scholarly journals Parallel hybrid enhanced inherited GA based scuc in a distributed cluster

2012 ◽  
Vol 1 (1) ◽  
pp. 96 ◽  
Author(s):  
Christopher Columbus C ◽  
Sishaj P. Simon
Keyword(s):  
Electricity Market ◽  
Message Passing ◽  
Message Passing Interface ◽  
Unit Commitment ◽  
Economic Dispatch ◽  
Relaxation Method ◽  
Master Problem ◽  
Solution Quality ◽  
System Operator ◽  
New Formulation

In the deregulated electricity market, secure operation is an enduring concern of the independent system operator (ISO). For a secure and economical hourly generation schedule of the day ahead market, ISO executes the security constrained unit commitment (SCUC) problem. In this paper, a new formulation of SCUC problem, considering more practical constraints are presented. The proposed SCUC formulation includes constraints, such as hourly power demand, system reserves, ramp up/down limits, minimum ON/OFF duration limits. Unlike the traditional SCUC techniques the proposed method solves the Security Constrained Economic Dispatch (SCED) from the UC. To solve such SCUC model, a hybrid solution method consists of an enhanced inherited genetic algorithm (EIGA) is used for unit commitment master problem and Lambda relaxation method is used for the economic dispatch sub-problem. The message passing interface (MPI) based technique is used to implement the hybrid EIGA in distributed memory model. The time complexity and the solution quality with respect to the number of processors in a cluster are thoroughly analyzed. The effectiveness of the proposed method to solve the SCUC problem is shown on different test systems.

scholarly journals Parallel Implementation of Non-slicing Floorplans with MPI and OpenMP

2021 ◽  
Author(s):  
Oluvaseun Owojaiye
Keyword(s):  
Message Passing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Parallel Implementation ◽  
Computation Time ◽  
Sequential Algorithm ◽  
Design Stage ◽  
Single Chip ◽  
Solution Quality ◽  
Early Design Stage

Advancement in technology has brought considerable improvement to processor design and now manufacturers design multiple processors on a single chip. Supercomputers today consists of cluster of interconnected nodes that collaborate together to solve complex and advanced computation problems. Message Passing Interface and Open Multiprocessing are the popularly used programming models to optimize sequential codes by parallelizing them on the different multiprocessor architecture that exist today. In this thesis, we parallelize the non-slicing floorplan algorithm based on Multilevel Floorplanning/placement of large scale modules using B*tree (MB*tree) with MPI and OpenMP on distributed and shared memory architectures respectively. In VLSI (Very Large Scale Integration) design automation, floorplanning is an initial and vital task performed in the early design stage. Experimental results using MCNC benchmark circuits show that our parallel algorithm produced better results than the corresponding sequential algorithm; we were able to speed up the algorithm up to 4 times, hence reducing computation time and maintaining floorplan solution quality. On the other hand, we compared both parallel versions; and the OpenMP results gave slightly better than the corresponding MPI results.

scholarly journals Continuous Reinforcement Algorithm and Robust Economic Dispatching-Based Spot Electricity Market Modeling considering Strategic Behaviors of Wind Power Producers and Other Participants

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Zhenyu Zhao ◽  
Shuguang Yuan ◽  
Qingyun Nie ◽  
Weishang Guo
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Gradient Descent ◽  
Economic Dispatch ◽  
Test System ◽  
Modeling Approach ◽  
Continuous State ◽  
Market Operation ◽  
System Operator ◽  
Action Spaces

