Cleaning-In-Place (CIP) is commonly used in the food and beverage industry to clean process equipment without disassembly. The thermal energy demand to heat up the cleaning fluids used in CIP-systems is usually covered using steam supplied by boilers. The dairy plant analysed in this work utilises a retrofitted heat pump directly integrated into the ammonia refrigerant circuit of a chiller to lift waste heat of the chiller to temperature levels suitable to preheat cleaning fluids of the CIP-system. The heat pump supplies a hot water thermal energy storage, which is used to preheat lye and acid tanks of the CIP-system and to supply additional processes within the cheese dairy to reduce the steam demand. Due to the complexity of the system and its operation in a producing facility, a model-based approach is used to study the dependency of the time-resolved load profile on the operating parameters with the aim to reduce the steam demand. The simulation model set up in Modelica combines a transient simulation of one part of the CIP-system based on the given cleaning processes and used equipment with measured load profiles for the second part of the CIP-system and the cheese dairy. The thermal energy storage is considered with a transient model, while for both chiller and heat pump simplified stationary models assuming constant second law efficiencies are used. This simulation model enables to study measures which increase the amount of heat supplied by the heat pump reducing the steam demand by up to 29%.

Integration of air-source to ground-source heat pump systems: Impact on borehole design and life-cycle cost in Canada

The increasing electrification of the heating and mobility sectors, in particular an increasing penetration of building-sector heat pumps, battery electric vehicles and rooftop photovoltaic, necessitates a spatially differentiated analysis of additional loads in order to understand when and where the electricity distribution grid might need to be extended. While numerous studies define national ramp up trajectories for heat pumps, rooftop photovoltaic systems, and battery electric vehicles, district level analyses remain scarce. This study addresses this gap by translating climate target based national pathways into development trajectories for seven representative neighborhood archetypes up to 2045. Building upon existing energetic archetype definitions, a classification of residential areas into seven archetypes is first developed to serve as the basis for scaling. National ramp-up trajectories from several studies are converted into annual growth curves and assigned to the seven neighborhood archetypes using census data and structural indicators such as building type, geometric rooftop potentials, and current as well as projected vehicle densities. The outcome is a set of archetype-specific, temporally resolved projections of building-sector heat pump, photovoltaic, and battery electric vehicle penetration until 2045 which show pronounced differences in the required ramp-up across neighborhood archetypes, with single- and two-family house areas driving most of the early adoption, while dense urban and commercial archetypes follow with delayed but steep increases. The presented work provides a robust and scalable framework applicable across Germany for simulating future energy and infrastructure developments across neighborhood structures aligned with national climate goals. • Climate-based tech ramp-ups downscaled to neighborhood archetypes to 2045. • Seven archetypes reflect Germany’s diverse energy-relevant building stock. • Yearly 2025–2045 HP, rooftop-PV and BEV penetration projections by archetype. • Method applicable across Germany.

Time-variable grid tariffs are a potential means to incentivize grid-friendly load shifting, thereby reducing power peaks and grid reinforcement needs. Various real-world studies have investigated the average consumption change during high- and low-price periods, but only a few have analyzed the effects on peaks. In contrast, this work focuses on daily power peaks at a transformer station level, testing a fixed time-of-use tariff and a dynamic real-time tariff in a real-world pilot. Both schemes consider automated control of electric water heaters, heat pumps, and electric vehicle charging. The analyses show that the real-time tariff led to higher injection peak reductions in summer, while neither scheme could consistently reduce consumption peaks in winter. Additionally, the work highlights challenges regarding experimenting in real-world settings with ongoing system changes and proposes a baseline load estimation approach addressing these changes. • Automated electric water heater, heat pump, and electric vehicle charging control. • Reinforcement learning-based decision-making with limited information. • Baseline load estimation approach considering ongoing system changes. • Real-time tariff yields higher injection peak reductions than Time-of-Use tariff. • Consumption peaks in winter cannot be consistently reduced.

