Institut für neue Energie-Systeme (InES)
Refine
Year of publication
Document Type
- Article (66)
- Conference Paper (29)
- Book (1)
- Part of a Compilation / Book Chapter (1)
Institute
Review
- peer-review (66)
- nein (11)
- editorial review (9)
- ja (6)
Version
- published (65)
PV-Optimized Heat Pump Control in Multi-Family Buildings Using a Reinforcement Learning Approach
(2024)
For the energy transition in the residential sector, heat pumps are a core technology for decarbonizing thermal energy production for space heating and domestic hot water. Electricity generation from on-site photovoltaic (PV) systems can also contribute to a carbon-neutral building stock. However, both will increase the stress on the electricity grid. This can be reduced by using appropriate control strategies to match electricity consumption and production. In recent years, artificial intelligence-based approaches such as reinforcement learning (RL) have become increasingly popular for energy-system management. However, the literature shows a lack of investigation of RL-based controllers for multi-family building energy systems, including an air source heat pump, thermal storage, and a PV system, although this is a common system configuration. Therefore, in this study, a model of such an energy system and RL-based controllers were developed and simulated with physical models and compared with conventional rule-based approaches. Four RL algorithms were investigated for two objectives, and finally, the soft actor–critic algorithm was selected for the annual simulations. The first objective, to maintain only the required temperatures in the thermal storage, could be achieved by the developed RL agent. However, the second objective, to additionally improve the PV self-consumption, was better achieved by the rule-based controller. Therefore, further research on the reward function, hyperparameters, and advanced methods, including long short-term memory layers, as well as a training for longer time periods than six days are suggested.
Integrating renewable energies into district heating systems has a large potential to reduce CO2-emissions in the heating sector. As district heating systems offer the possibility of incorporating renewable energies into the heat supply, new systems have to be built and the existing networks must be maintained. This study investigates ways to optimize existing district heating systems in order to ensure economic sustainability in the long-term. Previous case studies have elaborated on a variety of optimization measures. However, to date, these measures have neither been collected nor consistently assessed for a wider application range. Therefore, in the study presented here a system for assessing the ecological and economic benefits of optimization measures was developed and applied. The assessment method utilized showed that optimization of district heating consumer substations and adding of a central buffer storage tank has a high optimization potential in comparison to intermittent operation strategy, which has a significantly lower optimization potential. From this information and the transferability data, a district heating operator can determine which optimization measure should be prioritized, which is shown at the end of the paper on an example case.
This paper introduces a comprehensive approach for sizing grid-connected hybrid renewable energy systems tailored for electric bus fleet operations. The study involves two main steps. First, a mathematical model that optimizes the configuration of such systems by considering daily electric bus consumption, solar irradiance, wind speed, and biomass potential is formulated. The model utilizes Pareto frontier multi-objective optimization to minimize the net present cost, the cost of energy, and greenhouse gas emissions. Second, the model is rigorously applied and tested in a real-world case study in Fez, Morocco, using HOMER Pro; the case study centers on the daily energy requirements of the buses, estimated at 2.5 megawatt hours per day, with a peak demand of 345 kilowatts. Two scenarios are explored, revealing a discernible trade-off dilemma between the full hybrid renewable energy scenario (Scenario 1) and the grid-connected hybrid renewable energy scenario (Scenario 2). In Scenario 2, the grid-connected hybrid renewable energy system demonstrates a notable 42.8% reduction in the net present cost, totaling USD 984,624. Similarly, the levelized cost of energy experiences a significant decrease, reaching approximately 0.08 USD/kWh, marking a 38.1% reduction. However, this apparent economic advantage is juxtaposed with a critical consideration—an increase in greenhouse gas emissions from null to 330,418 kg/year.
Experimental, analytical, or numerical investigations are ordinarily conducted to reveal optimisation potential for solar thermal collectors. At the same time, the ‘Solar Keymark Database’ contains more than 2,000 test reports from certified laboratories featuring properties including thermal efficiency, dimensions, or optical properties. This dataset offers untapped potential for statistical analyses as an alternative optimisation approach. Hence, this paper aims to provide a) the first statistical findings of solar thermal collector properties listed in the Solar Keymark Database and b) insights into statistical relations between these properties. The key correlations observed from the analysis of flat-plate collectors were between efficiency and both gross height (R = 0.30) and gross area (R = 0.27). We concluded that preferable collector designs may be featured with larger area to height ratios. The analyses of evacuated tube collectors revealed a strong correlation between efficiency and transversal incidence angle modifier (R = -0.65) as a result from different tube spacing. It was noticeable that the quasi-dynamic test method reported significantly higher efficiencies (7.14 percentage points) for evacuated tube collectors, which should be carefully considered for future test procedures. Overall, the statistical analysis was in accordance with conventional bottom-up analyses and revealed insightful dependencies for the present collector data.
