The temperature difference between evaporating and condensing side of cascade high temperature heat pump (CHTHP) can be large. However, its heating coefficient of performance (COP) is not ideal due to the performance attenuation brought by large temperature lift. If both heating and cooling sides can be utilized, the whole COP will be greatly improved. In this work, a CHTHP prototype is established, along with three application scenarios, specifically dairy processing, liquor processing, and deep dehumidification, which simultaneously have cooling and heating demands consistent with the operating range of the unit. The experimental results indicate that the CHTHP prototype can supply cooling as low as 2 degrees C and heating up to 120 degrees C with comprehensive COP over 2.58, being more than 45.8 % higher than single heating system, showing impressive performance in combined cooling and heating (CCH) for industrial processes. Through the joint investigation of heat pump and application scenarios, it is revealed that the comprehensive performance of CHTHP can surpass conventional approach of using two separate heat pumps to provide cooling and heating respectively when the ratio of heating to cooling demand is high. In addition, the performance of CCH system can be further enhanced by optimizing corresponding process parameters in different scenarios. Based on the excellent performance of CHTHP in CCH and its practical industrial applications, this work will maximize the effectiveness of high temperature heat pump in the electrificaiton of industrial thermal energy consumption.

There has been an increase in solar photovoltaic/thermal (PVT) research in recent years, however, relatively little research has been dedicated to the design of PVT collectors as part of a heat pump system. This study aims to identify cost-effective design strategies for a PVT collector absorber to be integrated into a ground source heat pump (GSHP) circuit and enhance heat capture from the ambient air. The effect of geometry, material selection, fins, and forced convection on the overall U-value and thermal performance coefficients of the collector, are evaluated under steady state conditions using numerical modelling tool COMSOL Multiphysics. An annual mean fluid temperature profile is derived from a PVT + GSHP system simulation to calculate the annual thermal energy output, energy-to-mass and energy-to-cost ratios of the absorbers. Results show that the addition of fins and forced convection have the greatest influence on collector thermal performance, while material selection has a negligible impact. The corrugated, polycarbonate absorber with 10 mm fins, generates 55 % more thermal energy (1,464 kWhth/m2-yr) than the reference metallic sheet and tube collector at an energy-to-cost ratio 1/10th the reference, suggesting good market potential. An exergy analysis reveals that thermal exergy contributes 20 % to 50 % of the total exergy output, highlighting that low-temperature PVT designs exhibiting a smaller thermal share relative to electrical exergy compared to their higher temperature counterparts. This work's novelty and contribution comes from PVT design specifically for GSHP integration, examined at component and system levels, from both technical and economic perspectives.

Buildings consume significant energy worldwide and account for a substantial proportion of greenhouse gas emissions. Therefore, building energy management has become critical with the increasing demand for sustainable buildings and energy-efficient systems. Simulation tools have become crucial in assessing the effectiveness of buildings and their energy systems, and they are widely used in building energy management. These simulation tools can be categorized into white-box and black-box models based on the level of detail and transparency of the model's inputs and outputs. This review publication comprehensively analyzes the white-box, black-box, and web tool models for building energy simulation tools. We also examine the different simulation scales, ranging from single-family homes to districts and cities, and the various modelling approaches, such as steady-state, quasi-steady-state, and dynamic. This review aims to pinpoint the advantages and drawbacks of various simulation tools, offering guidance for upcoming research in the field of building energy management. We aim to help researchers, building designers, and engineers better understand the available simulation tools and make informed decisions when selecting and using them.

