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• We analyze time-of-use rates with respect to model predictive controls. • We show that thermal storage potential can vary greatly for differing climates. • We build a model predictive control framework for indoor temperature setpoints. • We analyze of the impacts of different variable electric rates. • The framework is capable of reducing cooling electricity costs by 30%. Heating, ventilation and air conditioning for residential and commercial buildings requires a substantial share of electric energy, and ultimately drives summer peak demand in the United States. Variable electric rates are becoming more common in the residential market, as utilities try to encourage users to shift their energy demand. Model predictive controls, one method of reducing energy usage, employ an optimization model to minimize peak demand, energy usage, or electricity costs. This paper details the development of a co-simulation framework to rapidly model and simulate building energy use and optimize cooling setpoint controls. The framework integrates commercially available software to: (i) simulate all energy interactions between the building, internal gains, outdoor environment, and heating and cooling systems via a building energy simulation program (EnergyPlus), (ii) algebraically formulate an optimization problem (with AMPL) using a black-box, reduced-order model for rapid calculations, (iii) employ Simulink as the environment that links calls to EnergyPlus and AMPL, and (iv) solve the optimization model (with CPLEX) to minimize electricity costs and user discomfort. Variable electric time-of-use rates are analyzed in the context of total cooling electricity costs, thermal comfort of users, and peak demand shedding. The framework uses a model predictive control formulation capable of reducing cooling electricity costs by up to 30%; however, cost savings and peak demand shedding are highly dependent on the time-of-use electricity rate schedule. [ABSTRACT FROM AUTHOR]

Interconnection wide electric transmission system has been envisioned to facilitate the growth of renewable energy, enhance reliability, improve system operating efficiency, and reduce emissions. In this paper, we propose an explicit planning approach for transmission design at the national level. It has been applied to the U.S. system to design transmission overlays for multiple future generation scenarios. Associated simulation results suggest that a national transmission overlay provides economic, environmental, and system performance benefits. [ABSTRACT FROM PUBLISHER]

The airline industry currently has a $40-billion plus market and is expected to grow rapidly with the population growth and growth in the overall economy. Everyday, thousands of aircrafts undergo maintenance, repair, and overhaul. The aircraft maintenance problem is one of the important logistic problems in the airline industry. It is aimed at scheduling the aircrafts' routing so that enough maintenance opportunities are provided to every aircraft in the fleet. In this paper, we present a new compact network representation of the aircraft maintenance routing problem (AMR) and propose a new mixed-integer linear programming formulation to solve the problem. The quality of this model was assessed on four real test instances from a major U.S. carrier, and compared with the flight string model proposed in the literature. The computational results show that the proposed model is able to obtain the optimal solutions to all test instances in reasonable time. This study suggests that this model can be applied to integrated problems of the AMR and other planning problems such as the fleet assignment problem and crew pairing problem. [ABSTRACT FROM AUTHOR]

This paper presents the development of a mathematical programming model for evaluating options for the continuance of the Essential Air Services (EAS) program, a provision of the Airline Deregulation Act of 1978. The EAS program was established to guarantee continued air service to small communities that suffered air service losses as a result of carrier withdrawals permitted by the Deregulation Act. The model has been designed to aid decision makers in allocating subsidized air services to communities in a region, given the goal of maximizing access to the air transportation system. In order to provide information on optimal service locations, service characteristics, and patterns of access, the authors have utilized a maximal covering formulation and incorporated elements of hierarchical and weighted benefit models. In addition, a stopover service option has been included in the model to permit evaluation of potential gains in economic efficiency that may be realized by utilizing stopover service, when appropriate, in place of more costly nonstop service. In addition to the coverage objective, a second objective of systemwide cost minimization is employed. By using a multiobjective approach, the trade-offs between the conflicting objectives of coverage and cost can be explored and the spatial patterns of service associated with each combination of coverage level and cost can be examined. The model is applied to a sample study of small community air service in North and South Dakota. [ABSTRACT FROM AUTHOR]

Coal is the second-largest source for electricity generation in the United States. However, the burning of coal produces dangerous gas emissions, such as carbon dioxide and Green House Gas (GHG) emissions. One alternative to decrease these emissions is biomass co-firing. To establish biomass as a viable option, the optimization of the biomass supply chain (BSC) is essential. Although most of the research conducted has focused on optimization models, the purpose of this paper is to incorporate machine-learning (ML) algorithms into a stochastic Mixed-Integer Linear Programming (MILP) model to select potential storage depot locations and improve the solution in two ways: by decreasing the total cost of the BSC and the computational burden. We consider the level of moisture and level of ash in the biomass from each parcel location, the average expected biomass yield, and the distance from each parcel to the closest power plant. The training labels (whether a potential depot location is beneficial or not) are obtained through the stochastic MILP model. Multiple ML algorithms are applied to a case study in the northeast area of the United States: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Multi-Layer Perceptron (MLP) Neural Network. After applying the hybrid methodology combining ML and optimization, it is found that the MLP outperforms the other algorithms in terms of selecting potential depots that decrease the total cost of the BSC and the computational burden of the stochastic MILP model. The LR and the DT also perform well in terms of decreasing total cost. [ABSTRACT FROM AUTHOR]