AbstractBuildings in rural Central Asia have unique characteristics as they were built during the Soviet era (during 1950–1960) without any energy efficiency measures. The special and aged building stock pose a crucial challenge on energy security and energy supply. However, accurate scientific data on their energy usage is lacking, highlighting a research gap about robust and validated methodology to determine the energy use of rural Central Asian buildings. In response to this need, this research paper proposes a set of generalised residential building typologies for rural Central Asia, with a focus on Kyrgyzstan. The study combines quantitative and qualitative methods, including household surveys, energy demand modelling, EnergyPlus simulations, and result validation. It derives energy-based building typologies from construction year and building envelope characteristics. Through numerous validated simulations, it was identified that the heat demand for rural Kyrgyz houses is 3–5 times here compared to European houses (250–400 kWh/m2). The study revealed the estimated heat demand for homes in rural Kyrgyzstan, indicating a significant potential for improving building energy efficiency in the region. These results can inform specific approaches, such as building renovation plans, energy certification, and renovation strategies. Furthermore, the validated methodology introduced offers opportunities for researchers in the field of building energy efficiency and can be applied to similar regions in Central Asia with comparable building stocks and climates.
The further expansion of renewable energies in Germany requires flexible consumers to balance fluctuations in electricity production from variable renewable energies. Cold storage warehouses, due to their inherent storage capacity and widespread use, are well-suited for integrating more renewable energies. The potential of cold storage warehouses is often viewed in isolation and not in conjunction with the cold chain’s upstream and downstream processes. By adjusting the temperatures within the processes, the individual links in the cold chain can be made flexible. To assess the effects of flexibilization on emissions and electricity costs, thermodynamic models of the individual links in the cold chain and of a yogurt pallet are developed and linked together. Due to temperature fluctuations in the products resulting from the flexibilization, emission evaluations must be considered throughout the cold chain. Results of the simulation for the study period show that emissions reductions and electricity cost savings can be achieved in all three links when they are made flexible. However, the savings vary in magnitude. Only minor savings can be achieved in the cooling tunnel. The greatest potential for savings is in refrigerated transport, if deeper cooling occurs in the process before, i.e., in the cold storage warehouse.
With the increasing number of electric vehicles in the transport sector, the relevance of accurate energy and power demand predictions of electromobility is growing. Thereby, different vehicle functions, especially heating and air conditioning and vehicle automation, have a significant influence. In accordance with the upcoming Euro-7 emissions standard, the energy consumption for heating even has to be contained in the manufacturer’s consumption data in the future. To increase the accuracy of energy and power demand predictions of electromobility, the energy consumption of vehicle functions such as heating and air conditioning as well as the energy savings through vehicle automation must be considered.
This paper presents approaches for modeling and simulation the energy consumption of heating, air conditioning and vehicle automation which can be used as an extension of electric vehicles WLTP (Worldwide Harmonized Light-Duty Vehicles Test Procedure) consumption simulation on the level of vehicle classes. The Germany-wide results of the electric vehicles energy demand for heating and air conditioning on the level of NUTS3-areas (Nomenclature of territorial units for statistics) and vehicle classes show regionally different results and confirm the relevance of the research approach. Vehicle automation results are described on the level of the five SAE automation levels (Society of Automotive Engineers automation levels) and the vehicle classes. The approaches and results can be used for single vehicles or assumed vehicle fleets.
Insulating glass flat-plate collectors can save cost by being produced quickly and automatically in insulated glass production facilities, and they can be filled with argon to reduce heat loss. During its lifetime, the collector is likely to lose argon because of gradual material degradation of the sealing. However, information on the influence of the argon concentration on the collector efficiency is limited. Therefore, the objective of this research work was to analyse this effect. A theoretical material property calculation of argon-air mixtures was carried out to determine the convective losses with variable argon concentrations. Thermal collector performance was measured experimentally using an outdoor solar tracker test rig. The results strongly suggest, that the influence of argon concentration on both the convective losses and the thermal efficiency is non-linear. The measurements revealed that an argon concentration of 90 % can increase average thermal performance by 6.7 ± 4.8 percentage points. An increase in argon concentration from 0 % to 50 % has almost twice the effect on average thermal efficiency as an increase from 50 % to 90 %. Concluding from these results, an argon leakage threshold of 2.5 percentage points per year is proposed to avoid disproportionate loss of efficiency over time.