Indirect evaporative chiller (IEC) can produce chilled water below the wet-bulb temperature of outdoor air. To evaluate the potential of applying indirect evaporative chillers for covering the high indoor sensible cooling loads in commercial buildings of Northern European countries, a case study based on a real commercial building in Stockholm was carried out assuming renovation of the existing air-conditioning system by replacing district cooling with an indirect evaporative chiller as cooling source. Numerical models were built for the indirect evaporative chiller as well as for the entire space cooling system, and techno-economic performance evaluation as well as sensitivity analyses were conducted with a validated numerical model to comprehensively evaluate renovation benefits. The simulation results show that, an indirect evaporative chiller fulfilling 80 % of the total sensible cooling load of the design outdoor condition with a dry-bulb outdoor air temperature of 26 degrees C and a relative humidity of 45 %, can produce chilled water below the wet-bulb outdoor air temperature when the relative humidity is lower than 0.4 for hours in July of year 2015 and 2018. Sensitivity analyses show that reducing the supply air flow rate of the ventilation system from the default setpoint of 1.2 L/(m 2 & sdot; s) to the minimum hygienically required level of 0.35 L/(m 2 & sdot; s) would double the seasonal energy efficiency rating of the air-conditioning system from 6.3 to 12.8. Compared to the original district cooling-based system, the operational expenditure of the renovated system can be saved for optimally 54 k SEK, justifying a capital expenditure of 588 k SEK assuming operation of 15 years. The case study shows that indirect evaporative chiller can potentially be applied for commercial buildings under the and climatic and market context of Sweden, providing an alternative cooling solution for similar applications.

This study empirically investigates the optimal design features of photovoltaic-thermal (PVT) collectors for integration with ground source heat pump (GSHP) systems, considering technical and economic factors. Outdoor experiments are conducted in Stockholm, Sweden, comparing five unglazed and uninsulated PVT collector designs a) Reference Sheet & Tube b) Sheet & Tube with a narrow air gap between PV and absorber plate c) Box-channel polypropylene d) Finned tube and e) Box-channel aluminum with fins at operating temperatures below ambient. The findings indicate that the box-channel aluminum design with fins, characterized by a superior combination of high zero-loss efficiency and a high U-value, emerges as the ideal PVT design for integration with ground source heat pumps, taking into account both technical and economic considerations. Despite having a relative specific thermal cost 9% higher than the reference collector, this design demonstrates the capability to generate 2,096 kWh/(m2a) of thermal energy, marking an 83.3% increase compared to the reference, with a 136% higher energy-to-mass ratio.

Market policies play a crucial role in facilitating the transition to a low-carbon society by restructuring the electricity market and influencing stakeholder behavior. Policymakers are concerned with how to implement these policies in terms of their intensity, combination, and timing. However, existing research lacks effective simulation tools that can accurately capture the impact of market policies on individual decision-making in the electricity sector, which is essential to represent the complex impacts of the policy mix. To address this gap, we present an agent-based model for analyzing the Low Carbon Transition (LCT) in the electricity sector. Using the Japanese electricity sector as a case study, we design various subsidies, incentives, and liberalization policy scenarios to evaluate the role of market policies in facilitating LCT. We observed that, within the Feed-in Premium (FIP) system, above a subsidy threshold of 2 JPY/kWh or 20% of the electricity cost leads to overcompensation, resulting in a stagnation of LCT promotion. To address this stagnation, it is imperative to not only enhance demand-side incentives, such as carbon taxes but also expedite the advancement of the free trade market to prevent market-induced stagnation. A synergistic implementation of these three policies is crucial for the most efficient progression of LCT.

The study investigated the characteristics of different natural refrigerants and their mixtures in a residential heat pump with low refrigerant charge. Three main evaluation criteria were utilized for comparing different mixtures: Coefficient of Performance (COP), Volumetric Heating Capacity (VHC), and a newly proposed indicator, the heating capacity at charge limit. Propane was used as a reference refrigerant. It was found that some mixtures significantly improved both COP and the heating capacity at charge limit while maintaining similar volumetric heating capacity and operating conditions. Alternative multi-criteria decision-making techniques were adopted to rank the best refrigerant mixtures. Mixtures rich in Dimethyl Ether (DME), such as DME-CO2 [0.96-0.04] and DME-Propylene [0.75-0.25] were found consistently among the best options. Those were followed by Propylene-rich mixtures such as Propylene-CO2, Propylene-Isobutane and Propylene-Butane. The levelized cost of heat (LCOH) could be improved by up to 12 %. To accelerate the transition to natural refrigerants, without compromises on efficiency and costs, further research and certification activities on non-fluorinated refrigerants based on Dimethyl Ether (R-E170), CO2 (R-744) and Propylene (R-1270) are recommended.

