Novel power flow problem solutions method’s based on genetic algorithm optimization for banks capacitor compensation using an fuzzy logic rule bases for critical nodal detections

The Reactive power flow’s is one of the most electrical distribution systems problem wich have great of interset of the electrical network researchers, it’s cause’s active power transmission reduction, power losses decreasing, and the drop voltage’s increase. In this research we described the efficiency of the FLC-GAO approach to solve the optimal power flow (OPF) combinatorial problem. The proposed approach employ tow algorithms, Fuzzy logic controller (FLC) algorithm for critical nodal detection and gentic algorithm optimization (GAO) algorithm for optimal seizing capacitor.GAO method is more efficient in combinatory problem solutions. The proposed approach has been examined and tested on the standard IEEE 57-bus the resulats show the power loss minimization denhancement, voltage profile, and stability improvement. The proposed approach results have been compared to those that reported in the literature recently. The results are promising and show the effectiveness and robustness of the proposed approach

Multi-Fuel Allocation for Power Generation Using Genetic Algorithms

The ever increasing growth of energy consumption has stimulated an energy crisis, not only in terms of energy demand, but also the impact of climate change from greenhouse gas (GHG) emissions. Renewable energy sources (RES) have high potential toward sustainable development, with a wide variety of socioeconomic benefits, including diversification of energy supply and creation of domestic industry. This paper presents a solution to optimal multi-fuel allocation for the electric power generation planning problem via genetic algorithms (GA). The objective is to maximize the electric power energy output and minimize generation cost. This is a difficult problem because of its data variation and volatility. GA can provide an appropriate heuristic search method and return an actual or near optimal solution. This paper uses some heuristics during crossover and mutation for tuning the system to obtain a better candidate solution. An experimental result showed significantly improved results compared with other techniques. The results in this paper should be useful for connecting power generation with economic growth

Contingency-Constrained Optimal Power Flow Using Simplex-Based Chaotic-PSO Algorithm

This paper proposes solving contingency-constrained optimal power flow (CC-OPF) by a simplex-based chaotic particle swarm optimization (SCPSO). The associated objective of CC-OPF with the considered valve-point loading effects of generators is to minimize the total generation cost, to reduce transmission loss, and to improve the bus-voltage profile under normal or postcontingent states. The proposed SCPSO method, which involves the chaotic map and the downhill simplex search, can avoid the premature convergence of PSO and escape local minima. The effectiveness of the proposed method is demonstrated in two power systems with contingency constraints and compared with other stochastic techniques in terms of solution quality and convergence rate. The experimental results show that the SCPSO-based CC-OPF method has suitable mutation schemes, thus showing robustness and effectiveness in solving contingency-constrained OPF problems

Particle Swarm Optimization for Total Operating Cost Minimization in Electrical Power System

This paper presents solution of economic dispatch problem via a particle swarm optimization algorithm (PSO). The objective is to minimize the total generation fuel cost and keep the power flows within the security limits. The PSO is simple in concept, easy in implementation .It does not require any derivative information, sure and fast convergence, Moreover; it is needs less computational time than other heuristic methods. These features increase the applicability of the PSO, particularly in power system applications .The effectiveness of the proposed algorithm is demonstrated on the IEEE 37-bus system and their performances are compared with the results of genetic algorithm (GA). The results show that PSO can converge to optimum solution with higher accuracy in comparison with GA

An Improved Tabu Search Algorithm Based on Grid Search Used in the Antenna Parameters Optimization

In the mobile system covering big areas, many small cells are often used. And the base antenna’s azimuth angle, vertical down angle, and transmit power are the most important parameters to affect the coverage of an antenna. This paper makes mathematical model and analyzes different algorithm’s performance in model. Finally we propose an improved Tabu search algorithm based on grid search, to get the best parameters of antennas, which can maximize the coverage area and minimize the interference

Optimising power flow in a volatile electrical grid using a message passing algorithm

Current methods of optimal power flow were not designed to handle increasing level of volatility in the electrical networks, this thesis suggests that a message passing-based approach could be useful for managing power distribution in electricity networks. This thesis shows the adaptability of message passing algorithms and demonstrates and validates its capabilities in addressing scenarios with inherent fluctuations, in minimising load shedding and generation costs, and in limiting voltages. Results are promising but more work is needed for this to be practical to real networks