scholarly journals Lagrangian Relaxation Based on Improved Proximal Bundle Method for Short-Term Hydrothermal Scheduling

2021 ◽  
Vol 13 (9) ◽  
pp. 4706
Author(s):  
Zhiyu Yan ◽  
Shengli Liao ◽  
Chuntian Cheng ◽  
Josué Medellín-Azuara ◽  
Benxi Liu
Keyword(s):  
Lagrangian Relaxation ◽  
Lagrange Multipliers ◽  
Large Scale ◽  
Orthogonal Design ◽  
Solution Space ◽  
Economic Benefits ◽  
Bundle Method ◽  
Short Term ◽  
Hydrothermal Scheduling ◽  
Proximal Bundle Method

Short-term hydrothermal scheduling (STHS) can improve water use efficiency, reduce carbon emissions, and increase economic benefits by optimizing the commitment and dispatch of hydro and thermal generating units together. However, limited by the large system scale and complex hydraulic and electrical constraints, STHS poses great challenges in modeling for operators. This paper presents an improved proximal bundle method (IPBM) within the framework of Lagrangian relaxation for STHS, which incorporates the expert system (ES) technique into the proximal bundle method (PBM). In IPBM, initial values of Lagrange multipliers are firstly determined using the linear combination of optimal solutions in the ES. Then, each time PBM declares a null step in the iterations, the solution space is inferred from the ES, and an orthogonal design is performed in the solution space to derive new updates of the Lagrange multipliers. A case study in a large-scale hydrothermal system in China is implemented to demonstrate the effectiveness of the proposed method. Results in different cases indicate that IPBM is superior to standard PBM in global search ability and computational efficiency, providing an alternative for STHS.

scholarly journals Solving the Short-Term Scheduling Problem of Hydrothermal Systems via Lagrangian Relaxation and Augmented Lagrangian

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Rafael N. Rodrigues ◽  
Edson L. da Silva ◽  
Erlon C. Finardi ◽  
Fabricio Y. K. Takigawa
Keyword(s):  
Lagrangian Relaxation ◽  
Large Scale ◽  
Augmented Lagrangian ◽  
Augmented Lagrangian Method ◽  
Real Life ◽  
Hydrothermal Systems ◽  
Bundle Method ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Short Term

This paper addresses the short-term scheduling problem of hydrothermal power systems, which results in a large-scale mixed-integer nonlinear programming problem. The objective consists in minimizing the operation cost over a two-day horizon with a one-hour time resolution. To solve this difficult problem, a Lagrangian Relaxation (LR) based on variable splitting is designed where the resulting dual problem is solved by a Bundle method. Given that the LR usually fails to find a feasible solution, we use an inexact Augmented Lagrangian method to improve the quality of the solution supplied by the LR. We assess our approach by using a real-life hydrothermal configuration extracted from the Brazilian power system, proving the conceptual and practical feasibility of the proposed algorithm. In summary, the main contributions of this paper are (i) a detailed and compatible modelling for this problem is presented; (ii) in order to solve efficiently the entire problem, a suitable decomposition strategy is presented. As a result of these contributions, the proposed model is able to find practical solutions with moderate computational burden, which is absolutely necessary in the modern power industry.

Guestworkers: Lessons from Western Europe

ILR Review ◽  
1980 ◽  
Vol 33 (3) ◽  
pp. 315-330 ◽  
Author(s):  
Philip L. Martin ◽  
Mark J. Miller
Keyword(s):  
Migrant Workers ◽  
Large Scale ◽  
Western Europe ◽  
Illegal Immigration ◽  
The United States ◽  
Economic Benefits ◽  
Federal Republic Of Germany ◽  
Short Term ◽  
Similar Program ◽  
Source Countries

This article appraises the postwar guestworker programs in France, Switzerland, and the Federal Republic of Germany in light of the proposal that a similar program be adopted in the United States. The authors agree that these programs provided significant short-term economic benefits in meeting the labor shortages experienced in Western Europe until recently. These programs also created several serious problems, however, leading the authors to conclude that a large-scale American temporary worker program (1) may reduce but not end illegal immigration; (2) will evolve into a resident, not short-term, worker program; (3) is likely to produce discrimination against migrant workers; (4) will not improve U.S. relations with labor-source countries; and (5) will exacerbate the employment problems of American minorities.

scholarly journals Short-Term Prediction of Energy Consumption in Demand Response for Blocks of Buildings: DR-BoB Approach

Buildings ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 221
Author(s):  
Nashwan Dawood
Keyword(s):  
Demand Response ◽  
Large Scale ◽  
Economic Benefits ◽  
Least Square ◽  
Percentage Error ◽  
Pilot Site ◽  
Short Term ◽  
Rls Algorithm ◽  
Term Prediction ◽  
Short Term Prediction

Load forecasting plays a major role in determining the prices of the energy supplied to end customers. An accurate prediction is vital for the energy companies, especially when it comes to the baseline calculations that are used to predict the energy load. In this paper, an accurate short-term prediction using the Exponentially Weighted Extended Recursive Least Square (EWE-RLS) algorithm based upon a standard Kalman filter is implemented to predict the energy load for blocks of buildings in a large-scale for four different European pilot sites. A new software tool, namely Local Energy Manager (LEM), is developed to implement the RLS algorithm and predict the forecast for energy demand a day ahead with a regular meter frequency of a quarter of an hour. The EWE-RLS algorithm is used to develop the LEM in demand response for blocks of buildings (DR-BOB), this is part of a large-scale H2020 EU project with the aim to generate the energy baselines during and after running demand response (DR) events. This is achieved in order to evaluate and measure the energy reduction as compared with historical data to demonstrate the environmental and economic benefits of DR. The energy baselines are generated based on different market scenarios, different temperature, and energy meter files with three different levels of asset, building, and a whole pilot site level. The prediction results obtained from the Mean Absolute Percentage Error (MAPE) offer a 5.1% high degree of accuracy and stability at a UK pilot site level compared to the asset and whole building scenarios, where it shows a very acceptable prediction accuracy of 10.7% and 19.6% respectively.

