horizon. The proposed model has a MINLP structure, which is solved using powerful well-known LINGO solver. Design capacities of different components of the system and the associated operating rules parameters are considered as decision variables that minimize total cost of operating costs over the planning horizon. Extensive simulation runs show that the derived operating rules perform quite satisfactory with non-significant deficits over the entire horizon.     

Chaotic Evolutionary Algorithms for Multi-Reservoir Optimization

The water sharing dispute in a multi-reservoir river basin forces the water resources planners to have an integrated operation of multi-reservoir system rather than considering them as a single reservoir system. Thus, optimizing the operations of a multi-reservoir system for an integrated operation is gaining importance, especially in India. Recently, evolutionary algorithms have been successfully applied for optimizing the multi-reservoir system operations. The evolutionary optimization algorithms start its search from a randomly generated initial population to attain the global optimal solution. However, simple evolutionary algorithms are slower in convergence and also results in sub-optimal solutions for complex problems with hardbound variables. Hence, in the present study, chaotic technique is introduced to generate the initial population and also in other search steps to enhance the performance of the evolutionary algorithms and applied for the optimization of a multi-reservoir system. The results are compared with that of a simple GA and DE algorithm. From the study, it is found that the chaotic algorithm with the general optimizer has produced the global optimal solution (optimal hydropower production in the present case) within lesser generations. This shows that coupling the chaotic algorithm with evolutionary algorithm will enrich the search technique by having better initial population and also converges quickly. Further, the performances of the developed policies are evaluated for longer run using a simulation model to assess the irrigation deficits. The simulation results show that the model satisfactorily meets the irrigation demand in most of the time periods and the deficit is very less.     

Optimizing Multiple Linear Rules for Multi-Reservoir Hydropower Systems Using an Optimization Method with an Adaptation Strategy

Water Resources Management - Water resources crisis has a significant impact on hydropower energy production, which highlights the importance of water resources management. Reservoirs are effective...     

Optimised Conjunctive Control Rules for a System of Water Supply Sources: Roadford Reservoir System (U.K.)

Due to the large and often competing demands for water andincreasing importance of sustainability criteria, waterresource managers have begun to examine closely ways inwhich the operation of existing and planned reservoirscould be optimised. Guidelines have been devised on theoperation of multi-purpose, multiple reservoir watersystems, but there remains no methodology generally acceptedby water resource managers for deriving multiple-reservoir operating policies.This article proposes a new approach to the optimisation ofthe operation of multiple reservoir systems. The revisedmethodology develops the concept of an extended droughtperiod to evaluate additional emergency storage reserveextending the reliability of the system. The operation ofthe Roadford Reservoir System, South West England,consisting of nine reservoirs was studied. Throughsimulation analysis, the control rules for each reservoirwere revised based on the concept of minimising demanddeficit. This article highlights the superior resultscompared with the current operating control rules.     

立轴冲击式水轮发电机组技术供水系统优化

水电站的技术供水系统要根据水电站的基本技术参数及设备要求的技术供水参数进行详细设计和论证,并结合电站所在区域的实际情况,使系统满足机组在各种工况下的正常安全稳定运行。主要阐述米角河一级水电站立轴冲击式水轮发电机组技术供水系统的优化设计,采用一次冷却供水和冷却水二次循环供水相结合的设计方案,避免供水水质变化时停机造成的经济损失,提高了供水系统的运行可靠性,减少了检修维护量,为电站"无人值班,少人值守"创造了必要条件。     

东莞市城市供水系统优化方案的探讨

通过对东莞市城市供水系统现状的调查,对城市供水系统中存在的问题进行了分析,并针对这些问题提出了供水水源、供水网络、供水管理方面的优化方案,供水资源优化配置时参考.     

Offline Optimization of Sluice Control Rules in the Urban Water System for Flooding Mitigation

A real-time control (RTC) system can substantially improve the efficiency in urban flooding mitigation by optimizing the capacity of drainage and storage in existing drainage systems. However, few studies have investigated the RTC for an Urban Water System (UWS) in cities with a high water surface ratio for flooding mitigation. In this study, control rules of the actuators in an UWS were developed by an offline optimization system that combines a hydraulic model, the stormwater management model (SWMM) with an optimization model solved by the differential evolution (DE) algorithm. Iteratively simulating only the downstream UWS hydraulic model, the objectives of this study were to (i) minimize the flooding volume from the UWS and (ii) minimize the cumulative water depths above the control lines in real-time. Results showed that the optimal control rules outperformed the current fully open and closed rules, indicating the UWS’s retaining and draining capacity was effectively utilized through the offline optimization. This study also found that the robust control rules might be biased and have little effect under most rainfalls, especially mild storms, as they are derived from the system’s average performance under various rainfall conditions.

     

Spatial Optimization Models for Water Supply Allocation

Climate change is likely to result in increased aridity, lower runoff, and declining water supplies for the cities of the Southwestern United States, including Phoenix. The situation in Phoenix is particularly complicated by the large number of water providers, each with its own supply portfolio, demand conditions, and conservation strategies. This paper details spatial optimization models to support water supply allocation between service provider districts, where some districts experience deficits and others experience surpluses in certain years. The approach seeks to reconcile and integrate projections derived from a complex simulation model taking into account current and future climate conditions. The formulated and applied models are designed to help better understand the expected increasingly complex interactions of providers under conditions of climate change. Preliminary results show cooperative agreements would reduce spot shortages that would occur even without climate change. In addition, they would substantially reduce deficits if climate change were to moderately reduce river flows in Phoenix’s major source regions, but have little effect under the most pessimistic scenarios because there are few surpluses available for re-allocation.     

Multi-Colony Ant Algorithm for Continuous Multi-Reservoir Operation Optimization Problem

Ant Colony Optimization (ACO) algorithms are basically developed for discrete optimization and hence their application to continuous optimization problems require the transformation of a continuous search space to a discrete one by discretization of the continuous decision variables. Thus, the allowable continuous range of decision variables is usually discretized into a discrete set of allowable values and a search is then conducted over the resulting discrete search space for the optimum solution. Due to the discretization of the search space on the decision variable, the performance of the ACO algorithms in continuous problems is poor. In this paper a special version of multi-colony algorithm is proposed which helps to generate a non-homogeneous and more or less random mesh in entire search space to minimize the possibility of loosing global optimum domain. The proposed multi-colony algorithm presents a new scheme which is quite different from those used in multi criteria and multi objective problems and parallelization schemes. The proposed algorithm can efficiently handle the combination of discrete and continuous decision variables. To investigate the performance of the proposed algorithm, the well-known multimodal, continuous, nonseparable, nonlinear, and illegal (CNNI) Fletcher–Powell function and complex 10-reservoir problem operation optimization have been considered. It is concluded that the proposed algorithm provides promising and comparable solutions with known global optimum results.