Security Constrained Optimal Power Flow Using Genetic Algorithms (original) (raw)
Related papers
Novel Genetic Algorithm Based Solutions for Optimal Power Flow under Contingency Conditions
Power system throughout the world is undergoing tremendous changes and developments due to rapid Restructuring, Deregulation and Open-access policies. Greater liberalization, larger market and increasing dependency on the electricity lead to the system operators to work on limited spinning reserve and to operate on vicinities to maximize the economy compromising on the reliability and security of the system for greater profits, which lead to establishment of a monitoring authority and accurate electronic system to prevent any untoward incidents like Blackouts. In any power system, unexpected outages of lines or transformers occur due to faults or other disturbances. These events may cause significant overloading of transmission lines or transformers, which in turn may lead to a viability crisis of the power system. The main role of power system control is to maintain a secure system state, i.e., to prevent the power system, moving from secure state into emergency state over the widest range of operating conditions. Security Constrained Optimal Power Flow (SCOPF) is major tool used to improve the security of the system. In this work, Genetic algorithm has been used to solve the OPF and SCOPF problems. As initial effort conventional GA (binary coded) based OPF and SCOPF is going to be attempted. The difficulties of binary coded GA in handling continuous search space lead to the evolution of real coded GA"s. Solutions obtained using both the algorithms are compared. Case studies are made on the IEEE30 bus test system to demonstrate the ability of real coded GA in solving the OPF and SCOPF problems.
Genetic Algorithm based Optimal Power Flow for Security Enhancement
Power-system-security enhancement deals with the task of taking remedial action against possible network overloads in the system following the occurrence of contingencies. Line overload can be removed by means of generation redispatching and by adjustment of phase-shifting transformers. The paper presents a genetic-algorithm (GA) based OPF algorithm for identifying the optimal values of generator active-power output and the angle of the phase-shifting transformer. The locations of phase shifters are selected based on sensitivity analysis. To overcome the shortcomings associated with the representation of real and integer variables using the binary string in the GA population, the control variables are represented in their natural form. Also, crossover and mutation operators which can deal directly with integers and floating-point numbers are used. Simulation results on IEEE 30-bus and IEEE 118-bus test systems are presented and compared with the results of other approaches.
Improved genetic algorithm for voltage security constrained optimal power flow problem
Voltage stability has become an important issue in the planning and operation of many power systems. Contingencies such as unexpected line outages in a stressed system may often result in voltage instability which may lead to voltage collapse. This paper presents a Genetic Algorithm (GA) approach for solving the Voltage Security Constrained Optimal Power Flow (VSC-OPF) problem. Base-case generator power output, voltage magnitude of generator buses, transformer tap position and reactive power generation of capacitor banks are taken as the control variables. Maximum L-index of load buses is used to specify the voltage stability level of the system. An improved GA which permits the control variables to be represented in their natural form is proposed to solve this combinatorial optimisation problem. For effective genetic operation, crossover and mutation operators which can directly operate on floating point numbers and integers are used. The proposed approach has been evaluated on the IEEE 30-bus test system. Simulation results show the effectiveness of the proposed approach for improving the voltage security of the system.
Genetic Algorithms Applications to Power System Security Schemes
2009
This thesis details the approaches which aim to automatically optimize power system security schemes. In this research, power system security scheme includes two main plans. The first plan, which is called the defence plan scheme, is about preventing cascading blackouts while the second plan, which is called the restoration plan, is about rebuilding the power system in case of failure of the first plan. Practically, the defence plan includes under-frequency load shedding and under-frequency islanding schemes. These two schemes are always considered the last stage of the defensive actions against any severe incident. It is recognized that it is not easy for any power system’s operational planner to obtain the minimum amount of load shedding or the best power system islanding formation. In the case of defence plan failure, which is always possible, a full or partial system collapse may occur. In this situation, the power system operator is urgently required to promptly restore the sys...
