A Stochastic Dynamic Programming Approach To Balancing Wind Intermittency With Hydropower

Hydropower is a rapid response energy source and thus a perfect complement to the intermittency of wind power. However, the effect wind energy has on conventional hydropower systems can be felt, especially if the system is subject to several other environmental and non-power use constraints. The goal of this paper is to develop a general method for optimizing short-term hydropower operations of a realistic multireservoir hydropower system in a deregulated market setting when there is a stochastic wind input. The approach used is a modification of stochastic dynamic programming (SDP). The methodology is applied to a representation of multiple projects in the Federal Columbia River Power System, which is currently being dispatched by the Bonneville Power Administration. Currently, studies on hydropower operations optimization with wind have involved linear programming orstochastic programming, which are based on linearity of the objective function and constraints. SDP, by contrast, is a stochastic optimization method that does not require assumptions of linearity of the objective function or the constraints. The true adaptive and stochastic nonlinear formulation of the objective function can be applied to multiple timesteps, and is efficient for many timesteps compared to stochastic programming

INFLUENCE OF APPLE CULTIVAR, TREE PHENOLOGY, AND LEAF QUALITY ON THE DEVELOPMENT AND MORTALITY OF CHORISTONEURA ROSACEANA (LEPIDOPTERA: TORTRICIDAE)

The obliquebanded leafroller, Choristoneura rosaceana (Harris), is a polyphagous insect that is occasionally a serious pest of apple trees. To determine how well adapted it is to this host plant, we studied its nutritional ecology by measuring the development and mortality of larvae and pupae reared on different sets of leaves. We investigated the influence of 3 apple cultivars, 2 time periods (June, July-August), and several leaf types, including those with different ages and different branch positions, on these processes. Larvae and pupae developed more rapidly with lower mortality on younger leaves than on older ones. When larvae were fed leaves collected from the same cultivar and branch position at different times during the season, developmental rates were faster and survival rates were higher earlier in the season. Development and survival were similar on leaves of all cultivar

Management of Potato Leafhopper, Empoasca fabae (Homoptera: Cicadellidae), on Alfalfa with the Aid of Systems Analysis

Efficient management of the potato leafhopper (Homoptera: Cicadellidae) on alfalfa requires a procedure for dealing with the complexities of the ecological and economic system. We developed a mathematical model to represent this agroecosystem and demonstrated how systems analysis can help to make management more efficient and less risky. Our management policies were based on two criteria: annual income calculated from the nutrient yields of three harvests, and level of carbohydrate reserve in the taproots at the end of the season, We determined the dynamic economic thresholds for controlling the leafhoppers as immigrants on each of the cuttings of alfalfa. During development of the thresholds we tested a variety of control tactics, including timing of harvests, We found that, for adult immigrants on the second crop, the economic thresholds increase exponentially as stem height increases, Tactics associated with these thresholds included insecticide treatments and early cutting of the second harvest. The results indicated that temperature pattern has an important effect on the economic thresholds and risk. Evaluation of the model and its results through sensitivity analysis, validation, and a comparison with current recommendations showed that the model can be a useful tool in research and managemen

Contextual Optimizer through Neighborhood Estimation for prescriptive analysis

We address the challenges posed by heteroscedastic noise in contextual decision-making. We propose a consistent Shrinking Neighborhood Estimation (SNE) technique that successfully estimates contextual performance under unpredictable variances. Furthermore, we propose a Rate-Efficient Sampling rule designed to enhance the performance of the SNE. The effectiveness of the combined solution ``Contextual Optimizer through Neighborhood Estimation"(CONE) is validated through theorems and numerical benchmarking. The methodologies have been further deployed to address a staffing challenge in a hospital call center, exemplifying their substantial impact and practical utility in real-world scenarios

Optimization Of Groundwater Remediation With New Efficient Parallel Algorithm For Global Optimization

Application of optimization algorithm to PDE modeling groundwater remediation can greatly reduce remediation cost. However, groundwater remediation analysis requires a computational expensive simulation, therefore, effective parallel optimization could potentially greatly reduce computational expense. The optimization algorithm used in this research is Parallel Stochastic radial basis function. This is designed for global optimization of computationally expensive functions with multiple local optima and it does not require derivatives. In each iteration of the algorithm, an RBF is updated based on all the evaluated points in order to approximate expensive function. Then the new RBF surface is used to generate the next set of points, which will be distributed to multiple processors for evaluation. The criteria of selection of next function evaluation points are estimated function value and distance from all the points known. Algorithms created for serial computing are not necessarily efficient in parallel so Parallel Stochastic RBF is different algorithm from its serial ancestor. The application for two Groundwater Superfund Remediation sites, Umatilla Chemical Depot, and Former Blaine Naval Ammunition Depot. In the study, the formulation adopted treats pumping rates as decision variables in order to remove plume of contaminated groundwater. Groundwater flow and contamination transport is simulated with MODFLOW-MT3DMS. For both problems, computation takes a large amount of CPU time, especially for Blaine problem, which requires nearly fifty minutes for a simulation for a single set of decision variables. Thus, efficient algorithm and powerful computing resource are essential in both cases. The results are discussed in terms of parallel computing metrics i.e. speedup and efficiency. We find that with use of up to 24 parallel processors, the results of the parallel Stochastic RBF algorithm are excellent with speed up efficiencies close to or exceeding 100%

Short-Term Optimization Model With ESP Forecasts For Columbia Hydropower System With Optimized Multi-Turbine Powerhouses

Hydroelectric generation is the major source of electric energy in the Pacific Northwestern region of the United States, and efficient operation of that system while meeting environmental constraints and reserve capacity demands is an important economic, environmental, and social issue. This paper describes efforts to develop a new stochastic short-term scheduling model (with perhaps a 3-week planning horizon) for the ten major reservoirs operated by the federal Bonneville Power Administration (BPA) on the Columbia and Snake River systems. The analysis incorporates time-delays (up to 24 hours in a model with time steps increasing from 6 hours initially perhaps to 24 hours); non-economic turbine dispatch with operational constraints; and inflow and load uncertainty (reflecting wind generation) through use of Ensemble Streamflow Predictions (ESP) augmented to include load uncertainties (ESLP). Synthetic ESLPs will be generated for the model testing effort. The intent is to evaluate the benefits of alternative representations of uncertainty subject to all of the operational constraints, both physical and those that result from environmental concerns. Large BPA storage projects can include many turbines of different types; for example, Grand Coulee has 27 turbines of 4 different types. To make system optimization faster and more reliable, concave “powerhouse” functions are pre-computed which are as economically efficient as possible given estimated turbine performance characteristics, and operational dispatch and release constraints. Powerhouse generation functions are forced to be concave if such constraints are consistent with the data; in other cases mandated fish-passage constraints result in non-economic turbine dispatch sequences and often limit allowable discharge ranges, both of which complicate the computation of the loading of individual turbines and the optimization of the hydropower system. Pre-computation of powerhouse functions is an effective decomposition technique for this large stochastic nonlinear optimization problem