scholarly journals Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources

2020 ◽  
Vol 12 (2) ◽  
pp. 518
Author(s):  
Yue Chen ◽  
Zhizhong Guo ◽  
Hongbo Li ◽  
Yi Yang ◽  
Abebe Tilahun Tadie ◽  
...  
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Test System ◽  
Power Sources ◽  
Renewable Power ◽  
Point Estimate Method ◽  
Optimal Power ◽  
Simulation Results ◽  
Point Estimation Method

With the increasing proportion of uncertain power sources in the power grid; such as wind and solar power sources; the probabilistic optimal power flow (POPF) is more suitable for the steady state analysis (SSA) of power systems with high proportions of renewable power sources (PSHPRPSs). Moreover; PSHPRPSs have large uncertain power generation prediction error in day-ahead dispatching; which is accommodated by real-time dispatching and automatic generation control (AGC). In summary; this paper proposes a once-iterative probabilistic optimal power flow (OIPOPF) method for the SSA of day-ahead dispatching in PSHPRPSs. To verify the feasibility of the OIPOPF model and its solution algorithm; the OIPOPF was applied to a modified Institute of Electrical and Electronic Engineers (IEEE) 39-bus test system and modified IEEE 300-bus test system. Based on a comparison between the simulation results of the OIPOPF and AC power flow models; the OIPOPF model was found to ensure the accuracy of the power flow results and simplify the power flow model. The OIPOPF was solved using the point estimate method based on Gram–Charlier expansion; and the numerical characteristics of the line power were obtained. Compared with the simulation results of the Monte Carlo method; the point estimation method based on Gram–Charlier expansion can accurately solve the proposed OIPOPF model

scholarly journals Tie-Line Reserve Power Probability Margin for Day-Ahead Dispatching in Power Systems with High Proportion Renewable Power Sources

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4742
Author(s):  
Yue Chen ◽  
Zhizhong Guo ◽  
Abebe Tilahun Tadie ◽  
Hongbo Li ◽  
Guizhong Wang ◽  
...  
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Prediction Error ◽  
Error Probability ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Power Sources ◽  
Renewable Power ◽  
Optimal Power ◽  
Tie Line

In power systems with a high proportion of renewable power sources (PSHPRPSs), the power constraints of the tie-line may limit the ability of the reserve power to accommodate uncertain power generation, resulting in difficulties for the grid power balance. As uncertain power generation cannot be predicted accurately and in accordance with the law of probability and statistics, it is necessary to use a probability model to calculate the uncertain power of the tie-line. Here, day-ahead prediction error probability optimal power flow (DPEPOPF) is proposed to calculate the tie-line reserve power probability margin (TRPPM) in day-ahead dispatching. In day-ahead dispatching, TRPPM is reserved for real-time dispatching to accommodate uncertain power generation, so as to avoid tie-line power congestion. This study classifies the area of the grid based on the principle of area control error accommodation, and the DPEPOPF is divided into two categories: An inter-area day-ahead prediction error probability optimal power flow mathematical model, and an intra-area day-ahead prediction error probability optimal power flow mathematical model. The point estimate optimization algorithm was implemented in MATLAB 8.3.0.532 (R2014a) to calculate the TRPPM. The simulation results verify the accuracy of the model and effectively avoid power congestion of the tie-line.

Bees Two-Hive Algorithm for Optimal Power Flow

2013 ◽  
Vol 313-314 ◽  
pp. 870-875
Author(s):  
Nopbhorn Leeprechanon ◽  
Prakornchai Phonrattanasak
Keyword(s):  
Energy Balance ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Test System ◽  
Bees Algorithm ◽  
Fuel Cost ◽  
Physical Constraints ◽  
Optimal Power ◽  
Transmission Limits ◽  
Simulation Results

This paper presents bees two-hive algorithm for solving the optimal power flow (OPF) problem with various constraints. The objective of the proposed technique is to improve the quality solution of the conventional bees algorithm that minimize the total fuel cost subject to operational and physical constraints i.e. energy balance, generation and transmission limits including security constraints. The proposed methodology is tested on the IEEE 30-bus test system. The results obtained using the proposed approach are compared to GA, PSO, BA and other conventional. The comparison of quality solution with other algorithms confirms performance of proposed technique. Simulation results demonstrate that bees two-hive algorithm provides better results than other heuristic techniques.

