Multi-objective adaptive clonal selection algorithm for solving optimal power flow considering multi-type FACTS devices and load uncertainty

This paper presents evolutionary technique to seek the optimal location of FACTS devices in a power system. FACTS controllers in the form of SVC type are introduced. This evolutionary technique is genetic algorithms (GAs). Provided optimal locations, FACTS devices can be used to achieve the optimal power flow (OPF) without any constraint violation and thus to increase the utilization of the lowest cost generation in power system. FACTS location should be reasonably chosen according to their contribution to the general objective of power system economic generation and dispatch. The objective cost function, which consists of the investment costs for this type of FACTS devices and the generation costs, is minimized. Numerical examples will be indicated to obtain the optimal allocation of SVC.

In this paper, a multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) was proposed for different multi-objective optimal power flow (MOOPF) problems. Optimal power flow (OPF) was formulated as a non-linear problem with various objectives and constraints. Pareto optimal front was obtained by using non-dominated sorting and crowding distance methods. Finally, an optimal compromised solution was selected from the Pareto optimal set by applying an ideal distance minimization method. The efficiency of the proposed MOHFPSO technique was tested on standard IEEE 30-bus and IEEE 57-bus test systems with various conflicting objectives. Simulation results were also compared with non-dominated sorting based multi-objective particle swarm optimization (MOPSO) and different optimization algorithms reported in the current literature. The achieved results revealed the potential of the proposed algorithm for MOOPF problems.

In this paper, the multi-objective optimal power flow (MOOPF) problem optimization objectives focus on four optimization objectives: generation cost, emission, real power loss and voltage deviation (VD). Three renewable energy sources with successful industrial applications, including wind energy, solar energy, and tidal energy are introduced. Renewable energy supply is uncertain, so Weibull distribution probability, lognormal probability and Gumbel probability are used to calculate the instability and intermittency of wind energy, solar energy and tidal energy, respectively. The inclusion of four energy supplies on the IEEE-30 test system and the consideration of renewable energy reserves and penalty cost calculation improve the realism of the model. In order to obtain the control parameters that minimize the four optimization objectives, a named multi-objective pathfinder algorithm (MOPFA) based on elite dominance and crowding distance was proposed to solve this multi-objective optimization problem. Simulation results show the feasibility of the model, and MOPFA can get more evenly distributed Pareto front and provide more diverse solutions. A compromise solution was selected by the fuzzy decision system. Comparison with the recently published literature also shows that the proposed model can effectively reduce emissions and other indicators. In addition, the statistical test results show that MOPFA's multi-objective optimization performance ranks first. In solving this complex optimization problem, results show the MOPFA is superior to other multi-objective algorithms in optimization accuracy and speed.

Enhanced Genetic Algorithm based computation technique for multi-objective Optimal Power Flow solution

In this paper, an original sensitivity index has been developed in order to evaluate, before the insertion of a STATCOM (STATic COMpensator) the interaction phenomena severity resulting from this insertion in a two-area four-machine test power system. The study has been carried out using small signal stability theory and eigenvalue analysis. This index, derived from the controllability and observability factors, can be calculated for each oscillation mode. The index has been tested on the poorly damped inter-area mode of the test system for three different configurations: the test system alone, the test system with a STATCOM in operation and the test system with a generator equipped with a PSS (power system stabilizer). Results have shown that, in all of these configurations, the index permits the selection of the location of the STATCOM in order to limit the interaction phenomena on the inter-area oscillation mode.

The feasibility of a parameter identification method based on symbolic time series analysis (STSA) and the adaptive immune clonal selection algorithm (AICSA) is studied. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. The effect of the parameters in STSA is theoretically evaluated and numerically verified. AICSA is employed to minimize the error between the state sequence histogram (SSH) that is transformed from raw acceleration data by STSA. The proposed methodology is evaluated by comparing it with AICSA using raw acceleration data. AICSA combining STSA is proved to be a powerful tool for identifying unknown parameters of structural systems even when the data is contaminated with relatively large amounts of noise.

A method to solve a multi‐objective optimal power flow (MOOPF) problem with multiple and competing objective functions (OF) is presented. The modified flower pollination algorithm and the normal boundary intersection method are used in a complementary way to determine the Pareto front solution of the MOOPF problem. To help in the decision making process, an intuitive criterion based on the centroid concept is proposed to select the best compromise solution from the Pareto frontier. To demonstrate the capabilities of the proposed method, different OFs are combined to calculate the Pareto front solution on the IEEE 30 bus test system. Finally, a comparison of the proposed centroid based method against the well‐known fuzzy membership and entropy criterions is provided in the results section.

