# A genetic algorithm approach to generator unit commitment

01 Nov 2003-International Journal of Electrical Power & Energy Systems (Elsevier)-Vol. 25, Iss: 9, pp 679-687

TL;DR: In this article, a new encoding and representation strategy is proposed that can handle large systems with an improvement in solution and faster convergence, and the authors formulated the problem as the minimization of the performance index, which is the sum of objectives (fuel cost, startup cost) and constraints (minimum up time (MUT), minimum down time (MDT), spinning reserve).

Abstract: Application of genetic algorithms for the solution of unit commitment with detailed problem formulation, solution methodology and representation is described in this paper. New Encoding and Representation strategy is proposed that can handle large systems with an improvement in solution and faster convergence. The unit commitment problem is formulated as the minimization of the performance index, which is the sum of objectives (fuel cost, startup cost) and constraints (minimum up time (MUT), minimum down time (MDT), spinning reserve). Solution methodology and Simulation Results are provided for a 10-generator unit commitment problem for 24 h duration.

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TL;DR: An approach to solving the unit commitment (UC) problem is presented based on a matrix real-coded genetic algorithm (MRCGA) with new repairing mechanism and window mutation and shows an improvement in the solution cost compared with the results obtained from other algorithms.

Abstract: An approach to solving the unit commitment (UC) problem is presented based on a matrix real-coded genetic algorithm (MRCGA) with new repairing mechanism and window mutation. The MRCGA chromosome consists of a real number matrix representing the generation schedule. Using the proposed coding, the MRCGA can solve the UC problem through genetic operations and avoid coping with a suboptimal economic dispatch (ED) problem. The new repairing mechanism guarantees that the generation schedule satisfies system and unit constraints. The window mutation improves the MRCGA searching performance. Numerical results show an improvement in the solution cost compared with the results obtained from other algorithms.

126 citations

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TL;DR: In this paper, the authors investigated thermal unit commitment with considerations for environmental constraints (ECUC), and (b) pumped-storage and thermal units commitment with consideration for environmental constraint (PSECUC) based on a new optimization methodology.

Abstract: Aside from their zero fuel costs, the pumped-storage units can reduce emissions of thermal generating units. The objective of this study is to investigate (a) thermal unit commitment with considerations for environmental constraints (ECUC), and (b) pumped-storage and thermal unit commitment with considerations for environmental constraints (PSECUC) based on a new optimization methodology. The PSECUC determines the start-up and shut-down schedules of pumped-storage and thermal generating units that meet the required demand so that the costs for fuel, start-up, and emissions (TC) are minimized. For the ECUC (thermal generating units only), the results show improvements of 0.03 and 0.50% in TC and excess emissions (EXEM), respectively. For the PSECUC, it is determined that 2-pumped-storage units can simultaneously decrease the TC and EXEM by 1.20 and 60%, respectively.

78 citations

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TL;DR: A 66-bus Indian utility system with 12 generating units and 93 transmission lines is considered to exhibit the effectiveness of the proposed OPF with line flow constraint incorporated in solving the Unit Commitment problem using Genetic Algorithm.

Abstract: In this paper Optimal Power Flow (OPF) with line flow constraint is incorporated in solving the Unit Commitment (UC) problem using Genetic Algorithm (GA). In this proposed approach the problem is solved in two phases. In the first phase unit commitment is solved with prevailing constraints, without line flow constraint by genetic algorithm. In the second phase the violations in the lines are minimized for a committed schedule using GA based OPF. The resulting solution minimizes line flow violations in the critical lines under unit’s decommitted hours by adjusting the unit generations. In this paper, a 66-bus Indian utility system with 12 generating units and 93 transmission lines is considered to exhibit the effectiveness of the proposed approach.

73 citations

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TL;DR: A new dynamic programming based direct computation Hopfield method for solving short term unit commitment (UC) problems of thermal generators using a linear input–output model for neurons to generate economic dispatch (ED).

Abstract: This paper develops a new dynamic programming based direct computation Hopfield method for solving short term unit commitment (UC) problems of thermal generators. The proposed two step process uses a direct computation Hopfield neural network to generate economic dispatch (ED). Then using dynamic programming (DP) the generator schedule is produced. The method employs a linear input–output model for neurons. Formulations for solving the UC problems are explored. Through the application of these formulations, direct computation instead of iterations for solving the problems becomes possible. However, it has been found that the UC problem cannot be tackled accurately within the framework of the conventional Hopfield network. Unlike the usual Hopfield methods which select the weighting factors of the energy function by trials, the proposed method determines the corresponding factor using formulation calculation. Hence, it is relatively easy to apply the proposed method. The Neyveli Thermal Power Station (NTPS) unit II in India with three units having prohibited operating zone has been considered as a case study and extensive study has also been performed for power system consisting of 10 generating units.

