How to measure flexibility – Performance indicators for demand driven power generation from biogas plants

Dotzauer, Martin; Pfeiffer, Diana; Lauer, Markus; Pohl, Marcel; Mauky, Eric; Bär, Katharina; Sonnleitner, Matthias; Zörner, Wilfried; Hudde, Jessica; Schwarz, Björn; Faßauer, Burkhardt; Dahmen, M.; Rieke, Christian; Herbert, Johannes; Thrän, Daniela

doi:10.1016/j.renene.2018.10.021

Cited by 42 publications

(26 citation statements)

References 24 publications

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“…The feedstock and transportation price data given in Table 3 are used as a base case in the simulations given below. Several different objective functions of the optimization problem, given in Equations (5)- (7), are used in the case study. Some results and discussion are given below.…”

Section: Results Of the Case Studymentioning

confidence: 99%

“…Optimal transportation distances and yearly needed feedstock masses for methane production cost below 0.35 EUR/m 3 (Equation 7). The ranges of decision variable values for Equations (6) and (7) are also obtained as in the case of Equation (5) (Figures 3-5) but were not presented here due to space limitation. The impact of the objective function, feedstock and transportation cost will be elaborated on in the following sections.…”

Section: Results Of the Case Studymentioning

confidence: 99%

“…Biogas power plants can help in better integration of other RES technologies into the power system. According to Reference [7], flexible power provision from biogas power plants can significantly contribute to energy systems with high shares of renewables. In this paper, eight different indicators are defined to shape "flexibility" and perform a downstream investigation of eight research projects focusing on flexible energy provision of biogas plants.…”

Section: Introductionmentioning

confidence: 99%

“…12, x FOR PEER REVIEW 19 of The methane production cost for Equation(7) in case of increased costs (feedstock cost 2 x…”

mentioning

confidence: 99%

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Optimization Model for Biogas Power Plant Feedstock Mixture Considering Feedstock and Transportation Costs Using a Differential Evolution Algorithm

Topić

Barukčić

Mandžukić³

et al. 2020

Energies

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In this paper, an optimization model for biogas power plant feedstock mixture with respect to feedstock and transportation costs using a differential evolution algorithm (DEA) is presented. A mathematical model and an optimization problem are presented. The proposed model introduces an optimal mixture of different feedstock combinations in a biogas power plant and informs about the maximal transportation distance for each feedstock before being unprofitable. In the case study, the proposed model is applied to five most commonly used feedstock in biogas power plants in Croatia and Hungary. The research is performed for a situation when the biogas power plant is not owned by the farm owner but by a third party. An optimization procedure is performed for each scenario with a cost of methane production that does not exceed 0.75 EUR/m3 in 1 MWe biogas power plant. The results show the needed yearly amounts and the maximum transportation distance of each feedstock.

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Section: Results Of the Case Studymentioning

confidence: 99%

Section: Results Of the Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…12, x FOR PEER REVIEW 19 of The methane production cost for Equation(7) in case of increased costs (feedstock cost 2 x…”

mentioning

confidence: 99%

See 2 more Smart Citations

Optimization Model for Biogas Power Plant Feedstock Mixture Considering Feedstock and Transportation Costs Using a Differential Evolution Algorithm

Topić

Barukčić

Mandžukić³

et al. 2020

Energies

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show abstract

“…In this context, the ability to generate electricity independently from weather conditions represents a major competitive advantage of bioenergy plants compared to other renewable energy technologies. Several biogas plants are already generating electricity according to the grid demand [3]. However, due to the slow load change dynamics, a flexible operation of solid biomass-fueled combined heat and power (CHP) plants has not been applied yet [4].…”

Section: Introductionmentioning

confidence: 99%

Steam storage systems for flexible biomass CHP plants - Evaluation and initial model based calculation

Stark

Conti

Saidi

et al. 2019

Biomass and Bioenergy

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Within the present study a novel concept for the demand-oriented power generation of a solid-biomass fueled combined heat and power (CHP) plant is investigated. The integration of a novel steam storage system into the plants process enables a decoupling of the steam (boiler) and the power generation (steam turbine). By buffering the steam, the power output of the turbine can be adjusted without changing the rated thermal capacity of the plant. The storage system consist of combination of steam accumulator and concrete storage. An initial model based simulation study is performed to identify the fundamental behavior of this system, integrated in a biomass CHP plant. The operation principle has proved their technical feasibility and seems to be applicable at a commercial scale. According to the modelling results flexible short term power generation in a time range from fifteen minutes to several hours is applicable. A load-range of almost the plant`s rated capacity can be achieved. The properties of the proposed concept are competitive to available energy storage systems.

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Defining bioenergy system services to accelerate the integration of bioenergy into a low‐carbon economy

Mäki,

Hennig,

Thrän

et al. 2024

Biofuels Bioprod Bioref

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The global energy system is in transition. It is attempting to reach net‐zero greenhouse gas emissions by 2050. The systemic changes mean that the role of bioenergy will change. The potential of bioenergy to make a flexible contribution to the energy system is key for the achievement of global emission reduction ambitions and the functioning of the low‐carbon energy system and economy. As the volume of sustainably available biomass resources is limited, defining the contributions from bioenergy to a low‐carbon energy system and finding balances – and ideally synergies – between the different possible energy and climate system services that biomass can provide will be very important. The recognized system services include, among others, the flexible operation of bioenergy plants to integrate variable renewable energy sources and to provide negative carbon dioxide (CO2) emissions. Interest in flexible operation of bioenergy value chains, bioenergy with carbon capture and utilization as well as synergies with renewable hydrogen‐based value chains has increased recently. The objective of this paper is to present a holistic definition of flexible bioenergy as a system service based on the work conducted in International Energy Agency (IEA) Bioenergy Technology Collaboration Programme's Task 44 Flexible Bioenergy and System Integration, and to provide some practical examples. The paper also presents the different bioenergy system services and considers their definitions and interactions, as this is important in energy system design. The definition of flexible bioenergy shows that the flexibility provision from bioenergy goes far beyond the traditional definition of providing short‐term flexibility in the power sector. Indicators to demonstrate the value of services as well as further quantitative assessment of synergies and trade‐offs are needed to valorize the different services from bioenergy and create viable business cases.

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How to measure flexibility – Performance indicators for demand driven power generation from biogas plants

Cited by 42 publications

References 24 publications

Optimization Model for Biogas Power Plant Feedstock Mixture Considering Feedstock and Transportation Costs Using a Differential Evolution Algorithm

Optimization Model for Biogas Power Plant Feedstock Mixture Considering Feedstock and Transportation Costs Using a Differential Evolution Algorithm

Steam storage systems for flexible biomass CHP plants - Evaluation and initial model based calculation

Defining bioenergy system services to accelerate the integration of bioenergy into a low‐carbon economy

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