Meta-Heuristic Approaches for the Optimization of Hydropower Energy: A Review

Meta-Heuristic Approaches for the Optimization of Hydropower Energy: A Review

Abdus Samad Azad, Pandian Vasant, Junzo Watada, Rajalingam Al Sokkalingam
DOI: 10.4018/978-1-7998-3645-2.ch015
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The concept of a multireservoir systems in hydropower introduces the function of multiple units simultaneously to reach the peak requirements. The reservoir optimized operation is a complex, extremely nonlinear, high dimensional, and multimodal task. The options that can be evaluated manually are generally limited in numbers, which made it difficult to identify the most appropriate option and should be taken into account while making decisions. Presently, for solving the optimization problems in multireservoir system, many modern heuristic stochastic search algorithms were established. That is possible because of the aspects of artificial and computational intelligence technologies. By connecting metaheuristic algorithms, the decision options can be identified to make the most suitable utilization of the scarce resources, best natural results for a given allotment can be attained, and the best trade-offs between contending goals can be established. In this chapter, the authors review the latest meta-heuristic optimization technique and their applications to maximize the economic factors.
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The power can be classified into two groups, the renewably and non-renewably generated ones as shown in Figure 1(Karaeren, 2014). A sustainable energy source like hydropower is depending on regular water cycle. It is most matured, biggest, dependable and profitable renewable power generation technology which is easily accessible. It creates the world’s electricity about 16% of and the world’s renewable electricity over 80%. At present, 90% of the electricity supply and more than 25 nations in the world rely upon hydropower.(Irena, 2012). Hydropower technology is flexible in meeting peak load demand with both higher and lower installed capacities. Although hydropower is considered the most economical power source but it is not true in case of multireservoir system by reason of the operation of many units at one time. Peak demand may vary from time to time and to meet this demand other alternative energy sources, for instance, the more valuable nuclear or thermal energy, have to be used. It increases the further cost of power generation. Hence It is needed to maximize the power generation by each operating unit while minimizing the use of other energy sources, as, in turn, the total operational cost will minimize (Srianan & Sangsawang, 2019). The plant operations can help in obtaining following benefits:

  • 1.

    The settings of separate units.

  • 2.

    The co-ordination of multiple unit operations.

  • 3.

    Release patterns from multiple reservoirs.

The energy price, power production and power generated through the alternative source of energy are the most significant determinants of revenue in the hydropower system and need to be optimized (Personal, Archive, & Fleten, 2010). The method of finding the optimum solution of an objective equation under the defined constraint is known as optimization (Zelinka, 2019). It targets to maximize or minimize the objective function (Edenhofer et al., 2012).

Figure 1.

Energy Resources (Karaeren, 2014)


Latterly, several novel meta-heuristic approaches were presented for high dimensional optimization problems. These techniques are promised for solving difficult real-world optimization problems, like engineering design problems

(Suresh & Lal, 2016, Naveen, Chandel, Vedik, & Topwal, 2016; Suresh & Lal, 2017). The NFL (No Free Lunch theorem) demonstrates that all optimization problems cannot be solved by a single meta-heuristic algorithm (Fister, Yang, Brest, & Fister, 2013). It has been considered as foundation of many studies in the field of optimization. Some widely-known meta-heuristic algorithms are Genetic Algorithm (GA) (Snaselova & Zboril, 2015), Particle Swarm Optimization (PSO) (Xin-She Yang, n.d.) and Elephant herding Optimization (EHO) (Wang, 2015). Monarch butterfly Optimization (MBO) (Wang, Deb, & Cui, 2015), Kidney-inspired Algorithm (KA) (Jaddi, Alvankarian, & Abdullah, 2016), Bat Algorithm (BA) (X. Yang, 2014), Firefly Algorithm (FA) (X. Yang, 2010), Krill Herd (KH) (Gandomi & Alavi, 2012) and Crow Search Algorithm (CSA) (Askarzadeh, 2016). Nonlinear dynamic techniques, exclusively chaos, can attract more consideration in many fields (Askarzadeh, 2016; Pan & Xu, 2016) high dimensional optimization problems. It is also observed that these techniques have some limitations in particular dimensionality problem, requirements of vast memory and lack of ability of handling nonlinear cost function. Hybrid methods combined with two or more methods were presented to get control of these problems (Mo, Lu, Wang, & Zhou, 2013).

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