A progressive method to solve large-scale AC optimal power flow with discrete variables and control of the feasibility

2014 ◽  
Author(s):  
Manuel Ruiz ◽  
Jean Maeght ◽  
Alexandre Marie ◽  
Patrick Panciatici ◽  
Arnaud Renaud
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Discrete Variables ◽  
Progressive Method ◽  
Optimal Power ◽  
And Control

scholarly journals Decomposed Iterative Optimal Power Flow with Automatic Regionalization

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4987
Author(s):  
Xinhu Zheng ◽  
Dongliang Duan ◽  
Liuqing Yang ◽  
Haonan Wang
Keyword(s):  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Optimization Techniques ◽  
Test Cases ◽  
Global Optimality ◽  
Large Scale Systems ◽  
Optimal Power ◽  
And Control

The optimal power flow (OPF) problem plays an important role in power system operation and control. The problem is nonconvex and NP-hard, hence global optimality is not guaranteed and the complexity grows exponentially with the size of the system. Therefore, centralized optimization techniques are not suitable for large-scale systems and an efficient decomposed implementation of OPF is highly demanded. In this paper, we propose a novel and efficient method to decompose the entire system into multiple sub-systems based on automatic regionalization and acquire the OPF solution across sub-systems via a modified MATPOWER solver. The proposed method is implemented in a modified solver and tested on several IEEE Power System Test Cases. The performance is shown to be more appealing compared with the original solver.

scholarly journals Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

2020 ◽  
Vol 34 (01) ◽  
pp. 630-637 ◽  
Author(s):  
Ferdinando Fioretto ◽  
Terrence W.K. Mak ◽  
Pascal Van Hentenryck
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Practical Applications ◽  
Fundamental Building Block ◽  
Optimal Power

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.

Fast Service Restoration of Multi-Source Active Distribution Network with Microgrids and Electric Vehicles

2014 ◽  
Vol 672-674 ◽  
pp. 1175-1178
Author(s):  
Guang Min Fan ◽  
Ling Xu Guo ◽  
Wei Liang ◽  
Hong Tao Qie
Keyword(s):  
Electric Vehicles ◽  
Distribution System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Distribution Network ◽  
Active Distribution Network ◽  
Optimal Power ◽  
Service Restoration ◽  
Restoration Method

The increasingly serious energy crisis and environmental pollution problems promote the large-scale application of microgrids (MGs) and electric vehicles (EVs). As the main carrier of MGs and EVs, distribution network is gradually presenting multi-source and active characteristics. A fast service restoration method of multi-source active distribution network with MGs and EVs is proposed in this paper for service restoration of distribution network, which takes effectiveness, rapidity, economy and reliability into consideration. Then, different optimal power flow (OPF) models for the service restoration strategy are constructed separately to minimize the network loss after service restoration. In addition, a genetic algorithm was introduced to solve the OPF model. The analysis of the service restoration strategy is carried out on an IEEE distribution system with three-feeder and eighteen nodes containing MGs and EVs, and the feasibility and effectiveness are verified

Solving large-scale reactive optimal power flow problems by a primal–dual $$\hbox {M}^{2}\hbox {BF}$$ approach

2019 ◽  
Vol 21 (2) ◽  
pp. 485-515 ◽  
Author(s):  
Ricardo B. N. M. Pinheiro ◽  
Leonardo Nepomuceno ◽  
Antonio R. Balbo
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Flow Problems ◽  
Optimal Power ◽  
Primal Dual

Large Scale Optimal Power Flow

1982 ◽  
Vol PAS-101 (10) ◽  
pp. 3722-3732 ◽  
Author(s):  
R.C. Burchett ◽  
H.H. Happ ◽  
K.A. Wirgau
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Optimal Power

A Robust Approach to Optimal Power Flow With Discrete Variables

2009 ◽  
Vol 24 (3) ◽  
pp. 1182-1190 ◽  
Author(s):  
Lin Liu ◽  
Xifan Wang ◽  
Xiaoying Ding ◽  
Haoyong Chen
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Discrete Variables ◽  
Robust Approach ◽  
Optimal Power
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