Energy Informatics最新文献

Buildings are responsible for about one-third of industrialised countries’ overall energy consumption and greenhouse gas emissions. As if this was not enough, recently, energy prices significantly increased and affected all economic areas. Making buildings more efficient and effective is the step needed toward cost reductions. Key enablers of cost-effectiveness are leveraging batteries, awareness of and adaptability to energy prices, and integrating powerful reasoning techniques to optimally and flexibly operate buildings. Researchers have tackled many of these aspects using a variety of approaches. Whereas a less investigated one is that of AI planning to coordinate actions and save energy in buildings. However, generating plans based on signals of energy prices and leveraging batteries is still an open research problem. To address this high-potential aspect, we engineer an AI planning system for improving the energy-cost effectiveness in buildings by coordinating the building’s operation based on day-ahead prices and the use of a battery, all without sacrificing the comfort of building occupants. We propose to exploit temporal planning due to its powerful modelling and reasoning features, especially in explicitly addressing time. We evaluate the effectiveness of the system in several scenarios with varying building environmental conditions. We compare the energy cost from using our planning system to a baseline cost, where we record a reduction of 43rage in favour of our system.

The creation of synthetic heat pump load profiles is essential for energy system modeling and simulations. This paper proposes a methodology to create synthetic heat pump load profiles based on the k-means algorithm and a data set from water-to-water heat pumps from Hamelin, Germany. The quality of the generated load profiles is shown according to load factors, load distribution curves and the Pearson correlation coefficient, and is also applied on two exemplary geographies in Germany. We publish our work open-source and provide a web-based heat pump load profile generator.

As power systems transition from controllable fossil fuel plants to variable renewable sources, managing power supply and demand fluctuations becomes increasingly important. Novel approaches are required to balance these fluctuations. The problem of determining the optimal deployment of flexibility options, considering factors such as timing and location, shares similarities with scheduling problems encountered in computer networks. In both cases, the objective is to coordinate various distributed units and manage the flow of either data or power. Among the methods for scheduling and resource allocation in computer networks, stochastic network calculus (SNC) is a promising approach that estimates worst-case guarantees for Quality of Service (QoS) indicators of computer networks, such as delay and backlog. Promising QoS indicators in the power system are given by the amount of stored energy, the serviced demand, and the demand elasticity. In this work, we investigate SNC for its capabilities and limitations to quantify flexibility service guarantees in power systems. We generate and aggregate stochastic envelopes for random processes, which was found useful for modeling flexibility in power systems at multiple time scales. In a case study on the reliability of a solar-powered car charging station, we obtain similar results as from a mixed-integer linear programming problem, which provides confidence that the chosen SNC approach is suitable for modeling power system flexibility.

In this article, we address the question of electric bus planning and operation under stochastic travel time and energy consumption. Uncertainties in the environment may cause disruptions to the planning and operation of electric buses, and a transportation planner must anticipate such conditions and be able to respond appropriately. One of the preconditions for planning robust strategies is understanding the existence and impact of multiple trade-off scenarios, which is the basis for this study. We model the travel time delays and trip energy consumption using estimated probability density functions and use a stochastic, mixed-integer formulation with chance constraints to evaluate several trade-off scenarios for electric bus fleets under uncertainty. The results show the existence of trade-off scenarios that lead to varying degrees of impacts related to network and environment. Careful fleet planning, dispatch, and charge control enable us to make the balance between these trade-offs and achieve better operational performances under uncertainty.

Urban energy system planning is vital for cities shifting towards a more sustainable and integrated energy system. Hydrogen is considered one of the most promising solutions in future energy systems. Previous work on hydrogen energy systems predominantly analysed hydrogen models on a national level or only parts of the mobility sector. This indicates a research gap for geospatial models that include multiple sectors in which hydrogen can be used. These models can be used to support decision-making processes around the hydrogen economy in cities. This study presents a holistic model addressing the geospatial modelling of hydrogen demand in urban areas. It proposes a method that integrates a variety of open source data, including geodata, earth observation data and energy data to estimate hydrogen demand top down for the industrial feedstock (steel, ammonia, organic chemistry), process heating, and mobility (buses, trucks, trains, airplanes, ships) sectors. The proposed method can also be extended to different sectors. The method is validated by modelling the hydrogen demand in all German cities and benchmarking it with national studies. This study’s results are within the same range as the results of national studies. For this paper, the method is applied for two case studies in Freiburg im Breisgau and Frankfurt am Main. Applying this method in urban areas shows potential hydrogen demand hotspots in these areas. The model’s results help policymakers and industry stakeholders make informed decisions about the development of hydrogen infrastructure and facilitate the adoption of hydrogen as a low-carbon energy carrier. Future research could explore the temporal aspects of hydrogen demand and the spatial influence of hydrogen demand on future hydrogen production facilities such as electrolysers.

