exchange market algorithm

Optimization is a process of finding the best solution for a problem. Heuristic and metaheuristic optimization algorithms are commonly used where the search space is complex. In recent decades, the ubiquity of trusses as structural systems has made their optimization an important engineering endeavor. The primary aim of structural optimization is to determine the most suitable combination of design variables, so as to achieve satisfactory performance of the structures subjected to constraints. the three basic features of the structural optimization problem are: The design variables, the objective function, the constraints. This study evaluates the performances of the exchange market algorithm (EMA) in the structural optimization field for the first time. This optimization algorithm is inspired by the procedure of trading the shares on stock market. In the proposed method there are two different modes in EMA. In the first mode, there is no oscillation in the market where as in the second mode, the market has oscillation. For the first mode, the algorithm’s duty is to recruit people toward successful individuals, while in the second case the algorithm seeks optimal points. the member’s section area has assumed to be a decision variable, and the objective function is to minimize their weight. The member stresses and node displacements are the constraints that must maintain within the allowed limits for each condition. The implementation of exchange market algorithm has been done in MATLAB software. to quantitatively assess the performance of the algorithm, three planar trusses (10 bar, 18 bar, and 200 bar) and three space trusses (22 bar, 25 bar and 72 bar) with multiple loading conditions and design constraints have been considered. The results demonstrate that the exchange market algorithm is very effective and efficient for the optimization designs of medium scale truss structural problems.

In this paper, the design of damping supplementary controller in VSC HVDC transmission systems, which is the interface of Offshore Wind Power Plant (OWPP) with the main power system, is studied. First, it is shown that the speed-power curve in a wind turbine affects the damping of oscillation and electromechanical modes of the power system, and depending on the operating conditions of the turbine, the extent of this effect varies. Then, to improve the dynamic stability of the power system, the use of an optimized supplementary controller in the VSC HVDC system will be proposed. The proposed controller is added as an additional loop to the converter control circuits in VSC HVDC and will amplify the damping torque in the generators by correcting the damping coefficient of the system oscillation modes. In addition, a solution is provided to use the supplementary controller in the most optimal path, so that the most controllability on the oscillation modes and the least interference with other channels between the input-output signals are provided. To design the proposed controller, a fractional order PID controller will be used whose coefficients are adjusted through an optimized exchange market algorithm. The optimization of the algorithm is done by using mutation and crossover operators in the genetic algorithm with the aim of avoiding bats being trapped at local extremum. The simulation results show that the method proposed in this paper not only improves the dynamic stability of the power system but also strengthens the voltage profile.

The complexity of engineering problems and the existence of various constraints on these issues, encourage the researchers to use of innovative methods based on a heuristic algorithm to find the optimal solution for practical problems at a cost-effective time and non-consistency tolerance. A distinction has been made between various issues and, therefore, extensive research has been done to improve heuristic algorithms in order to enhance their ability to solve engineering and practical problems. In this paper, due to the ability to global search some of the metaheuristic search patterns (such as the EMA algorithm) and the ability to local search for some meta-heuristic search patterns (such as the FPA algorithm), a novel combination method is proposed to use the ability of both types of algorithms. Then, using the proposed method, a hybrid search pattern with new abilities is presented, whose abilities are proven on standard benchmark testing functions as well as solving engineering problems.

IRAN and the world are moving away from central energy resource to distributed generation (DG) in order to lower carbon emissions. Renewable energy resources comprise a big percentage of DGs and their optimal integration to the grid is the main attempt of planning/developing projects with in electricity network. Feasibility and thorough conceptual design studies are required in the planning/development process as the most of the electricity networks are designed in the passed decades, not considering the challenges imposed by DGs. As an example, the issue of optimal placement and the capacity of DG’s become problematic when large amount of dispersed generation is connected to a distribution network. Therefore, optimized algorithms have been developed over the last decade in order to do the planning purpose optimally such as to alleviate the unwanted effects of DGs. In this article, after explaining the two proposed methods, the modified non-sorting genetic algorithm (NSGA)’s and Exchange Market Algorithm (EMA)’s results, based on MATPOWER’s systems have been compared, in order to find a fast and reliable solution to optimum planning.

Since distribution networks incorporate a large share of the losses in power systems, reducing losses in these networks is one of the most important issues of global networks, including issues that have always been taken into consideration. There are several ways to reduce losses in distribution networks, one of which is the installation of distributed generation units. Renewable sources can supply a clean and smart solution to the increased demands. Thus, Photovoltaic (PV) and Wind Turbine (WT) are taken here as resources of Distributed Generation (DG). Location and sizing of distributed generation have affected largely on the system losses. In this article, Exchange Market Optimization Algorithm (EMA) is proposed for optimal location and sizing of DG based renewable sources for the 69 buses distribution system. The exchange market algorithm on IEEE radial distribution system is simulated and studied using Matlab software and the results of the exchange market algorithm are compared with other algorithms. The results show the effectiveness of the exchange market algorithm in finding the optimal location and sizing of DG to reduce losses and improve the voltage profile.