• Energy Research

Analysis and simulation of wind turbine availability and cost of energy Presented at the First Swiss Wind Energy R&D Forum

Der steigende Anteil von Erneuerbaren Energieträgern im Energieversorgungssystem führt zu zunehmenden Herausforderungen bei deren Integration in das elektrische Netz. Zentrales Anliegen dieser Untersuchung ist es, am Beispiel eines Windparks (Fallstudie 1) bzw. einer PV-Anlage im Privathaushalt (Fallstudie 2) mögliche Energiespeicher-Dienstleistungen, die zur verbesserten Netz- und Marktintegration der erneuerbaren Energieträger führen, darzustellen und zu bewerten. Fraunhofer UMSICHT hat in den letzten Jahren das Modell GOMES® (Generic Optimization Model for Energy Storage) entwickelt, welches zur Einsatzoptimierung von Energiespeichern (stationäre und mobile elektrische Speicher, thermische Speicher sowie intelligente Haushaltslasten als virtuelle Stromspeicher) dient. Zielfunktion ist die Maximierung der Erlöse beziehungsweise im Bereich der privaten Endkunden die Minimierung der Strombezugskosten. Mit Hilfe von GOMES® wurden die beiden o.g. Szenarien modelliert und in viertelstündlich aufgelösten Jahresrechnungen analysiert. In Fallstudie 1 wird der Windpark in Verbindung mit einem großtechnischen, stationären Stromspeicher untersucht. In Fallstudie 2 werden verschiedene Energiespeicherformen (Stromspeicher, thermischer Speicher bzw. verschiebbare Haushaltslasten) in Verbindung mit der PV-Anlage eingesetzt. Fallstudie 1: Für viele Windenergieanlagen ist bei den derzeitigen Spotmarktpreisen eine Teilnahme an der Direktvermarktung im Vergleich zur EEG-Vergütung noch nicht rentabel - selbst wenn sie durch einen Stromspeicher mit idealisierten technischen und wirtschaftlichen Eigenschaften unterstützt werden. Prinzipiell geeignete Speichertechnologien für die Kombination mit Windparks sind Druckluftspeicherkraftwerke bzw. Großbatterien (Natrium-Schwefel- bzw. Redox-Flow-Batterie). Bei der Dimensionierung des Stromspeichers muss beachtet werden, dass bei Technologien mit eingeschränktem Teillastbereich, wie z.B. Druckluftspeicherkraftwerken, die Einspeicherleistung genau auf die installierte Leistung des Windparks abgestimmt werden muss. Dagegen sollte die Ausspeicherleistung zur Maximierung des jährlichen Betriebserlöses möglichst groß gewählt werden. Als Richtlinie für die Wahl des Verhältnisses zwischen installierter Speicherkapazität und Speicherleistung gilt, dass bei Stromspeichern, deren Investitionskosten von der Speicherkapazität dominiert werden (Batterien), für die hier untersuchte Speicheranwendung eine Volllaststundenzahl von 1-4 Stunden angestrebt werden sollte. Bei Druckluftspeicherkraftwerken ist eine Volllaststundenzahl von 6-8 Stunden sinnvoll. Fallstudie 2: In der verbrauchernahen Fallstudie, bei welcher die durch Energiespeicher unterstützte Nutzung der Eigenverbrauchsregelung für PV-Anlagen in Einfamilienhäusern im Vordergrund steht, hat sich gezeigt, dass - vor Berücksichtigung der Investitionskosten - der Batteriespeicher, welcher sowohl PV-Strom zwischenspeichert als auch den Bezugszeitpunkt von Strom aus dem Netz optimiert den größten, betriebswirtschaftlich Nutzen einbringt. Die alleinige Zwischenspeicherung von PV-Strom ist betriebswirtschaftlich dagegen am uninteressantesten. Aus betriebswirtschaftlicher Sicht die zweitbeste Alternative ist die Erweiterung der Wärmepumpe um einen thermischen Speicher. Die zeitliche Verschiebung von Haushaltslasten bringt einen vergleichsweise kleinen Nutzen. Aus Sicht des Netzes, welches durch die Erhöhung des PV-Eigenverbrauchanteils entlastet werden soll, sind die meisten untersuchten Maßnahmen wenig effektiv. Ein wichtiger Grund hierfür ist die Interaktion zeitlich variabler Endkundenstrompreise mit der PV-Eigenverbrauchsregelung. Weisen diese eine Tarifstufe auf, bei der der Strombezug aus dem Netz wirtschaftlich attraktiver ist als der PV-Eigenverbrauch, wird eine negative Wechselwirkung erzeugt. Lediglich die alleinige Zwischenspeicherung von PV-Strom im Batteriespeicher erbrachte eine nennenswerte Steigerung des PV-Eigenverbrauchanteils.

