scholarly journals Distributed Learning Algorithms and Lossless Convex Relaxation for Economic Dispatch with Transmission Losses and Capacity Limits

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Kwang-Ki K. Kim
Keyword(s):  
Power Generation ◽  
Learning Algorithm ◽  
Convex Relaxation ◽  
Economic Dispatch ◽  
Optimal Solution ◽  
Necessary Optimality Conditions ◽  
Distributed Learning ◽  
Coefficient Matrix ◽  
Test Case ◽  
Capacity Limits

This paper considers problems of economic dispatch in power networks that contain independent power generation units and loads. For efficient distributed economic dispatch, we present a mechanism of multiagent learning in which each agent corresponding to a generation unit updates the power generation based on the received information from the neighborhood. The convergence of the proposed distributed learning algorithm to the global optimal solution is analyzed. Another method of distributed economic dispatch we propose is a decentralized iterative linear projection method in which the necessary optimality conditions are solved without considering the generation capacities and the obtained solutions are iteratively projected onto the convex set corresponding to the generation capacities. A centralized method based on semidefinite programming for economic dispatch with a loss coefficient matrix is also presented for comparisons. For demonstration, the proposed methods of distributed economic dispatch are applied to a 6-generator test case and the three different methods of economic dispatch give the same solutions. We also analyze parametric dependence of the optimal power generation profiles on varying power demands in economic dispatch.

scholarly journals Optimal economic dispatch of power generation solution using lightning search algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 371
Author(s):  
Murad Yahya Nassar ◽  
Mohd Noor Abdullah ◽  
Asif Ahmed Rahimoon
Keyword(s):  
Power Generation ◽  
Power System ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Thermal Power ◽  
Economic Dispatch ◽  
Optimal Solution ◽  
Fuel Price ◽  
Solution Quality ◽  
Generation Cost

Economic dispatch (ED) is the power demand allocating process for the committed units at minimum generation cost while satisfying system and operational constraints. Increasing cost of fuel price and electricity demand can increase the cost of thermal power generation. Therefore, robust and efficient optimization algorithm is required to determine the optimal solution for ED problem in power system operation and planning. In this paper the lightning search algorithm (LSA) is proposed to solve the ED problem. The system constraints such as power balance, generator limits, system transmission losses and valve-points effects (VPE) are considered in this paper. To verify the effectiveness of LSA in terms of convergence characteristic, robustness, simulation time and solution quality, the two case studies consists of 6 and 13 units have been tested. The simulation results show that the LSA can provide optimal cost than many methods reported in literature. Therefore, it has potential to solve many optimization problems in power dispatch and power system applications.

scholarly journals Energy Efficiency Oriented Access Point Selection for Cognitive Sensors in Internet of Things

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
ChunHua Ju ◽  
Qi Shao
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
Distributed Learning ◽  
Access Point ◽  
Selection Problem ◽  
Potential Game ◽  
Point Selection ◽  
Selection For

This paper studies the distributed energy efficient access point (AP) selection for cognitive sensors in the Internet of Things (IoT). The energy consumption is critical for the wireless sensor network (WSN), and central control would cause extremely high complexity due to the dense and dynamic deployment of sensors in the IoT. The desired approach is the one with lower computation complexity and much more flexibility, and the global optimization is also expected. We solve the multisensors AP selection problem by using the game theory and distributed learning algorithm. First, we formulate an energy oriented AP selection problem and propose a game model which is proved to be an exact potential game. Second, we design a distributed learning algorithm to obtain the globally optimal solution to the problem in a distributed manner. Finally, simulation results verify the theoretic analysis and show that the proposed approach could achieve much higher energy efficiency.

scholarly journals Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3654
Author(s):  
Nastaran Gholizadeh ◽  
Petr Musilek
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Learning Algorithm ◽  
Single Point ◽  
Distributed Learning ◽  
Large Data ◽  
Multi Agent Systems ◽  
Power Quality Monitoring ◽  
Multi Agent ◽  
Learning Frameworks

In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.

scholarly journals Sensor Location Problem for Network Traffic Flow Derivation Based on Turning Ratios at Intersection

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Minhua Shao ◽  
Lijun Sun ◽  
Xianzhi Shao
Keyword(s):  
Network Flow ◽  
Location Problem ◽  
Optimal Solution ◽  
Coefficient Matrix ◽  
Sensor Location ◽  
The Road ◽  
Minimum Number ◽  
Flow Conservation ◽  
The Greedy Algorithm ◽  
Counting Points

The sensor location problem (SLP) discussed in this paper is to find the minimum number and optimum locations of the flow counting points in the road network so that the traffic flows over the whole network can be inferred uniquely. Flow conservation system at intersections is formulated firstly using the turning ratios as the prior information. Then the coefficient matrix of the flow conservation system is proved to be nonsingular. Based on that, the minimal number of counting points is determined to be the total number of exclusive incoming roads and dummy roads, which are added to the network to represent the trips generated on real roads. So the task of SLP model based on turning ratios is just to determine the optimal sensor locations. The following analysis in this paper shows that placing sensors on all the exclusive incoming roads and dummy roads can always generate a unique network flow vector for any network topology. After that, a detection set composed of only real roads is proven to exist from the view of feasibility in reality. Finally, considering the roads importance and cost of the sensors, a weighted SLP model is formulated to find the optimal detection set. The greedy algorithm is proven to be able to provide the optimal solution for the proposed weighted SLP model.

