Modified method of dynamic programming for optimization of hydraulic modes of distribution heating networks

Author(s):  
Николай Николаевич Новицкий ◽  
Александр Викторович Луценко

Предложена оригинальная модификация метода динамического программирования, предназначенная для оптимизации гидравлических режимов распределительных тепловых сетей, опирающаяся на специальные свойства задачи. Продемонстрировано, что предложенная модификация метода динамического программирования обладает высокой вычислительной эффективностью по сравнению с возможными альтернативными методами дискретно-непрерывной оптимизации и гарантирует получение оптимального решения задачи. The paper discusses the problem of optimizing the hydraulic modes of the distribution of heat networks (RTS), which arises at the stage of preparing heating systems for the next heating season. The urgency of this task is due to the significant reserves of energy saving, improving the reliability and quality of heat supply to consumers, which can be realized through the optimal organization of RTS operation modes. Currently, there are no formally rigorous and simultaneously computationally efficient methods for solving this problem. A new effective method for optimizing RTS modes is presented, based on a dynamic programming scheme, which takes into account the specifics of the problem (specified flow distribution) and RTS topology (a tree in a single-line representation, multicontourness in a two-line representation, symmetry of supply and return pipelines). The proposed solution overcomes the main problem of applying the traditional dynamic programming scheme when optimizing multi-loop pipe networks associated with the need to comply with the second Kirchhoff law on contours when building conditionally optimal trajectories. The basic idea is to reduce the contours of the design scheme to parallel connections of branches (on the direct course of the algorithm) with simultaneous cutting of both non-optimal and inadmissible fragments of trajectories. And the latter here are easily cut off both in terms of the membership of the phase variables of the admissible region and in satisfying Kirchhoff’s second law. The reverse move is reduced to a simple procedure of unfolding the design scheme in the reverse sequence of reduction, in order to restore the optimal values of phase variables on it. Numerical examples illustrate the effectiveness of the proposed method, its suitability for solving problems with discrete and continuous optimality criteria, multi-criteria optimization, the possibility of solving several optimization problems simultaneously.

2001 ◽  
Vol 7 (2) ◽  
pp. 106-114
Author(s):  
Ela Chraptovič ◽  
Juozas Atkočiūnas

The theory of mathematical programming widely spread as a method of a solution of extreme problems. It accompanies the study of plastic theory problem from its posing up to final solution. However, here again from our point of view not all possibilities are realized. Unfortunately, the use of mathematical programming as an instrument of a numerical solution for structural analysis frequently is also restricted by that. The possibilities of mechanical interpretation of optimality criteria of applied algorithms are not uncovered. The global solution of the problem of mathematical programming exists, if Kuhn-Tucker conditions are satisfied. These conditions do not depend on the applied algorithm of a problem solution. The identity of Kuhn-Tucker conditions with a optimality criteria of Rosen algorithm is finding out in this research. The role of a design matrix for the creating of strain compatibility equations is clarified. The Kuhn-Tucker conditions mean the residual strain compatibility equations in analysis of elastic-plastic systems. It is proved in the article that for problems of limiting equilibrium the Kuhn-Tucker conditions include the dependences of the associated law of plastic flow. The Kuhn-Tucker conditions together with limitations of a source problem of account represent a complete set of dependences of the theory of shakedown. The correct mathematical and mechanical interpretation of the Kuhn-Tucker conditions allows to refuse a direct solution of a dual problem of mathematical programming. It makes easier the solution of optimization problems of structures at shakedown.


2012 ◽  
Vol 34 (5) ◽  
pp. A2625-A2649 ◽  
Author(s):  
Simone Cacace ◽  
Emiliano Cristiani ◽  
Maurizio Falcone ◽  
Athena Picarelli

2014 ◽  
Vol 3 (4) ◽  
pp. 34-54 ◽  
Author(s):  
Vikram Kumar Kamboj ◽  
S.K. Bath

Biogeography Based Optimization (BBO) algorithm is a population-based algorithm based on biogeography concept, which uses the idea of the migration strategy of animals or other spices for solving optimization problems. Biogeography Based Optimization algorithm has a simple procedure to find the optimal solution for the non-smooth and non-convex problems through the steps of migration and mutation. This research paper presents the solution to Economic Load Dispatch Problem for IEEE 3, 4, 6 and 10-unit generating model using Biogeography Based Optimization algorithm. It also presents the mathematical formulation of scalar and multi-objective unit commitment problem, which is a further extension of economic load dispatch problem.


Author(s):  
Chao-Chin Wu ◽  
Jenn-Yang Ke ◽  
Heshan Lin ◽  
Syun-Sheng Jhan

Dynamic Programming (DP) is an important and popular method for solving a wide variety of discrete optimization problems such as scheduling, string-editing, packaging, and inventory management. DP breaks problems into simpler subproblems and combines their solutions into solutions to original ones. This paper focuses on one type of dynamic programming called Nonserial Polyadic Dynamic Programming (NPDP). To run NPDP applications efficiently on an emerging General-Purpose Graphic Processing Unit (GPGPU), the authors have to exploit more parallelism to fully utilize the computing power of the hundreds of processing units in it. However, the parallelism degree varies significantly in different phases of the NPDP applications. To address the problem, the authors propose a method that can adjust the thread-level parallelism to provide a sufficient and steadier parallelism degree for different phases. If a phase has insufficient parallelism, the authors split threads into subthreads. On the other hand, the authors can limit the total number of threads in a phase by merging threads. The authors also examine the difference between the conventional problem of finding the minimum on a GPU and the NPDP-featured problem of finding the minimums of many independent sets on a GPU. Finally, the authors examine how to design an appropriate data structure to apply the memory coalescing optimization technique. The experimental results demonstrate our method can obtain the best speedup of 13.40 over the algorithm published previously.


Author(s):  
Ali A. Al-Arbo ◽  
Rana Z. Al-Kawaz

<span>This paper proposes a new spectral conjugate gradient (SCG) approach for solving unregulated nonlinear optimization problems. Our approach proposes Using Wolfe's rapid line scan to adjust the standard conjugate descent (CD) algorithm. A new spectral parameter is a mixture of new gradient and old search path. The path provided by the modified method provides a path of descent for the solution of objective functions. The updated method fits the traditional CD method if the line check is correct. The stability and global convergence properties of the current new SCG are technically obtained from applying certain well-known and recent mild assumptions. We test our approach with eight recently published CD and SCG methods on 55 optimization research issues from the CUTE library. The suggested and all other algorithms included in our experimental research were implemented in FORTRAN language with double precision arithmetic and all experiments were conducted on a PC with 8 GB ram Processor Intel Core i7. The results indicate that our proposed solution outperforms recently reported algorithms by processing and performing fewer iterations in a shorter time.</span>


2011 ◽  
Vol 323 ◽  
pp. 222-228
Author(s):  
Xiao Wei Gu ◽  
Peng Fei Wang ◽  
Qing Wang ◽  
You Yi Zheng ◽  
Jian Ping Liu ◽  
...  

A dynamic sequencing method has been developed that can simultaneously optimize the final pit and the production schedule of an open-pit coal mine. The method first establishes a geological seam model of a bedded coal deposit which estimates the relevant attributes of coal seams at the center of each block on the X-Y plane. Based on the seam model, a sequence of “geologically optimum final pits” is generated and, in each of these pits, a sequence of “geologically optimum push-backs” is generated. The geologically optimum push-backs are then put into a dynamic programming scheme and the best production schedule which has the highest NPV is obtained for each final pit. After the best production schedules for all the final pits are obtained, the one with the highest overall NPV is the optimum final pit and its associated best schedule is the overall best production schedule.


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