Heat Pump Demand Research Articles

Dynamic model of integrated electricity and district heating for remote communities

District heating networks offer promising solutions for remote communities, providing centralized heat supply, improved efficiency, and diverse energy sources, especially with existing diesel generation. Hence, this paper bridges gaps in the existing literature by developing comprehensive dynamic models of combined district heating networks within existing electric power networks in remote communities, which allows identifying challenges and benefits of district heating networks for these communities. It is shown that district heating networks allow utilizing waste energy to enable energy exchanges between the electricity and heating systems, enabling the provision of necessary ancillary services for remote microgrids with renewable energy sources. The presented dynamic district heating network model incorporates particular considerations in remote, northern communities such as soil limitations, extreme cold conditions, and piping insulation to minimize heat loss. It also addresses accurate sizing of heat pumps based on realistic thermal load requirements, weather conditions, and consumer profiles, proposing demand management controls to enhance frequency regulation for the integration of variable renewable energy sources. The main contributions of the paper include detailed dynamic modeling for district heating network operation, heat pump demand response control system design, and a comparative analysis between centralized district heating networks and decentralized electric thermal storage units that have been deployed for thermal supply in remote areas. The presented dynamic models are applied, tested, and validated in an existing electric microgrid at Kasabonika Lake First Nation in Northern Ontario, showcasing the role of a potential district heating network in facilitating renewable energy sources integration in isolated microgrids.

Peak regulation strategies for ground source heat pump demand response of based on load forecasting: A case study of rural building in China

The increase in electricity consumption in rural areas has led to an overall increase in the peak load in both winter and summer, challenging the reliability of low-voltage distribution networks. A typical rural house in Xi ‘an area of Shaanxi Province is considered a case study. To aid the investigation, a ground source heat pump system is developed, installed, and used as the experimental platform. The simulation model of the rural residential building's energy consumption is established, and the demand response peak regulation strategy for the rural residential building in winter is investigated. Using the developed model, two demand response peak regulation models, Direct Compressor Control Mechanism (DCCM) and Thermostat Set-point Control Mechanism (TSCM) are formulated. Combined with a long-term and short-term memory neural network power load forecasting model, a simulation is carried out to analyze and study the indoor temperature, operating energy consumption, and other parameters under the two peak regulation scenarios. The proposed strategy is assessed and evaluated. Compared with the baseline model, the proposed strategy allows a reduction in the total building operating energy consumption of 41.9 % for the DCCM model and 17.2 % for the TSCM model.

Data-based, high spatiotemporal resolution heat pump demand for power system planning

Decarbonizing the residential building sector by replacing gas boilers with electric heat pumps will dramatically increase electricity demand. Existing models of future heat pump demand either use daily heating demand profiles that do not capture heat pump use or do not represent sub-national heating demand variation. This work presents a novel method to generate high spatiotemporal resolution residential heat pump demand profiles based on heat pump field trial data. These spatially varied demand profiles are integrated into a generation, storage, and transmission expansion planning model to assess the impact of spatiotemporal variations in heat pump demand. This method is demonstrated and validated using the British power system in the United Kingdom (UK), and the results are compared with those obtained using spatially uniform demand profiles. The results show that while spatially uniform heating demand can be used to estimate peak and total annual heating demand and grid-wide systems cost, high spatiotemporal resolution heating demand data is crucial for spatial power system planning. Using spatially uniform heating demand profiles leads to 15.1 GW of misplaced generation and storage capacity for a 90% carbon emission reduction from 2019. For a 99% reduction in carbon emissions, the misallocated capacity increases to 16.9-23.9 GW. Meeting spatially varied heating load with the system planned for uniform national heating demand leads to 5% higher operational costs for a 90% carbon emission reduction. These results suggest that high spatiotemporal resolution heating demand data is especially important for planning bulk power systems with high shares of renewable generation.

Demand response with heat pumps: Practical implementation of three different control options.

Three case studies of different heat pump demand response control strategies in real homes are presented. All three households reduced their electricity consumption during a peak period but delivered unintended consequences where the heat pump's logic did not correspond to the demand response requirements. This study highlights that the implementation of heat pump demand response to support electricity system operation requires a clear definition of electricity system need as well as practical demand response mechanisms to be integrated into heating system design.

