K. Chandrasekaran

Multi-objective scheduling problem: Hybrid approach using fuzzy assisted cuckoo search algorithm

Abstract This article proposes a hybrid cuckoo search algorithm (CSA) integrated with fuzzy system for solving multi-objective unit commitment problem (MOUCP). The power system stresses the need for economic, non-polluting and reliable operation. Hence three conflicting functions such as fuel cost, emission and reliability level of the system are considered. CSA mimics the breeding behavior of cuckoos, where each individual searches the most suitable nest to lay an egg (compromise solution) in order to maximize the egg’s survival rate and achieve the best habitat society. Fuzzy set theory is used to create the fuzzy membership search domain where it consists of all possible compromise solutions. CSA searches the best compromise solution within the fuzzy search domain simultaneously tuning the fuzzy design boundary variables. Tuning of fuzzy design variables eliminate the requirement of expertise needed for setting these variables. On solving MOUCP, the proposed binary coded CSA finds the ON/OFF status of the generating units while the real coded CSA solves economic dispatch problem (EDP) and also tunes the fuzzy design boundary variables. The proposed methodology is tested and validated for both the single and multi-objective optimization problems. The effectiveness of the proposed technique is demonstrated on 6, 10, 26 and 40 unit test systems by comparing its performance with other methods reported in the literature.

Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs

This paper proposes a nodal ant colony optimization (NACO) technique to solve profit based unit commitment problem (PBUCP). Generation companies (GENCOs) in a competitive restructured power market, schedule their generators with an objective to maximize their own profit without any regard for system social benefit. Power and reserve prices become important factors in decision process. Ant colony optimization that mimics the behavior of ants foraging activities is suitably implemented to search the UCP search space. Here a search space consisting of optimal combination of binary nodes for unit ON/OFF status is represented for the movement of the ants to maintain good exploration and exploitation search capabilities. The proposed model help GENCOs to make decisions on the quantity of power and reserve that must be put up for sale in the markets and also to schedule generators in order to receive the maximum profit. The effectiveness of the proposed technique for PBUCP is validated on 10 and 36 generating unit systems available in the literature. NACO yields an increase of profit, greater than 1.5%, in comparison with the basic ACO, Muller method and hybrid LR-GA.

Optimal Deviation Based Firefly Algorithm Tuned Fuzzy Design for Multi-Objective UCP

Restructuring of power system stresses the need for economic and reliable generation of power. Therefore generating units should be committed considering fuel cost and reliability level of the system. This necessitates the need for multi-objectives to be met in a unit commitment problem (UCP). Since the above objectives are conflicting in nature, a novel methodology employing optimal deviation based firefly algorithm tuned fuzzy membership function is applied to multi-objective unit commitment problem (MOUCP). The ON/OFF status of the generating units is obtained by binary coded FF whereas the sub-problem economic dispatch (ED) is obtained by real coded FF. Here the conflicting functions are formulated as a single objective function using fuzzy weighted optimal deviation. The fuzzy membership design variables are tuned using real coded FF; thereby the requirement of expertise for setting these variables are eliminated. The proposed methodology is validated on 100-unit system, IEEE RTS 24-bus system, IEEE 118-bus system and a practical Taiwan Power (Taipower) 38-unit system over a 24-h period. Effective strategy on scheduling spinning reserve is demonstrated by comparing its performance with other methods reported in the literature.

Application of Touring Ant colony Optimization technique for optimal power flow incorporating thyristor controlled series compensator

In this work, a Touring Ant colony Optimization (TACO) for the solution of the optimal power flow (OPF) with use of controllable FACTS devices is studied. This method can provide an enhanced economic solution with the use of controllable FACTS devices. Thyristor controlled series compensators (TCSC) is considered in this method. In deregulated power system environment it is necessary to maintain specified power in contract transmission line path. Hence the specified power flow control, constraints due to the use of FACTS devices are included in the OPF problem. The sensitivity analysis is carried out for the location of FACTS devices. The proposed method can be considered for solving two subproblems. The first sub-problem is a power flow control problem by incorporating TCSC in the contract path of power system network and the second sub-problem is the conventional OPF problem. The two sub-problems are simultaneously solved using Touring Ant Colony Optimization (TACO) algorithm. The solutions are compared and tested using a standard IEEE 30-bus system thereby validating feasibility of this approach.