This research project aimed at establishing an interconnection between multiple battery storage units as well as defining and testing operation strategies for battery energy systems in different use cases. The project took full benefit of the existing battery storage infrastructure located in Helsinki (Suvilahti), Suomenniemi (storage in an LVDC microgrid) and Lappeenranta (LUT Green Campus stationary and mobile storages). Each of these storages was in active operation already before the project started. However, the storages are operated independently of each other, and their operation is not yet fully optimized for the needs of electricity markets and the power system. In the research project, a storage system has a stakeholder-specific multi-objective role, which means that the storage system has to respond to several service requests simultaneously. This may mean, for instance, operating at the same time in the frequency control in the electricity markets, trading electricity in the day-ahead, intraday, and ancillary markets, simultaneously offering various services to local network operations and several other stakeholders. This kind of multi-objective operation requires full understanding of interactions of different markets and stakeholders and risks related to the conflicting objectives of the stakeholders. One of the key outcomes of the project work was the establishment of a connection to the Suvilahti BESS unit through an IEC 104 protocol. Another outcome was constructing a simulation tool in Matlab that enables testing of numerous scenarios of a single BESS unit operation with different operating parameters and various operating strategies. The major part of the analyses was done based on the results of the simulation tool. There are two further main outcomes of the project. The first one is that it is technically possible to remotely control multiple BESS units against multiple tasks according to a pre-defined logic. The second outcome of the project is that a BESS can and should be operated against multiple tasks simultaneously. During such an operation, there may or may not emerge a conflict of objectives between the involved stakeholders. The nature of the conflicts has been investigated and the methods to mitigate the conflict have been analysed. The aggregation of BESS resources is one way to mitigate the conflict of objectives. Publishers version

Battery systems are extensively used in smart energy systems in many different applications, such as Frequency Containment Reserve or Self-Consumption Increase. The behavior of a battery in a particular operation scenario is usually summarized using different key performance indicators (KPIs). Some of these indicators such as efficiency indicate how much of the total electric power supplied to the battery is actually used. Other indicators, such as the number of charging-discharging cycles or the number of charging-discharging swaps, are of relevance for deriving the aging and degradation of a battery system. Obtaining these indicators is very time-demanding: either a set of lab experiments is run, or the battery system is simulated using a battery simulation model. This work instead proposes a machine learning (ML) estimation of battery performance indicators derived from time series input data. For this purpose, a random forest regressor has been trained using the real data of electricity grid frequency evolution, household power demand, and photovoltaic power generation. The results obtained in the research show that the required KPIs can be estimated rapidly with an average relative error of less than 10%. The article demonstrates that the machine learning approach is a suitable alternative to obtain a very fast rough approximation of the expected behavior of a battery system and can be scaled and adapted well for estimation queries of entire fleets of battery systems.

This work proposes a methodology to evaluate the performance of operating wind farms via the use of Supervisory Control and Data Acquisition System (SCADA) and modeled data. The potential annual energy is calculated per individual turbine considering underperforming/loss events to have their power output in accordance with a representative derived operational power curve. Losses/underperformance events are calculated and categorized into several groups aiming at identifying and quantify their causes. The methodology requires both anemometry data from SCADA system as well as modeled data. The discrepancy of the data representing the valid points of the power curve is taken into consideration as well when assessing the performance, i.e. wind speed vs power output of events that are not loss/underperformance. Production loss and relative standard deviation of power output of what is defined as “valid sample” in this work (per each turbine) are the main results obtained in this work. Finally, a number of optimization measures are suggested in order to enhance the performance, which can lead to a boost in the financial output of a wind farm. Aiming at judging the reliability of the proposed methodology, a case study is conducted and evaluated. The investigated case study shows that the methodology is capable of determining potential energy and associated losses/underperformance events. Several questions were raised during the assessment and are discussed in this report, recommendation for optimization measures are presented at the end of the study. Also, a discussion on the limitations and uncertainties associated to the presented methodology and the case study.