Residential demand-side management using integrated solar-powered heat pump and thermal storage

Due to the mismatch between rooftop solar energy generation and residential electrical demand, considerable solar energy is exported into electricity networks causing a range of issues such as voltage instability and connection overload. To address these problems, we propose and analyse a residential hot water, heating and cooling system, which features a heat pump combined with thermal energy storage to align peak thermal loads with output from a rooftop solar system. This work quantifies the impacts of thermal storage on residential space-conditioning peak load reduction. Annual hourly thermal and domestic hot water loads were determined for a representative Australian house, located in Brisbane, using building energy simulation software and verified using measured data. Combined with the measured sub-metered electrical loads of other electrical appliances, this data was used to simulate the solar system export, heat pump demand, and thermal storage system performance. Results show that by combining a 5-kW solar system, the proposed system can reduce annual grid-electricity demand by approximately 76% compared with a conventional non-thermal storage system. Peak electrical load was also observed to undergo a temporal shift and reduce by approximately 45%. Furthermore, the solar fraction for air-conditioning and domestic hot water loads reached 84%, while solar self-consumption increased to about 56%. This study demonstrates that the proposed system is an effective means of managing electricity demand, shifting peak load and improving solar utilization, thus relieving stress on electricity networks from high penetration of solar photovoltaics.

Providing Grid Services With Heat Pumps: A Review

Abstract The integration of variable and intermittent renewable energy generation into the power system is a grand challenge to our efforts to achieve a sustainable future. Flexible demand is one solution to this challenge, where the demand can be controlled to follow energy supply, rather than the conventional way of controlling energy supply to follow demand. Recent research has shown that electric building climate control systems like heat pumps can provide this demand flexibility by effectively storing energy as heat in the thermal mass of the building. While some forms of heat pump demand flexibility have been implemented in the form of peak pricing and utility demand response programs, controlling heat pumps to provide ancillary services like frequency regulation, load following, and reserve have yet to be widely implemented. In this paper, we review the recent advances and remaining challenges in controlling heat pumps to provide these grid services. This analysis includes heat pump and building modeling, control methods both for isolated heat pumps and heat pumps in aggregate, and the potential implications that this concept has on the power system.

Predictive Thermal Relation Model for Synthesizing Low Carbon Heating Load Profiles on Distribution Networks

The introduction of electric heat pumps as a low carbon option for space heating offers a potential pathway for reducing the carbon emissions resulting from domestic heating demand in the UK. However, the additional power demands of heat pumps over conventional domestic loads have the potential to significantly erode network headroom, particularly at the distribution level. The uptake of this technology within the UK is currently limited and the effects of widespread adoption on distribution networks are not well characterized due to the sparse availability of operational heat pump demand data. This paper outlines a methodology for quantifying the demand impact of heat pumps on Low Voltage networks sensitive to local temperature by deriving fundamental thermal relationships from real heat pump electrical demand data. These relations can then be applied to predict demand for new studies independent of the geographic specifics of the original dataset. The strength of this model is in the ability to predict an aggregated hourly heat pump electrical demand profile that reflects local temperature conditions and intra-day usage as well as population size, thereby also accounting for diversity effects that are difficult to capture in physics based models. This work augments the usability of limited existing data by facilitating demand analysis sensitive to local temperature conditions, rather than blanket rescaling of existing customer data as has been performed in previous studies. This creates future opportunities for examining heat pump demand sensitivity for different geographic locations against existing heat pump assessments, as well as performing studies which incorporate multiple low carbon technologies connected to a Low Voltage network.

Ensuring statistics have power: Guidance for designing, reporting and acting on electricity demand reduction and behaviour change programs

In this paper we address ongoing confusion over the meaning of statistical significance and statistical power in energy efficiency and energy demand reduction intervention studies. We discuss the role of these concepts in designing studies, in deciding what can be inferred from the results and thus what course of subsequent action to take. We do this using a worked example of a study of Heat Pump demand response in New Zealand to show how to appropriately size experimental and observational studies, the consequences this has for subsequent data analysis and the decisions that can then be taken. The paper then provides two sets of recommendations. The first focuses on the uncontroversial but seemingly ignorable issue of statistical power analysis and sample design, something regularly omitted in the energy studies literature. The second focuses on how to report energy demand reduction study or trial results, make inferences and take commercial or policy-oriented decisions in a contextually appropriate way. The paper therefore offers guidance to researchers tasked with designing and assessing such studies; project managers who need to understand what can count as evidence, for what purpose and in what context and decision makers who need to make defensible commercial or policy decisions based on that evidence. The paper therefore helps all of these stakeholders to distinguish the search for statistical significance from the requirement for actionable evidence and so avoid throwing the substantive baby out with the p-value bathwater.