An Efficient Chaotic Cuckoo Search Framework for Solving Non-Convex Optimal Power Flow Problem

2017 ◽  
Vol 33 ◽  
pp. 84-99 ◽  
Author(s):  
Bachir Bentouati ◽  
Saliha Chettih ◽  
Rabah Djekidel ◽  
Ragab Abdel-Aziz El-Sehiemy
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Inequality Constraints ◽  
Cuckoo Search ◽  
Test System ◽  
Optimal Power ◽  
Equality And Inequality Constraints ◽  
Elitism Scheme ◽  
Complicated Task

The optimal power flow (OPF) problem is a very complicated task in power systems. OPF problem has a set of equality and inequality constraints. This paper looks at a chaotic cuckoo search (CCS) algorithm for solving non-convex OPF problem. The proposed CCS is a bio-inspired optimization calculation that is inspired by the behaviour of cuckoos people in nature. The chaotic guide is a variation of qualities combined with CS. A sinusoidal chaotic is integrated with CS algorithm and tested on standard IEEE 30-bus test system to the point of improving its global speed of convergence and enhancing its performance. The elitism scheme is also serves to save the best cuckoo during amid the procedure when updating the cuckoo. The results show clearly the superiority of CCS in searching for the best function values results when compared with well-known metaheuristic search algorithms.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

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.

scholarly journals SOCP Relaxations of Optimal Power Flow Problem Considering Current Margins in Radial Networks

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3164 ◽  
Author(s):  
Yuwei Chen ◽  
Ji Xiang ◽  
Yanjun Li
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Additional Term ◽  
Transmission Losses ◽  
Stability Margins ◽  
Optimal Power ◽  
Second Order Cone ◽  
Convex Problem ◽  
Generation Costs

Optimal power flow (OPF) is a non-linear and non-convex problem that seeks the optimization of a power system operation point to minimize the total generation costs or transmission losses. This study proposes an OPF model considering current margins in radial networks. The objective function of this OPF model has an additional term of current margins of the line besides the traditional transmission losses and generations costs, which contributes to thermal stability margins of power systems. The model is a reformulated bus injection model with clear physical meanings. Second order cone program (SOCP) relaxations for the proposed OPF are made, followed by the over-satisfaction condition guaranteeing the exactness of the SOCP relaxations. A simple 6-node case and several IEEE benchmark systems are studied to illustrate the efficiency of the developed results.

Optimal TCSC placement for optimal power flow

2012 ◽  
Vol 63 (5) ◽  
pp. 316-321 ◽  
Author(s):  
Fatiha Lakdja ◽  
Fatima Zohra Gherbi ◽  
Redouane Berber ◽  
Houari Boudjella
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Models ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Analysis ◽  
Test System ◽  
Facts Devices ◽  
Optimal Power ◽  
Current Transmission ◽  
Flexible Alternating Current Transmission

Very few publications have been focused on the mathematical modeling of Flexible Alternating Current Transmission Systems (FACTS) -devices in optimal power flow analysis. A Thyristor Controlled Series Capacitors (TCSC) model has been proposed, and the model has been implemented in a successive QP. The mathematical models for TCSC have been established, and the Optimal Power Flow (OPF) problem with these FACTS-devices is solved by Newtons method. This article employs the Newton- based OPF-TCSC solver of MATLAB Simulator, thus it is essential to understand the development of OPF and the suitability of Newton-based algorithms for solving OPF-TCSC problem. The proposed concept was tested and validated with TCSC in twenty six-bus test system. Result shows that, when TCSC is used to relieve congestion in the system and the investment on TCSC can be recovered, with a new and original idea of integration.

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