Minimization of load shedding by sequential use of linear programming and particle swarm optimization (original) (raw)
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International Journal of Electrical and Computer Engineering (IJECE), 2022
This paper proposes an under-frequency load shedding (UFLS) method by using the optimization technique of artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm to determine the minimum load shedding capacity. The suggested technique using a hybrid algorithm ANN-PSO focuses on 2 main goals: determine whether process shedding plan or not and the distribution of the minimum of shedding power on each demand load bus in order to restore system’s frequency back to acceptable values. In the hybrid algorithm ANN-PSO, the PSO algorithm takes responsible for searching the optimal weights in the neural network structure, which can help to optimize the network training in terms of training speed and accuracy. The distribution of shedding power at each node considering the primary control and secondary control of the generators’ unit and the phase electrical distance between the outage generators and load nodes. The effectiveness of the proposed method is experimented with multiple generators outage cases at various load levels in the IEEE-37 Bus scheme where load
An Improved Algorithm for Optimal Load Shedding in Power Systems
A blackout is usually the result of load increasing beyond the transmission capacity of the power system. A collapsing system enters a contingency state before the blackout. This contingency state is characterized by a decline in the bus voltage magnitudes. To avoid blackouts, power systems may start shedding load when a contingency state occurs called under voltage load shedding (UVLS). The success of a UVLS scheme in arresting the contingency state depends on shedding the optimum amount of load at the optimum time and location. This paper proposes a hybrid algorithm based on genetic algorithms (GA) and particle swarm optimization (PSO). The proposed algorithm can be used to find the optimal amount of load shed for systems under stress (overloaded) in smart grids. The proposed algorithm uses the fast voltage stability index (FVSI) to determine the weak buses in the system and then calculates the optimal amount of load shed to recover a collapsing system. The performance analysis shows that the proposed algorithm can improve the voltage profile by 0.022 per units with up to 75% less load shedding and a convergence time that is 53% faster than GA.
Application of Particle Swarm Optimization Technique to Solve Economic Load Dispatch Problem
2012
This paper presents the application of particle swarm optimization (PSO) technique to find the optimal location of flexible AC transmission system (FACTS) devices with minimum cost of installation of FACTS devices and to improve system loadability (SL). While finding the optimal location, thermal limit for the lines and voltage limit for the buses are taken as constraints. Three types of FACTS devices, thyristor controlled series compensator (TCSC), static VAR compensator (SVC) and unified power flow controller (UPFC) are considered. The optimizations are performed on the parameters namely the location of FACTS devices, their setting, their type, and installation cost of FACTS devices. Two cases namely, single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC and UPFC) are considered. Simulations are performed on IEEE 6, 30 and 118 bus systems and Tamil Nadu Electricity Board (TNEB) 69 bus system, a practical system in India for optimal location of FACTS devices. The results obtained are quite encouraging and will be useful in electrical restructuring.
An Application of PSO in Optimal Load Shedding Considering Voltage Stability
Optimal power system load shedding in restructured power system considering market issues and voltage stability usingParticle Swarm Optimization (PSO)is presented in this paper. A multi-objective function that comprises economic and technical sub-optimization problems is considered to provide an optimal load shedding scheme with maximumGenerating Company (GenCo) profit, and as a consequence maximum social welfareas well as maximum loadability limit. Particle swarm optimization (PSO) is utilized to find the optimal scheme of load shedding when a component outage occurs. The proposed methodology is obtained using modifications in MATPOWER 4.1 software integrating continuation power flow and PSO algorithm. In the smart market (SM) procedure, the generators offers and dispatchableloads bids have been considered where the Independent System Operator (ISO) as the smart market operator decides on the market clearing price and generators rescheduled amount of power generation in precontingency as well as post-contingency situations. The proposed methodology is applied to a modified IEEE 30-Bus test system. The results obtained show the effectiveness of the proposed method in procurement of an optimal load shedding satisfying both GenCo and ISO aims to maximize profit and social welfare as well as maintaining the power system voltage stability margin.