In the course of digitization, there is an increased interest in sensor data, including data from old systems with a service life of several decades. Since the installation of sensor technology can be quite expensive, soft sensors are often used to enhance the monitoring capabilities. Soft sensors use easy-to-measure variables to predict hard-to-measure variables, employing arbitrary models. This is particularly challenging if the observed system is complex and exhibits dynamic behavior, e.g., transient responses after changes in the system. Data-driven models are, therefore, often used. As recent studies suggest using Transformer-based models for regression tasks, this paper investigates the use of Transformer-based soft sensors for modelling the dynamic behavior of systems. To this extent, the performance of Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM) models are compared to Transformers, based on two data sets featuring dynamic behavior in terms of time-delayed variables. The outcomes of this paper demonstrate that while the Transformer can map time delays, it is outperformed by MLP and LSTM. This deviation from previous Transformer evaluations is noteworthy as it may be influenced by the dynamic characteristics of the input data set, and its attention-based mechanism may not be optimized for sequential data. It is important to mention that the previous studies in this area did not focus on time-delayed dynamic variables.

We describe a solution for secure and verifiable handling of energy certificates. Such certificates are increasingly used to claim and prove responsible use of green energy, and there is a strong need for transparency and public verifiability. While the proposed solution is designed for handling electricity it applies to different types of energy as well and the concepts may also be applied to other domains. Transmission System Operators are trusted to record consumption and production of electricity. The movement from volume-based MWh yearly certificates to spot-market aligned hourly or 15 min time-volume based intervals, creates challenges in relation to handling large amounts of data and subsequent transactions. Small discrete intervals gives the certification increased accuracy of energy consumption, as a means to prevent greenwashing, with the cost of higher amounts of transactional data and complexity. To ensure trust in the certification, these certificates must in addition be unique and publicly verifiable. This paper describes how blockchain technology can be used to create the required transparency and public verifiability. We show how large amounts of data can be efficiently handled on blockchains and how confidential data such as the amount of used energy in the certificates can be protected, ensuring privacy and correctness of the certificates.

Data integration in the energy sector, which refers to the process of combining and harmonizing data from multiple heterogeneous sources, is becoming increasingly difficult due to the growing volume of heterogeneous data. Schema matching plays a crucial role in this process by giving each representation a unique identity by matching raw energy data to a generic data model. This study uses an energy domain language model to automate schema matching, reducing manual effort in integrating heterogeneous data. We developed two energy domain language models, Energy BERT and Energy Sentence Bert, and trained them using an open-source scientific corpus. The comparison of the developed models with the baseline model using real-life energy domain data shows that Energy BERT and Energy Sentence Bert models significantly improve the accuracy of schema matching.

We consider line failure cascading in power networks where an initial random failure of a few lines leads to consecutive other line overloads and failures before the system settles in a steady state. Such cascades are rooted in non-obvious, long-range, and higher-order couplings among the lines’ flows induced by physical constraints on the network. Failure interaction graph encodes which and to what extent other lines in a networked system are affected after each line failure and can help to predict the final state after an initial disturbance. We perform data analytics on the final lines’ steady states of cascade trajectories to infer a specific line’s state given the states of others. We use a generative model to reconstruct possible steady states, and a predictive model aims to predict the probability of each line’s failures after the initial failure as a regression problem. The generative model uses regularized pseudolikelihood estimator to infer interaction weights by solving the inverse Ising problem and deploys Glauber dynamics to generate steady states. The discriminative model uses boosted trees to efficiently learn over training and predict over test data the state of each line as a target finding an appropriate subset of other lines’ states as explanatory variables. We analyze the degree distribution of the corresponding interaction graphs to study the number of other components affected by each line failure (out-degree) or the number of lines that affect the state of a given line (in-degree). Both models show that the in-degree follows a power-law distribution. Finally, we discuss the possible application of the interaction graph for early link removal to mitigate the failure-cascading consequences.

The planned massive increase of producers and consumers such as electric vehicles, heat pumps and photovoltaic systems in distribution grids will lead to new challenges in the electrical power system. These can include grid congestions at the low voltage level but also at higher voltage levels. Control strategies can enable the efficient use of flexibilities and therefore help mitigate upcoming problems. However, they need to be evaluated carefully before their application in the energy system to avoid any unwanted effects and to choose the most fitting strategy for each application. In this publication, a Python-based modular simulation tool for developing and analysing control strategies for prosumers, which uses pandapower (Thurner et al. 2018), is presented. It is intended for sequential simulations and enables detailed operational analyses, which include evaluating the influence on grid situations, the necessary behavior of energy system components, required measurements and communications. This publication also gives an overview of control strategies, existing simulation tools, how the modular simulation tool fits in and illustrates its functionalities in an application example, which further highlights its versatility and efficiency. Time series simulations with the tool allow analyses regarding the effect of control strategies on power flow results. Moreover, the simulation tool also facilitates evaluating the behavior of energy system components (e.g. distribution substations), necessary communications and measurements as well as any faults that might occur.