Abstract
Solar energy has many advantages, such as being abundant, clean and environmentally friendly. Solar power generation has been widely deployed worldwide as an important form of renewable energy. The solar thermal power generation is one of a few popular forms to utilize solar energy, yet its modelling is a complicated problem. In this paper, an improved bare bone multi-objective particle swarm optimization algorithm (IBBMOPSO) is proposed based on the bare bone multi-objective particle swarm optimization algorithm (BBMOPSO). The algorithm is first tested on a set of benchmark problems, confirming its efficacy and the convergency speed. Then, it is applied to optimize two typical solar power generation systems including the solar Stirling power generation and the solar Brayton power generation; the results show that the proposed algorithm outperforms other algorithms for multi-objective optimization problems.
Highlights
The energy issue is viewed as one of many global problems in the 21st century and it is projected that the global energy demand will increase by almost a quarter by 2040 [1]
In order to evaluate the optimization performance of IBBMOPSO algorithm, this paper compares it with three representative multi-objective evolutionary algorithms
An IBBMOPSO algorithm has been proposed based on the bare bone multi-objective particle swarm optimization algorithm (BBMOPSO)
Summary
The energy issue is viewed as one of many global problems in the 21st century and it is projected that the global energy demand will increase by almost a quarter by 2040 [1]. This paper investigates the modelling of the aforementioned two typical solar power generation systems, namely, solar Stirling cycle power generation system and solar Brayton cycle power generation system These two systems are highly non-linear, and it is a challenging multi-objective optimization problem to identify a suitable model. Mohammad et al used the NSGA2 algorithm to optimize the dish solar Stirling generator system with maximum power output, maximum entropy generation rate and maximum thermal effici ency and three different multi-objective decision-making methods are used [27]. Particle swarm optimization (PSO) proposed by Kennedy [30] is inspired by the foraging phenomenon of birds It has the advantages of being simple to implement and having strong global search ability, can be used to handle a range of MOPs. For example, Tripathi [31] proposed an adaptive MOPSO algorithm that uses inertia weights and learning factors as part of the decision variables.
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