Smart Energy最新文献

Aligning prosumers' electricity consumption to the availability of self-generated electricity decreases CO₂ emissions and costs. Nudges are proposed as one behavioral intervention to orchestrate such changes. At the same time, fragmented findings in the literature make it challenging to identify suitable behavioral interventions for specific households and contexts - specifically for optimizing self-consumption. We test three sequentially applied interventions (feedback, benchmark, and default) delivered by digital tools in a field experiment with 111 German households with rooftop-photovoltaics. The experiment design with a control-group, baseline measurements, and high-frequency smart-meter-data allows us to examine the causal effects of each intervention for increasing self-consumption. While feedback and benchmark deliver small self-consumption increases (3–4 percent), the smart changing default leads to a 16 percent increase for active participants. In general, households with controllable electric vehicles show stronger effects than those without. For upscaling behavioral interventions for other prosumers, we recommend interventions that require little interaction and energy literacy because even the self-selected, motivated sample rarely interacted with the digital tools.

The buildings energy consumption is a great part of Europe's overall energy demand. The development of diagnostic methods capable of promptly alerting users in case of issues (e.g. mild and progressive decrease in systems components performance) is crucial for the smart management of buildings. Machine learning-based building energy monitoring is a reliable approach for identifying subtle anomalies in the building energy demand behaviour. This study presents the application of a systematic procedure to develop a reliable monitoring method based on machine learning predictive models, ensuring minimal user knowledge requirements. The proposed method applied to the electricity demand of various components of the heating, ventilation and air conditioning system of a real Italian healthcare facility. The obtained models are exploited to apply the building energy monitoring method, assessing its capability to highlight mild changes in building energy demand behaviour. Considering that its application on specific system components implies an increased technical and economic effort to carry out data collection, the present work is aimed at assessing the benefits of such applications. Because of its high reproducibility and relatively simple integration into centralized building energy management systems, the proposed method offers a practical solution to enhance the smart management of building energy systems.

As the transition towards a net-zero gains momentum, smart local energy systems (SLES) will play a key role in delivering clean and sustainable energy in various forms of usage such as heat, electricity, and transportation, to communities where these projects are implemented. Successful SLES have previously shown a combination of cutting-edge engineering technology, as well as social and economic factors coming into play to achieve a set goal. The interdependencies between these contributing factors illustrates the multi-attribute nature of SLES. This article highlights how insightful models can be in upscaling SLESs. The models considered were categorized according to their modes of application, and instances where they have been used for modelling a multi-energy system upscale. Multi-criteria analysis was used to rank these models according to their ability to represent SLES. Four major aspects of upscaling (growth, replication, accumulation, and transformation) were used to weight the criteria using the entropy method and CRITIC method, respectively. The TOPSIS method was used to rank the models and the result indicated that among 21 models considered, the hybrid combination of an optimization model, a weighting model, and a multi-criteria decision model was the closest to the ideal solution.

Price-responsive demand and dynamic electricity price contracts can play a vital role in balancing renewable energy production and alleviating energy shortages such as those experienced in the European energy crisis. This study focuses on the implicit demand flexibility of residential consumers during extraordinarily high electricity prices in winter 2021/22 in Norway where most households have electric heating and spot price contracts. An econometric model is developed that compares the demand with pre-crisis levels, adjusts for factors influencing electricity consumption, such as outdoor temperature, and utilises a comprehensive dataset including hourly electricity demand data. The results reveal a quick response since the price signal was passed immediately to the customers and substantial energy savings of 11.4 % during winter. While the average household showed no significant short-term price response to daily or hourly price variations, several subgroups did. Particularly, households actively monitoring hourly prices via real-time information channels and those with automatic smart charging of electric cars showed higher load reductions in peak price hours and load shifting to low-price hours. Thus, the study concludes that households are able to respond to variable hourly electricity prices and suggests the promotion of spot price contracts to incentivise residential demand response.

Especially in densely populated areas, district cooling represents an opportunity to reduce energy consumption and emissions. Nevertheless, this technology is characterised by large capital costs which impede its diffusion. As a consequence, optimization tools can significantly help to unleash their potential. In this paper, a methodology is proposed to combinedly optimize the design and operation of a district cooling system based on a Mixed Integer Quadratic Programming. The model is compared to the design only optimization, based on a properly tailored heuristic approach. The models, when applied to a case study characterized by seasonal demand, provide similar solutions, which differ by 0.5 % in terms of objective value for a standard scenario. The simultaneous design and operation optimization does not provide sensible savings with respect to optimizing solely the design. A sensitivity analysis is performed to prove the robustness of the results. The results showed that the simultaneous operation and design optimization would be limited to 1 % of total costs in the case of seasonal cooling demand. On the other hand, if the cooling demand persists throughout the year, as in tropical climates, the combined optimization provides significant benefits, since these savings reach 4.7 % of total costs.

