MODELS FOR WATER AND POWER SCHEDULING FOR THE CALIFORNIA STATE WATER PROJECT1

ABSTRACT: A large-scale simulation/optimization model provides schedules for operation of water and power for the California State Water Project (SWP). The SWP consists of a series of reservoirs linked by rivers, pumping plants, canals, tunnels, and generating plants and is operated by the California Department of Water Resources. The Department provides water to municipal and agricultural users, and manages its electrical loads and resources. The model, therefore, performs hydraulic and electrical computations leading to optimal operation of the entire system. It consists of hydraulic network programming components to meet the storage objectives at all the reservoirs, a linear programming component to determine the schedules at pumping and generating plants, an electrical network programming component to balance electrical loads and resources, and a number of other simulation components. It operates on yearly, weekly, and daily bases. It is primarily used for real-time operation of the SWP and can provide hourly detail schedules which are implemented by the SWP staff via a computerized system.     

APPROACHES TO WATER RESOURCE POLICY AND PLANNING IN TEXAS1

ABSTRACT: Texas is one of the states in which limitations in water supplies could severely constrain economic growth in certain areas. The traditional planning approach for addressing this problem has involved devising schemes for large water development projects, which for many years included the importation of water from other states. Now the attitude towards water resource management is changing, and it is generally agreed that better management of existing supplies is the preferred approach. In this paper we review some of the changes that have recently occurred in Texas, including attempts to streamline the water institutions in such a way that they might be more responsive to the need for more comprehensive management of water resources statewide, with greater emphasis on social and environmental concerns.     

DERIVING THE NONLINEAR RISK-BENEFIT ALGORITHM FOR RESERVOIRS1

ABSTRACT: The Nonlinear Risk-Benefit (NRB) Algorithm includes risk as one of the objectives in a multiple-objective optimization problem. The NRB Algorithm is derived by extending the Surrogate Worth Trade-Off method to quadratic programming. This category of problem is common in water resources planning and design, especially multipurpose reservoir systems. Consequently, an example is given using the algorithm for optimally operating a multipurpose reservoir.     

提高交通安全——实现智能汽车的研究

主要介绍了通过智能汽车的研究 ,提高城市交通安全的必要性 ;综述了发达国家关于智能汽车研究的现状与趋势 ,并通过国外智能汽车发展的趋势 ;进一步论证智能汽车的研究是汽车工业发展的方向 ,也是提高交通安全的重要手段之一。此外 ,论文提出利用模式识别技术的智能汽车的结构框架 ;指出神经网络方法应用与模式识别技术结合的优势。最后 ,论文在阐述我国需要发展智能汽车的同时 ,提出应结合中国国情相应研究相关理论 ,为今后的实施奠定基础的近期目标 ,并提出展望     

INTEGRATED MODELING FOR WATERSHED MANAGEMENT: MULTIPLE OBJECTIVES AND SPATIAL EFFECTS1

ABSTRACT: This paper presents an optimization framework for prioritizing sites for wetlands restoration on a watershed or landscape scale. The framework is designed for analyzing the potential environmental impacts of alternative management strategies while accounting for economic constraints, thereby aiding decision makers in explicitly considering multiple management objectives. The modeling strategy consists of two phases. First, relationships between the configuration of land use types in a watershed and valued ecosystem services are specified mathematically. Second, those functions are incorporated into a spatial optimization model that allows comparisons of the expected environmental impacts and economic costs of management strategies that change the configuration of land use in the watershed. By way of a stylized example, this paper develops the general structure of the framework, presents simulation results based on two production functions for ecosystem services, and discusses the potential utility of the methodology for watershed management.     

