S. Patra

Fuzzy and simulated annealing based dynamic programming for the unit commitment problem

A dynamic programming technique with a fuzzy and simulated annealing based unit selection procedure has been proposed in this paper for the solution of the UC problem. The curse of dimensionality of the dynamic programming technique is eliminated by minimizing the number of prospective solution paths to be stored at each stage of the search procedure. Heuristics like priority ordering of the units, unit grouping, fast economic dispatch based on priority ordering, avoidance of repeated economic dispatch through memory action have been employed to make the algorithm fast. The proposed method produced comparable results with the best performing methods found in the literature.

Differential Evolution Algorithm for Solving Unit Commitment with Ramp Constraints

Abstract This article proposes a differential evolution algorithm to solve the unit commitment problem with ramp constraints. Two implementations of the proposed algorithm—the first one using the binary code, and the second one using the integer code—have been developed. Both of these implementations have been found to converge to the same optimum solution requiring a different number of generations and a different CPU time. The proposed algorithm shows competitive performance with the best of the similar methods proposed earlier.

A Binary Differential Evolution Algorithm For Transmission And Voltage Constrained Unit Commitment

The paper proposes a binary version of the differential evolution algorithm and then solves the transmission and voltage constrained unit commitment using it. The proposed algorithm uses binary coding of the selection variables and implementing OR/XOR/NOT type Boolean logics on variable strings. The inclusion of the ramp rate, transmission/voltage constraints ensure the feasibility of the schedule. The solution algorithm has been found to be capable of finding the optimum/near optimum solution.

Fuzzy based fast dynamic programming solution of unit commitment with ramp constraints

: A fast dynamic programming technique based on a fuzzy based unit selection procedure is proposed in this paper for the solution of the unit commitment problem with ramp constraints. The curse of dimensionality of the dynamic programming technique is eliminated by minimizing the number of prospective solution paths to be stored at each stage of the search procedure. Heuristics like priority ordering of the units, unit grouping, fast economic dispatch based on priority ordering, and avoidance of repeated economic dispatch through memory action have been employed to make the algorithm fast. The proposed method produced comparable results with the best performing methods found in the literature.

Voltage stability analysis using conventional methods

Power systems all over the world have to operate very close to their stability limits owing to the ever increasing power demands. Voltage collapses usually occur on power systems which are heavily loaded or faulted or have shortage of reactive power. This paper analyses the voltage stability of the power system under increasing load conditions. Modal analysis technique and Continuation Power flow methods have been used to identify the weakest buses prone to voltage collapse. Eigen values are used to determine the system voltage stability and participation factors are used to determine the weakest bus which has the maximum contribution to voltage collapse of the system. FACTS controllers (SVC and STATCOM) are then applied to the weakest buses for voltage improvement. Comparison is made between the performances of the power system with and without FACTS compensation. The tests have been carried out on IEEE 30 and 57 bus systems. Further, this analysis has been extended to the North-Eastern Grid of India. This paper aims to study the improvement of voltage profile of power systems using FACTS devices.

Multi objective optimal power flow using particle swarm optimization technique

Objective of OPF (Optimal power flow) is to achieve reduction of power generation cost or loss by adjusting certain control variables while satisfying physical and operating limits on various other controls, dependent variables and function of those variables. Achieving OPF within power systems has been a major problem and this paper aims at addressing that challenge by presenting an efficient and reliable evolutionary based approach. PSO (Particle Swarm Optimization), a population based stochastic optimization technique, can be leveraged to overcome OPF challenge. This technique is initialized with a population of random feasible solutions and searches for optima by updating generations. The IEEE-30 bus system is considered throughout this research work to test the proposed algorithm.