Cost related sensitivity analysis for optimal operation of a grid-parallel PEM fuel cell power plant

El-Sharkh, M.Y.; Tanrıöven, Muğdeşem; Rahman, Arief; Alam, Mohammad Saad

doi:10.1016/j.jpowsour.2006.06.046

Cited by 71 publications

(29 citation statements)

References 6 publications

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“…The FCPP produces as much thermal as electrical energy [57,58]. PEM fuel cells have various advantages that make them preferred relative to other fuel cells and permit them to be used in hybrid energy systems [59][60][61][62]. A sound strategy is necessary for management of excess electrical and/or thermal energy.…”

Section: Fuel Cellmentioning

confidence: 99%

“…Details about PEMFCs are provided elsewhere [48,[59][60][61][62]. Table 3 provides the technical specifications of the fuel cell system [14,48].…”

Section: Fuel Cellmentioning

confidence: 99%

“…In this paper, the model in [14,48,59,61,62] is extended to include wind turbine, solar collector, and photovoltaic costs and revenues, so as to examine whether it is feasible to add wind turbines, solar PV panels and fuel cells to the local electrical grid serving a residential building in Iran. System operating costs can often be reduced through the utilization of such a hybrid renewable energy system and by connecting to the local electrical grid.…”

Section: Objective Functionmentioning

confidence: 99%

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Optimal Operation of a Grid-Connected Hybrid Renewable Energy System for Residential Applications

2017

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Abstract:The results of a study on incorporating solar-thermal collectors into a hybrid renewable energy system are reported. A photovoltaic-wind turbine-fuel cell-solar-thermal collector system is designed and an economic model is introduced for supplying the residential thermal and electrical loads via the grid-connected hybrid system. Since determining the optimal operation of a hybrid system such as a combined heat and power system constitutes a complex optimization problem requiring a sophisticated optimization method, a modified heuristic approach-based particle swarm optimization is proposed for solving the optimization problem. The results are compared with those obtained by an efficient metaheuristic optimization method, namely a genetic algorithm, in terms of accuracy and run time. The results show that, using the grid-connected hybrid combined heat and power system, among the cases considered, decreases the total cost of the system. The results also demonstrate that the reductions in daily cost relative to the base case by the modified particle swarm optimization algorithm for Cases 1-4 are 5.01%, 25.59%, 19.42%, and 22.19%, respectively. Finally, Case 2 is the most cost-effective and reliable. Moreover, the modified particle swarm optimization algorithm leads to better results than the genetic algorithm.

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Section: Fuel Cellmentioning

confidence: 99%

“…Details about PEMFCs are provided elsewhere [48,[59][60][61][62]. Table 3 provides the technical specifications of the fuel cell system [14,48].…”

Section: Fuel Cellmentioning

confidence: 99%

Section: Objective Functionmentioning

confidence: 99%

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Optimal Operation of a Grid-Connected Hybrid Renewable Energy System for Residential Applications

2017

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“…Unit commitment of large polymer electrolyte membrane (PEM) fuel cell systems has been investigated [7,8] using evolutionary programming techniques 4 , although these were applied to consider performance driver rather than to explicitly consider system design. Also, cost-effective operating strategies for micro-CHP including fuel cells have been investigated using non linear programming, demonstrating that simple strategies such as following electricity demand can result in poor performance [9].…”

Section: Selected Literature Reviewmentioning

confidence: 99%

“…The primary issues relating to formulating a valid economic metric for analysis are ensuring relevance to both deployment pathways and circumventing the lack of established capital cost data for micro-CHP systems 7 , without which calculation of typical investment metrics (such as net present value) are not possible.…”

Section: The Primary Performance Metricmentioning

confidence: 99%

Fuel cell micro-CHP techno-economics: Part 1 – model concept and formulation

Giarola

Brett

Brandon

2009

International Journal of Hydrogen Energy

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This article presents the concept and mathematical treatment for a techno-economic modelling framework designed to enable exploration of fuel cell micro combined heat and power (micro-CHP) system design and control. The aim is to provide a tool that can help to focus research and development attention on the system characteristics critical for commercial success of these technologies, present cost targets for developers, and to ensure policy makers provide appropriate instruments to support commercialisation. The model is distinctive in that it applies mixed integer unit commitment formulation to link design and control decisions for micro-CHP, and explicitly characterises stack degradation in a techno-economic framework. It is structured to provide depiction of the fuel cell stack and balance-of-plant, supplementary thermal-only system (e.g. tail gas burner), thermal energy storage, and electrical power storage. Technically, the fuel cell stack is characterised by steady-state thermal and electrical efficiencies for full and part-load operation, its nameplate capacity, minimum operating set point, and stack degradation via performance loss rate proportional to power density and thermal cycling rate. The dynamics of operation are emulated via ramp limits, minimum up-time and minimum down-time constraints, and start-up and shutdown costs and energy consumptions. The primary performance evaluation metric adopted is the 1 Corresponding author. E-mail a.hawkes@imperial.ac.uk maximum additional capital cost a rational investor would pay for the fuel cell micro-CHP system over and above what they would pay for a competing conventional heating system. The companion article (Part 2) applies the developed model to consider the impact of stack degradation on economic and environmental performance. Keywords: micro-CHP, cogeneration, unit commitment, mixed integer, degradation Nomenclature Objective LifetimeCostThe operation and maintenance cost of meeting energy demand over the fuel cell's lifetime. Decision Variables AMCombined lifetime maintenance costs for all equipment (£). OPCombined lifetime fuel and electricity cost minus revenue (£) ω z Weighting for day-types in the problem, for each day-type z. EDem t Electricity demand in time period t HDem tHeat demand in time period t T Number of time periods in one sample day. L Number of piecewise linear elements for fuel cell electricity production cost function.Z Number of representative sample days analysed in the problem.

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Stochastic load effect on home energy system scheduling optimization

Karami

Sanjari

Hadavi

et al. 2014

Int. Trans. Electr. Energ. Syst.

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Summary This paper deals with a grid‐connected Home Energy System (HES), including Combined Heat and Power (CHP) and a battery as an Energy Storage System (ESS). The energy routing and scheduling of different resources in this integrated system are optimized to decrease the system operation costs. The optimization procedure leads to generating an efficient look‐up table, by which the generated power of each energy resource is determined for all time intervals. Considering the consumption uncertainties, the stochastic load behavior is modeled, and its effect on the system operation cost is investigated. The effects of the electricity tariff and battery on the optimization of the energy resources scheduling are also studied in this paper. Copyright © 2014 John Wiley & Sons, Ltd.

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Cost related sensitivity analysis for optimal operation of a grid-parallel PEM fuel cell power plant

Cited by 71 publications

References 6 publications

Optimal Operation of a Grid-Connected Hybrid Renewable Energy System for Residential Applications

Optimal Operation of a Grid-Connected Hybrid Renewable Energy System for Residential Applications

Fuel cell micro-CHP techno-economics: Part 1 – model concept and formulation

Stochastic load effect on home energy system scheduling optimization

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