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Stochastic optimization can be used to model predictable but uncertain element failures, in an attempt to enhance system reliability in power system operation and planning. In practical applications, such as preventive operation during severe weather, the uncertainty set is often very large. This will lead to two challenges: (i) every possible scenario cannot be practically identified; and (ii) the computational demands of stochastic optimization with a large scenario set cannot be met. To address these challenges, this paper develops a multidimensional scenario selection method, which creates a rather small but representative set of scenarios. The developed method makes use of failure features as well as network features of the elements that may fail, to achieve a superior performance. The simulations studies on a synthetic large-scale Texas system, show the dominant performance of the method compared to existing algorithms in the literature, as well as common industry practices. Due to its effectiveness, the method presented in this paper enables computationally efficient implementation of stochastic power system operation and planning software tools. Such stochastic tools will improve system reliability and efficiency through enhanced use of the existing resources, without requiring any expensive system upgrade. [ABSTRACT FROM AUTHOR]

Highway maintenance and rehabilitation (M&R) planning is a multifaceted decision-making process that requires the consideration of multiple but often conflicting objectives. Traditionally, state highway agencies (SHAs) have focused on maximizing the condition of a pavement network or minimizing agency costs needed to keep the network at an acceptable physical and functional level of service. However, other objectives like minimizing the road user costs and greenhouse gas (GHG) emissions are seldom factored into the M&R decision-making process. In this paper, a mixed integer program that accounts for these three objectives is presented. The proposed model is then implemented on a subset network in Texas consisting of 100 pavement sections. The results demonstrate the Pareto-optimal relationships that exist between the road condition improvements, road user costs, and GHG emissions. Notably, there is a positive relationship between condition score improvements and the observed marginal GHG emissions from the case network implementation. Furthermore, this network's results indicate that GHG emissions from materials and construction contribute to the most significant proportion of the total estimated GHG emissions. The results of the sensitivity analysis confirm the observed trends in the network case examined and provide valuable insights concerning the impact of variability in the budget size. The principal contribution of this study is in providing an approach for highway decision-makers to perform trade-offs in road condition improvements, road user costs, and GHG emissions of alternate pavement M&R programs or policies. [ABSTRACT FROM AUTHOR]

A feasible alternative to the production of fossil fuels is the production of biofuels. In order to minimize the costs of producing biofuels, we developed a stochastic programming formulation that optimizes the inbound delivery of biomass. The proposed model captures the variability in the moisture and ash content in the biomass, which define its quality and affect the cost of biofuel. We propose a novel hub-and-spoke network to take advantage of the economies of scale in transportation and to minimize the effect of poor quality. The first-stage variables are the potential locations of depots and biorefineries, and the necessary unit trains to transport the biomass. The second-stage variables are the flow of biomass between the network nodes and the third-party bioethanol supply. A case study from Texas is presented. The numerical results show that the biomass quality changes the selected depot/biorefinery locations and conversion technology in the optimal network design. The cost due to poor biomass quality accounts for approximately 8.31 % of the investment and operational cost. Our proposed L-shaped with connectivity constraints approach outperforms the benchmark L-shaped method in terms of solution quality and computational effort by 0.6 % and 91.63 % on average, respectively. [ABSTRACT FROM AUTHOR]

With an increasing emphasis on sustainable planning, highway decision makers need to shift their focus from developing a cost-effective highway program to developing a more comprehensive sustainable highway program. It includes incorporating the three components of sustainability, namely economics, the environment, and social equity. Studies in the literature have primarily focused on economic and environmental objectives. However, there are limited studies incorporating social equity in highway maintenance and rehabilitation (M&R) programming. Additionally, a review of the existing literature indicated that although studies have focused on single or dual objectives of sustainability, the existing literature failed to address all the three components of sustainability simultaneously. Accordingly, this study aimed to address this issue by proposing an M&R budget allocation model for developing a sustainable highway asset management plan that concurrently focuses on all the three objectives of sustainability. The proposed model assists decision makers with a range of potential decision alternatives that address multiple sustainability objectives. The proposed model was applied to a case-study example on a road network in Texas. The findings from the case study indicated that decision makers can implement sustainable M&R programs that can efficiently achieve social equity objectives without specifically compromising the environmental and economic objectives. [ABSTRACT FROM AUTHOR]

We propose an approach to solving the AC network-constrained unit commitment problem (AC-NCUC) of large-scale power systems. This approach relies on solving convex (continuous and mixed-integer) optimization problems. First, we formulate a second-order conic relaxation of the original optimization problem, which renders a mixed-integer second-order conic optimization problem. To solve this problem, we use Benders’ decomposition, which decomposes it into a master problem, which is mixed-integer linear, and a subproblem, which is continuous and second-order conic. To improve the convergence of the Benders’ algorithm, we add several linear feasibility constraints to the master problem. Since the second-order relaxation of the original problem cannot guarantee AC feasibility of the solution found, we ensure such feasibility by solving a sequence of continuous convex optimization problems. We numerically validate the proposed approach using a Texas 2000-bus system. [ABSTRACT FROM AUTHOR]

This work focuses on developing methods to better manage significant imbalances between water supply and demand during droughts. A service-driven approach (Model as a Service, or MaaS) is used to couple river modeling services with optimization services for determining optimal water allocation strategies under daily drought scenarios. It demonstrates the promise of coupling simulation-optimization model services to improve real-time water management in a service driven framework, which should be beneficial to many other water resource applications. The approach is implemented using the DataWolf workflow tool and AzureML Cloud machine learning services and applied to an April 2015 drought event in the Upper Guadalupe River Basin, Texas. Weather and water demand uncertainty are considered through scenario-based optimization. The optimization objective is to minimize the daily total curtailment hours across all groups of permit holders. The scenario analysis shows that the current permit grouping system has a significant impact on the optimal water allocation strategy. The scenarios also demonstrate that noncompliance of junior water users is predicted to have a much greater effect on the river system than noncompliance of senior water users. The resulting framework can be deployed for water allocation in any area by updating water user information, water allocation policy constraints, and river data that can be obtained from publicly available sources. [ABSTRACT FROM AUTHOR]

