Application of Chaotic Univariate Marginal Distribution Algorithm to Economic Dispatch Control of Cascade Hydropower Plants

2012 ◽  
Vol 516-517 ◽  
pp. 1326-1331
Author(s):  
Wei Gu ◽  
Yong Gang Wu ◽  
Jin Cheng Wu
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Marginal Distribution ◽  
Economic Dispatch ◽  
Constrained Optimization Problem ◽  
Chaotic Search ◽  
Chaotic Sequences ◽  
Hydropower Plants ◽  
Simulation Results ◽  
Linear Constrained Optimization

The economic dispatch control of cascade hydropower plants is a large scale non-linear constrained optimization problem, which plays an important role in cascade reservoirs daily optimal. This paper proposes a chaotic univariate marginal distribution algorithm (CUMDA) to solve the economic dispatch problem of cascade hydropower plants. In the proposed method, a chaotic search is integrated with univariate marginal distribution algorithm (UMDA) to effectively avoid premature convergence, chaotic sequences combine with adaptive approach are applied to help algorithm escape from local optimal trap. The feasibility of the proposed method is demonstrated for economic dispatch control of a test cascade hydro system. The simulation results show that the proposed method can obtain higher quality solution.

scholarly journals Learning Compact Model for Large-Scale Multi-Label Data

2019 ◽  
Vol 33 ◽  
pp. 5385-5392 ◽  
Author(s):  
Tong Wei ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Performance Metrics ◽  
State Of The Art ◽  
Optimization Method ◽  
Model Parameters ◽  
Generalization Capability ◽  
Constrained Optimization Problem ◽  
Unseen Data ◽  
Model Size

Large-scale multi-label learning (LMLL) aims to annotate relevant labels from a large number of candidates for unseen data. Due to the high dimensionality in both feature and label spaces in LMLL, the storage overheads of LMLL models are often costly. This paper proposes a POP (joint label and feature Parameter OPtimization) method. It tries to filter out redundant model parameters to facilitate compact models. Our key insights are as follows. First, we investigate labels that have little impact on the commonly used LMLL performance metrics and only preserve a small number of dominant parameters for these labels. Second, for the remaining influential labels, we reduce spurious feature parameters that have little contribution to the generalization capability of models, and preserve parameters for only discriminative features. The overall problem is formulated as a constrained optimization problem pursuing minimal model size. In order to solve the resultant difficult optimization, we show that a relaxation of the optimization can be efficiently solved using binary search and greedy strategies. Experiments verify that the proposed method clearly reduces the model size compared to state-of-the-art LMLL approaches, in addition, achieves highly competitive performance.

scholarly journals A Fast Clustering-Based Min-Cut Placement Algorithm With Simulated-Annealing Performance

VLSI Design ◽  
1996 ◽  
Vol 5 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Youssef Saab
Keyword(s):  
Simulated Annealing ◽  
Integrated Circuits ◽  
Large Scale ◽  
Optimization Problem ◽  
Wire Length ◽  
Constrained Optimization Problem ◽  
Placement Problem ◽  
Constructive Methods ◽  
Placement Algorithm ◽  
Min Cut

Placement is an important constrained optimization problem in the design of very large scale (VLSI) integrated circuits [1–4]. Simulated annealing [5] and min-cut placement [6] are two of the most successful approaches to the placement problem. Min-cut methods yield less congested and more routable placements at the expense of more wire-length, while simulated annealing methods tend to optimize more the total wire-length with little emphasis on the minimization of congestion. It is also well known that min-cut algorithms are substantially faster than simulated-annealing-based methods. In this paper, a fast min-cut algorithm (ROW-PLACE) for row-based placement is presented and is empirically shown to achieve simulated-annealing-quality wire-length on a number of benchmark circuits. In comparison with Timberwolf 6 [7], ROW-PLACE is at least 12 times faster in its normal mode and is at least 25 times faster in its faster mode. The good results of ROW-PLACE are achieved using a very effective clustering-based partitioning algorithm in combination with constructive methods that reduce the wire-length of nets involved in terminal propagation.

