Optimal design and operation of a CHP based district heating system including a heat storage and electrode boiler to increase self-consumption

Dancker, Jonte; Gotze, Jens; Schulz, F.; Konneke, Nicola; Beyrau, Frank; Wolter, Martin

doi:10.1109/isgt-la.2019.8895427

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…In fact, the setting up of distributed generation, when exploited in a self-sufficiency manner, may decrease the utilisation peaks [29]. Scientists and experts demonstrated that this kind of arrangement might enhance a microgrid's resilience and improve the level of self-consumption whilst decreasing the grid loads, which means being cost-effective up to 60% of self-consumption [30].…”

Section: Overview Of Literaturementioning

confidence: 99%

“…It was assumed that the electric grid is operating as intended, and the stability issues have been excluded from the analysis. For example, the study in [30] describes large signal stability of an inverter-based generator using a Lyapunov function; in [31], the work is related to microgrids decentralised and hierarchical control, and in [32], the study provides as a new model of a converter-based generator. These topics are an important aspect in the implementation of such systems, however, in this investigation, only the energy flows between the renewable generator, the storage equipment, consumers and UG are examined, while the stability issues associated with the operation of inverters are out of scope of this paper.…”

Section: Overview Of Literaturementioning

confidence: 99%

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Modelling of Consumption Shares for Small Wind Energy Prosumers

et al. 2021

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This article describes a simulation of energy distribution in an average household where electricity is produced with a small wind generator or purchased from the public electricity grid. Numerical experiments conducted within an average of five minutes were performed using annual production and consumption graphs. Virtual storage devices, a water tank and a battery were used to buffer energy inside the household. The energy required for non-shiftable consumption and hot water consumption were taken directly from the utility grid. Surplus energy remaining from wind generator production after providing for consumption and storage needs were redirected there. A cover factor was used as a measure of the efficiency of energy distribution. One of the aims of the article was to determine by simulations the change of the cover factor in a virtually designed situation where the expected energy output of the wind generator was known in advance over one to three hours. The results found that for the configuration of the proposed nanogrid option, the positive results were readily achieved when the expected wind generator production was known an hour ahead. Then, the cover factor increased from 0.593 to 0.645. The side result of using projected/expected production is an increase in asymmetrical energy exchanges bilaterally between nanogrid and utility grid in favour of grid sales. Another finding was that the cover factor depended on the wind generator’s production intensity but less on the intensity of consumption within the household.It is hoped/expected that future research will address the prediction of output using mathematical methods.

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Section: Overview Of Literaturementioning

confidence: 99%

Section: Overview Of Literaturementioning

confidence: 99%

Modelling of Consumption Shares for Small Wind Energy Prosumers

et al. 2021

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“…The peak load is defined by (22) and (23) as well as the annual transmitted energy (24) in whichḢ out,ggf andḢ in,ggf depict gas that is imported from and exported to upper gas networks. Because gas grid fees are based on the higher heating value and the chemical power flow of the connectors is based on the lower calorific value, f u,ggf is used in (25) and (26) as a conversion factor in the piecewise linearization.Ḣ in,ggf,t ≤Ḣ peak,ggf,t ∀t ∈ T…”

Section: Economic Elementsmentioning

confidence: 99%

“…x p,ggf = f u,ggfḢpeak,ggf , y p,ggf = C a,p,ggf (25) x e,ggf = f u,ggfḢa,ggf , y e,ggf = C a,e,ggf (26) C pv,ggf = c pv,ggf y p,ggf + y e,ggf (27) The energy trading is modelled considering the current spot market price c eex in (28) and (29) as well as the energy related trading fees c trad,eex in (30). Because trading of gas is based on the higher heating value [31] a conversion factor f u,eex is used to convert from lower to higher heating value in (28)- (30).…”

Section: Economic Elementsmentioning

confidence: 99%

“…Within this framework, a method is implemented that displays the connections between the Energy Hub's components based on energy flows instead of a coupling matrix. Besides these three contributions (general framework, connections based on energy flows, automated building of Energy Hub), this paper investigates the impact of network charges on the optimization results, which has so far only been investigated in [25].…”

Section: Introductionmentioning

confidence: 99%

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A general MILP based optimization framework to design Energy Hubs

Gotze

Dancker

Wolter

2019

At - Automatisierungstechnik

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To optimally design integrated energy systems a widely used approach is the Energy Hub. The conversion, storage and transfer of different energy vectors is represented by a coupling matrix. Yet, the coupling matrix restricts the configuration of the Energy Hub and the constraints, that can be included. This paper proposes a MILP based optimization framework, which allows a high variability and adaptability and is based on energy flows. The functionality of the developed framework is tested on four use cases depicting different system sizes and Energy Hub configurations. It is shown that the framework is able to simplify the design process of an Energy Hub.

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