scholarly journals A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Guojiang Xiong ◽  
Jing Zhang ◽  
Xufeng Yuan ◽  
Dongyuan Shi ◽  
Yu He ◽  
...  
Keyword(s):  
Power Grid ◽  
Economic Dispatch ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Search Space ◽  
Neighborhood Search ◽  
Solution Quality ◽  
Promising Alternative ◽  
Comparison Results ◽  
Population Based Algorithm

Economic dispatch (ED) is of cardinal significance for the power system operation. It is mathematically a typical complex nonlinear multivariable strongly coupled optimization problem with equality and inequality constraints, especially considering the valve-point effects. In order to effectively solve the problem, a simple yet very young and efficient population-based algorithm named across neighborhood search (ANS) is implemented in this paper. In ANS, a group of individuals collaboratively navigate through the search space for obtaining the optimal solution by simultaneously searching the neighborhoods of multiple superior solutions. Four benchmark test cases with diverse complexities and characteristics are firstly employed to comprehensively verify the feasibility and effectiveness of ANS. The experimental and comparison results fully demonstrate the superiority of ANS in terms of the final solution quality, convergence speed, robustness, and statistics. In addition, the sensitivities of ANS to variations of population size and across-search degree are studied. Furthermore, ANS is applied to a practical provincial power grid of China. All the comparison results consistently indicate that ANS is highly competitive and can be used as a promising alternative for ED problems.

scholarly journals Dynamic Economic Dispatch On Micro Grid Electrical Systems Using Quadratic Programming

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Heri Suryoatmojo
Keyword(s):  
Power Generation ◽  
Quadratic Programming ◽  
Electric Power ◽  
Renewable Energy Sources ◽  
Economic Dispatch ◽  
Small Scale ◽  
Distributed Power Generation ◽  
Optimal Capacity ◽  
Micro Grid ◽  
Dynamic Economic Dispatch

Currently the needs of electric power increased rapidly along with the development of technology. The increase in power requirements is contrary to the availability of sources of energy depletion of oil and coal. This problem affects the national electrical resistance. To meet the needs of large electric power with wide area coverage is required small scale distributed power generation. This distributed generation (DG) of renewable energy sources sought to minimize the use of energy resources such as oil and coal and connected to the micro grid and use the battery as a power balance. Because of there are many DGs and the use of batteries, therefore it is important to determine the optimal power generation of each plant as well as the use of battery based on the optimal capacity so that requirement of electric power can be met with minimal cost each time. This optimization is known as Dynamic Economic Dispatch. In this study, the methods of Quadratic Programming is required to solve the optimization problem.

scholarly journals Multi-objective optimization of solar thermal photovoltaic hybrid power generation system based on NSGA-II algorithm

2021 ◽  
Vol 336 ◽  
pp. 02022
Author(s):  
Liang Meng ◽  
Wen Zhou ◽  
Yang Li ◽  
Zhibin Liu ◽  
Yajing Liu
Keyword(s):  
Power Generation ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Solar Thermal ◽  
Multi Objective Optimization ◽  
Generation System ◽  
Nsga Ii ◽  
Multi Objective ◽  
Hybrid Power ◽  
Power Generation System

In this paper, NSGA-Ⅱ is used to realize the dual-objective optimization and three-objective optimization of the solar-thermal photovoltaic hybrid power generation system; Compared with the optimal solution set of three-objective optimization, optimization based on technical and economic evaluation indicators belongs to the category of multi-objective optimization. It can be considered that NSGA-Ⅱ is very suitable for multi-objective optimization of solar-thermal photovoltaic hybrid power generation system and other similar multi-objective optimization problems.

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

scholarly journals Enhanced Meta-Learning for Cross-Lingual Named Entity Recognition with Minimal Resources

2020 ◽  
Vol 34 (05) ◽  
pp. 9274-9281
Author(s):  
Qianhui Wu ◽  
Zijia Lin ◽  
Guoxin Wang ◽  
Hui Chen ◽  
Börje F. Karlsson ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Target Language ◽  
Test Case ◽  
Named Entity ◽  
Meta Learning ◽  
Target Languages ◽  
Cross Lingual ◽  
The Given

For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER). While all existing methods directly transfer from source-learned model to a target language, in this paper, we propose to fine-tune the learned model with a few similar examples given a test case, which could benefit the prediction by leveraging the structural and semantic information conveyed in such similar examples. To this end, we present a meta-learning algorithm to find a good model parameter initialization that could fast adapt to the given test case and propose to construct multiple pseudo-NER tasks for meta-training by computing sentence similarities. To further improve the model's generalization ability across different languages, we introduce a masking scheme and augment the loss function with an additional maximum term during meta-training. We conduct extensive experiments on cross-lingual named entity recognition with minimal resources over five target languages. The results show that our approach significantly outperforms existing state-of-the-art methods across the board.

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