Optimal scheduling of thermal generating units in electric power systems

The Unit Commitment Problem (UCP) in electric power system problem that consists of finding the startup and shutdown schedule of generating units over a period of time (e.g., 24 hrs) so that the operating cost is minimized; The UCP is often characterized by its prohibitive computational time and memory space requirement. The thesis investigates some computational aspects of the problem in an effort to improve the CPU time as well as the quality of the solution. Two algorithms that show significant improvement over existing methods are presented: One is based on the dynamic programming approach and designed for implementation on high performance computing machines with vector and parallel processing capabilities. The other is based on genetic algorithm techniques and designed for implementation on regular engineering workstations or fast personal computers; Finally, the effect of transmission losses on the quality of the optimal scheduling and the computational time are investigated. Simulation results on 26- and 44-unit power systems are presented to illustrate the effectiveness of the proposed algorithms

An extensible genetic algorithm framework for problem solving in a common environment

An object-oriented framework is described for solving mathematical programs using genetic algorithms (GA). The advantages of this framework are its extensibility, modular design, and accessibility to existing programming code. The framework also incorporates a graphical user's interface that may be used to build new GA's as well as run GA simulations. Two power system problems are solved by implementing genetic algorithms using the framework. The first is a continuous optimization problem and the second an integer programming problem. We illustrate the flexibility of the framework as well as its other features on our test problems.published_or_final_versio

Recent Results on Approximate Optimization Methods for the Unit Commitment Problem

International audienceThis work provides an account of recently proposed methods to address the Unit Commitment (UC) problem. In the UC problem, the goal is to schedule a subset of a given group of electrical power generating units and also to determine their production output in order to meet energy demands at minimum cost. In addition, the solution must satisfy a set of technological and operational constraints. Here, computational results are reported for the most effective methodologies. Amongst the problems chosen to report the computational results are the most frequently used benchmark problems, due to Kazarlis, Bakirtzis and Petridis. In the problems considered, the units, which can be up to 100, have to be scheduled for 24-hour period

A Differential Evolution Approach to Optimal Generator Maintenance Scheduling of the Nigerian Power System

The goal of optimal generator maintenance scheduling is to evolve optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system while satisfying system load demand and crew constraints. In this paper, the differential evolution (DE), an evolutionary computation algorithm that utilizes the differential information to guide its further search, is applied to effectively solve the generator maintenance scheduling (GMS) optimization problem. The proposed method can handle mixed integer discrete continuous optimization problems. Results are presented with the DE algorithm on two different case studies for Nigerian power system

Genetic algorithms for scheduling generation and maintenance in power systems

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Optimal Location of Distributed Generation Sources and Capacitance of Distribution Network to Reduce Losses, Improve Voltage Profile, and Minimizing the Costs Using Genetic and Harmonic Search Algorithm

International audienceReducing losses and improving the voltage profile have been the main objectives of electrical power system designers. One of the suggested solutions for achieving these goals is the use of parallel capacitors and distributed generation sources in distribution systems. A location that is optimized for DG installation may not be the best place to minimize losses in improving the system voltage profile. In this paper, determining the optimal location of the dispersed generation unit and the capacitive bank with the goal of optimizing a target function, including losses, improving the voltage profile, and the cost of investment in capacitors and dispersed production. In this paper, IEEE standard 33 buses is considered for simulation, and the results are obtained by using genetic and harmonic search algorithm indicate that DG optimization and capacitor with a target function in which the loss reduction and improvement of the voltage profile is considered to reduce costs, reduce losses, and improve the voltage profile, which are remarkable improvements

Performance Optimisation of Standalone and Grid Connected Microgrid Clusters

Remote areas usually supplied by isolated electricity systems known as microgrids which can operate in standalone and grid-connected mode. This research focus on reliable operation of microgrids with minimal fuel consumption and maximal renewables penetration, ensuring least voltage and frequency deviations. These problems can be solved by an optimisation-based technique. The objective function is formulated and solved with a Genetic Algorithm approach and performance of the proposal is evaluated by exhaustive numerical analyses in Matlab

Using simulations and artificial life algorithms to grow elements of construction

'In nature, shape is cheaper than material', that is a common truth for most of the plants and other living organisms, even though they may not recognize that. In all living forms, shape is more or less directly linked to the influence of force, that was acting upon the organism during its growth. Trees and bones concentrate their material where thy need strength and stiffness, locating the tissue in desired places through the process of self-organization. We can study nature to find solutions to design problems. That’s where inspiration comes from, so we pick a solution already spotted somewhere in the organic world, that closely resembles our design problem, and use it in constructive way. First, examining it, disassembling, sorting out conclusions and ideas discovered, then performing an act of 'reverse engineering' and putting it all together again, in a way that suits our design needs. Very simple ideas copied from nature, produce complexity and exhibit self-organization capabilities, when applied in bigger scale and number. Computer algorithms of simulated artificial life help us to capture them, understand well and use where needed. This investigation is going to follow the question : How can we use methods seen in nature to simulate growth of construction elements? Different ways of extracting ideas from world of biology will be presented, then several techniques of simulated emergence will be demonstrated. Specific focus will be put on topics of computational modelling of natural phenomena, and differences in developmental and non-developmental techniques. Resulting 3D models will be shown and explained