Increased loading of power systems, and environmental restrictions, combined with a world wide deregulation of the power industry, require more effective control means for power flow control and voltage support. From the point view of steady-state analysis, this paper concentrates on building up a versatile model for all types of FACTS devices. In this respect, the decomposed power injection model (PIM) is proposed. Based on the model, a novel framework for power flow control, using FACTS devices, is adopted to derive FACTS control parameters. In order to take full advantage of the optimal multiplier Newton-Raphson power flow method (OMNR) for power flow with embedded FACTS devices and interior point linear programming (IPLP) for power flow control, a rectangular-polar hybrid formulation is adopted. Numerical results on a modified IEEE 5-node network are presented to illustrate the proposed approach.

In electricity market, both power system safety and cost need to be considered during the electricity trading. So the multi-objective optimal power flow (MOOPF) mathematical model is formulated for the power system with reactive power cost, which contains various operational constraints, power system equation and inequality constraints. In addition, a modified sooty tern optimization algorithm (MSTOA) is proposed to solve the MOOPF problem, which fully validated on the IEEE30 test system. Moreover, a constrained prior strategy based on Pareto-dominant (CPSP) is proposed for solving the inequality constraints on state variables. The simulation results show that MSTOA appears to find the best compromise solution (BCS) with higher feasibility and the Pareto front distribution of the obtained solution set is more uniform. Finally, the proposed MSTOA can provide more and better economic solutions for decision-makers while facing the MOOPF problem in the electricity trading.

Voltage stability is an important issue in power system operation. Flexible AC transmission systems, so-called FACTS devices, help to improve voltage stability and minimize real power losses. The effectiveness of FACTS devices depend on their proper location and rating. This paper presents a method, based on line flow sensitivity factors such as bus voltage stability index and line voltage stability index, to find suitable locations of multi-type FACTS devices. Also this paper proposes an application of particle swarm optimization (PSO) and harmony search algorithm (HSA) in optimizing the rating of FACTS devices. The proposed approaches are evaluated with three different objective functions namely, minimization of real power loss, improvement of voltage profile and enhancement of voltage stability. The performance of proposed methods is analyzed on IEEE 14 bus system by implementing FACTS devices such as static var compensator (SVC), thyristor controlled series capacitor (TCSC) and unified power flow controller (UPFC).The analysis shows that there is a reduction in real power loss and improvement in voltage stability and voltage profile of the system after employing FACTS devices. It also shows that both real power loss and bus voltage stability index (BVSI) have been reduced more with FACTS ratings obtained from PSO than with that from HSA.

With the improved Artificial Fish Swarm Algorithm (AFSA), a new multi-objective Optimal Power Flow (OPF) calculation method was proposed. To accelerate the algorithm convergence speed and satisfy the requirement of accuracy, Chaos with good ergodicity and stochasticity was employed to initialize the fish school. After improvement the step in the behaviour of AF_prey can be adjusted dynamically, which can speed up the convergence. The behaviour of AF_swarm was also modified to improve the searching accuracy. The improved AFSA was employed to the optimal power flow calculation, which has capability of dynamic optimization and is suitable to deal with the multidimensional and nonlinear issues. The objective function and constraint conditions were treated separately, which turned the optimization issue with constraint conditions to non-constraint optimization issue. IEEE standard model was employed to the OPF calculation with Microsoft Visual C. The simulation results proved the good performance in the algorithm convergence speed and calculation accuracy of optimal power flow calculation with the improved AFSP. And The simulation results verified the feasibility and validity of multi-objective OPF calculation based on improved AFSA.

