Comparison of control approaches for variable speed air source heat pumps considering time variable electricity prices and PV

Fischer, David; Bernhardt, Josef; Madani, Hatef; Wittwer, Christof

doi:10.1016/j.apenergy.2017.06.110

Cited by 87 publications

(62 citation statements)

References 14 publications

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“…Progressively decreasing prices for sensing, communication, and computing devices will open up possibilities for improved controls for demand response (DR) as future management systems will be more affordable. A number of studies have investigated the building energy flexibility with a special focus on building heating systems [9][10][11][12][13][14][15][16]. In those studies, DR measures have been applied to electric heating systems, such as heat pump systems or direct electric heating systems.…”

Section: Introductionmentioning

confidence: 99%

“…DR measures can be applied to control the electricity use for the heating of buildings depending on power grid signals [12]. The most common signal for DR is the electricity spot price [15][16][17][18][19][20], but the CO 2eq. intensity of the electricity mix [15,[21][22][23][24][25], the share of renewables in the electricity mix [9,26] or voltage fluctuations [27] are applied as well.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation Method for the Hourly Average CO2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating

Clauß

Stinner

Solli³

et al. 2019

Energies

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This work introduces a generic methodology to determine the hourly average CO2eq. intensity of the electricity mix of a bidding zone. The proposed method is based on the logic of input–output models and avails the balance between electricity generation and demand. The methodology also takes into account electricity trading between bidding zones and time-varying CO2eq. intensities of the electricity traded. The paper shows that it is essential to take into account electricity imports and their varying CO2eq. intensities for the evaluation of the CO2eq. intensity in Scandinavian bidding zones. Generally, the average CO2eq. intensity of the Norwegian electricity mix increases during times of electricity imports since the average CO2eq. intensity is normally low because electricity is mainly generated from hydropower. Among other applications, the CO2eq. intensity can be used as a penalty signal in predictive controls of building energy systems since ENTSO-E provides 72 h forecasts of electricity generation. Therefore, as a second contribution, the demand response potential for heating a single-family residential building based on the hourly average CO2eq. intensity of six Scandinavian bidding zones is investigated. Predictive rule-based controls are implemented into a building performance simulation tool (here IDA ICE) to study the influence that the daily fluctuations of the CO2eq. intensity signal have on the potential overall emission savings. The results show that control strategies based on the CO2eq. intensity can achieve emission reductions, if daily fluctuations of the CO2eq. intensity are large enough to compensate for the increased electricity use due to load shifting. Furthermore, the results reveal that price-based control strategies usually lead to increased overall emissions for the Scandinavian bidding zones as the operation is shifted to nighttime, when cheap carbon-intensive electricity is imported from the continental European power grid.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation Method for the Hourly Average CO2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating

Clauß

Stinner

Solli³

et al. 2019

Energies

View full text Add to dashboard Cite

show abstract

“…Other literature has concentrated on the storage and scheduling of distributed generation, the evaluation of solar energy for household heating, the utilization of solar PV system in the residential buildings and a comparison of heat supply for various alternative energy sources, etc. [11][12][13][14][15][16][17]. However, few studies have explored the combination of distributed generation and electric heating.…”

Section: Introductionmentioning

confidence: 99%

A Rural Household Heating Method that Combines a Regenerative Electric Boiler with PV Power Generation

Zhang¹,

Gao²

2019

Pol. J. Environ. Stud.

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The heating mode of a regenerative electric boiler in China's rural area includes many problems such as the increase of heating cost, the high expense of government subsidies and tr investment cost, etc. Consequently, this paper proposes a new heating method that combines a regenerative electric boiler with distributed photo-voltaic power generation. The photovoltaic software PVsyst was used to simulate generating capacity, and a system-wide economic efficiency calculation model was established. Taking a family in Gaobeidian City as an example, a comparative analysis of the method before and after optimization was conducted. The results showed that the new heating method has good economics, environmental benefits and promotional value. This new rural household heating method is of great significance for realizing the rural household energy cleanness in the Beijing-Tianjin-Hebei metropolitan region, promoting the green and low-carbon development of rural energy, and realizing beautiful rural construction.

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“…Studies have previously compared control strategies and have identified the advantages associated with the use of predictive controls [6]. Additionally, Model Predictive Control (MPC) has been highlighted as being a particularly useful class of predictive control [7] at improving economic and technical performance metrics (e.g. reduced imported electricity costs and increased thermal comfort).…”

Section: Introductionmentioning

confidence: 99%

Heat pump and thermal storage sizing with time-of-use electricity pricing

Lyden¹,

Tuohy²

2019

Proceedings of the 13th International Renewable Energy Storage Conference 2019 (IRES 2019)

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Heat pump and thermal storage sizing studies require modelling to ensure capital and operational costs are minimised. Modelling should consider added flexibility, e.g. grid services, sector coupling benefits, e.g. utilising excess wind production, and access to electricity markets, e.g. time-of-use tariffs. This paper presents a two-step methodology for sizing heat pump and thermal storage systems with a time-of-use electricity tariff. The first step is a modelling method for decentralised energy systems, with the broader aim of assisting planning-level design, and consists of resource assessment, demand assessment, electrical components, thermal components, storage components, and control strategies. The second step is a parametric analysis of heat pump and thermal storage size combinations. This is then applied to a sizing study for an existing residential district heating network including a time-of-use electricity tariff. The performance metrics: % of heat pump thermal output at low-cost period, % of heat demand met by heat pump, electricity import cost, and capital cost, were plotted and tabulated to compare sizing combinations. Graphs explore the operation of the heat production units and the thermal storage. Future development involving use of model predictive control and grid services, and alternative applications including operational planning and feasibility studies, are then discussed.

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Comparison of control approaches for variable speed air source heat pumps considering time variable electricity prices and PV

Cited by 87 publications

References 14 publications

Evaluation Method for the Hourly Average CO2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating

Evaluation Method for the Hourly Average CO2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating

A Rural Household Heating Method that Combines a Regenerative Electric Boiler with PV Power Generation

Heat pump and thermal storage sizing with time-of-use electricity pricing

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