District heating is a common means of providing residential heating in urban areas in Northern, Eastern, and Central Europe. Shifting external parameters, such as fuel price fluctuations and policy implementations encouraging decarbonization, can lead to significant cost increases that take years to resolve, as production facilities are built to operate for multiple decades. In this article, a mixed-integer linear optimization model minimizing total network costs is developed to assess how flexible networks are to such changes and how ideal investment pathways depend on existing infrastructure. Utilizing temporal and technology aggregation methods, the operation of a given network is simulated over a multi-decade timeframe, with the option to invest in new production facilities every five years as existing facilities reach end-of-life. The model is applied to two Nordic district heating networks located in Turku, Finland, and Gothenburg, Sweden. Progressive expansion of power-to-heat utilization in the form of heat pumps and electric boilers is shown to be economically advantageous in both networks as existing facilities are decommissioned. The effect of path dependence becomes apparent under varying simulated scenarios, such as in Turku, where significant investments are postponed until the decommissioning of a single large combined heat and power plant. Air-source heat pumps are found to be uncompetitive in the colder Nordic climate compared to heat pumps utilizing industrial waste heat at a constant temperature, but may be incorporated in cases where access to such waste heat sources is constrained. • An optimization model is built to determine long-term district heating investments • Technology and temporal aggregation enable efficient multi-decade optimization • Two Nordic district heating networks are analysed as comparative case studies • Optimal investment pathways are strongly influenced by the existing infrastructure • Gradual electrification via power-to-heat expansion is cost-optimal in most cases

• This comprehensive workflow is applicable across diverse fields, including unconventional hydrocarbons, CO2 storage, geothermal systems, and mineral resources exploration. • This integrated workflow combines lithological, geochemical, petrophysical, and rock mechanical data for immature organic-rich carbonate reservoirs, providing continuous high-resolution profiles along the core interval that capture rock heterogeneity and improve understanding of the complex nature. Understanding the vertical and lateral heterogeneity of immature organic-rich carbonate reservoirs is essential for predicting reservoir quality, maintaining wellbore stability, and identifying sweet-spot intervals in unconventional systems. Although geological, geochemical, petrophysical, and mechanical techniques are extensively employed for rock characterization, they are frequently applied in isolation, which can hinder a systematic assessment of variability. This paper introduces a structured, reproducible workflow that integrates lithological description, multivariate geochemical classification, continuous petrophysical profiling, and non-destructive mechanical testing into a unified framework for evaluating heterogeneity. The methodological innovation resides not in the individual analytical techniques but in their sequential integration into a facies-guided workflow that seamlessly links continuous non-destructive measurements with targeted laboratory analyses and predictive data integration. The workflow combines lithological descriptions, rock classification using unsupervised machine learning methods such as silhouette analysis, principal component analysis (PCA), and hierarchical clustering on principal component analysis (HCPC), bulk geochemistry, petrophysical profiling, non-destructive rock mechanical testing, and integrated data interpretation. This integrated method allows for accurate characterization of these complex reservoirs, supporting a systematic assessment of their heterogeneity, quality, and petrophysical and mechanical behavior. Continuous measurements are first used to capture spatial variability along the core interval, after which representative samples from identified facies domains are selected for laboratory analyses, including mineralogy, organic geochemistry, and petrophysical measurements. The integration of continuous and discrete datasets enables the evaluation of key properties across the entire core interval and systematic identification of heterogeneous zones. Lateral heterogeneity is identified by comparing lithofacies transitions, chemofacies shifts, and coordinated changes in porosity, velocity (Vp), total organic carbon (TOC), and strength (UCS) and hardness (HLD) logs. Moreover, the proposed workflow provides a clear methodological pathway for assessing vertical and lateral heterogeneity in immature organic-rich carbonate systems and can be adapted to unconventional hydrocarbon reservoirs, CO 2 storage formations, geothermal systems, and mineral exploration studies. Summited separately

Study on the integration of photovoltaic hydrogen production system with energy storage and heat pump: economic analysis under multiple scenarios and multiple configurations