In a spot wholesale electricity market containing strategic bidding interactions among wind power producers and other participants such as fossil generation companies and distribution companies, the randomly fluctuating natures of wind power hinders not only the modeling and simulating of the dynamic bidding process and equilibrium of the electricity market but also the effectiveness about keeping economy and reliability in market clearing (economic dispatching) corresponding to the independent system operator. Because the gradient descent continuous actor-critic algorithm is demonstrated as an effective method in dealing with Markov’s decision-making problems with continuous state and action spaces and the robust economic dispatch model can optimize the permitted real-time wind power deviation intervals based on wind power producers’ bidding power output, in this paper, considering bidding interactions among wind power producers and other participants, we propose a gradient descent continuous actor-critic algorithm-based hour-ahead electricity market modeling approach with the robust economic dispatch model embedded. Simulations are implemented on the IEEE 30-bus test system, which, to some extent, verifies the market operation economy and the robustness against wind power fluctuations by using our proposed modeling approach.

scholarly journals Parallel Implementation of Non-slicing Floorplans with MPI and OpenMP

2021 ◽  
Author(s):  
Oluvaseun Owojaiye
Keyword(s):  
Message Passing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Parallel Implementation ◽  
Computation Time ◽  
Sequential Algorithm ◽  
Design Stage ◽  
Single Chip ◽  
Solution Quality ◽  
Early Design Stage

Advancement in technology has brought considerable improvement to processor design and now manufacturers design multiple processors on a single chip. Supercomputers today consists of cluster of interconnected nodes that collaborate together to solve complex and advanced computation problems. Message Passing Interface and Open Multiprocessing are the popularly used programming models to optimize sequential codes by parallelizing them on the different multiprocessor architecture that exist today. In this thesis, we parallelize the non-slicing floorplan algorithm based on Multilevel Floorplanning/placement of large scale modules using B*tree (MB*tree) with MPI and OpenMP on distributed and shared memory architectures respectively. In VLSI (Very Large Scale Integration) design automation, floorplanning is an initial and vital task performed in the early design stage. Experimental results using MCNC benchmark circuits show that our parallel algorithm produced better results than the corresponding sequential algorithm; we were able to speed up the algorithm up to 4 times, hence reducing computation time and maintaining floorplan solution quality. On the other hand, we compared both parallel versions; and the OpenMP results gave slightly better than the corresponding MPI results.

A new representation of conventional hydropower in unit commitment economic dispatch models to support resource adequacy and reliability studies

2021 ◽  
Author(s):  
Nathalie Voisin ◽  
Konstantinos Oikonomou ◽  
Sean Turner ◽  
Mitch Clement ◽  
Tim Magee ◽  
...  
Keyword(s):  
Electricity Market ◽  
Water Availability ◽  
Power Plants ◽  
Large Scale ◽  
Unit Commitment ◽  
Economic Dispatch ◽  
System Optimization ◽  
Hydropower Plant ◽  
Thermo Electric ◽  
Water Conditions

<p>The Western U.S. relies heavily on hundreds of water-dependent power plants, with hydropower and fresh surface water dependent thermo-electric plants accounting for over 60% of generating capacity.  The Western Interconnect overlays 11 States, over three different electricity market areas, and 9 large river basins with tens of unconnected watersheds as well as tens of coordinated watersheds. Such complexity requires computational tradeoffs for the representation of the water-energy dependencies, including a centrally controlled unit commitment and economic dispatch as well as an offline representation of hydropower’s availability and operations. Benchmark hydropower representations for application to resource adequacy studies include i) fixed daily time series and ii) a parameterized monthly representation involving three constraints: a monthly energy target, and hourly minimum and maximum generation. The representations are derived for one year and under average water conditions. We propose a large-scale approach to represent medium-term (weekly) hydropower flexibility for grid-scale reliability studies, as driven by weekly water availability. Using a combination of hydrological models, reservoir operation schemes, and statistical tools, we develop datasets of hydropower plant-specific weekly energy targets, with weekly minimum and maximum hourly generation, for multiple years with varying water conditions. The assumption – and computational tradeoff - is that water availability guides the weekly operations and range of daily operations, leaving enough flexibility for the power system optimization to accommodate intra-day, week days and weekends load variations. We present the hydropower datasets and evaluate how this new representation influences the simulated contribution of hydropower to grid operations as part of resource adequacy and reliability studies.</p>