Solution of Economic Dispatch Problems with Smooth and Non Smooth Cost Function Using Particle Swarm Optimization

2014 ◽  
Vol 984-985 ◽  
pp. 1295-1300
Author(s):  
S.R. Darsana ◽  
K. Dhayalini ◽  
S. Sathiyamoorthy
Keyword(s):  
Particle Swarm Optimization ◽  
Cost Function ◽  
Large Scale ◽  
Optimization Problem ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Relaxation Method ◽  
Cost Functions ◽  
Practical Consideration ◽  
Swarm Optimization

This paper deals with solution of economic dispatch problem with smooth and non smooth cost function. With practical consideration, ED will have non smooth cost functions with equality and inequality constraints that make the problem, a large-scale highly constrained nonlinear optimization problem. Here particle swarm optimization (PSO) technique is used to solve economic dispatch. PSO based algorithm is easy to implement and it performs well on optimization problem. To demonstrate the effectiveness of the proposed method it is being applied to test ED problems, with smooth and non smooth cost functions. Comparison with lagrangian relaxation method showed the superiority of the proposed approach to check the efficiency, studies have been performed for 6 generating unit with smooth cost function. Numerical simulations indicate an improvement in total fuel cost savings.

scholarly journals “Peer-to-Peer Plus” Electricity Transaction within Community of Active Energy Agents Regarding Distribution Network Constraints

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2408
Author(s):  
Min Fu ◽  
Zhiyu Xu ◽  
Ning Wang ◽  
Xiaoyu Lyu ◽  
Weisheng Xu
Keyword(s):  
Optimization Problem ◽  
Distribution Network ◽  
Test System ◽  
Peer To Peer ◽  
Constrained Optimization Problem ◽  
Community Based ◽  
Numeric Simulation ◽  
Distribution Company ◽  
Simulation Results ◽  
Centralized Coordination

This paper proposes the concept “active energy agent (AEA)” to characterize the autonomous and interactive entities of power system. The future distribution network is a peer-to-peer (P2P) community based on numbers of AEAs. A two-stage “P2P Plus” mechanism is developed to address the electricity transaction within AEA community. In the first “P2P” stage, electricity is directly traded among AEAs via P2P price bidding. The model of P2P transaction is established, and the method of multi-dimensional willingness is adopted in price bidding. In the second “Plus” stage, the centralized coordination by distribution company (DisCo) is formulated as a constrained optimization problem, in which the objective is to maximize profit and the constraints are the basic rights of AEAs and line ratings of distribution network. A 30-bus test system including 29 AEAs and main grid is investigated. Numeric simulation results verify the effectiveness of the proposed models and methods regarding flow constraint. Comparative study reveals the economic motivations of AEAs to participate in P2P transaction, the efficiency of combined search, and the benefit of DisCo from pricing control.

Analytical Reason for Smaller Lateral Sway in Angled-Plane Scissor Linkage

2020 ◽  
Vol 12 (5) ◽  
Author(s):  
Sunil Kumar Singh ◽  
Sangamesh R. Deepak
Keyword(s):  
Rigid Body ◽  
Optimization Problem ◽  
Linear Optimization ◽  
Parallel Plane ◽  
Design Parameters ◽  
Finite Limit ◽  
Constrained Optimization Problem ◽  
Linear Optimization Problem ◽  
Non Linear ◽  
Linear Constrained Optimization

Abstract Scissor linkages are widely used with scissor links arranged in two parallel planes. When small misalignment of revolute joint axes are permissible, the linkage can undergo lateral sway. This paper, using rigid-body kinematics and a modeling of misalignment, converts the task of finding lateral sway into a non-linear constrained optimization problem. Through linearization of the optimization problem, this paper analytically proves that (1) maximum lateral sway increases as the number of units in the parallel-plane scissor linkage increases whereas in angled-plane scissor linkage, the lateral sway tends to a finite limit as the number of units is increased and (2) the lateral sway is independent of connector length in parallel-plane scissor linkage whereas it is dependent on the length of the connector in angled-plane scissor linkage. These results are further substantiated with numerical solution of the non-linear optimization problem. The results imply that the angled-plane scissor linkage can substantially limit lateral sway in comparison to parallel-plane scissor linkage under similar conditions of joint misalignment. The analytical expression derived in this paper helps in identifying the influence of design parameters on lateral sway.

scholarly journals A Novel Heap based Optimizer for Scheduling of Large-scale Combined Heat and Power Economic Dispatch

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ahmed R. Ginidi ◽  
Abdallah M. Elsayed ◽  
Abdullah M. Shaheen ◽  
Ehab E. Elattar ◽  
Ragab A. El-Sehiemy
Keyword(s):  
Large Scale ◽  
Economic Dispatch ◽  
Combined Heat And Power ◽  
Power Economic

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

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