A More Realistic Heat Pump Control Approach by Application of an Integrated Two-Part Control

Schulz, Maximilian; Kemmler, Thomas; Kumm, Julia; Hufendiek, Kai; Thomas, Bernd

doi:10.3390/en13112752

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…The optimization model E2M2_DES represents a mixed-integer linear programming (MILP) optimisation model formulated by GAMS minimising energy cost for a defined energy system by computing optimum schedules for its technologies. We have already introduced the model in [25] for application to the heat supply of a single family household by a heat pump system. For the following analysis, we enhanced its functionalities, especially regarding its applicability to optimize EV charging processes and to consider different price incentives and GHG emission targets, which we will present in more detail below.…”

Section: The General Optimisation Model E2m2_desmentioning

confidence: 99%

“…The electrical demand profile of the HH and the PV generation profile stems from real measured data, corresponding to [25], presented in more detail in Figure 3. Both profiles show a time resolution of 15 min, referring to relevant time intervals for billing and balancing in the German system.…”

Section: Structure Of the Local Energy System Of The Indivdual Householdmentioning

confidence: 99%

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Discussing the Actual Impact of Optimizing Cost and GHG Emission Minimal Charging of Electric Vehicles in Distributed Energy Systems

Schulz

Hufendiek

2021

Energies

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Electric vehicles represent a promising opportunity to achieve greenhouse gas (GHG) reduction targets in the transport sector. Integrating them comprehensively into the energy system requires smart control strategies for the charging processes. In this paper we concentrate on charging processes at the end users home. From the perspective of an end user, optimizing of charging electric vehicles might strive for different targets: cost minimization of power purchase for the individual household or—as proposed more often recently—minimization of GHG emissions. These targets are sometimes competing and cannot generally be achieved at the same time as the results show. In this paper, we present approaches of considering these targets by controlling charging processes at the end users home. We investigate the influence of differently designed optimizing charging strategies for this purpose, considering the electrical purchase cost as well as the GHG emissions and compare them with the conventional uncontrolled charging strategy using the example of a representative household of a single family. Therefore, we assumed a detailed trip profile of such a household equipped with a local generation and storage system at the same time. We implemented the mentioned strategies and compare the results concerning effects on annual GHG emissions and annual energy purchase costs of the household. Regarding GHG emissions we apply a recently proposed approach by other authors based on hourly emission factors. We discuss the effectivity of this approach and derive, that there is hardly no real impact on actual GHG emissions in the overall system. As incorporating this GHG target into the objective function increases cost, we appraise such theoretical GHG target therefore counterproductive. In conclusion, we would thus like to appeal for dynamic electricity prices for decentralised energy systems, leading at the same time to cost efficient charging of electric vehicles unfolding clear incentives for end users, which is GHG friendly at the end.

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Section: The General Optimisation Model E2m2_desmentioning

confidence: 99%

Section: Structure Of the Local Energy System Of The Indivdual Householdmentioning

confidence: 99%

Discussing the Actual Impact of Optimizing Cost and GHG Emission Minimal Charging of Electric Vehicles in Distributed Energy Systems

Schulz

Hufendiek

2021

Energies

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“…This algorithm can be interpreted as the canonical implementation of SC maximization. Regarding the data input, the electric load profile is provided by measured data of a single-family household in the German city Düsseldorf with a temporal resolution of 15 min; see [22,23]. The PV production profile is provided by a PV plant in the Stuttgart area with a temporal resolution of 15 min.…”

Section: Battery Operation Mode 1: Maximization Of Prosumer Self-cons...mentioning

confidence: 99%

Impact of Network Charge Design in an Energy System with Large Penetration of Renewables and High Prosumer Shares

2021

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The transformation of our energy system toward zero net CO2 emissions correlates with a stronger use of low energy density renewable energy sources (RES), such as photovoltaic (PV) energy. As a source of flexibility, distributed PV systems, in particular, are oftentimes installed in combination with battery storage systems. These storage systems are dispatchable, i.e., controllable by the operating owners, who can thereby take over an active market role as energy prosumers. The particular battery operation modes are based on the individual prosumer decisions, which, in turn, are strongly affected by the regulatory framework in place. Regulatory frameworks differ from country to country, but almost all regulatory frameworks feature a network charge mechanism, which allocates network infrastructure and operating costs to the end customers. This raises the question of the extent to which different network charges lead to different prosumer decisions, i.e., battery operation modes, and thus different energy system configurations (system costs). In order to evaluate this question we apply (a) a fundamental linear optimization model of the energy wholesale market, which we stringently link to (b) an analysis of peak-coincident network capacity utilization as well as (c) an evaluation of the complete costs of energy for prosumers and consumers. This stringent cycle of analysis is applied to two prototypical network allocation schemes. We demonstrate that network allocation schemes that are orientated to peak-coincident network capacity utilization could both better incentivize a distribution network-oriented behaviour and better share financial burdens between prosuming and purely consuming households than would be the case for volumetric network charge designs. This paper further demonstrates that network-oriented battery operation does not, per se, result in optimal RES integration at the wholesale market level and CO2 emissions reduction. To identify effects from increasing sector integration, an analysis is both performed for a setting without and with consideration of widespread e-mobility. As a broader conclusion, our results demonstrate that future regulatory frameworks should have a stronger focus on prosumer integration by means, among other things, of an adequate network charge design reflecting the increasingly distributed nature of our future energy system.

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“…When it comes to the HP, the power consumed depends on the cooling necessities for the highpower equipment. Since the amount of power losses within the high-power components increases naturally with the increased power circulating through these, based on [42], the power consumption of the HP is considered linearly dependent on the power consumed from the network PN(i,t). Consequently, a 2 % quota of the power consumed from the network was considered.…”

Section: ) Power Consumption Of the Hvac Unitmentioning

confidence: 99%

Novel Decentralized Voltage-Centered EV Charging Control Algorithm Using DSRC System in Low Voltage Distribution Networks

Kriukov

Gavrilaș²,

Ivanov

et al. 2021

IEEE Access

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A More Realistic Heat Pump Control Approach by Application of an Integrated Two-Part Control

Cited by 6 publications

References 20 publications

Discussing the Actual Impact of Optimizing Cost and GHG Emission Minimal Charging of Electric Vehicles in Distributed Energy Systems

Discussing the Actual Impact of Optimizing Cost and GHG Emission Minimal Charging of Electric Vehicles in Distributed Energy Systems

Impact of Network Charge Design in an Energy System with Large Penetration of Renewables and High Prosumer Shares

Novel Decentralized Voltage-Centered EV Charging Control Algorithm Using DSRC System in Low Voltage Distribution Networks

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