Sizing Algorithm for a PV-battery-H2-hybrid System Employing Particle Swarm Optimization

Paulitschke, Martin; Bocklisch, Thilo; Böttiger, Michael

doi:10.1016/j.egypro.2015.07.664

Cited by 32 publications

(9 citation statements)

References 5 publications

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“…Therefore, intelligent HESS-design algorithms take into account both, component sizes and energy management parameters. A particle-swarm optimization algorithm was successfully employed demonstrating good convergence, fast computation speed and an excellent handling of the complex, non-linear optimization problem [35]. Fig.4 presents a PV-HESS-example and demonstrates in a simplified manner the decomposition of the difference power ΔP (PV minus load power) for a 5kW PV-plant and a four-people household (with 4MWh/a energy consumption) at a reference site in Chemnitz.The first step is the peak shaving (power-to-heat conversion) for ΔP values greater than the threshold value ΔP PS .…”

Section: Control and Energy Management Concepts For Hessmentioning

confidence: 99%

Hybrid Energy Storage Systems for Renewable Energy Applications

Bocklisch

2015

Energy Procedia

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173

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The paper gives an overview of the innovative field of hybrid energy storage systems (HESS). An HESS is characterized by a beneficial coupling of two or more energy storage technologies with supplementary operating characteristics (such as energy and power density, self-discharge rate, efficiency, life-time, etc.). The paper briefly discusses typical HESS-applications, energy storage coupling architectures, basic energy management concepts and a principle approach for the power flow decomposition based on peak shaving and double low-pass filtering. Four HESS-configurations, suitable for the application in decentralized PVsystems: a) power-to-heat/battery, b) power-to-heat/battery/hydrogen, c) supercap/battery and d) battery/battery, are briefly discussed. The paper ends with a short description of the HESS-experimental test-bed at Chemnitz University of Technology.

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Section: Control and Energy Management Concepts For Hessmentioning

confidence: 99%

Hybrid Energy Storage Systems for Renewable Energy Applications

Bocklisch

2015

Energy Procedia

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173

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“…Operation of a hybrid fuel cell/PV/electrolyser system for micro co-generation applications has been studied in [18]. In [19], particle swarm optimization (PSO) algorithm has been utilized to ideally size a hybrid battery/PV/H2 energy system. A fuzzy-logic based Energy management of a grid-connected fuel cell/PV hybrid system has been investigated in [20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since the heat production of fuel cell is proportional with its electrical power production, then according to Figs. 19 and 20, fuel cell heat generation is reduced in case 2 which increases fuel cell's lifespan.…”

Section: Item Unit Valuementioning

confidence: 99%

Optimal stochastic short-term thermal and electrical operation of fuel cell/photovoltaic/battery/grid hybrid energy system in the presence of demand response program

Majidi

Zare

2017

Energy Conversion and Management

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“…In [10], the popular technique, genetic algorithm, was employed to calculate the best possible size and place of a wind-PV-battery-diesel hybrid system for installation in buildings in remote areas. In [11], a hybrid energy system including battery storage, fuel cell, and PV was ideally sized through the particle swarm optimization algorithm. In [12], with the aim of minimizing total investment cost and also considering environmental emission, the optimal size of a hybrid energy system consisting of a diesel generator, PV system, and battery storage was found.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The amount of natural gas required by the µCHP to provide electricity is formulated in Equation 10, and the amount of heat recovered by the µCHP is formulated in Equation (11). Equation 12was established to link the amount of heat recovered by the µCHP to the shares used to meet the thermal demand and to meet the cooling demand through the absorption chiller.…”

Section: Operation Constraints For Generation Technologiesmentioning

confidence: 99%

Designing of Cost-Effective and Low-Carbon Multi-Energy Nanogrids for Residential Applications

et al. 2020

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Recently, hybrid multi-energy systems consisting of multiple generation, conversion, and storage technologies have been receiving great attention as a promising option to meet the multi-energy demands of residential end-users, by transforming them from passive consumers to active prosumers, who both produce and consume energy. The design problem of such systems is challenging due to the large number of degrees of freedom in the design and operation phases, so the system as a whole must be optimized. Moreover, both economic and low-carbon priorities should be considered in the design problem to foster an effective implementation and deployment. The aim of this paper was to present a methodology for the optimal design of multi-energy nanogrids (MENs) operating in grid-connected and islanded modes. Based on a pre-defined MEN superstructure, a multi-objective linear problem was established to find the types and sizes of the technologies in the MEN, with the aim to reduce the total annual cost and the fossil primary energy input, while satisfying the assigned time-varying user multi-energy demand. With reference to the latter, the thermal behavior of the building was simulated by using the dynamic simulation software TRNSYS. The Pareto frontier was found by minimizing a weighted sum of the total annual cost and fossil primary energy input, and the problem was solved by using branch-and-cut. In the numerical testing, a single-family house of 200 m2 located in Italy was considered as the residential end-user. Results show the effectiveness of the model for providing good balancing solutions for end-users based on economic and energetic priorities. Moreover, it was found that the MEN operating in grid-connected mode showed economic and environmental performances much better than those found for the configuration operating in islanded mode.

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Sizing Algorithm for a PV-battery-H2-hybrid System Employing Particle Swarm Optimization

Cited by 32 publications

References 5 publications

Hybrid Energy Storage Systems for Renewable Energy Applications

Hybrid Energy Storage Systems for Renewable Energy Applications

Optimal stochastic short-term thermal and electrical operation of fuel cell/photovoltaic/battery/grid hybrid energy system in the presence of demand response program

Designing of Cost-Effective and Low-Carbon Multi-Energy Nanogrids for Residential Applications

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