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With the large-scale expansion of decentralized power generation from renewable energy resources (RES) and reduction of fossil fuel-based power generation, this leads to fundamental structural changes in the power supply system. The transition is characterized by a shift from central to decentral, from directional to bi-directional. As the share of renewable power systems in the electricity grid increases, the inherent uncertainty of renewable energies poses challenges to the grid system’s stability.
To achieve increased efficiency, decarbonisation, decentralization, and digitisation in energy sector, a flexible and resilient energy system is required. Electrical energy data and non-electrical energy data are generated from various sources, including the supervisory control and data acquisition system (SCADA), geographic information system (GIS), and weather information system. Based on these data, different spatial and temporal models of energy systems and their databases are created and connected. This thesis contributes to the analysis of regional power performance. To this end, an efficient simplified grid-oriented network cluster is presented to account for the regional allocation of RES power systems. The model clustering includes the grid topology, temporal and spatial resolution, and the structure of electrical and non-electrical data. This approach contributes to methodological aspects of energy system modelling and analysis.
In addition, based on the proposed cluster model, this thesis presents the physical profile-based and data-driven methods to estimate the regional large-scale photovoltaic (PV) and wind power generations. In order to adapt to the spatial-temporal heterogeneity of the regional renewable power generations and to improve the local power utilization rate, this thesis conducts empirical analyses of the regional energy storage and combined PV and wind power systems using cable pooling with shared grid connection.
In summary, this thesis presents a flexible modelling approach that takes into account the regional allocation of renewable power systems and enables a more accurate estimation of regional power generation. The proposed model and analytical methods can help the efficiency and resiliency of the energy system and support the transition towards renewable energies.
Zur Einhaltung der Dekarbonisierungsziele und Bewältigung der Herausforderungen bei der ganzheitlichen Transformation des Energiesystems im Kontext der politisch geforderten Energiewende, ist zunehmend der Begriff Sektorenkopplung zu verzeichnen. Eine wesentliche Rolle nimmt dabei die auf den Begriff Power-to-X (P2X) zusammengefasste Elektrifizierung der Sektoren Gas, Wärme, Kälte und Mobilität ein. P2X-Energiewandler und die damit einhergehende Nutzung Erneuerbarer Energien (EE), z.B. aus Photovoltaik (PV) und Wind, sind aufgrund der volatilen Stromerzeugung der EE-Anlagen als dargebotsabhängige Energiewandlung zu verstehen. Aus diesem Grund werden P2X-Energiewandler mit sektoralen Energiespeichern und flexiblen bedarfsgerechten Energiewandlern hin zu sektorenübergreifenden Energiespeichern aggregiert. Diese sind nicht nur in der Lage den Herausforderungen eines zunehmend aus EE bestehenden Energieversorgungssystems (EVS) entgegenzuwirken, sondern einen Beitrag zur sektorenübergreifenden Energiebedarfsdeckung und Dekarbonisierung zu leisten.
In diesem Zusammenhang ist in den vergangenen Jahren auf dem Campus der Brandenburgischen Technischen Universität Cottbus-Senftenberg (BTU) eine Forschungs- und Demonstrationsanlage entstanden, welche sektorale und sektorenübergreifende Energiespeicher topologisch lokal aggregiert und hinsichtlich der Netzbetriebsführung verknüpft. Dieser dezentrale Anlagenverbund bietet eine herausragende Möglichkeit, einen sektorenübergreifenden Betrieb der dezentralen EVS auf lokaler Ebene zu untersuchen.
Als Forschungsgegenstand und Teilsystem der Forschungs- und Demonstrationsanlage gilt im Folgenden der sektorenübergreifende Energiespeicher bestehend aus den Technologien der Kraft-Wärme-Kopplung, Power-to-Heat und einem thermischen Energiespeicher. Dieser Anlagenverbund wird innerhalb der Systemgrenzen eines Microgrid unter größtmöglicher Nutzung erneuerbarer Energien am Beispiel der PV untersucht. Ein Simulationsmodell erweitert diesen Forschungsgegenstand und zielt auf modellgestützte Untersuchungen zur Planung und zum Betrieb des sektorenübergreifenden Energiespeichers, insbesondere zur stromnetzdienlichen Systemintegration. Die Ergebnisse der Untersuchungen werden im Folgenden dargestellt und gelten als Beitrag zur Systemintegration von sektorenübergreifenden Energiespeichern in die lokale dezentrale Energieversorgung.
Energy demand of continents, countries, communities and individuals will continue to increase in the phase of increasing population and improvement in the living standards of people. The attempt to meet this ever increasing demand and at the same time protect the environment has resulted in the fast growth of power generation from renewable sources of energy especially from wind through wind power plants and solar through photovoltaic power plants. This growth has been facilitated by various support schemes such as feed-in-tariff scheme, feed-in-premium and quota scheme. Further growth is expected in the future. This is because of the existing support schemes and the expectation of the emergence of improved technologies for harvesting renewable energy.
This development of power generation from renewable sources of energy although positive lead to some distinctive negative effects on the existing electrical network to which they are connected. These negative effects are known and well documented. The fluctuating nature of wind and solar radiation at any given location over a given period of observation is seen to translate into the power they feed into the power network. This fluctuating infeed requires more active management of the network by system operators so as to ensure continuous reliable power generation and delivery. Sometimes the management process lead to non-utilization of power produced by the renewables sources. Secondly, expansion and reinforcement of some existing networks are needed in other to accommodate renewable power generators. These come at a cost. Many studies and researches have been dedicated to finding solutions to these issues.
This work agrees with the use of storage systems as means of solving these issues but the question that remains unanswered is what the optimal way is. There is also a further push given to the view of installing renewable energy plants together with storage systems as a unit in this work. The main task presented in this work, however, is a concept of sizing renewable energy plant and storage systems as a unit. The resulting renewable energy plant-storage unit has the objective of supporting the electrical network to which it will be connected. Firstly the support should be by reducing the fluctuating effect from renewable production. Secondly by helping improve the load hosting capacity of the electrical network. This will be by supplying the part of the load demand leading to the reduction of the overall power drawn by connected loads from the electrical power network.
Historic data of renewable resource and also the load demand at the point or bus of connection are the drivers of this concept. With the earlier mentioned objectives and random or stochastic nature of data involved, particle swarm optimization method is employed in implementing the concept of sizing to arrive at an optimal solution of required sizes of the renewable energy plant-storage system.
The concept of sizing is based on proposing an ideal load demand that can be supplied by a utility under normal operating condition at all time. It follows that any extra demand should be supplied by the optimally sized renewable energy plant-storage unit. In this work sizing results of three scenarios presented. A single node network with three different types of the load was used in testing the effect of optimally sized renewable energy plant-storage system on an electrical network. The outcome of this test showed that the optimally sized renewable energy storage-system improved the ability of the test electrical network to support additional load hence load hosting capacity of test network was improved. The process required modelling and simulation all of which were carried out using MATLAB Simulink software.