Modern heat pump systems often come equipped with sensors, enabling the collection of substantial operational data.However, many residential heat pumps installed in preceding decades lack pressure sensors, energy meters, or mass flow meters, primarily due to financial limitations.As a result of these incomplete measurements, the direct analysis of the heat pump system's performance or the leveraging of the amassed data for inventive applications like prognosticating energy consumption, detecting and diagnosing faults, and implementing intelligent control becomes challenging.In existing literature, the focus of soft sensors in heat pump systems has been on estimating a single parameter.This approach, however, overlooks the reality that multiple parameters are often missing due to the lack of all-encompassing physical meters and sensors.Furthermore, current soft sensor models are typically developed using inputs such as compressor power consumption, pressures, evaporation, and condensation temperatures.These inputs, unfortunately, tend to be inaccessible within existing heat pump monitoring installations.In practice, it is a challenge to compensate for several critical measurements, encompassing mass flow rate, pressures, power consumption, and heating capacity, by using only commonly available sensors such as secondary loop temperatures and compressor frequency are available.Currently, there is a notable gap in research concerning this practical issue.To address the problems associated with inadequate measurements, this study presents the development and validation of soft sensors based on a data-driven approach, which can compensate for the parameters often unavailable with data collected from a limited number of commonly used sensors.Each component model employs a multivariate polynomial regression that calculates the evaporation temperature, condensation temperature, mass flow rate, and compressor power consumption, respectively.Subsequently, we present an integrated heat pump model that combines these component models into a comprehensive heat pump model.Finally, we validate the data-driven model against field test installations, demonstrating its accuracy with a relative root mean squared error (RRMSE) ranging from 10% to 20%.

Social sustainability requires both technological innovations and societal changes within energy systems, with decentralization playing a critical role. This shift emphasizes the increasing importance of individual user decision-making, posing significant management challenges. An individual’s environmental awareness has a key influence on their energy decisions. However, the relationship between individual environmental awareness and social sustainability, particularly from a systemic perspective, remains underexplored. Our study uses agent-based modeling to examine this relationship within Japan’s electricity market, focusing on social learning and consumer heterogeneity. We find that social learning leads to the formation of consumer clusters with specific electricity preferences, affecting environmental awareness differently across high- and low-carbon groups. This process reveals the nuanced role of social learning in promoting low-carbon technology adoption, which varies according to the market share of low-carbon energy. Additionally, our results suggest that initial heterogeneity in environmental awareness among consumers has a limited and varied effect on sustainable transition pathways. However, the diversity resulting from social learning significantly shapes these trajectories. These insights highlight the complex interplay between individual behaviors, societal dynamics, and technological advancements in steering the sustainable transition, providing valuable considerations for future energy system management.

As the transition away from fluorinated refrigerants occurs due to F-gas and PFAS regulations, heat pumps face the challenge of adapting to new non-fluorinated refrigerants. Evaluating heat pump performance during this transition is challenging due to limited operational data on the new refrigerants. Conducting long-term tests to fully understand a heat pump’s performance with all possible refrigerants is labor-intensive and economically burdensome. This study introduces two complementary reduced-parameter models to assess heat pump performance across multiple new natural refrigerants despite limited data. A transfer learning model, leveraging knowledge from existing data-rich refrigerants, has been developed to evaluate the performance of heat pumps using new, data-scarce natural refrigerants. However, due to the lack of transparency in transfer learning models, semi-empirical models are being developed in parallel. The semi-empirical models, across multiple natural refrigerants, are capable of analyzing the thermodynamics and heat transfer processes within the heat pump system by utilizing only limited easy-to-measure variables as inputs. The transfer learning model demonstrates high accuracy for all outputs across seven refrigerants with RRMSE all below 7%. In comparison, the semi-empirical models are less accurate, with RRMSE results under 25% for all parameters except compressor power. By integrating these two models, a comprehensive framework is established for assessing heat pump performance with both high accuracy and a deeper understanding of the system.