Preheating Quantification for Smart Hybrid Heat Pumps Considering Uncertainty

The deployment of smart hybrid heat pumps (SHHPs) can introduce considerable benefits to electricity systems via smart switching between electricity and gas while minimizing the total heating cost for each individual customer. In particular, the fully optimized control technology can provide flexible heat that redistributes the heat demand across time for improving the utilization of low-carbon generation and enhancing the overall energy efficiency of the heating system. To this end, an accurate quantification of the preheating is of great importance to characterize the flexible heat. This paper proposes a novel data-driven preheating quantification method to estimate the capability of the heat pump demand shifting and isolate the effect of interventions. Varieties of fine-grained data from a real-world trial are exploited to estimate the baseline heat demand using Bayesian deep learning while jointly considering epistemic and aleatoric uncertainties. A comprehensive range of case studies are carried out to demonstrate the superior performance of the proposed quantification method, and then, the estimated demand shift is used as an input into the whole-system model to investigate the system implications and quantify the range of benefits of rolling out the SHHPs developed by PassivSystems to the future GB electricity systems.

Benefits of smart control of hybrid heat pumps: An analysis of field trial data

Smart hybrid heat pumps have the capability to perform smart switching between electricity and gas by employing a fully-optimized control technology with predictive demand-side management to automatically use the most cost-effective heating mode across time. This enables a mechanism for delivering flexible demand-side response in a domestic setting. This paper conducts a comprehensive analysis of the fine-grained data collected during the world’s first sizable field trial of smart hybrid heat pumps to present the benefits of the smart control technology. More specifically, a novel flexibility quantification framework is proposed to estimate the capability of heat pump demand shifting based on preheating. Within the proposed framework, accurate estimation of baseline heat demand during the days with interventions is fundamentally critical for understanding the effectiveness of smart control. Furthermore, diversity of heat pump demand is quantified across different numbers of households as an important input into electricity distribution network planning. Finally, the observed values of the Coefficient of Performance (COP) have been analyzed to demonstrate that the smart control can optimize the heat pump operation while taking into account a variety of parameters including the heat pump output water temperature, therefore delivering higher average COP values by maximizing the operating efficiency of the heat pump. Finally, the results of the whole-system assessment of smart hybrid heat pumps demonstrate that the system value of smart control is between 2.1 and 5.3 £ bn/year.

Modeling and matching performance of a hybrid-power gas engine heat pump system with continuously variable transmission

Based on the steady-state efficiency of the engine and the motor and characteristic curve of Lithium iron phosphate (LiFePO4) battery, an energy management strategy is proposed for a coaxial parallel hybrid-power gas engine heat pump (HPGHP) with V-belt continuously variable transmission (CVT) by using logic threshold approach to control the dynamical transitions between the various operation modes. The torque distribution between engine and motor, and CVT ratio are regulated through predetermined optimal operation lines of hybrid powertrain system in different operations. The results demonstrate that the energy management strategy can satisfy heat pump demand performance and reduce gas consumption effectively, as well as keep battery state of charge (SOC) in reasonable range. The average thermal efficiency of driving system in three main operating modes is 3.8%, 1.9% and 2.5% higher than that of multi-stage transmission system respectively, indicating that the system has better energy saving performance.

A Communication Performance Evaluation on Smoothing Power Fluctuations Based on Demand Response Control of Thermostatically-controlled Appliances

Cyber security problem in energy system has been paid more and more attention recently. In this paper, impact of the packet loss and bit errors in the communication process on the performance of demand response was evaluated. Thermodynamic modelling mechanism of the typical electric heat pumps was first discussed. And, a novel heat pump demand response control strategy based on Optimal Temperature Regulation (OTR) was proposed. The control strategy was then applied to balance the power fluctuations caused by renewable energy. Packet loss and bit errors in the signal transmission were discussed, and their impact on the performance of the control strategy was analysed. Finally, case studies were used to validate the given method.