Multi-objective dynamic unit commitment optimization for energy-saving and emission reduction with wind power
Title | Multi-objective dynamic unit commitment optimization for energy-saving and emission reduction with wind power |
Publication Type | Conference Paper |
Year of Publication | 2015 |
Authors | Wang, J., Zhou, Y. |
Conference Name | 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT) |
Date Published | nov |
Keywords | air pollution, carbon compounds, CO2, commitment discrete magnitude, double optimization, double-optimization strategy, emission reduction, energy-saving, energy-saving and emission reducing, fault time, fuzzy satisfaction-maximizing method, fuzzy set theory, genetic algorithms, Heuristic algorithms, Linear programming, load distribution, mixed-integer variable, multi-objective, multiobjective dynamic unit commitment optimization, multiobjective unit commitment modeling, nondominated sorting genetic algorithm-II, NSGA-D, NSGA-II algorithm, Optimization, Pareto optimisation, Pareto solution, pollution emission, power generation dispatch, power generation scheduling, pubcrawl170110, satisfaction-maximizing decision, SO2, Spinning, sulphur compounds, unit commitment optimization, Wind power generation, wind power integrated system, wind power plants, Wind speed, wind turbines |
Abstract | As a clean energy, wind power is massively utilized in net recent years, which significantly reduced the pollution emission created from unit. This article referred to the concept of energy-saving and emission reducing; built a multiple objective function with represent of the emission of CO2& SO2, the coal-fired from units and the lowest unit fees of commitment; Proposed a algorithm to improving NSGA-D (Non-dominated Sorting Genetic Algorithm-II) for the dynamic characteristics, consider of some constraint conditions such as the shortest operation and fault time and climbing etc.; Optimized and commitment discrete magnitude and Load distribution continuous quantity with the double-optimization strategy; Introduced the fuzzy satisfaction-maximizing method to reaching a decision for Pareto solution and also nested into each dynamic solution; Through simulation for 10 units of wind power, the result show that this method is an effective way to optimize the Multi-objective unit commitment modeling in wind power integrated system with Mixed-integer variable. |
DOI | 10.1109/DRPT.2015.7432582 |
Citation Key | wang_multi-objective_2015 |
- satisfaction-maximizing decision
- NSGA-D
- NSGA-II algorithm
- optimization
- Pareto optimisation
- Pareto solution
- pollution emission
- power generation dispatch
- power generation scheduling
- pubcrawl170110
- nondominated sorting genetic algorithm-II
- SO2
- Spinning
- sulphur compounds
- unit commitment optimization
- Wind power generation
- wind power integrated system
- wind power plants
- Wind speed
- wind turbines
- fuzzy satisfaction-maximizing method
- carbon compounds
- CO2
- commitment discrete magnitude
- double optimization
- double-optimization strategy
- emission reduction
- energy-saving
- energy-saving and emission reducing
- fault time
- air pollution
- fuzzy set theory
- genetic algorithms
- Heuristic algorithms
- Linear programming
- load distribution
- mixed-integer variable
- multi-objective
- multiobjective dynamic unit commitment optimization
- multiobjective unit commitment modeling