scholarly journals Effect of the 1985 Farm Bill Provisions on Farmers' Soil Conservation Decisions

1990 ◽  
Vol 22 (2) ◽  
pp. 179-189 ◽  
Author(s):  
Jeffrey M. Gillespie ◽  
L. Upton Hatch ◽  
Patricia A. Duffy
Keyword(s):  
Soil Conservation ◽  
Soil Loss ◽  
Programming Model ◽  
Conservation Reserve Program ◽  
Mixed Integer ◽  
Farm Bill ◽  
Bid Price ◽  
Land Retirement ◽  
Farmer Survey ◽  
Conservation Decisions

Abstract Conservation initiatives in the 1985 Farm Bill affected farmers' decisions regarding soil conservation. A farmer survey was conducted and a multi-period mixed-integer programming model was developed to determine an optimal farm plan with choices of crop-tillage combinations and land retirement Results indicate that farmers' incentives to reduce soil loss in the Sand Mountain region in Alabama are not substantially affected by provisions of the 1985 Farm Bill. The bid price for the Conservation Reserve Program will have to be considerably higher than 1988 levels to provide an incentive to remove land from production.

scholarly journals Incorporating the 1990 Farm Bill into Farm-Level Decision Models: An Application to Cotton Farms

1993 ◽  
Vol 25 (2) ◽  
pp. 119-133 ◽  
Author(s):  
Patricia A. Duffy ◽  
Danny L. Cain ◽  
George J. Young
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Programming Model ◽  
Decision Models ◽  
Mixed Integer ◽  
Farm Income ◽  
Farm Bill ◽  
Farm Level ◽  
Integer Programming Model ◽  
Cross Compliance

AbstractA five-year, 0-1, mixed integer programming model was developed to analyze the effects of 1990 Farm Bill legislation on the crop-mix decisions made on cotton farms. Results showed that, when compared to the 1985 Farm Bill, the 1990 Farm Bill can result in higher whole-farm income despite new "triple base" provisions limiting payment acres. The increase in income results from elimination of limited cross-compliance provisions and the change to a three-year base calculation. The model was also used to assess the likely impact of possible changes in the current legislation.

Analysis on the Penetration Level of Wind Farm Considering Transient Stability Constraint and Uncertainty of Wind Power

2020 ◽  
Vol 13 (7) ◽  
pp. 1078-1086
Author(s):  
Bai Hao ◽  
Huang Andi ◽  
Zhou Changcheng
Keyword(s):  
Wind Power ◽  
Energy Function ◽  
Wind Farm ◽  
Transient Stability ◽  
Programming Model ◽  
Forecast Error ◽  
Credibility Theory ◽  
Mixed Integer ◽  
Stability Constraint ◽  
Level Problem

Background: The penetration level of a wind farm with transient stability constraint and static security constraint has been a key problem in wind power applications. Objective: The study explores maximum penetration level problem of wind considering transient stability constraint and uncertainty of wind power out, based on credibility theory and corrected energy function method. Methods: According to the corrected energy function, the transient stability constraint of the power grid is transferred to the penetration level problem of a wind farm. Wind speed forecast error is handled as a fuzzy variable to express the uncertainty of wind farm output. Then this paper builds a fuzzy chance-constrained model to calculate wind farm penetration level. To avoid inefficient fuzzy simulation, the model is simplified to a mixed integer linear programming model. Results: The results validate the proposed model and investigate the influence of grid-connection node, wind turbine characteristic, fuzzy reliability index, and transient stability index on wind farm penetration level. Conclusion: The result shows that the model proposed in this study can consider the uncertainty of wind power out and establish a quantitative transient stability constraint to determine the wind farm penetration level with a certain fuzzy confidence level.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals New general mixed-integer linear programming model for mobile workforce management

2021 ◽  
Author(s):  
András Éles ◽  
István Heckl ◽  
Heriberto Cabezas
Keyword(s):  
Programming Model ◽  
Time Windows ◽  
Mutual Exclusion ◽  
Parallel Execution ◽  
Mixed Integer ◽  
Management Problem ◽  
Routing Problem ◽  
Workforce Management ◽  
Computational Performance ◽  
Wide Range

AbstractA mathematical model is introduced to solve a mobile workforce management problem. In such a problem there are a number of tasks to be executed at different locations by various teams. For example, when an electricity utility company has to deal with planned system upgrades and damages caused by storms. The aim is to determine the schedule of the teams in such a way that the overall cost is minimal. The mobile workforce management problem involves scheduling. The following questions should be answered: when to perform a task, how to route vehicles—the vehicle routing problem—and the order the sites should be visited and by which teams. These problems are already complex in themselves. This paper proposes an integrated mathematical programming model formulation, which, by the assignment of its binary variables, can be easily included in heuristic algorithmic frameworks. In the problem specification, a wide range of parameters can be set. This includes absolute and expected time windows for tasks, packing and unpacking in case of team movement, resource utilization, relations between tasks such as precedence, mutual exclusion or parallel execution, and team-dependent travelling and execution times and costs. To make the model able to solve larger problems, an algorithmic framework is also implemented which can be used to find heuristic solutions in acceptable time. This latter solution method can be used as an alternative. Computational performance is examined through a series of test cases in which the most important factors are scaled.

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