PARMENIDES addresses challenges in the energy system by providing interoperable solutions that harness the potential of Hybrid Energy Storage Systems. A key innovation is the PARMENIDES Energy Community Ontology streamlining energy community operations through optimized energy flows and local energy maximization. The project introduces an Energy Management System for Hybrid Energy Storage Systems, utilizing ontology as a knowledge base and for extended information inference. Diverse PARMENIDES use cases cover scenarios ranging from passive energy community participation to fully automated optimization. These use cases vary in automation levels, optimization features, and flexibility strategies. The developed Information and Communication Technology architecture ensures interoperability, reliability, and security. Components include a Grid Capacity System, Grid Monitoring Devices and Smart Meters, an Information and Configuration System as a central repository for knowledge and data and an Energy Management System. Specific instantiations of the architecture will be implemented in the Austrian and Swedish pilots.

This paper proposes a generic, extensible, and scalable definition of hybrid energy storage systems (HESS) and provides a corresponding information model applicable for energy management system (EMS) implementation. Given the need for flexibility in both energy supply and demand due to the energy transition, multiple energy carriers have been coupled, energy storage mediums have been leveraged, and their characteristics have been optimized. EMS are adapting to these developments, which can be facilitated by having common definitions and information models. There are at least two prevailing descriptions of HESS: one based on complementary characteristics, and another based on the constituent energy storage mediums. The proposed definition is an extension and specific application of the concept of “energy hubs” and a clarification of the multiple descriptions of HESS. On a larger scale, this work aims to facilitate the interoperability of various EMS that involve HESS and to provide a foundational resource for projects related to HESS architectures, control, and optimization. This work is a contribution to the development of an open ontology tailored for EMS applications in the context of energy communities with HESS.

Low carbon transition (LCT) is a key issue in the future development of human society, and the full application of renewable electricity is the top priority. The process of transition is extremely complex, involving policies, markets, technologies, and other aspects. Only when all fields work simultaneously can they effectively promote the diffusion of low carbon energy. Therefore, various countries have proposed different policies to stimulate the development of low carbon energy according to the structures of their own electricity systems. In this study, the Japan’s electricity market is regarded as an object, an agent-based model is presented to describe the process of LCT in the said market. The model takes the market as core, integrates the subsidy promotion and technological development mechanisms, and restores all aspects involved in LCT as much as possible. Simulation results are highly consistent with historical data on the development of the Japanese electricity market in the past 10 years. The development of low carbon energy soon (within 10 years) is predicted, and the results are very close to the goals set by the Japanese government. Finally, we find that the main driving force for Japan’s low carbon energy development in this stage is the country’s feed-in tariff (FIT) policy, so we focus on the subsidy promotion mechanism and sum up an empirical formula which can be used as a quantitative analysis tool for FIT policy.

International Energy Agency predicts that the global number of installed heat pumps (HP) will increase from 180 million in 2020 to approximately 600 million by 2030, covering 20% of buildings heating needs. Electric power consumption is one of the main key performance indicators for the heat pump systems from technoeconomic perspective. However a common issue prevalent in many existing heat pumps is the lack of electric power measurement. The modern installations might be equipped with electric power measurement sensors but this comes at a higher system cost for the manufacturers and end-users. The primary objective of this work is to propose a virtual measurement for estimating power consumption, thereby eliminating the need for field measurement of power for heat pumps. To achieve the objective, a data-driven approach is proposed. Firstly, the in-situ data is preprocessed through data merging, cleaning, and normalization. Then, input features are pre-selected using Spearman correlation coefficients, and further refined by addressing multicollinearity problem. Following this, Extreme Gradient Boosting (XGBoost) models and polynomial models are developed by considering different features as inputs. All models are finally validated against the in-situ data from multi units of ground source heat pump (GSHP) and air source heat pump (ASHP) installations. The results showed that the electric power consumption of GSHP can be estimated with high accuracy (99% for R-2, 10 W for MAE, and 1% for MAPE) through generic data-driven models using only four easy-to-measure input features. Taking three input features as inputs for ASHP generic model, the accuracy can be reached to 83% for R-2, 125 W for MAE, and 9% for MAPE. The method presented in this paper can be applied to estimate power consumption of millions of heat pumps and consequently add a significant value as well as provide different types of services, such as cost-saving benefits for manufacturers and end-users, flexibility services for aggregators and electricity grids.