Building cooling and heating account for around 25% of global greenhouse gas emissions, with a rising trend. While the growing cooling demand is largely met by chillers, decarbonization and electrification efforts are boosting the adoption of heat pumps. Both technologies rely on electricity-intensive vapor compression cycles, which contribute to environmental impacts and place stress on power grids. To mitigate these effects, previous research has suggested improving the performance of vapor compression systems (VCSs) through optimization and fault detection. In this regard, digital twins (DTs) offer a promising approach by enabling real-time simulation with minimal manual effort. However, their adoption in the building sector remains limited. This paper presents a systematic literature review of modeling methods for VCSs, discussing their strengths and limitations in the context of DT applications. Additionally, it introduces a set of evaluation criteria to analyze the performance of DTs. The findings highlight that (i) model selection is highly dependent on contextual factors such as load and weather variations, (ii) commonly used modeling methods often face challenges when applied to DT scenarios, and (iii) focusing only on error-based metrics may overlook other aspects such as generalizability, resiliency, scalability, and interpretability which might be crucial in final applications. These insights facilitate the development and validation of VCS DTs and supports their adaptation in the building sector. • A systematic review of existing modeling methods for vapor compression systems (VCSs) is conducted. • Strengths and limitations of VCS modeling methods are discussed in the context of digital twinning. • A structured set of evaluation criteria for DT-oriented modeling is introduced. • The importance and role of each evaluation criterion are discussed in relation to practical DT applications. • A case study comparing seven commonly used modeling methods in terms of accuracy, reliability, robustness, scalability, and interpretability is provided. • Results suggest that model selection should consider multiple aspects beyond accuracy alone.

Power control systems (PCSs) can exploit low-carbon technologies (LCTs) to provide grid ancillary services. This work develops a bilevel mixed-integer linear programming PCS of photovoltaics (PVs), electric vehicles (EVs), heat pumps (HPs), and battery energy storage systems (BESS), for providing automatic frequency restoration reserves (aFRR) with energy arbitrage, PV self-consumption, and customers’ thermal and charging comfort. The contribution of the BESS and the flexible loads is evaluated under different seasons, grid types and sizes, and energy/reserve prices. Validating against a PCS solely for minimum grid energy cost (energy arbitrage), the findings demonstrate the increased cost savings when a PCS participates in the reserve market with BESS and EV combined. The cost of solely energy arbitrage was found consistently higher than 100% (e.g. 40 compared to only 19 with aFRR provision). These benefits become more important recently in 2023, with the higher energy prices, and much higher reserve prices compared to 2018 (up to 540% increase). While the always present BESS is able to contribute more to ancillary services compared to the uncertain EV fleets, the contribution of EVs increased to a notable 38.5% of the total provided aFRR energy share at larger grids. Finally, mixed nodes that comprise both residential-commercial buildings and home-public chargers have a higher potential for ancillary services provision, demonstrating a 5x and 12x higher potential compared to residential and commercial nodes, respectively. Overall, this work highlights the importance of PCSs in large grids or with a variety of loads to provide ancillary services for enhanced savings. • Providing aFRR by EVs and BESS on top of energy arbitrage yields double cost savings. • EVs’ contribution to ancillary services can become comparable to BESS in large grids. • Higher potential for ancillary services for grids of diverse buildings and chargers. • In contrast with solely energy arbitrage, V2G is highly used in ancillary services. • Increasingly varied imbalance prices favor participation in the reserve market.

A new system of energy tower heat pump combined with boilers and solar energy

Energy management and optimal control of a multifarious water heating systems with waste heat recovery and a solar-assisted heat pump: A case of large-capacity healthcare building

Transient magnetohydrodynamic flow over a rotating vertical porous surface incorporating thermal radiation, hall and ion-slip effects: Using finite element method

Thermodynamic analysis of power generation and waste heat recovery in CO2-plume geothermal systems in aquifers of varying heterogeneity

The thermal role of granitic bodies in geothermal system: Insights from South China

Optimizing operational strategy for medium-depth hydrothermal geothermal cascade heating system

Influence of the burial depth of series-connected shallow vertical U-tube ground heat exchangers on rock mass temperature

Energy efficiency evaluation of operation modes for ground source heat pump coupled with phase change thermal storage heating system

Theoretical analysis, experimental research, and industrial verification of ultra-high temperature heat pump heating system

A new concept of adsorption heat pumps: Proof-of-concept and feasibility assessment

Vapor bypass technology to enhance the performance of an air source heat pump system with 5 mm diameter finned tube heat exchanger