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.

Transient Stability Constrained Optimal Power Flow Using Teaching Learning-Based Optimization

2014 ◽  
Vol 3 (4) ◽  
pp. 55-71 ◽  
Author(s):  
Aparajita Mukherjee ◽  
Sourav Paul ◽  
Provas Kumar Roy
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Nonlinear Problems ◽  
Optimal Power ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Bus System

Transient stability constrained optimal power flow (TSC-OPF) is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. In order to solve the TSC-OPF problem efficiently, a relatively new optimization technique named teaching learning based optimization (TLBO) is proposed in this paper. TLBO algorithm simulates the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The authors have explained in detail, the basic philosophy of this method. In this paper, the authors deal with the comparison of other optimization problems with TLBO in solving TSC-OPF problem. Case studies on IEEE 30-bus system WSCC 3-generator, 9-bus system and New England 10-generator, 39-bus system indicate that the proposed TLBO approach is much more computationally efficient than the other popular methods and is promising to solve TSC-OPF problem.

Transient Stability Constrained Optimal Power Flow Using Teaching Learning Based Optimization

2015 ◽  
Vol 4 (1) ◽  
pp. 18-35 ◽  
Author(s):  
Aparajita Mukherjee ◽  
Sourav Paul ◽  
Provas Kumar Roy
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Nonlinear Problems ◽  
Optimal Power ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Bus System

Transient stability constrained optimal power flow (TSC-OPF) is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. In order to solve the TSC-OPF problem efficiently, a relatively new optimization technique named teaching learning based optimization (TLBO) is proposed in this paper. TLBO algorithm simulates the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The authors have explained in detail, the basic philosophy of this method. In this paper, the authors deal with the comparison of other optimization problems with TLBO in solving TSC-OPF problem. Case studies on IEEE 30-bus system WSCC 3-generator, 9-bus system and New England 10-generator, 39-bus system indicate that the proposed TLBO approach is much more computationally efficient than the other popular methods and is promising to solve TSC-OPF problem.

scholarly journals Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6066
Author(s):  
Khaled Nusair ◽  
Lina Alhmoud
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Energy Resources ◽  
Solar Pv ◽  
Renewable Energy Resources ◽  
Real Power ◽  
Optimal Power ◽  
Bus System

In recent decades, the energy market around the world has been reshaped to accommodate the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’ constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system is performed using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of simulation and statistical results of EO with other optimization techniques showed that EO is more effective and superior and provides the lowest optimization value in term of electric power generation, real power loss, emission index and voltage deviation.

Transient Stability Constrained Line-Flow-Based Optimal Power Flow Analysis

2012 ◽  
Vol 13 (2) ◽  
Author(s):  
Lazarus O. Uzoechi ◽  
Satish M. Mahajan ◽  
Ghadir Radman
Keyword(s):  
New England ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Flow Analysis ◽  
Computation Time ◽  
Internal Node ◽  
Machine Model ◽  
Optimal Power ◽  
Line Flow

This paper establishes a new method that adopts the line-flow-based (LFB) approach to develop a transient stability constrained optimal power flow (OPF) analysis called LFB-TSCOPF. The transient energy function (TEF) serves as a direct means of carrying out the stability analysis. The reduction technique was adopted in which the classical machine model was reduced to the internal node model. The proposed method was tested on the WECC 9-bus, three-machine, IEEE 14-bus, five-machine, and the New England 39-bus, ten-machine test systems. The results were compared with other known results from different methods in literature. The results of the active power and total optimal costs are quite promising and consistent with other known methods. The LFB-TSCOPF re-dispatches real power by applying the energy margin performance index as an indication of the generator unit(s) to be rescheduled. The LFB-TSCOPF provides a more comprehensive linear model, reduces computation time and can be useful for online stability studies.

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.

Sign in / Sign up

Export Citation Format

Share Document