56 citations

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TL;DR: In this article, the authors proposed a probabilistic cost optimization scheme under uncertain environment for the MGs with several multiple Distributed Generation (DG) units for a Combined Heat and Power (CHP) system, where a PEMFCPP (Proton Exchange Membrane Fuel cell power plant) is considered as a prime mover of the CHP system.

Abstract: Micro Grids (MGs) are clusters of the DER (Distributed Energy Resource) units and loads which can operate in both grid-connected and island modes. This paper addresses a probabilistic cost optimization scheme under uncertain environment for the MGs with several multiple Distributed Generation (DG) units. The purpose of the proposed approach is to make decisions regarding to optimizing the production of the DG units and power exchange with the upstream network for a Combined Heat and Power (CHP) system. A PEMFCPP (Proton Exchange Membrane Fuel cell power plant) is considered as a prime mover of the CHP system. An electrochemical model for representation and performance of the PEMFC is applied. In order to best use of the FCPP, hydrogen production and storage management are carried out. An economic model is organized to calculate the operation cost of the MG based on the electrochemical model of the PEMFC and hydrogen storage. The proposed optimization scheme comprises a self-adaptive Charged System Search (CSS) linked to the 2m + 1 point estimate method. The 2m + 1 point estimate method is employed to cover the uncertainty in the following data: the hourly market tariffs, electrical and thermal load demands, available output power of the PhotoVoltaic (PV) and Wind Turbines (WT) units, fuel prices, hydrogen selling price, operation temperature of the FC and pressure of the reactant gases of FC. The Self-adaptive CSS (SCSS) is organized based on the CSS algorithm and is upgraded by some modification approaches, mainly a self-adaptive reformation approach. In the proposed reformation method, two updating approaches are considered. Each particle based on the ability of those approaches to find optimal solutions in the past iterations, chooses one of them to improve its solution. The effectiveness of the proposed approach is verified on a multiple-DG MG in the grid-connected mode.

52 citations

##### References

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01 Sep 1988

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.

Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required

52,797 citations

01 Jan 1989

TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.

Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.

33,034 citations

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01 Jan 1984

TL;DR: In this paper, the authors present a graduate-level text in electric power engineering as regards to planning, operating, and controlling large scale power generation and transmission systems, including characteristics of power generation units, transmission losses, generation with limited energy supply, control of generation, and power system security.

Abstract: Topics considered include characteristics of power generation units, transmission losses, generation with limited energy supply, control of generation, and power system security. This book is a graduate-level text in electric power engineering as regards to planning, operating, and controlling large scale power generation and transmission systems. Material used was generated in the post-1966 period. Many (if not most) of the chapter problems require a digital computer. A background in steady-state power circuit analysis is required.

6,344 citations

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TL;DR: Power Generation Operation And Control Solution pdf Free April 17th, 2019 Free download Ebook Handbook Textbook User Guide PDF files on the internet quickly and easily And Distribution Third Edition electric Power Engineering Geyser Load Control Timer Isg1201 Operation Manual Using Excess Conduit Hydro generation Power For Bitcoin Mining Electric Power as mentioned in this paper

Abstract: Power Generation Operation And Control Solution pdf Free April 17th, 2019 Power Generation Operation And Control Solution pdf Free download Ebook Handbook Textbook User Guide PDF files on the internet quickly and easily And Distribution Third Edition electric Power Engineering Geyser Load Control Timer Isg1201 Operation Manual Using Excess Conduit Hydro generation Power For Bitcoin Mining Electric Power

1,478 citations

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TL;DR: This paper presents a genetic algorithm (GA) solution to the unit commitment problem using the varying quality function technique and adding problem specific operators, satisfactory solutions to theunit commitment problem were obtained.

Abstract: This paper presents a genetic algorithm (GA) solution to the unit commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple GA algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the varying quality function technique and adding problem specific operators, satisfactory solutions to the unit commitment problem were obtained. Test results for power systems of up to 100 units and comparisons with results obtained using Lagrangian relaxation and dynamic programming are also reported.

1,119 citations