Power systems operation using particle swarm optimization technique
Electric Power Systems Research, 2008
This paper presents a proposed technique for solving different optimization problems using particle swarm optimization (PSO) technique as a modern optimization technique. The security constrained optimal active power dispatch is solved by a proposed optimal effective localized area (OELA) in large-scale power system at different operating conditions. However, the boundaries of this area can be increased or decreased depending on the amount and type of the operation problems as well as the control action requirements to remove these problems. Hence, minimum control variables are adjusted in a smalllocalized area to steer the system to secure and reliable operation condition. The optimal operation of ready reserve is introduced using an efficient proposed procedure considering the security constraints of the transmission lines power flows. Different emergency condition problems are solved using the OELA applied to different standard test systems.
2018
Power systems operating under stress may approach a collapse point resulting in blackouts. To avoid this problem corrective measures such as load shedding are required. Conventional techniques are fail to provide optimal load shed.This paper focuses on optimal load shed as well as enhancing the system voltage profile using a hybrid optimization algorithm based on the well-known Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). GA has traditionally been known for its accuracy while the PSO algorithm is popular for its fast convergence time. GA algorithms require longer convergence times due to the complex nature of their cost functions; therefore, in this work PSO is applied to the GA construction to solve this problem. This result in a fast and accurate algorithm named GAPSO. This paper focuses on optimal load shed by using hybrid optimization termed as GAPSO. The proposed algorithm is utilized to minimize the total amount of load shed on the weak buses. Weak buses are i...
Implementing Particle Swarm Optimization to Solve Economic Load Dispatch Problem
2009 International Conference of Soft Computing and Pattern Recognition, 2009
Economic Load Dispatch (ELD) is one of an important optimization tasks which provides an economic condition for a power systems. In this paper, Particle Swarm Optimization (PSO) as an effective and reliable evolutionary based approach has been proposed to solve the constraint economic load dispatch problem. The proposed method is able to determine, the output power generation for all of the power generation units, so that the total constraint cost function is minimized. In this paper, a piecewise quadratic function is used to show the fuel cost equation of each generation units, and the B-coefficient matrix is used to represent transmission losses. The feasibility of the proposed method to show the performance of this method to solve and manage a constraint problems is demonstrated in 4 power system test cases, consisting 3,6,15, and 40 generation units with neglected losses in two of the last cases. The obtained PSO results are compared with Genetic Algorithm (GA) and Quadratic Programming (QP) base approaches. These results prove that the proposed method is capable of getting higher quality solution including mathematical simplicity, fast convergence, and robustness to solve hard optimization problems.
2019
With the increasing demand for power today, it becomes necessary to operate power plants most economically. This gives rise to economic load dispatch. Economic load dispatch is the process for allocating the generation among the available generating unit to fulfill the load demand in such a way to minimize the total generation cost and satisfying the equality and inequality constraints. On the other hand, the optimal power flow problem focuses on minimizing the generation cost considering the equality constraints, inequality constraints and the state and control variables. This project is focused on providing a solution to the economic load dispatch problem and analyzing the optimal power flow constraints of a test 30- bus system using the particle swarm optimization technique and the lambda iteration method. Using the particle swarm optimization technique in MATLAB and lambda iteration method in GAMS, we obtained the generation cost, system power losses and the respective power output of the generators. When optimal power flow is considered with more constraints such as the voltage constraints and the line limits, the cost of generation, power generated and the power loss, are observed to increase. This shows that the need to consider all the transmission constraints to obtain accurate optimization of a power system.
Particle Swarm Optimization Approach For Economic Load Dispatch: A Review
Particle swarm optimization (PSO) is an effective & reliable evolutionary based approach .Due to its higher quality solution including mathematical simplicity, fast convergence & robustness it has become popular for many optimization problems. There are various field of power system in which PSO is successfully applied. Economic Load Dispatch(ELD) is one of important tasks which provide an economic condition for a power system. it is a method of determine the most efficient, low cost & reliable operation of a power system by dispatching the available electricity generation resources to supply the load on the system. This paper presents a review of PSO application in Economic Load Dispatch problems.