Smart Energy最新文献

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.

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.

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.

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.

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.

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.

The fourth and fifth industrial revolutions (Industry 4.0 and Industry 5.0) have driven significant advances in digitalization and integration of advanced technologies, emphasizing the need for sustainable solutions. Smart Energy Systems (SESs) have emerged as crucial tools for addressing climate change, integrating smart grids and smart homes/buildings to improve energy infrastructure. To achieve a robust and sustainable SES, stakeholders must collaborate efficiently through an energy management framework based on the Internet of Things (IoT). Demand Response (DR) is key to balancing energy demands and costs. This research proposes an edge-based automation cloud solution, utilizing Eclipse Arrowhead local clouds, which are based on Service-Oriented Architecture that promotes the integration of stakeholders. This novel solution guarantees secure, low-latency communication among various smart home and industrial IoT technologies. The study also introduces a theoretical framework that employs AI at the edge to create environment profiles for smart buildings, optimizing DR and ensuring human comfort. By focusing on room-level optimization, the research aims to improve the overall efficiency of SESs and foster sustainable energy practices.

In the context of the digital transformation of the electricity sector, the most recent technologies in power substations has promised benefits related to improved engineering, operations, and maintenance, transforming them into “smart” substations. However, methods and tools to analyse the impacts and estimate the benefits of electrical substation digitalisation to support investment decision-making have rarely been reported. We propose a comprehensive framework for assessing the impacts of the digitalisation of electrical power substations. Literature review, interviews with stakeholders and an expert survey were carried out to better understand those impacts and support the definition of the conceptual model. The framework identifies six key technological drivers and a total of 12 impacts, establishes the relationships between them, and defines key performance indicators for quantifying the impacts. The proposed framework was applied in a case study of the digital retrofit of a distribution substation on the scope of a research and development project on intelligent substations. The application demonstrates the framework's usefulness as a tool to systematically assess digital substations and highlights how digitalisation can benefit engineering and construction, and operations and maintenance activities. The proposed framework can also be applied to other contexts, thus contributing to an increasingly digital and connected electricity sector.

Sector integration is one of the major components considered when designing future smart energy systems. Due to the lack of data, current partitioning of consumers into residential, commercial, industrial, and transportation sectors is based on assumptions on their respective energy demand. In reality though, there is a diversity in preferences and behaviors so that consumers from the same sector may have different demand and consumers from different sectors may have similar demand. With the increasing amount of individual data, it is becoming possible to study this diversity and more accurately partition consumers using advanced analysis techniques such as clustering. However, while this approach does allow for an accurate grouping, the complex mechanisms at the roots of identified clusters are still unclear. Indeed, energy demand depends on multiple factors such as economic, political, cultural, social, or historical besides environmental conditions. The present study uses households' data provided by the Japanese Ministry of the Environment with the objective of finding patterns associated with residential energy consumption profiles. It is found that the probability distribution of households' energy consumption seems to be log-normal so that clusters are revealed by first applying a logarithmic nonlinear transformation. Furthermore, k-means clustering, which is commonly used in energy systems study, fails here to correctly identify the clusters when compared with density-based clustering. After identifying clusters, we look for statistically significant specificities in the corresponding households' data such as their geographical location, number of residents, income, buildings' construction year, equipment and vehicles and suggest interpretations for each.