Multi-level Parallelization of Genotype Imputation on Supercomputers

2020 ◽  
Vol 15 ◽  
Author(s):  
Weiwen Zhang ◽  
Long Wang ◽  
Theint Theint Aye ◽  
Juniarto Samsudin ◽  
Yongqing Zhu
Keyword(s):  
Association Study ◽  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Genome Wide Association Study ◽  
Job Scheduling ◽  
Genotype Imputation ◽  
Job Level ◽  
Multi Level ◽  
High Performance Requirement

Background: Genotype imputation as a service is developed to enable researchers to estimate genotypes on haplotyped data without performing whole genome sequencing. However, genotype imputation is computation intensive and thus it remains a challenge to satisfy the high performance requirement of genome wide association study (GWAS). Objective: In this paper, we propose a high performance computing solution for genotype imputation on supercomputers to enhance its execution performance. Method: We design and implement a multi-level parallelization that includes job level, process level and thread level parallelization, enabled by job scheduling management, message passing interface (MPI) and OpenMP, respectively. It involves job distribution, chunk partition and execution, parallelized iteration for imputation and data concatenation. Due to the design of multi-level parallelization, we can exploit the multi-machine/multi-core architecture to improve the performance of genotype imputation. Results: Experiment results show that our proposed method can outperform the Hadoop-based implementation of genotype imputation. Moreover, we conduct the experiments on supercomputers to evaluate the performance of the proposed method. The evaluation shows that it can significantly shorten the execution time, thus improving the performance for genotype imputation. Conclusion: The proposed multi-level parallelization, when deployed as an imputation as a service, will facilitate bioinformatics researchers in Singapore to conduct genotype imputation and enhance the association study.

scholarly journals Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2284
Author(s):  
Krzysztof Przystupa ◽  
Mykola Beshley ◽  
Olena Hordiichuk-Bublivska ◽  
Marian Kyryk ◽  
Halyna Beshley ◽  
...  
Keyword(s):  
Distributed Systems ◽  
Singular Value Decomposition ◽  
Data Processing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Recommendation Systems ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Value Decomposition ◽  
Svd Method

The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

A high-performance, portable implementation of the MPI message passing interface standard

1996 ◽  
Vol 22 (6) ◽  
pp. 789-828 ◽  
Author(s):  
William Gropp ◽  
Ewing Lusk ◽  
Nathan Doss ◽  
Anthony Skjellum
Keyword(s):  
Message Passing ◽  
High Performance ◽  
Message Passing Interface

Stochastic control for risk under deregulated electricity market — a case study using a new formulation

2005 ◽  
Vol 32 (4) ◽  
pp. 719-725 ◽  
Author(s):  
Joyce Li Zhang ◽  
K Ponnambalam
Keyword(s):  
Electricity Market ◽  
Explicit Method ◽  
State Variables ◽  
Risk Minimization ◽  
Solution Approach ◽  
Second Moments ◽  
Deregulated Electricity Market ◽  
New Formulation ◽  
Dual Dynamic Programming

This paper describes the implementation of a new solution approach — Fletcher-Ponnambalam model (FP) — for risk management in hydropower system under deregulated electricity market. The FP model is an explicit method developed for the first and second moments of the storage state distributions in terms of moments of the inflow distributions. This method provides statistical information on the nature of random behaviour of the system state variables without any discretization and hence suitable for multi-reservoir problems. Also avoiding a scenario-based optimization makes it computationally inexpensive, as there is little growth to the size of the original problem. In this paper, the price uncertainty was introduced into the FP model in addition to the inflow uncertainty. Lake Nipigon reservoir system is chosen as the case study and FP results are compared with the stochastic dual dynamic programming (SDDP). Our studies indicate that the method could achieve optimum operations, considering risk minimization as one of the objectives in optimization.Key words: reservoir operations, explicit method, uncertainty, stochastic programming, risk.

Sign in / Sign up

Export Citation Format

Share Document