• The study developed Equity-based optimization models for highway maintenance and rehabilitation (M&R) programming. • The models were implemented on a numerical case study based in Texas (in the United States). • Social equity was assessed by using adaptations of the Gini coefficient and the Theil index. • Different equity policies used for M&R programming lead to varying equity levels in pavement conditions. • In general, equity improves with an increase in budget. With limited funds and an aging highway infrastructure network, agency decision-makers are tasked with making the most cost-effective decisions while accounting for the environment and social equity. Several studies have accounted for the economic and environmental considerations in infrastructure management decisions. However, there have been limited studies which have proposed quantitative approaches for integrating social equity as part of the highway Maintenance and Rehabilitation (M&R) decision-making process. To address this gap in the literature, this paper proposes four optimization models (corresponding to policies) for achieving social equity in highway M&R programming. The underlying rationales behind these models include combinations of egalitarian, utilitarian, socialism, and Rawlsian equity theories. The developed models were then implemented in a numerical case study consisting of a network of 500 pavement sections. The case study results were evaluated using average condition scores and adaptations of the Gini Coefficient and the Theil Ind ex. An inter-policy analysis suggests that for a similar performance in network condition, different policies yield varying inequity levels over time. Furthermore, step changes in the budget size also suggest that a higher budget size generally leads to a reduction in inequity although some policies perform better than others. [ABSTRACT FROM AUTHOR]

The maximum entropy model, a commonly used species distribution model (SDM) normally combines observations of the species occurrence with environmental information to predict the geographic distributions of animal or plant species. However, it only produces point estimates for the probability of species existence. To understand the uncertainty of the point estimates, we analytically derived the variance of the outputs of the maximum entropy model from the variance of the input. We applied the analytic method to obtain the standard deviation of dengue importation probability and Aedes aegypti suitability. Dengue occurrence data and Aedes aegypti mosquito abundance data, combined with demographic and environmental data, were applied to obtain point estimates and the corresponding variance. To address the issue of not having the true distributions for comparison, we compared and contrasted the performance of the analytical expression with the bootstrap method and Poisson point process model which proved of equivalence of maximum entropy model with the assumption of independent point locations. Both Dengue importation probability and Aedes aegypti mosquito suitability examples show that the methods generate comparatively the same results and the analytic method we introduced is dramatically faster than the bootstrap method and directly apply to maximum entropy model. [ABSTRACT FROM AUTHOR]

An experimental investigation was performed to assess the performance of a hybrid fiber-reinforced polymer/reinforced concrete bridge system. The full-scale laboratory specimen was representative of an 813 mm(32 in.) wide strip of a completed bridge in San Patricio County, Tex. The specimen was first subjected to static loading prior to casting the reinforced concrete deck. Displacement, strain, and acoustic emission were recorded. After completion of the nondestructive static loading a reinforced concrete deck was cast in the laboratory to represent one unit of the completed bridge. Load was statically applied with several increased load cycles until failure occurred at a load level exceeding 18 times the calculated design load. The results of the static testing indicated that the original design of the hybrid bridge was very conservative. An optimized design of the hybrid bridge was then derived. The static load testing program and the resulting optimized design are described. [ABSTRACT FROM AUTHOR]

Aquifer storage recovery (ASR) is an innovative technology with the potential to augment dwindling water resources in regions experiencing rapid growth and development. Planning and design of ASR systems requires quantifying how much water should be stored and appropriate times for storage and withdrawals within a planning period. A monthly scale planning model has been developed in this study to derive optimal (least cost) long-term policies for operating ASR systems and is solved using a recursive deterministic dynamic programming approach. The outputs of the model include annual costs of operation, the amount of water to be imported each month as well as the schedule for storage and extraction. A case study modeled after a proposed ASR system for Mustang Island and Padre Island service areas of the city of Corpus Christi is used to illustrate the utility of the developed model. The results indicate that for the assumed baseline demands, the ASR system is to be kept operational for a period of 4 months starting from May through August. Model sensitivity analysis indicated that increased seasonal shortages can be met using ASR with little additional costs. For the assumed cost structure, a 16% shortage increased the costs by 1. 6%. However, the operation time of ASR increased from 4 to 8 months. The developed dynamic programming model is a useful tool to assess the feasibility of evaluating the use of ASR systems during regional-scale water resources planning endeavors. [ABSTRACT FROM AUTHOR]

A multistage stochastic optimization model using linear programming was developed to provide planning tools for Lower Colorado River Authority in the operation of the Highland Lakes system, as well as a framework for examining the operation of four irrigation districts. Three primary objectives were maximized in the model: (1) Revenues from rice production, (2) recreation benefits associated with lake use, and (3) revenues from hydropower generation. The model includes stochastic inflows, weather-dependent irrigation demands, an interruptible contract decision function, a reservoir space rule to balance storage volumes between reservoirs, hydropower production, municipal and irrigation return flows, and bay and estuary inflow requirements. Model weights and coefficients were calibrated to reflect actual market prices and economic constraints, or to represent water management priorities. Stochastic optimization is used to account for meteorological uncertainty using representative inflow scenarios. Considering the uncertainty in the inflows, the model predicts optimum acreage levels, reservoir storage levels, reservoir releases, and other decisions, to maximize total expected benefits in the system. [ABSTRACT FROM AUTHOR]