Improved Immune Algorithm for Hydrothermal Joint Economic Scheduling

2014 ◽  
Vol 1030-1032 ◽  
pp. 2582-2585
Author(s):  
Wei Zhou Wang ◽  
Gang Zhang ◽  
Yu Li ◽  
Jing Jing Zheng
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Considerable Proportion ◽  
Reservoir Capacity ◽  
Short Term ◽  
Hydrothermal Scheduling ◽  
Scheduling Model ◽  
Scale Optimization ◽  
Hydropower Construction

China is in a period of rapid economic development, Hydropower construction scale is increasingly expanding, Hydropower accounts for a considerable proportion of the grid, therefore it becomes more important to conduct the hydrothermal joint economic scheduling in gird. Hydrothermal joint scheduling optimization can make water fire and electric play to their strengths and so that to make a great contribution on conserving energy and protecting the environment. Medium and long-term hydrothermal scheduling seems to be more important because its long scheduling period, and can guide the short-term hydrothermal operation. However, under the medium and long-term hydrothermal scheduling, time span and the changing range of reservoir capacity is big, respect to short-term joint scheduling it is a more complex large-scale optimization problem. Therefore, it is very important to establish a reasonable medium and long-term hydrothermal joint scheduling model within the accuracy range and study the corresponding algorithm.

scholarly journals Short-term Hydro-thermal Coordination By Lagrangian Relaxation: A New Algorithm for the Solution of the Dual Problem

2020 ◽  
Author(s):  
Pedro Bento ◽  
Filipe Pina ◽  
Sílvio Mariano ◽  
Maria do Rosario Calado
Keyword(s):  
Lagrangian Relaxation ◽  
Lagrange Multipliers ◽  
Large Scale ◽  
Dual Problem ◽  
Unit Commitment ◽  
Subgradient Methods ◽  
Step Size ◽  
Coordination Problem ◽  
Unit Commitment Problem ◽  
Primal Problem

For decades, researchers have been studying the unit commitment problem in electrical power generation. To solve this complex, large scale and constrained optimization (primal) problem in a direct manner is not a feasible approach, which is where Lagrangian relaxation comes in as the answer. The dual Lagrangian problem translates a relaxed problem approach, that indirectly leads to solutions of the original (primal) problem. In the coordination problem, a decomposition takes place where the global solution is achieved by coordinating between the respective subproblems solutions. This dual problem is solved iteratively, and Lagrange multipliers are updated between each iteration using subgradient methods. To tackle, time-consuming tuning tasks  or user related experience, a new adaptative algorithm, is proposed to better adjust the step-size used to update Lagrange multipliers, i.e., avoid the need to pre-select  a set of parameters. A results comparison against a traditionally employed step-size update mechanism, showed that the adaptive algorithm manages to obtain improved performances in terms of the targeted primal problem. Keywords: Hydro-Thermal coordination, Lagrangian relaxation, Lagrangian dual problem, Lagrange multipliers, Subgradient methods

Screening for the FV: Q506 Mutation – Evaluation of Thirteen Plasma-based Methods for their Diagnostic Efficacy in Comparison with DNA Analysis

1997 ◽  
Vol 77 (03) ◽  
pp. 436-439 ◽  
Author(s):  
Armando Tripodi ◽  
Barbara Negri ◽  
Rogier M Bertina ◽  
Pier Mannuccio Mannucci
Keyword(s):  
Venous Thrombosis ◽  
Large Scale ◽  
Dna Analysis ◽  
Genetic Defect ◽  
Laboratory Diagnosis ◽  
Economic Benefits ◽  
Factor V ◽  
Factor X ◽  
Diagnostic Efficacy ◽  
Dna Test

SummaryThe factor V (FV) mutation Q506 that causes resistance to activated protein C (APC) is the genetic defect associated most frequently with venous thrombosis. The laboratory diagnosis can be made by DNA analysis or by clotting tests that measure the degree of prolongation of plasma clotting time upon addition of APC. Home-made and commercial methods are available but no comparative evaluation of their diagnostic efficacy has so far been reported. Eighty frozen coded plasma samples from carriers and non-carriers of the FV: Q506 mutation, diagnosed by DNA analysis, were sent to 8 experienced laboratories that were asked to analyze these samples in blind with their own APC resistance tests. The APTT methods were highly variable in their capacity to discriminate between carriers and non-carriers but this capacity increased dramatically when samples were diluted with FV-deficient plasma before analysis, bringing the sensitivity and specificity of these tests to 100%. The best discrimination was obtained with methods in which fibrin formation is triggered by the addition of activated factor X or Russell viper venom. In conclusion, this study provides evidence that some coagulation tests are able to distinguish carriers of the FV: Q506 mutation from non-carriers as well as the DNA test. They are inexpensive and easy to perform. Their use in large-scale clinical trials should be of help to determine the medical and economic benefits of screening healthy individuals for the mutation before they are exposed to such risk factors for venous thrombosis as surgery, pregnancy and oral contraceptives.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Sign in / Sign up

Export Citation Format

Share Document