A Genetic Algorithm for Mixed-Integer Optimisation in Power and Water System Design and Control

In semiconductor factories and electronics factories, a large multi-chiller system is needed to satisfy strict cooling load requirements. In order to save energy, it is worthwhile to design the chilled water system operation. In this paper, a design methodology is developed for multi-chiller system in which chiller units consist of different cooling capacities chiller that run in parallel. The optimal design problem can be expressed as a mixed integer nonlinear programming (MINLP) which minimizes the system energy consumption within a future time period. In order to solve this complex problem, the two-stage strategy is employed to divide the problem into a scheduling stage and an operating stage. Instead of a lag scheme obtained with general method, this forecasting scheme consists of a series of optimal scheme at each sub-time period and can offer operating reference for operator. The effectiveness of the proposed method is illustrated by an industrial case

Download Full-text

A Tabu search-based algorithm for mixed-integer nonlinear problems and its application to integrated process and control system design

Computers & Chemical Engineering ◽

10.1016/j.compchemeng.2007.10.008 ◽

2008 ◽

Vol 32 (8) ◽

pp. 1877-1891 ◽

Cited By ~ 43

Author(s):

Oliver Exler ◽

Luis T. Antelo ◽

Jose A. Egea ◽

Antonio A. Alonso ◽

Julio R. Banga

Keyword(s):

Control System ◽

Tabu Search ◽

System Design ◽

Nonlinear Problems ◽

Mixed Integer ◽

Control System Design ◽

Integrated Process ◽

Mixed Integer Nonlinear Problems ◽

And Control

Download Full-text

Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽

10.3390/electronics10121452 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1452

Author(s):

Cristian Mateo Castiblanco-Pérez ◽

David Esteban Toro-Rodríguez ◽

Oscar Danilo Montoya ◽

Diego Armando Giral-Ramírez

Keyword(s):

Genetic Algorithm ◽

Objective Function ◽

Programming Model ◽

Distribution Networks ◽

Optimization Method ◽

Optimal Placement ◽

Mixed Integer ◽

Power Balance ◽

Continuous Version ◽

Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

Download Full-text

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Water ◽

10.3390/w11071392 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1392 ◽

Cited By ~ 4

Author(s):

Iram Parvez ◽

JianJian Shen ◽

Mehran Khan ◽

Chuntian Cheng

Keyword(s):

Genetic Algorithm ◽

Dynamic Programming ◽

Stochastic Programming ◽

Short Interval ◽

Optimization Techniques ◽

Mixed Integer ◽

Fuzzy Logics ◽

Spinning Reserve ◽

Lagrange Relaxation ◽

Generation Scheduling

The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

Download Full-text

Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy ◽

10.1177/0957650911420930 ◽

2011 ◽

Vol 225 (8) ◽

pp. 1041-1051 ◽

Cited By ~ 8

Author(s):

H Sayyaadi ◽

H R Aminian

Keyword(s):

Genetic Algorithm ◽

Gas Turbine ◽

Heat Exchangers ◽

Mixed Integer ◽

Design Stage ◽

Tube Length ◽

Pareto Optimal ◽

Multi Objective Optimization ◽

Multi Objective ◽

Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

Download Full-text