Comparison of particle swarm and genetic algorithm based design algorithms for PV-hybrid systems with battery and hydrogen storage path

Paulitschke, Martin; Bocklisch, Thilo; Böttiger, Michael

doi:10.1016/j.egypro.2017.09.509

Cited by 16 publications

(6 citation statements)

References 10 publications

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“…In this paper we chose a simple reference application of a stand-alone photovoltaic (PV) hybrid system with battery and hydrogen storage for a single family home (s. Figure 1). Numerous example applications demonstrate the great potentials of this HESS configuration [3][4][5][6] and point out both the necessity of a good, adapted design and sizing [5] of installed capacities and powers of the energy storage components and the importance of an intelligent energy management (EM) for the optimal control of the power flows within the HESS [4]. There are energy management concepts [4,6,7], with fuzzy logic controller based approaches being commonly used.…”

Section: Fc Elmentioning

confidence: 99%

Adaptive Fuzzy Logic Controller Based Energy Management for a Stand-alone PV Hybrid System with Battery and Hydrogen Storage Path

Warner¹,

Bocklisch²

2023

Atlantis Highlights in Engineering

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The paper describes a novel adaptive fuzzy logic controller based energy management concept (A-FLC-EM) for a stand-alone photovoltaic (PV) hybrid system with battery and hydrogen storage path. The reference application is a single family home. The basic idea is to switch and optimally adjust the energy management parameters according to identified changes of distinct longterm energy supply and/or demand situations. Key elements of the offline learning phase are the analysis of the energy time series and the automatic determination of distinct energy situations on the basis of a segmentation algorithm and a vector of suitable statistical features calculated for a short-term, sliding observation window. A bottom-up approach is used, ranking and selecting statistical features that are particularly good at distinguishing certain long-term energy situations. The selected features form the basis for a clustering algorithm to detect and describe distinct energy situations. For each energy situation, the calculation of optimal energy management parameters is performed for a training data set employing particle swarm optimization (PSO). The performance of the novel A-FLC-EM is demonstrated compared to a conventional fuzzy logic controller based energy management (FLC-EM) with an all-year fixed parameter setting. Qualitative and quantitative improvements as well as further challenges are discussed.

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Section: Fc Elmentioning

confidence: 99%

Adaptive Fuzzy Logic Controller Based Energy Management for a Stand-alone PV Hybrid System with Battery and Hydrogen Storage Path

Warner¹,

Bocklisch²

2023

Atlantis Highlights in Engineering

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“…Hence, there should be a focus on increasing component lifetime to defer reinvestment. For a new system, a co-design of component sizes and control approach is possible and desirable [31,32]. Increased efficiency allows smaller sized components, saving costs and resources.…”

Section: Energy Management: Problem Structure and Objectivesmentioning

confidence: 99%

Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Gerlach

Bocklisch

2021

Energies

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Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

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“…Whereas in some studies, PSO algorithms generally have performance that is as good as genetic algorithms. Even in some cases, PSO can exceed the performance of genetic algorithms [20]. Another advantage is that PSO has fewer function evaluations than genetic algorithms, so that it has a relatively better level of computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Rainfall-runoff Model GR4J using Particle Swarm Optimization on Planting Calendar

Aufar¹,

Sitanggang²,

Annisa³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Challenges in food production in the future are certainly more complex in developing countries like Indonesia. The Agricultural Research and Development Agency developed an Integrated Planting Calendar (Katam) information system to decide the water discharge for rice planting time using rainfall-runoff modeling called GR4J (Genie Rural a four parameters Journalier). This study aims to improve the accuracy of the GR4J model for determining water discharge in an area. The study areas in this study were the Progo Watershed and the Wuryantoro Watershed. The GR4J model is measured based on four free parameters in the form of Maximum Capacity Production Store (X1), Coefficient Changes in Groundwater (X2), Maximum Capacity Routing Store (X3), and Peak Time Ordinate unit hydrograph (X4). The four parameters are optimized using Particle Swarm Optimization (PSO). This study shows that the parameter optimization of the GR4J model with PSO was successfully carried out. As a determinant of the model's success, the Nash-Sutcliffe Efficiency (NSE) equation and the Root Mean Square Error (RMSE) is used. We have found that NSE is increasing and RMSE decreases in RMSE in each watershed after using PSO optimization. The area affects the accuracy of the GR4J model. The smaller the area, the more it can show the characteristics of a watershed. Using the PSO algorithm in the GR4J model will be more effective than using manual or trial methods based on expert knowledge because it can achieve optimization quickly.

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Comparison of particle swarm and genetic algorithm based design algorithms for PV-hybrid systems with battery and hydrogen storage path

Cited by 16 publications

References 10 publications

Adaptive Fuzzy Logic Controller Based Energy Management for a Stand-alone PV Hybrid System with Battery and Hydrogen Storage Path

Adaptive Fuzzy Logic Controller Based Energy Management for a Stand-alone PV Hybrid System with Battery and Hydrogen Storage Path

Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Parameter Optimization of Rainfall-runoff Model GR4J using Particle Swarm Optimization on Planting Calendar

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