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In recent years, local energy markets have become an important concept in more decentralized energy systems. Implementations in pilot projects provide first insights into different hypotheses and approaches. From a technical perspective, the requirements for the IT infrastructure of a local energy market are diverse, and a holistic view of its arch...
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Context 1
... the project duration, technical problems occured repeatedly. Besides the LoRaWAN technology, there is no backup transmission technology in the project. Therefore, no data is transmitted in the event of a malfunction. As Fig. 3 shows, about 60% of all malfunctions are one period (15 min) long. 85% of all occurring malfunctions are one hour or shorter. We track these malfunction problems back to transmission errors by the LoRa sensor. They are partly related to the distance of the sensor to the antenna. Two participants live near the transmission limit of the ...
Context 2
... to the short-term issues, the installed LoRA sensors show issues in long-term usage and stop transmitting. These malfunctions periods are longer and occur randomly. We solved this problem by restarting the sensors manually. Nevertheless, these issues contributed to longer downtimes that lasted for several days for one participant. However, as Fig. 3 shows, the proportion of these failures is less than 1%. In addition to these smaller, individual disruptions, there are major technical failures (over several weeks) during the project caused by technical issues of the LoRaWAN antenna. For example, a storm damaged the antenna, which had to be repaired. Also, the web socket failed one ...
Citations
... Similarly, the locally traded energy is cheaper, and the market participants benefit from local transactions by saving money. On the one hand, an accurate PV forecast provides means for prosumers to maximize the usage of the PV system [10]. On the other hand, a revenue can be obtained by selling the surplus as long as the PV output can be accurately predicted [11,12]. ...
In this paper, we investigate two types of photovoltaic (PV) systems (on-grid and off-grid) of different sizes and propose a reliable PV forecasting method. The novelty of our research consists in a weather data-driven feature engineering considering the operation of the PV systems in similar conditions and merging the results of deterministic and stochastic models, namely Machine Learning algorithms (Random Forest—RF, eXtreme Gradient Boost—XGB) and Deep Learning algorithms (Deep Neural Networks—DNN, Gated Recurrent Unit—GRU) into a Hybrid Meta-learning Forecasting method. It combines the estimations of the above-mentioned algorithms with relevant features to predict the PV output using a Long Short-Term Memory model. To design the PV forecast for off-grid systems, that are equally important for prosumers, and approximate the potential power of these systems, the level of load and charging state of the batteries are considered. In this context, feature engineering using the weather and PV output data, including PV characteristics, is relevant to obtaining a performant and robust PV forecast for various use cases taking into account the size and connectivity of the PV systems. On average, the Mean Absolute Error and Mean Absolute Percentage Error have halved compared to values obtained with deterministic methods and are 25% lower than the stochastic models.
... In the project, the user behavior of 37 households within a peer-to-peer energy market is evaluated. An example for a local energy community in Germany is the Landau microgrid project [17]. The project covers 11 households that buy and sell energy between each other. ...
The success of incentives for investments in sustainable residential energy technologies depends on individual households actively participating in the energy transition by investing in electrification and by becoming prosumers. This willingness is influenced by the return on investments in electrification and preferences towards environmental sustainability. Returns on investment can be supported by a preferential regulation of Citizen Energy Communities, i.e. a special form of a microgrid regulation. However, the exact effect of such regulation is debated and therefore analyzed in this study. We propose a multi-periodic community development model that determines household investment decisions over a long time horizon, with heterogeneous individual preferences in regards to sustainability and heterogeneous energy consumption profiles. We consider that investment decisions which increase individual utility might be delayed due to inertia in the decision process. Decisions are determined in our model based on individual preferences using a multi-objective evolutionary algorithm embedded in an energy system simulation. In a case study, we investigate the development of a neighborhood in Germany consisting of 30 households in regards to community costs and community emissions with and without Citizen Energy Community regulation as proposed by the European Union. We find that Citizen Energy Community regulation always reduces community costs and emissions, while heterogeneous distributions of economic and ecologic preferences within the community lead to higher gains. Furthermore, we find that decision inertia considerably slows down the transformation process. This shows that policymakers should carefully consider who to target with Citizen Energy Community regulation and that subsidies should be designed such that they counterbalance delayed private investment decisions.
Modern problems and challenges in the electricity sector are caused by the instability of renewable energy sources, the increase in their share in the structure of generating capacities, the uncertainty of the behavior of energy consumers, the decentralization of energy systems, etc. Such problems require comprehensive scientific research in the field of energy informatics. In the article, in the context of the development of energy informatics methods and tools, the concept of research into the functioning of electric power objects is proposed as a sequence of components: object–energy–information–model–measure–algorithm–program–result. A brief description of each component is provided. A number of examples of the use of components of this concept in various studies related to the functioning of electric power facilities are given. In particular, the stochastic model of electric signals of general-purpose networks and the determination of its components using the Hilbert transformation, the use of the discrete Hilbert transformation in the analysis of electric power quality characteristics, the model and methods of statistical processing of vibration noise signals in the tasks of monitoring the technical condition of wind turbines are presented.
Zusammenfassung
Der stetige Wandel von einer auf fossilen Rohstoffen basierenden Energieversorgung hin zu einer Versorgung auf Basis erneuerbarer Energien dient u. a. dem Ziel der Reduzierung des Kohlenstoffdioxidausstoßes. Dieser Wandel führt neben der Einsparung von Kohlenstoffdioxid zu einem Handlungsbedarf im Stromnetz, da durch die Substitution von steuerbaren durch dargebotsabhängige Erzeuger der Ausgleich zwischen Last und Erzeugung nicht mehr wie bisher durchgeführt werden kann. Ein möglicher Ansatz zur Lösung dieses Problems, ist zusätzlich zum Netzausbau die Ausgestaltung eines Smart Grids, welches durch Steuersysteme anhand verschiedener Netz‑, Umwelt- sowie ökonomischer Parameter erreicht werden kann.
Dieser Artikel untersucht verschiedene zentrale und dezentrale Konzepte für die Datenhaltung in Smart Grid-Steuersystemen sowie die Datenverarbeitung, wobei hier die Aspekte der lokalen und der cloudbasierten Verarbeitung betrachtet werden. Betrachtungsgegenstand bei der Datenhaltung sind relationale Datenbanken, Blockchain und NoSQL-Datenbanken. Nach einem Vergleich der verschiedenen Konzepte anhand der Parameter Datensicherheit, Konsistenz, Skalierbarkeit, Kosten, Latenz, Verfügbarkeit und Komplexität sowie einer Abstimmung mit den Anforderungen eines zuvor definierten Use-Case wird eine Beurteilung darüber abgegeben, welches Konzept am besten den Anforderungen entspricht. Der Beitrag schließt mit einem Ausblick und einer Handlungsempfehlung. Eine ausführliche Analyse für die Datenverarbeitung wird in einem zukünftigen Paper veröffentlicht.
To reduce climate change, considerable behavioral changes are required from private households, who often have a low energy literacy and are therefore unaware of the necessary behavioral change.We introduce a Design Science Research project with the aim to increase energy literacy. To this end, we contribute a theory-grounded design theory for a Smart Home Energy Application based on effective use.In comparison to previous approaches for designing Smart Home Energy Applications, the design process is user-centered.We combine semi-structured interviews with a structured survey and a literature review to derive meta requirements and deduct preliminary design principles mapping them to a prototype.The intermediate results of this study inform research and practice by providing valuable insights on how users interact with a Smart Home Energy Application. The design principles enable the design of information systems allowing for effective use and contribute to a more sustainable energy behavior of households.KeywordsEnergy literacyDesign scienceEffective use theory