High-temperature aquifer thermal energy storage systems for storage and utilization of excess heat are a promising element for decarbonization strategies of district heating systems. Based on a combination of literature review and expert consultation, this study aims to identify potential environmental and economic key factors determining a sustainable integration of high-temperature aquifer thermal energy storage systems into district heating networks. For this objective, we use several methods in five steps to narrow down the potentially high number of influencing factors. We identify hard boundary constraints for project development, the most relevant life cycle phases and related internal factors. Moreover, we identify influencing external factors and methodological factors that impact environmental and economic outcomes from a systemic perspective. Our findings suggest that potential key factors mainly pertain to the construction and operation phases, which are significantly affected by drilling, heat production, and the electricity required for submersible pumps and heat pumps for injection and extraction of stored heat. Identifying these factors enhances the comprehension and transparency of decision support based on life cycle assessment and life cycle costing. The results further guides research and practical improvement actions towards the most pertinent factors.

This research explores the potential for reducing reliance on fossil fuels through the combined utilisation of photovoltaic energy and local Vertical Axis Wind Turbines (VAWTs). In urban settings, VAWTs offer advantages such as lower noise emissions, independence from wind direction, operational efficiency at both low and high wind speeds, and enhanced stability due to their unique helical blade design. These characteristics make VAWTs a suitable and effective option for generating sustainable energy in urban environments. A Vertical Axis Wind Turbine (VAWT) will be virtually introduced to 2050 Homes Development in Nottingham, a cluster of 27 houses configured to use a micro–Low Temperature District Heating (LTDH) network.

This paper investigates the possibility to move 2050 Homes scheme into zero energy level by using Quite Revolution (QR6) helical blade VAWT along with photovoltaic energy generation. The preliminary results show that two QR6 VAWT can bring the 27 terrace homes from the 2050 Homes scheme into zero energy class performance.

This study has been undertaken to understand whether business models for heating, cooling and hot water can be categorized as circular. The study addresses the case of new, combustion-free technology by resorting to low-temperature district energy. Such systems necessitate smart infrastructure with efficient demand and supply matching ensuring the most cost-efficient use of heat supply over time. By studying 10 cases in a research project stretching across 3 years, it is identified that all cases display circular economy features, across the categories of reuse, reduce and recycle. The category of reverse logistics is only identified in 7 cases where energy is circulated within the networks. The integration of excess heat exhibits a particularly strong circularity case, covering all four circular economy dimensions. The circularity of low temperature district energy business models is, however, not free, but comes at a cost compared to conventional combustion-based technology, as new key resources and consequential investments are needed. The major conclusion of the study is that low temperature district energy business models are inherently circular, an important information for European policy making, fostering a circular energy transition.

Traditionally, building control systems for heating, ventilation, and air conditioning (HVAC) relied on rule-based scheduler systems. Deep reinforcement learning techniques have the ability to learn optimal control policies from data without the need for explicit programming or domain-specific knowledge. However, these data-driven methods require considerable time and data to learn effective policies without prior knowledge. Performing transfer learning using pre-trained models avoids the need to learn the underlying data from scratch, thus saving time and resources. In this work, we evaluate reinforcement learning as a method of pretraining and fine-tuning neural networks for HVAC control. First, we train an RL agent in a building simulation environment to obtain a foundation model. We then fine-tune this model on two separate simulation environments such that one environment simulates the same building under different weather conditions while the other environment simulates a different building under the same weather conditions. We perform these experiments with two different reward functions to evaluate their effect on transfer learning. The results indicate that transfer learning agents outperform the rule-based controller and show improvements in the range of 1% to 4% when compared to agents trained from scratch.

Sector coupling and system integration are key concepts in the energy transition from fossil fuels to fully decarbonized energy systems based on renewable energy. An intelligent use of sector coupling – such as that expressed in the concept of a smart energy system –accommodates for the identification of a more energy-efficient and affordable green transition. However, these benefits are often not fully identified in scenario modelling for the simple reason that not all energy systems analysis tools are equipped to do so. Here, we use the EnergyPLAN tool to replicate the EU Baseline and 1.5 TECH scenarios of the report “A Clean Planet for All”, which we then compare to a smart energy systems scenario for Europe. Due to its focus on sector coupling, we show how such a smart energy Europe scenario can be more energy efficient and affordable than the other scenarios.