An efficient solver for multi-objective onshore wind farm siting and network integration

Existing planning approaches for onshore wind farm siting and network integration often do not meet minimum cost solutions or social and environmental considerations. In this paper, we develop an approach for the multi-objective optimization of turbine locations and their network connection using a Quota Steiner tree problem. Applying a novel transformation on a known directed cut formulation, reduction techniques, and heuristics, we design an exact solver that makes large problem instances solvable and outperforms generic MIP solvers. Although our case studies in selected regions of Germany show large trade-offs between the objective criteria of cost and landscape impact, small burdens on one criterion can significantly improve the other criteria. In addition, we demonstrate that contrary to many approaches for exclusive turbine siting, network integration must be simultaneously optimized in order to avoid excessive costs or landscape impacts in the course of a wind farm project. Our novel problem formulation and the developed solver can assist planners in decision making and help optimize wind farms in large regions in the future

Integration of renewable energy sources into the future European power system using a verified dispatch model with high spatiotemporal resolution

The requirements for reducing the greenhouse gas emissions of the power sector in Europe will result in a significant increase of generation from variable renewable energy sources (VRES). The presence of such technologies may pose significant challenges to the traditional operation and structure of the existing transmission grid. In this thesis, the integration of VRES into the future European power system is investigated until the year 2050.The introduced challenges translate to challenges of modeling the power system as well. Hence, the numerical modeling as well as the existing European framework of power system operation is described in detail, including the corresponding literature. In this thesis, a novel multi-level methodology for the generation dispatch that respects transmission constraints and includes flexible demand operation is introduced to model the pan-European power system. The final development of the model is completed via the determination of the system’s boundary conditions and technical parameters with respect to grid infrastructure, generation and demand in high spatiotemporal resolution. The resulting model is verified for the year2015 against historical conditions and forms the basis for the implementation of all future European scenarios. The future power system is analyzed for the years 2030, 2040 and 2050 with respect to VRES integration and the impact of demand flexibility. It is found that the main grid congestion occurs between the North and Baltic Sea regions and Central Europe. This congestion becomes responsible for the majority of the resulting VRES curtailments, which are related to wind generation. The total amount of curtailments for the reference case is 88 TWh for Germany and 729 TWh for Europe, out of which it is concluded that the most suitable locations for exploiting the corresponding curtailment energy occurs in western Denmark and western Ireland. Regarding the impact of demand flexibility, it is found that the overall impact is relatively small (7.6% reduction in VRES curtailments) and therefore more flexibility options should be considered. Moreover, it is found that VRES integration is more sensitive to the shifting duration rather than to the available flexibility especially when seasonal flexibility is allowed, while also it is shown that shifting in space can also become very beneficial (27%reduction). However load shifting cannot constitute the only solution for their mitigation but further alternatives may be required as well. Examining all scenarios for 2050, it is found that the average amount of VRES curtailments becomes 592 TWh and that this value approximately doubles every 10 years from 2030 to 2050. Finally, it is shown that the level of the spatial resolution for the transmission grid representation plays a significant role with respect to VRES integration, where even models with 100-200 nodes can underestimate the total curtailments by half

Integration of Renewable Energy Sources into the Future European Power System Using a Verified Dispatch Model with HighSpatiotemporal Resolution

