crow search algorithm

This paper presents the system stability-constrianted optimal protection coordination (SSCOPC) as stochastic problem in the microgrid with grid-connected and islanded operation modes, including renewable energy sources (RESs) and energy storage systems (ESSs). To regulate optimally the dual setting overcurrent relays (DSORs) and to selectin the reactance size of fault current limiter (FCL), the system minimizes the total relays operation time in the primary and backup protection modes. Also, it is subject to the limits of the coordination time interval (CTI), DSORs setting parameters, FCL size, and microgrid stability in the fault condition. Generally, the microgrid includes RESs and ESSs; thus, the proposed SSCOPC needs a daily operation of microgrid as stochastic model to calculate network variables before fault occurrence. Hence, s this situation minimizes the microgrid operation cost and load shedding cost of islanded microgid which are subjected to optimal power flow equations in the presence of RESs and ESSswhile considering the uncertainty of the load and its generation. Therefore, by applying the proposed problem on the 9 and 32 bus microgrid, solving the problem by the Crow Search Algorithm (CSA), and selecting optimal size of RESs and ESSs, based on operation and protection coordination indices, the proposed SSCOPC can obtain a fast protection solution that considers system stability in faulty conditions.

By incremental deployment of renewable energy sources on microgrid frameworks, new technical and economic issues have emerged in the power system industry. The optimal operation of microgrids in the presence of intermittent renewable sources has been counted as a new challenge within the last decade. Microgrids are off-grid or grid-connected power systems on a very small scale encompassing different types of distributed generation sources and local loads. Generally, in isolated microgrids, the demanded energy of consumers is maintained by hybrid models of internal energy sources. The principal purpose of hybrid systems is to supply the electrical power demanded by consumers instantaneously as well as storing surplus energy for critical conditions. In this paper, a techno-economic and environmental base approach for optimal energy management of microgrids using crow search algorithm is presented. Under study microgrid include renewable energy resources, battery and diesel generator as backup power generator. Annual cost and the released emission are considered as the objective function of the proposed method. The crow search algorithm calculates power dispatch scheduling among generation units. Simulation results of the proposed method show the appropriate configuration of the hybrid system that lead to decrease the annual cost of the system and the released emission.

Installing new energy sources as redundant black-start (BS) units is an efficient way to enhance the speed of power system restoration, especially when there is a high risk that the available power plants considered as BS units fail to operate. In this regard, this paper provides a new optimal design for the placement of the Gas Turbine (GT) as the redundant energy source to improve the power system performance during both restoration and normal conditions. In doing so, there will be contradictory objective functions to be minimized. Therefore, a multi-objective problem (MOP), as a mixed integer linear programming (MILP), is defined. The Pareto optimal solutions of the MOP are obtained by using a new population-based meta-heuristic technique, called Crow Search Algorithm (CSA). Two power systems are used for the validation of the proposed method. The simulation results show that the system can benefit from this method not only to increase the capability of black-start generation, but also to improve the power system performance in normal conditions. During the restoration process, it also provides the optimal start-up sequences of non-black-start (NBS) units with the optimal transmission paths.

In this paper, a framework for multi-objective optimization of a hybrid photovoltaic/diesel generator (PV/DG) system has been presented. This study follows the design of the PV/DG system at the lowest cost, lowest emission and highest reliability.The total net present cost (TNPC), CO2 emissions and the loss of power supply probability (LPSP) are selected asthe problem objectives. This system has been designed in three scenarios: 1- without considering uncertainty and operating reserve, 2- with considering uncertainties of the system and 3- with considering uncertainties of the system and using DG as operating reserve. In order to effectively solve this problem, multi-objective versionof crow search algorithm (MO-CSA) has been developed and the results are compared with the results obtained by well-known non-dominated sorting genetic algorithm II (NSGA-II). The coding of the problem has been done in MATLAB environment and the Pareto fronts have been compared. Simulation results indicate that combination of DG and PV is a promising alternative for supplying the electrical demand of stand-alone areas. Moreover, the results obtained by MO-CSA outperform the results obtained by NSGA-II.

The need for more efficient power systems has prompted the use of a new technologies includes Flexible AC transmission system (FACTS) devices. FACTS devices provides new opportunity for controlling the line power flow and minimizing losses while maintaining the bus voltages within a permissible limit. In this thesis a new method is proposed for optimal placement and sizing of Thyristor controlled series compensator (TCSC) and Static VAR compensator (SVC) for improvement power system operation using crow search algorithm. Optimal place for installation of TCSC and SVC is determined based on line’s reactive power flow and voltage collapse proximity indication (VCPI), respectively. Optimal size of FACTS devices as long as reactive power of generating units and optimal tap of transformers is computed by solving an optimization problem. The objective function is defined as minimizing the sum of the network costs includes costs of energy losses and costs of FACTS devices. Crow search algorithm is a novel metaheuristic optimization method that inspired from the intelligent behavior of crows in storing and retrieving foods. That’s this thesis is used for the first time in optimal placement and sizing of TCSC and SVC. The proposed method is simulated on IEEE 30 bus and 57 bus sample networks in MATLAB environment. Simulation results shows that crow search algorithm converges to optimal solution while meets all technical constraints.And Improves the power system efficiency.