A Simulated Annealing Algorithm For Fuzzy Unit Commitment Problem

This paper presents a new algorithm based on integrating simulated annealing and fuzzy logic methods to solve the unit commitment problem. The uncertainties in the load demand and the spinning reserve constraints are formulated in a fuzzy logic frame. The simulated annealing is used to solve the combinatorial part of the unit commitment problem, while the nonlinear part of the problem is solved via a quadratic programming routine. A simple cooling schedule has been implemented to apply the simulated annealing test in the algorithm. Numerical results show the superiority of the solutions obtained compared to the classical methods and the simulated annealing method as individual

A Simulated Annealing Algorithm For Fuzzy Unit Commitment Problem

This paper presents a new algorithm based on integrating simulated annealing and fuzzy logic methods to solve the unit commitment problem. The uncertainties in the load demand and the spinning reserve constraints are formulated in a fuzzy logic frame. The simulated annealing is used to solve the combinatorial part of the unit commitment problem, while the nonlinear part of the problem is solved via a quadratic programming routine. A simple cooling schedule has been implemented to apply the simulated annealing test in the algorithm. Numerical results show the superiority of the solutions obtained compared to the classical methods and the simulated annealing method as individual

A New Genetic-Based Tabu Search Algorithm For Unit Commitment Problem

This paper presents a new algorithm based on integrating the use of genetic algorithms and tabu search methods to solve the unit commitment problem. The proposed algorithm, which is mainly based on genetic algorithms incorporates tabu search method to generate new population members in the reproduction phase of the genetic algorithm. In the proposed algorithm, genetic algorithm solution is coded as a mix between binary and decimal representation. A fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the algorithm, a simple short term memory procedure is used to counterthe danger of entrapment at a local optimum by preventing cycling of solutions, and the premature convergence of the genetic algorithm. A significant improvement of the proposed algorithm results, over those obtained by either genetic algorithm or tabu search, has been achieved. Numerical examples also showed the superiority of the proposed algorithm compared with two classical methods in the literature

A New Genetic-Based Tabu Search Algorithm For Unit Commitment Problem

This paper presents a new algorithm based on integrating the use of genetic algorithms and tabu search methods to solve the unit commitment problem. The proposed algorithm, which is mainly based on genetic algorithms incorporates tabu search method to generate new population members in the reproduction phase of the genetic algorithm. In the proposed algorithm, genetic algorithm solution is coded as a mix between binary and decimal representation. A fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the algorithm, a simple short term memory procedure is used to counterthe danger of entrapment at a local optimum by preventing cycling of solutions, and the premature convergence of the genetic algorithm. A significant improvement of the proposed algorithm results, over those obtained by either genetic algorithm or tabu search, has been achieved. Numerical examples also showed the superiority of the proposed algorithm compared with two classical methods in the literature

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

"A Global ANN Algorithm for Induction Motor Based On Optimal Preview Control Theory"

In this paper a global Artificial Neural Network (ANN), algorithm for on-line speed control of a threephase induction motor (IM), is proposed. This algorithm is based on the optimal preview controller. It comprises a novel error system and vector control of the 1M. The IM model includes thee input variables, which are the stator angular frequency and the two components of the stator space voltage vector, and three output variables, which are the rotor angular velocity and the two components of the stator space flux linkage. The objective of the proposed algorithm is to achieve rotor speed control, field orientation control and wnstant flux control. In order to emulate the characteristic of the optimal preview controller within global and accurate performance system, a neural network-based technique for the on-line purpose of speed control of IM, is implemented. This technique is utilized based on optimizing the speed control problem using the optimal preview control law. The numerical solution is used to train a feed Ah” using the radial basis method. Successive trained data is utilized to obtain global stability operation for the IM over the whole control intervals. This data includes, several desired speed trajectories and different load torque operations in addition to the motor parameter variations. Digital computer simulation results have ken carried-out to demonstrate the feasibility, reliability and effectiveness of the proposed global ANN algorithm

"A Global ANN Algorithm for Induction Motor Based On Optimal Preview Control Theory"

In this paper a global Artificial Neural Network (ANN), algorithm for on-line speed control of a threephase induction motor (IM), is proposed. This algorithm is based on the optimal preview controller. It comprises a novel error system and vector control of the 1M. The IM model includes thee input variables, which are the stator angular frequency and the two components of the stator space voltage vector, and three output variables, which are the rotor angular velocity and the two components of the stator space flux linkage. The objective of the proposed algorithm is to achieve rotor speed control, field orientation control and wnstant flux control. In order to emulate the characteristic of the optimal preview controller within global and accurate performance system, a neural network-based technique for the on-line purpose of speed control of IM, is implemented. This technique is utilized based on optimizing the speed control problem using the optimal preview control law. The numerical solution is used to train a feed Ah” using the radial basis method. Successive trained data is utilized to obtain global stability operation for the IM over the whole control intervals. This data includes, several desired speed trajectories and different load torque operations in addition to the motor parameter variations. Digital computer simulation results have ken carried-out to demonstrate the feasibility, reliability and effectiveness of the proposed global ANN algorithm

An Improved OPP Problem Formulation for Distribution Grid Observability

Phasor measurement units (PMUs) are becoming popular and populating the power system grids rapidly due to their wide range of benefits and applications. This research paper proposes a comprehensive, effective, and revised formulation of the optimal PMU placement (OPP) problem with a view to minimizing the required number of PMU and ensuring the maximum number of measurement redundancy subjected to the full observability of the distribution grids. The proposed formulation also incorporates the presence of passive measurements/zero injection buses (ZIB) and the channel availability of the installed PMU. Additionally, the formulation is extended to various contingency cases i.e., the single line outage and single PMU loss cases. This paper solves the proposed OPP formulation employing a heuristic technique called backtracking search algorithm (BSA) and tests its effectiveness through different IEEE standard distribution feeders. Additionally, this study compares the obtained results with the mixed integer linear programming (MILP)-based approach and the referenced works. The obtained results demonstrate the superiority of the proposed formulation and solution methodology compared to other methodologies