TY - GEN
T1 - Application of evolutionary multiobjective algorithms for solving the problem of energy dispatch in hydroelectric power plants
AU - Marcelino, Carolina G.
AU - Carvalho, Leonel M.
AU - Almeida, Paulo E.M.
AU - Wanner, Elizabeth F.
AU - Miranda, Vladimiro
PY - 2015
Y1 - 2015
N2 - The Brazilian population increase and the purchase power growth have resulted in a widespread use of electric home appliances. Consequently, the demand for electricity has been growing steadily in an average of 5% a year. In this country, electric demand is supplied predominantly by hydro power. Many of the power plants installed do not operate efficiently from water consumption point of view. Energy Dispatch is defined as the allocation of operational values to each turbine inside a power plant to meet some criteria defined by the power plant owner. In this context, an optimal scheduling criterion could be the provision of the greatest amount of electricity with the lowest possible water consumption, i.e. maximization of water use efficiency. Some power plant operators rely on “Normal Mode of Operation” (NMO) as Energy Dispatch criterion. This criterion consists in equally dividing power demand between available turbines regardless whether the allocation represents an efficient good operation point for each turbine. This work proposes a multiobjective approach to solve electric dispatch problem in which the objective functions considered are maximization of hydroelectric productivity function and minimization of the distance between NMO and “Optimized Control Mode” (OCM). Two well-known Multiobjective Evolutionary Algorithms are used to solve this problem. Practical results have shown water savings in the order of million m3/s. In addition, statistical inference has revealed that SPEA2 algorithm is more robust than NSGA-II algorithm to solve this problem.
AB - The Brazilian population increase and the purchase power growth have resulted in a widespread use of electric home appliances. Consequently, the demand for electricity has been growing steadily in an average of 5% a year. In this country, electric demand is supplied predominantly by hydro power. Many of the power plants installed do not operate efficiently from water consumption point of view. Energy Dispatch is defined as the allocation of operational values to each turbine inside a power plant to meet some criteria defined by the power plant owner. In this context, an optimal scheduling criterion could be the provision of the greatest amount of electricity with the lowest possible water consumption, i.e. maximization of water use efficiency. Some power plant operators rely on “Normal Mode of Operation” (NMO) as Energy Dispatch criterion. This criterion consists in equally dividing power demand between available turbines regardless whether the allocation represents an efficient good operation point for each turbine. This work proposes a multiobjective approach to solve electric dispatch problem in which the objective functions considered are maximization of hydroelectric productivity function and minimization of the distance between NMO and “Optimized Control Mode” (OCM). Two well-known Multiobjective Evolutionary Algorithms are used to solve this problem. Practical results have shown water savings in the order of million m3/s. In addition, statistical inference has revealed that SPEA2 algorithm is more robust than NSGA-II algorithm to solve this problem.
KW - energy efficiency
KW - multiobjective optimization
KW - NSGA-II
KW - SPEA2
UR - http://link.springer.com/chapter/10.1007%2F978-3-319-15892-1_27
UR - http://www.scopus.com/inward/record.url?scp=84925325521&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-15892-1_27
DO - 10.1007/978-3-319-15892-1_27
M3 - Conference publication
AN - SCOPUS:84925325521
T3 - Lecture Notes in Computer Science
SP - 403
EP - 417
BT - Evolutionary multi-criterion optimization
A2 - Gaspar-Cunha, António
A2 - Henggeler Antunes, Carlos
A2 - Coello Coello, Carlos
PB - Springer
CY - Cham (CH)
T2 - 8th International Conference on Evolutionary Multi-Criterion Optimization
Y2 - 29 March 2015 through 1 April 2015
ER -