The development of smart sensors, low cost communication, and computation technologies enables continuous monitoring and accumulation of tremendous amounts of data for heat pump systems. But the measurements, especially for domestic heat pump, usually suffer from incompleteness given technical and/or economic barriers, which prevents database of measurements from being exploited to its full potential. To this end, this work proposes a data-driven soft sensor approach for compensating multiple missing information. The soft sensors are developed based on an ANN model, an integrated multivariate polynomial regression model and empirical model by considering different constrains like data and information availability during model establishing process. All the three models have been validated against the data from a field test installation, and showed good performance for all the compensated variables. Of the three models, the ANN model shows the best performance for all soft sensors, but it has the highest requirement for additional resources to collect training data. While the integrated multivariate polynomial regression model demonstrates excellent accuracy for the majority of soft sensors with manufacturers' subcomponent data which needs no extra cost. Even though empirical model is not as accurate as the other two models, it still performs good accuracy with limited information from performance map. The methods developed in the present study paves the way for available measured data in thousands of installations to be fully utilized for innovative services including but not limited to: improved heat pump control strategies, fault detection and diagnosis, and communication with local energy grids.

In this study, we develop a dynamic system model that exhibits only one stable solution (sink) to analyze the impact of various factors on low-carbon transition, including policy implications, energy consumption, and energy capacity. Our aim is to provide policy recommendations based on the simulation results of the model, which reproduces five representative scenarios of distributed energy systems. We also compare our simulation results with those obtained from a set of agent-based models. Our analysis shows that the most effective way to achieve a low-carbon transition is by combining two approaches: limiting the use of high-carbon energy sources through certain market rules and reducing energy consumption. We further obtain different paths of low-carbon transition based on phase diagrams that do not harm the economy. For developed countries, the most feasible approach is to directly increase the market regulation ratio. For countries or regions with middle-level technical and energy conditions and relatively high energy consumption, such as some Eastern European countries, reducing energy consumption and then implementing low-carbon energy incentives policy is the best strategy. Finally, for countries or regions with lower technical and energy conditions, such as African countries, the main task is to improve energy capacity to increase the possibility of a low-carbon transition.

Heat pumps and water tanks can be used to increase PV self-consumption in buildings without any additional equipment, but there is sometimes a lack of economic incentives to maximize it that limits economic gains. Therefore, pricing conditions need to change in order to make self-consumption strategies more interesting for prosumers. This study aims at determining what, if any, unsubsidized market conditions could lead to economically motivated self-consumption control strategies with solar heat pumps. A sensitivity analysis is used on multiple pricing models based on current market conditions for a solar PV and ground source heat pump system for a single-family house in Norrköping, Sweden. The results show that control strategies aimed at maximizing self-consumption have very little impact on net costs, regardless of pricing model or variation in price. Feed-in-bonus is the most important aspect when comparing different pricing schemes, and no other sensitivity comes close.

Natural refrigerants hold some drawbacks which can challenge their application in residential heat pumps, especially those located indoors. Natural mixtures may compensate for the unwanted characteristics of the pure fluids by extending the range of applicability of natural refrigerants, while possibly providing superior performance. A modelling framework was developed in Python and utilized to predict the steady-state operation characteristics of a single-stage heat pump. A variety of key performance indicators considering uncertainties were analysed. DME, R432A and R510A were the best alternatives regarding COP and required refrigerant charge for an application with small temperature glides. Propylene showed the highest volumetric heating capacity while Isobutane showed the lowest discharge temperature. Uncertainties and local sensitivity analysis were evaluated through error propagation. The most sensitive parameters differed significantly from one refrigerant to another. For mixtures containing CO2, mixture composition resulted in being a critical parameter.