Binary/real coded particle swarm optimization for unit commitment problem

This paper presents a new approach for binary and real coded particle swarm optimization (BRPSO) algorithm to solve the unit commitment problem (UCP). On solving UCP, the proposed binary coded PSO finds the ON/OFF status of the generating units while the economic dispatch problem (EDP) is solved using the real coded PSO. Tanh function is proposed to improve the particle searching mechanism in binary PSO. The proposed methodology is tested and validated on 3, 17, 26 and 38 generating unit system for 24 hour scheduling horizon. The effectiveness of the proposed technique is demonstrated by comparing its performance with the other methods reported in the literature.

Touring Ant colony Optimization technique for Optimal Power Flow incorporating thyristor controlled series compensator

In this work, a study about Touring And Colony Optimization ( TACO) for the solution of Optimal Power Flow with the use of controllable FACTS device is done. Its proven that this method can provide an enhanced economic solution with the use of controllable FACTS devices. Thyristor controlled series compensators (TCSC) is considered in this method. In deregulated power system environment it is necessary to maintain specified power in contract transmission line path. Hence the specified power flow control, constraints due to the use of FACTS devices are included in the OPF problem. The sensitivity analysis is carried out for the location of FACTS devices. The proposed method can be considered for solving two sub-problems. The first sub-problem is a power flow control problem by incorporating TCSC in the contract path of power system network and the second sub-problem is the conventional OPF problem. The two sub-problems are simultaneously solved using Touring Ant Colony Optimization (TACO) algorithm. The solutions are compared and tested using a standard IEEE 30-bus system thereby validating feasibility of this approach.

Unit commitment in composite generation and transmission systems using Genetic Algorithm

This paper proposes a new method for the incorporation of the generation unit and transmission line unavailability in the solution of the unit commitment problem. The above parameters are taken into account in order to assess the required spinning reserve capacity at each hour of the dispatch period, so as to maintain an acceptable reliability level. The unit commitment problem is solved by a Genetic Algorithm resulting in near-optimal unit commitment solutions. The evaluation of the required spinning reserve capacity is performed by implementing reliability constraints, based on the expected unserved energy and loss of load probability indexes. In this way, the required spinning reserve capacity is effectively scheduled according to the desired reliability level. The results are compared with LR to prove the efficiency of the proposed method.

Wind-Thermal Integrated Power System Scheduling Problem Using Cuckoo Search Algorithm

A new nature inspired metaheuristic algorithm known as the cuckoo search algorithm (CSA) is presented in this paper, to solve the unit commitment problem (UCP) for hybrid power system. The utilization of wind energy sources is increasing throughout the world. It is therefore important to develop the protocol for the integration of wind generation system with conventional thermal unit generation system. High wind penetration can lead to high-risk level in power system reliability. In order to maintain the system reliability, wind power dispatch is usually restricted and energy storage is considered for smoothing out the fluctuations. On solving UCP, the proposed binary coded CSA finds the ON/OFF status of the generating units while the economic dispatch problem (EDP) is solved using the real coded CSA. The proposed methodology is tested and validated on 3, 4, 9, 12 38 and 100 unit systems for 24 hour scheduling horizon. The effectiveness of the proposed technique is demonstrated by comparing its performance with the other methods reported in the literature.

Optimal Placement of Cellular Transceiver for Transmission Line Monitoring Using Genetic Algorithm

In recent decades, automation and modernization inhabit a vital role in the electric power industry. For reliable operation, the transmission network has to be continuously monitored and smarter decisions could be executed to satisfy the real time needs. In this respect, electrical transmission towers are installed with wireless sensor network (WSN) to gather the information from the transmission line network. The collected information is transmitted to the control centre for further actions through hybrid hierarchical network architecture. The hybrid architecture consists of a combination of wired, wireless and cellular technologies. WSNs are facing the bottleneck problem during transmission of data due to the bandwidth and latency of low data rate devices. Installation of cellular transceiver in transmission towers can reduce bottleneck problems remarkably. Whereas the installation of cellular transceivers cause high cost. Hence, this paper explains the optimistic ways for optimal placement of cellular transceivers with minimum cost. The objective is to define cellular node locations in order to improve network performance in terms of bandwidth and end-to-end delay. To attain the cost optimistic location of cellular transceivers, a heuristic algorithm based on genetic strategy is developed and applied to a benchmark set of problems. Experiments by simulations are conducted to evaluate the performance of the proposed algorithm in this paper. Model solutions specify both where to place the cellular transceivers and the optimal data paths to route the data. The optimization problem is carried with the objective of minimizing the installation cost. Through a comprehensive evaluation in simulation we show that our approach is effective in accomplishing the desired objectives for corridor of several hundreds of towers.