Decision-making under uncertainty has been studied for a long time by the operations management research community. In the past, uncertainty models were often derived based on domain knowledge. However, the availability of vast amounts of data in the recent years has shifted interests towards data-driven approaches for uncertainty quantification. More specifically, statistical models are employed within this framework for characterizing the uncertain components of a stochastic optimization problem based on historical data.In this dissertation, we focus on applications of data-driven decision-making under uncertainty in the healthcare and energy management sectors. The first part of our work provides a mathematical framework for efficient call assignment under Direct Load Control (DLC) contracts (i.e. an incentive-based demand-response program that is widely used by utility firms for balancing the supply and demand of electricity during peak times). Specifically, we employ a model for forecasting energy consumption and develop a large-scale integer stochastic dynamic optimization problem. We then propose a novel hierarchical approximation scheme for efficient execution of the contracts. We evaluate the quality of our proposed approach using real-world data obtained from California Independent System Operator (CAISO), which is the umbrella organization of utility firms in California. A large utility firm in California has implemented our model and informed us that they have experienced a 4\% additional r duction in their cost.Following a similar predict-then-optimize methodological framework, the second part of this dissertation studies data-driven healthcare intervention planning. Specifically, we develop a continuous-time latent-space Markovian model for describing disease progression based on discrete-time irregularly-spaced observations. Our model is capable of incorporating the effect of interventions on progression of disease. We discuss the computational challenges of parameter estimation for this model and ...

This paper presents a control scheme in order to minimize energy cost with annually energy positive constraint as following daily demand fluctuations. Generally, the more energy positive by biomass fuelled combined heat and power (CHP), the more expensive in energy cost. The control scheme consists of Lagrangian relaxation based two optimization functions: (A) the annual scheduling function which does forecast correction and rescheduling monthly, and (B) the daily scheduling function which does the gap evaluation between the forecasted and the actual load every thirty minutes and does forecast correction and rescheduling if needed. By the two functions rescheduling, the control scheme create robust operation schedule. The proposed scheme is verified by numerical simulations for HIKARI building in Lyon, France. The simulation shows that the scheme can achieve cost minimization and Positive Energy Buildings (PEB) by rescheduling under conditions of demand fluctuations.

Fossil and other traditional energy have been bringing serious challenges on energy consumption. Making full use of clean and renewable energy becomes the inevitable requirement of social development. With the development of smart grids, micro-networks and the rapidly developing Internet technology, researchers put forward the concept of energy internet synthesizing the energy network and information network, which aims to improve the energy efficiency, realize the mutual complementary of multiple energies, promote the change of energy usage, and provide new paradigm for sustainable energy development. First of all, this paper summarizes the state-of-the-art researches on energy internet in different countries. Then, in the view of optimal operation of energy internet, optimization operation models, communication and data for the optimal operation of energy internet, and the fault propagation mechanism in energy internet optimization operation are investigated. furthermore, this paper points out the significant research direction and the key techniques for the optimization operation of the energy internet, in order to provide a potential help for China's Energy Internet research.

What are the legal implications of operational efficiency, and how can the inefficient artefacts of shipping contracts be phased out while driving uptake of specific clauses that encourage the transparency, cooperation, and benefit sharing that allow for more efficient operation of ships? This paper explores this question as part of a series that examines the undervalued opportunity presented by operational efficiencies to reduce shipping emissions in the short term and pave the way for long-term decarbonisation solutions. The learnings presented here have emerged from a series of meetings and workshops gathering perspectives from experts across the maritime value chain—shipowners, operators, charterers, ports, and NGOs—as part of the Short Term Actions Taskforce. Other papers in the series provide an overview of the issue, and dive deeper into the identified solutions and enablers: the role of data, and the role of pilots.

This paper presents an operation strategy of energy hubs in the presence of electrical, heating, and cooling demand as well as renewable power generation uncertainties. The proposed strategy can be used for optimal decision makingof energy providers companies, as well as, other private participants of hub operators. The presence of electrical energy storage devise in the assumed energy hub can handle the fluctuations in the operating points raised by suchuncertainties. In order to modeling of hourly demands and renewable power generation uncertainties a scenario generation model is adopted in this paper. The considered energy hub in this study follows a centralized framework andthe energy hub operator is responsible for optimal operation of the hub assets based on the day-ahead scheduling. The simulation result illustrates that in the presence of electrical energy storage devices the optimal operation of hub assets can be attained.