MULTIOBJECTWE OPTIMIZATION OF RESERVOIR SYSTEMS OPERATION1

ABSTRACT: Development of optimal operational policies for large-scale reservoir systems is often complicated by a multiplicity of conflicting project uses and purposes. A wide range of multiobjective optimization methods are available for appraising tradeoffs between conificting objectives. The purpose of this study is to provide guidance as to those methods which are best suited to dealing with the challenging large-scale, nonlinear, dynamic, and stochastic characteristics of multireservoir system operations. As a case study, the selected methodologies are applied to the Han River Reservoir System in Korea for four principal project objectives: water supply and low flow augmentation; annual hydropower production, reliable energy generation, and minimization of risk of violating firm water supply requirements. Additional objectives such as flood control are also considered, but are imposed as fixed constraints.     

SOME CONJUNCTIVE DESALTING STUDIES EN THE UNITED KINGDOM1

ABSTRACT .Operating rule procedures are developed for the optimal use of desalination in conjunction with surface water impoundments. Results obtained from the use of hill-climbing and dynamic programming methods are compared. Benefits to be obtained from the integrated use of desalination are demonstrated through a case study for the Barcelona area of Spain.     

SIMULATION/OPTIMIZATION TECHNIQUES FOR MULTI-BASIN WATER RESOURCE PLANNING1

A set of simulation and optimization tools capable of analyzing the development and operation of a complex, multi-basin, interconnected water resource are explained. These models provide valuable information regarding the important questions: (1) “When should new projects be build?” (2) “How big should they be?” and (3) “How should the system be operated?” Since these tools were developed by and for practicing engineers, their applicability to real-world problems is mandatory. To assure this, testing was done on an actual proposed project, the Texas Water System.     

Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm

ABSTRACT

Wind speed forecasting plays an important role in power grid dispatching management. This article proposes a short-term wind speed forecasting method based on random forest model combining ensemble empirical modal decomposition and improved harmony search algorithm. First, the initial wind speed data set is decomposed into several ensemble empirical mode functions by EEMD, then feature extraction of each sub-modal IMF is performed using fast Fourier transform to solve the cycle of each sub-modal IMF. Next, combining the high-performance parameter optimization ability of the improved harmony search algorithm, two optimal parameters of random forest model, number of decision trees, and number of split features are determined. Finally, the random forest model is used to forecast the processing results of each submodal IMF. The proposed model is applied to the simulation analysis of historical wind data of Chaoyang District, Liaoning Province from April 27, 2015 to May 22, 2015. To illustrate the suitability and superiority of the EEMD-RF-IHS model, three types of models are used for comparison: single models including ANN, SVM, RF; EMD combination models including EMD-ANN, EMD-SVM, EMD-RF; EEMD combination models including EEMD-ANN, EEMD-SVM, EEMD-RF. The analysis results of evaluation indicators show that the proposed model can effectively forecast short-term wind data with high stability and precision, providing a reference for forecasting application in other industry fields.     

Multi-objective-based optimal transmission switching and demand response for managing congestion in hybrid power systems

ABSTRACT

This paper proposes a novel congestion management (CM) approach by using the optimal transmission switching (OTS) and demand response (DR) for a system with conventional thermal generators and renewable energy sources (RESs). In this paper, wind and solar PV units are considered as the RESs. The stochastic behavior of wind and solar PV powers are modeled by using the appropriate probability density functions (PDFs). The proposed CM methodology simultaneously optimizes the generation dispatch, demand response, and also the network topology of the power system. The OTS identifies the branches that should be taken out of service by significantly reducing the operating cost of the system while respecting the system security. Here, the total operating cost minimization/social welfare maximization and system losses minimization are considered as the objectives to be optimized. The proposed CM problem is solved using the multi-objective Jaya algorithm and it is used to determine a set of Pareto-optimal solutions. The Jaya algorithm is simple and it does not have any algorithmic-specific parameters to be tuned. This aspect reduces the designer’s effort in tuning the parameters to arrive at the optimum objective function value. A fuzzy logic-based approach is used to identify the best compromise solution. The effectiveness of the proposed CM approach is examined on modified IEEE 30 and practical Indian 75 bus test systems. The obtained simulation results are analyzed and they show the effectiveness of the proposed approach.