This paper presents a genetic algorithm based approach for solving security constrained optimal power flow problem (SCOPF) including FACTS devices. The optimal location of FACTS devices are identified using an index called overload index and the optimal values are obtained using an enhanced genetic algorithm. The optimal allocation by the proposed method optimizes the investment, taking into account its effects on security in terms of the alleviation of line overloads. The proposed approach has been tested on IEEE-30 bus system to show the effectiveness of the proposed algorithm for solving the SCOPF problem.
Genetic based algorithm for security constrained power system economic dispatch
Electric Power Systems Research, 2000
Genetic algorithms are relatively new, however they represent a powerful technique based on biological concepts and are very suitable for solving optimization problems. One such optimization problem is power system security with consideration of economic dispatch. This paper presents a new approach based on constrained genetic algorithms to solve such optimization problems. The operation of power systems demands a high degree of security in order to keep the system operating satisfactorily when subjected to disturbances, while at the same time it is required to take economic aspects into account. A pattern recognition technique is used first to assess dynamic security. Linear classifiers that determine the stability of electric power systems are presented and added to other system stability and operational constraints. The problem is formulated as a constrained optimization problem in a way that ensures a secure and economic system of operation. A genetic algorithm is then used to solve the formulated optimization problem. The method is tested using two different test systems and the results obtained show that the it is very useful for solving such problems.
Optimal Power Flow for Steady state security enhancement using Genetic Algorithm with FACTS devices
2008
This paper presents an enhanced Genetic Algorithm (EGA) based approach to solve the Optimal Power Flow (OPF) with FACTS devices to eliminate line over loads in the system following single line outages. The optimizations are performed on two parameters: the location of the devices, and their values. Two different kinds of FACTS controllers are used for steady state studies: Thyristor Controlled Series Capacitors (TCSCs) and Thyristor controlled Phase shifting Transformers (TCPSTs).
Security constrained optimal power flow by modern optimization tools
International Journal of Engineering, Science and Technology
In this paper, two approaches are studied for scheduling the power generators of the least cost. This is called security constrained optimal power flow [SCOPF]. This problem is represented as a two stage, security constrained OPF problem, in which the first stage optimizes the problem using a genetic algorithm for the purpose of comparison, while the second stage optimize the problem of each contingency using flower pollination algorithms (FPA) as a new trend. Case studies based on IEEE 30 bus system show that the discussed techniques are advantageous and can guarantee operational reliability and economy.
Multi-objective Evolutionary Algorithm for Power System Security Enhancement
2010
This paper reports an application of multi-objective genetic algorithm to solve the multi-objective optimal power flow problem. MOGA characterizes the pareto optimal frontier (non dominated solutions) and evaluates the fitness of an individual according to the pareto dominance relationship. A fuzzy set theory is employed to extract the best compromise solution over the trade-off curve. In this paper, two objective functions are considered: maximization of system security and minimization of investment cost of Thyristor Controlled Series Capacitor (TCSC) .TCSC is used to alleviate the line overload. The probable locations of TCSC are pre-selected based on Single Contingency Sensitivity (SCS) index which ranks the system branches according to their severity. Genetic algorithm is proposed to identify the optimal parameters of TCSC, generator active power and voltage magnitude .The proposed approach has been evaluated on the IEEE 30-bus test system. Simulation results show the effective...
Implementation and Analysis of Genetic Algorithms (GA) to the Optimal Power Flow (OPF) Problem
2018
Recently, there is an increasing need for Optimal Power Flow (OPF) to solve problems of today's deregulated power systems and the unsolved problems in the vertically integrated power systems. The most important aspects related to OPF are the solution methodologies, and the application areas. This work is an implementation and analysis of an optimization method based on Meta-Heuristics in the form of Genetic Algorithms (GA) for solving the OPF problem (GA-OPF) considering transformer tap, and shunt VAR compensation settings. The effects of the parameters of the presented GA on the performance the OPF problem solution is analyzed in details. Moreover, the GA-OPF solutions are compared to solution obtained from classical optimization techniques presented in MATPOWER program. The effectiveness of the presented GA-OPF technique is demonstrated on the IEEE 9-bus system and the IEEE 14-bus system.