Probabilistic Transient Stability Constrained Optimal Power Flow for Power Systems With Multiple Correlated Uncertain Wind Generations

2016 ◽  
Vol 7 (3) ◽  
pp. 1133-1144 ◽  
Author(s):  
Shiwei Xia ◽  
Xiao Luo ◽  
Ka Wing Chan ◽  
Ming Zhou ◽  
Gengyin Li
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Optimal Power

A Novel Optimization Algorithm for Transient Stability Constrained Optimal Power Flow

2016 ◽  
pp. 147-176
Author(s):  
Sourav Paul ◽  
Provas Kumar Roy
Keyword(s):  
Power Systems ◽  
New England ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Linear Optimization ◽  
Standard Test ◽  
Optimal Power ◽  
Teaching Learning ◽  
Bus System

Optimal power flow with transient stability constraints (TSCOPF) becomes an effective tool of many problems in power systems since it simultaneously considers economy and dynamic stability of power system. TSC-OPF is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. This paper presents a novel and efficient optimisation approach named the teaching learning based optimisation (TLBO) for solving the TSCOPF problem. The quality and usefulness of the proposed algorithm is demonstrated through its application to four standard test systems namely, IEEE 30-bus system, IEEE 118-bus system, WSCC 3-generator 9-bus system and New England 10-generator 39-bus system. To demonstrate the applicability and validity of the proposed method, the results obtained from the proposed algorithm are compared with those obtained from other algorithms available in the literature. The experimental results show that the proposed TLBO approach is comparatively capable of obtaining higher quality solution and faster computational time.

scholarly journals Adaptive Unified Differential Evolution Algorithm for Optimal Operation of Power Systems with Static Security, Transient Stability and Statcom Device

2019 ◽  
Vol 8 (4) ◽  
pp. 3309-3324
Keyword(s):  
Power Systems ◽  
Power System ◽  
Differential Evolution ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Voltage Stability ◽  
Transient Stability ◽  
Generator System ◽  
Optimal Power ◽  
Voltage Transient

The complexity of a power system operating with transient stability/security constraints increases with increased interconnection of power transmission networks. Many of the power system’s secure operations are affected with the voltage/transient instability problems. Thereby, the modern power systems have considered solving optimal power flow (OPF) problems using voltage/transient stability constraints as a tedious and challenging task. Algebraic and differential equations of the voltage/stability constraints are included in non-linear optimal power flow optimization problems. In this work, the OPF problems with voltage/stability constraints are solved using a newly developed reliable and robust technique. Moreover, the impact of a FACTS device such as STATCOM device was investigated to test its impact in the enhancement of power system performance. An adaptive unified differential evolution (AuDE) technique is proposed to search in the non-convex and nonlinear problems to obtain the global optimal solutions. Compared to other existing methods and basic DE, the proposed AuDE algorithm has achieved better results under simulation conditions. The power system’s performance is considerably enhanced with STATCOM device. Efficiency of the proposed method in solving the transient and security constrained power systems for optimal operations were demonstrated using the numerical results obtained from IEEE 39-bus, 10-generator system and IEEE 30-bus, 6-generator system. Due to page limitation only 30-bus systems results are presented.

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 Solution techniques for transient stability-constrained optimal power flow – Part I

2017 ◽  
Vol 11 (12) ◽  
pp. 3177-3185 ◽  
Author(s):  
Shrirang Abhyankar ◽  
Guangchao Geng ◽  
Mihai Anitescu ◽  
Xiaoyu Wang ◽  
Venkata Dinavahi
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Optimal Power ◽  
Flow Part ◽  
Solution Techniques
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