For the past century, ac networks have been established as the standard technology for electrical power transmission system s. However, the de technology has not disappeared completely from this picture. The capability of de systems to transmit higher power over longer distances, the possibility of interconnecting asynchronous networks, and their high efficiency has maintained the interest of both industry and academia. Historically, systems based on dc-generators and mercury valves were used for de power transmission applications, but, by the 90's, all installations were thyrsi tor-based line commutated converters (LCC). In 1999, the first system based on voltage source converters (VSC) was installed in Gotland, Sweden, marking the beginning of a new era for de transmission. Over the past 15 years, the power rating of VSC-based de transmission systems has increased from 50 to 700 MVV, the operating voltage from 120 to 500 kV, meanwhile , the covered distances have become as long as 950 km (ABB's HVDC-light installation in Namibia in 2010). The work presented in this thesis is oriented towards the control and operation of multi-terminal VSC de (MTDC) networks. The proposed approach is a hierarchical control architecture, inspired by the well-established automatic generation control strategy applied to ac networks. In the proposed architecture, the primary control of the MTDC system is decentralized and implemented using a generalized droop strategy More than analyzing the behavior of the primary control, this thesis provides a methodology for designing the various parameters that influence this behavior. The importance of correctly dimensioning the VSC's output capacitor is underlined as this element, when set in the context of a MTDC network, becomes the inertial element of the grid and it has a direct impact on the voltage overs hoots that appear during transients. Further on, an improved droop control strategy that attenuates the voltage oscillations during transients is proposed. Also part of the proposed hierarchical control, the secondary control is centralized and it regulates the operating point of the network so that optimal power flow (OPF) is achieved . Compared to other works, this thesis elaborates, both analytically and through simulations, on the coordination between the primary and secondary control layers. Durante el siglo pasado, las redes de corriente alterna se han consolidado como la tecnología estándar para los sistemas de transmisión de energía eléctrica. Sin embargo, los sistemas de transmisión en continua se han seguido utilizando en algunas aplicaciones. La capacidad de estos para transmitir mayores potencias a distancias más largas, la posibilidad de interconectar redes asincrónicas, y su alta eficiencia han propiciado que se mantuviera el interés académico, de investigación e industrial en esta tecnología . Aunque históricamente se utilizaron sistemas basados en generadores de continua y válvulas de mercurio para las redes de transmisión, en la década de los 90 todas las instalaciones ya contaban con convertidores conmutados basados en tiristores (LCC). En 1999, se instaló el primer sistema basado en convertidores en fuente de tensión (VSC) en Gotland, Suecia, marcando el comienzo de una nueva era para la transmisión en corriente continua. En los últimos 15 años, la potencia de los sistemas de transmisión en continua basados en VSC ha aumentado desde los 50 hasta los 700 MN, la tensión de servicio de 120 a 500 kV y las distancias recorridas han llegado a ser, en algunos casos, de hasta 950 kilómetros (HVDC-light de ABB en Namibia en 201 O). El trabajo presentado en esta tesis se centra en el control y operación de redes de corriente continua VSC multi-terminal (MTDC). El enfoque propuesto se basa en una arquitectura de control jerárquico, inspirada en la estrategia de control de generación automática aplicada a redes de corriente alterna. En la arquitectura propuesta, el control primario del sistema MTDC está descentralizado e implementado mediante una estrategia de 'droop' generalizada. Más allá del análisis del comportamiento del control primario, esta tesis presenta una metodología para el diseño de los diferentes parámetros que influyen en el mismo. Se destaca la importancia de dimensionar correctamente condensador de salida del VSC, ya que este elemento, cuando se encuentra en el contexto de una red MTDC, se convierte en el elemento inercial de la red y tiene un impacto directo en el comportamiento transitorio de las tensiones. Asimismo, se propone una estrategia de control de 'droop' mejorada que atenúa las oscilaciones de tensión durante los transitorios. En el marco del control jerárquico propuesto, el control secundario está centralizado y regula el punto de funcionamiento de la red de manera que se consigue un flujo de potencia óptimo (OPF). En comparación con otros trabajos, esta tesis lleva a cabo, tanto de forma analítica como a través de simulaciones, un estudio detallado sobre la coordinación entre las capas de control primario y secundario en redes MTDC.

This article applies the grey wolf optimizer and differential evolution (DE) algorithms to solve the optimal power flow (OPF) problem. Both algorithms are used to optimize single objective functions sequentially under the system constraints. Then, the DE algorithm is utilized to solve multi-objective OPF problems. The indicator of the static line stability index is incorporated into the OPF problem. The fuzzy-based Pareto front method is tested to find the best compromise point of multi-objective functions. The proposed algorithms are used to determine the optimal values of the continuous and discrete control variables. These algorithms are applied to the standard IEEE 30-bus and 118-bus systems with different scenarios. The simulation results are investigated and analyzed. The achieved results show the effectiveness of the proposed algorithms in comparison with the other recent heuristic algorithms in the literature.

A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution

This paper presents a new approach for environmental/economic transaction planning model of the electricity market. The environmental/economic transaction planning problem is formulated as a multi-objective optimal power flow (MOPF) problem. A novel algorithm using multiobjective particle swarm optimization (MOPSO) and non-stationary multi-stage assignment penalty function is proposed to solve this problem. PSO is modified by using dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. Incorporating of non-stationary multistage assignment penalty function in solving OPF problem can improve the convergence. The proposed method is demonstrated on the IEEE 30-bus system. The results show that the proposed approach can efficiently gain multiple pareto optimal transaction planning that match with the sustainable development strategy