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.

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 °C and heating up to 120 °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.

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.

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%.

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.

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.

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.

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.

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.

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.