The requirements for reducing the greenhouse gas emissions of the power sector in Europewill result in a significant increase of generation from variable renewable energy sources (VRES). The presence of such technologies may pose significant challenges to the traditional operation and structure of the existing transmission grid. In this thesis, the integration of VRES into the future European power system is investigated until the year 2050. The introduced challenges translate to challenges of modeling the power system as well. Hence, the numerical modeling as well as the existing European framework of power system operation is described in detail, including the corresponding literature. In this thesis, a novel multi-level methodology for the generation dispatch that respects transmission constraints and includes flexible demand operation is introduced to model the pan-European power system. The final development of the model is completed via the determination of the system’s boundary conditions and technical parameters with respect to grid infrastructure, generation and demand in high spatio temporal resolution. The resulting model is verified for the year 2015 against historical conditions and forms the basis for the implementation of all future European scenarios. The future power system is analyzed for the years 2030, 2040 and 2050 with respect to VRES integration and the impact of demand flexibility. It is found that the main grid congestion occurs between the North and Baltic Sea regions and Central Europe. This congestion becomes responsible for the majority of the resulting VRES curtailments, which are related to wind generation. The total amount of curtailments for the reference case is 88 TWh for Germany and 729 TWh for Europe, out of which it is concluded that the most suitable locations for exploiting the corresponding curtailment energy occurs in western Denmark and western Ireland. Regarding the impact of demand flexibility, it is found that the overall impact is relatively small (7.6% reduction in VRES curtailments) and therefore more flexibility options should be considered. Moreover, it is found that VRES integration is more sensitive to the shifting duration rather than to the available flexibility especially when seasonal flexibility is allowed, while also it is shown that shifting in space can also become very beneficial (27% reduction). However load shifting cannot constitute the only solution for their mitigation but further alternatives may be required as well. Examining all scenarios for 2050, it is found that the average amount of VRES curtailments becomes 592 TWh and that this value approximatelydoubles every 10 years from 2030 to 2050. Finally, it is shown that the level of the spatial resolution for the transmission grid representation plays a significant role with respect to VRES integration, where even models with 100-200 nodes can underestimate the total curtailments by half

Integration of Large-Scale Variable Renewable Energy Sources into the Future European Power System: On the Curtailment Challenge

The future European power system is projected to rely heavily on variable renewable energy sources (VRES), primarily wind and solar generation. However, the difficulties inherent to storing the primary energy of these sources is expected to pose significant challenges in terms of their integration into the system. To account for the high variability of renewable energy sources VRES, a novel pan-European dispatch model with high spatio-temporal resolution including load shifting is introduced here, providing highly detailed information regarding renewable energy curtailments for all Europe, typically underestimated in studies of future systems. which also includes modeling of load shifting. The model consists of four separate levels with different approaches for modeling thermal generation flexibility, storage units and demand as well as with spatial resolutions and generation dispatch formulations. Applying the developed model for the future European power system follows the results of corresponding transmission expansion planning studies, which are translated into the desired high spatial resolution. The analysis of the “large scale-RES” scenario for 2050 shows considerable congestion between northern and central Europe, which constitutes the primary cause of VRES curtailments of renewables. In addition, load shifting is shown to mostly improve the integration of solar energy into the system and not wind, which constitutes the dominant energy source for this scenario. Finally, the analysis of the curtailments time series using ideal converters shows that the best locations for their exploitation can be found in western Ireland and western Denmark

Geo-referenced synthetic low-voltage distribution networks: A data-driven approach

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Classification of Building Types in Germany: A Data-Driven Modeling Approach

Details on building levels play an essential part in a number of real-world application models. Energy systems, telecommunications, disaster management, the internet-of-things, health care, and marketing are a few of the many applications that require building information. The essential variables that most of these models require are building type, house type, area of living space, and number of residents. In order to acquire some of this information, this paper introduces a methodology and generates corresponding data. The study was conducted for specific applications in energy system modeling. Nonetheless, these data can also be used in other applications. Building locations and some of their details are openly available in the form of map data from OpenStreetMap (OSM). However, data regarding building types (i.e., residential, industrial, office, single-family house, multi-family house, etc.) are only partially available in the OSM dataset. Therefore, a machine learning classification algorithm for predicting the building types on the basis of the OSM buildings’ data was introduced. Although the OSM dataset is the fundamental and most crucial one used for modeling, the machine learning algorithm’s training was performed on a dataset that was prepared by combining several features from three other datasets. The generated dataset consists of approximately 29 million buildings, of which about 19 million are residential, with 72% being single-family houses and the rest multi-family ones that include two-family houses and apartment buildings. Furthermore, the results were validated through a comparison with publicly available statistical data. The comparison of the resulting data with official statistics reveals that there is a percentage error of 3.64% for residential buildings, 13.14% for single-family houses, and −15.38% for multi-family houses classification. Nevertheless, by incorporating the building types, this dataset is able to complement existing building information in studies in which building type information is crucial