A novel method to solve sustainable economic power loading dispatch problem

Publication Date14 May 2018
AuthorLingling Li,Yanfang Yang,Ming-Lang Tseng,Ching-Hsin Wang,Ming K. Lim
SubjectInformation & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems
A novel method to solve
sustainable economic power
loading dispatch problem
Lingling Li and Yanfang Yang
Department of Electrical Engineering, Hebei University of Technology,
Tianjin, China
Ming-Lang Tseng
Institute of Innovation and Circular Economy, Asia University,
Taichung City, Taiwan and
Lunghwa University of Science and Technology, Taoyuan City, Taiwan
Ching-Hsin Wang
Institute of Project Management, National Chin-Yi University of Technology,
Taichung City, Taiwan, and
Ming K. Lim
Centre for Business in Society, Coventry University, Coventry, UK
Purpose The purpose of this paper is to deal with the economic requirements of power system loading dispatch
and reduce the fuel cost of generation units. In order to optimize the scheduling of power load, an improved chicken
swarm optimization (ICSO) is proposed to be adopted, for solving economic load dispatch (ELD) problem.
Design/methodology/approach The ICSO increased the self-foraging factor to the chicks whose
activities were the highest. And the evolutionary operations of chicks capturing the rooster food were
increased. Therefore, these helped the ICSO to jump out of the local extreme traps and obtain the global
optimal solution. In this study, the generation capacity of the generation unit is regarded as a variable, and the
fuel cost is regarded as the objective function. The particle swarm optimization (PSO), chicken swarm
optimization (CSO), and ICSO were used to optimize the fuel cost of three different test systems.
Findings The result showed that the convergence speed, global search ability, and total fuel cost of the
ICSO were better than those of PSO and CSO under different test systems. The non-linearity of the input and
output of the generating unit satisfied the equality constraints; the average ratio of the optimal solution
obtained by PSO, CSO, and ICSO was 1:0.999994:0.999988. The result also presented the equality and
inequality constraints; the average ratio of the optimal solution was 1:0.997200:0.996033. The third test
system took the non-linearity of the input and output of the generating unit that satisfied both equality and
inequality constraints; the average ratio was 1:0.995968:0.993564.
Practical implications This study realizes the whole fuel cost minimization in which various types of
intelligent algorithms have been applied to the field of load economic scheduling. With the continuous
evolution of intelligent algorithms, they save a lot of fuel cost for the ELD problem.
Originality/value The ICSO is applied to solve the ELD problem. The quality of the optimal solution and
the convergence speed of ICSO are better than that of CSO and PSO. Compared with PSO and CSO, ICSO can
dispatch the generator more reasonably, thus saving the fuel cost. This will help the power sector to achieve
greater economic benefits. Hence, the ICSO has good performance and significant effectiveness in solving the
ELD problem.
Keywords Economic load dispatch, Global optimal solution, Improved chicken swarm optimization,
Total fuel cost
Paper type Research paper
1. Introduction
The economic load dispatch (ELD) problem is an important part of power planning and
management. ELD is related to the reliability of the users electricity and the economic
benefits of the entire power industry (Mandal et al., 2014). As efficiency departments, electric
Industrial Management & Data
Vol. 118 No. 4, 2018
pp. 806-827
© Emerald PublishingLimited
DOI 10.1108/IMDS-04-2017-0145
Received 7 May 2017
Revised 20 July 2017
18 September 2017
Accepted 3 October 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
power enterprises must consider the economic benefits of sustainability; thus, the power
sector has continuously investigated this problem. In recent years, many new energy
sources have posed significant challenges in the power system, and the importance of the
economic dispatch of the power load has become more prominent. In conclusion, the ELD
problem is a reasonable load allocation scheme under the power demand and generator
running constraints that can be used to minimize the total fuel costs and to maximize
the economic benefits (He et al., 2016; Zou et al., 2016). The ELD problem must consider
the valve point effect, transmission loss, and other factors; thus, it is a multi-objective,
multi-constrained non-convex optimization problem (Parouha and Das, 2016).
Similar to other problems (Tseng and Bui, 2017; Guo et al., 2017), the ELD problem has
always been an important research topic for the power sector, and new research methods
for this problem are continuously being proposed (Aoki and Satoh, 1984; Balamurugan
and Subramanian, 2008; Ghorbani and Babaei, 2016). Several traditional mathematical
methods were proposed to solve the problem, such as the lambda iteration method
(Aravindhababu and Nayar, 2002), linear programming technique (Parikh and
Chattopadhyay, 1996), non-linear programming (Takriti and Krasenbrink, 1999),
and Lagrangian relaxation approach (El-Keib et al., 1994). However, the quality of the
optimal solution of these algorithms is not high; thus, the optimization effect must be
improved (Noman and Iba, 2008). Because several factors must be considered, including
the value point effect and power generation condition of the turbo unit in the thermal
power plant, the traditional mathematical methods cannot be used to find the optimal
solution to the problem (Parouha and Das, 2016).
Many intelligent algorithms have been proposed to solve the ELD problem, and these
algorithms have achieved good optimization effects (Abdelaziz et al., 2016a). The use of
intelligent algorithm to optimize the ELD problem can improve the quality of the optimal
solution and increase the convergence speed and the economic efficiency of the power
system (Yuan and Hesamzadeh, 2017; Pradhan et al., 2016). However, there are various
problems when using intelligent algorithms to solve the ELD problem. As the dimension of
the ELD problem to be solved increases, the genetic algorithm (GA) becomes more likely to
fall into local optima (Gaing, 2003). Particle swarm optimization (PSO) often exhibits the
two steps forward, one step backphenomenon because of the lack of a search strategy
(Van and Engelbrecht, 2004), which results in an insufficient search ability and slow
convergence (Zhan et al., 2011). The ELD problem has non-convex, non-smooth, and
non-different characteristics (Nguyen et al., 2016; Beigvand et al., 2017; Short et al., 2017;
Nazari-Heris et al., 2017); thus, the power sector has been committed to developing
better optimization methods to obtain economic benefits (Zou et al., 2017; Meng et al., 2016;
Al-Betar et al., 2016).
Nagib et al. (2016) considered the value point effect of turbo-generator units. An
invasive-weed optimization algorithm was used to solve the ELD problem. The algorithm
obtained the optimal operation effect more effectively than PSO; however, the algorithm did
not consider the inequality constraints of power generation, and thus, the quality of the
optimal solution must be further proven. In addition,Pothiya et al. (2008) proposed a multiple
tabu search algorithm to solve the problem of dynamiceconomic dispatch. The algorithmhad
a better convergence rate than that of the simulatedannealing (SA) algorithm and GA when
the ELD problem considered ramp rate constraints. However, the study considered only the
inequality constraints of power generation. Therefore, the quality of the optimal solution
obtained using the multiple tabu search algorithm must be proven when considering the
non-linear characteristics of the input and output of the generating units. Therefore,
the optimal solution and the convergence rate of the intelligent algorithm may vary under
different conditions, and a more stable and efficient intelligent algorithm should be
proposed to solve the ELD problem.
power loading

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