Impact of different time series aggregation methods on optimal energy system design

Modelling renewable energy systems is a computationally-demanding task due to the high fluctuation of supply and demand time series. To reduce the scale of these, this paper discusses different methods for their aggregation into typical periods. Each aggregation method is applied to a different type of energy system model, making the methods fairly incomparable. To overcome this, the different aggregation methods are first extended so that they can be applied to all types of multidimensional time series and then compared by applying them to different energy system configurations and analyzing their impact on the cost optimal design. It was found that regardless of the method, time series aggregation allows for significantly reduced computational resources. Nevertheless, averaged values lead to underestimation of the real system cost in comparison to the use of representative periods from the original time series. The aggregation method itself, e.g. k means clustering, plays a minor role. More significant is the system considered: Energy systems utilizing centralized resources require fewer typical periods for a feasible system design in comparison to systems with a higher share of renewable feed-in. Furthermore, for energy systems based on seasonal storage, currently existing models integration of typical periods is not suitable

Historic drivers of onshore wind power and inevitable future trade-offs

The required acceleration of onshore wind deployment requires the consideration of both economic and social criteria. With a spatially explicit analysis of the validated European turbine stock, we show that historical siting focused on cost-effectiveness of turbines and minimization of local disamenities, resulting in substantial regional inequalities. A multi-criteria turbine allocation approach demonstrates in 180 different scenarios that strong trade-offs have to be made in the future expansion by 2050. The sites of additional onshore wind turbines can be associated with up to 43% lower costs on average, up to 42% higher regional equality, or up to 93% less affected population than at existing turbine locations. Depending on the capacity generation target, repowering decisions and spatial scale for siting, the mean costs increase by at least 18% if the affected population is minimized – even more so if regional equality is maximized. Meaningful regulations that compensate the affected regions for neglecting one of the criteria are urgently needed

Worldwide LCOEs of decentralized off-grid renewable energy systems

Recent events mean that the security of energy supplies is becoming more uncertain. One way to achieve a more reliable energy supply can be decentralised renewable off-grid energy systems, for which more and more case studies are conducted in research. This review gives a global overview of the costs, in terms of levelised cost of electricity (LCOE), for these autonomous energy systems, which range from $0.03/kWh to about$1.00/kWh worldwide in 2021. The average LCOEs for 100% renewable energy systems have decreased by 9% annually between 2016 and 2021 from $0.54/kWh to$0.29/kWh, presumably due to cost reductions in renewable energy and electricity storage. Our overview can be employed to verify findings on off-grid systems, and to assess where these systems might be deployed and how costs are evolving

Extreme events in time series aggregation: A case study for optimal residential energy supply systems

To account for volatile renewable energy supply, energy systems optimization problems require high temporal resolution. Many models use time-series clustering to find representative periods to reduce the amount of time-series input data and make the optimization problem computationally tractable. However, clustering methods remove peaks and other extreme events, which are important to achieve robust system designs. We present a general decision framework to include extreme events in a set of representative periods. We introduce a method to find extreme periods based on the slack variables of the optimization problem itself. Our method is evaluated and benchmarked with other extreme period inclusion methods from the literature for a design and operations optimization problem: a residential energy supply system. Our method ensures feasibility over the full input data of the residential energy supply system although the design optimization is performed on the reduced data set. We show that using extreme periods as part of representative periods improves the accuracy of the optimization results by 3% to more than 75% depending on system constraints compared to results with clustering only, and thus reduces system cost and enhances system reliability

A comparative co-simulation analysis to improve the sustainability of cogeneration-based district multi-energy systems using photovoltaics, power-to-heat, and heat storage

For an extensive decarbonization of district multi-energy systems, efforts are needed that go beyond today\u27s cogeneration of heat and power in district multi-energy systems. The multitude of existing technical possibilities are confronted with a large variety of existing multi-energy system configurations. The variety impedes the development of universal decarbonization pathways. In order to tackle the decarbonization challenge in existing and distinct districts, this paper calculates a wide range of urban district configurations in an extensive co-simulation based on domain specific submodels. A district multi-energy system comprising a district heating network, a power grid, and cogeneration is simulated for two locations in Germany with locally captured weather data, and for a whole year with variable parameters to configure a power-to-heat operation, building insolation/refurbishment, rooftop photovoltaic orientation, future energy demand scenarios, and district sizes with a temporal resolution of 60 seconds, in total 3840 variants. The interdependencies and synergies between the electrical low-voltage distribution grid and the district heating network are analysed in terms of efficiency and compliance with network restrictions. Thus, important sector-specific simulations of the heat and the electricity sector are combined in a holistic district multi-energy system co-simulation. The clearly most important impact on emission reduction and fuel consumption is a low heat demand, which can be achieved through thermal refurbishment of buildings. Up to \SI{46}{\percent} reduction in $CO_2$ emissions are possible using the surplus electricity from photovoltaics for power-to-heat in combination with central heat storage in the district\u27s combined heat and power plant. Domestic hot water heated by district heating network in combination with power-to-heat conversion distributed in the district reduces the load on the distribution power grid. Even though the investigated measures already improve the sustainability significantly, providing the energy needed for the production of synthetic fuels remains the crucial challenge on the further path towards net-zero

Implementing FAIR through a distributed data infrastructure

Within the research project LOD-GOESS (https://lod-geoss.gitub.io ) we are developing a distributed data architecture for sharing and improved discovery of research data in the domain of energy systems analysis. A central element is the databus (https://databus.dbpedia.org ) which acts as a central searchable metadata catalog. Research data can be registered to the databus. The metadata improves the findability of the data, direct links to the data sources accessibility. If the metadata is annotated with an ontology (e.g. the open energy ontology), semantic searches can be performed to find suitable research data. This improves interoperability and reusability of the data. Currently we are developing several demonstrators which show the benefit of open and transparent data handling for the publication of scenario data, model coupling and shared technology data bases. The infrastructure can also be used to track the provenance of data which is used in energy systems analysis. With our presentation we want to show how this infrastructure can be used to improve transparency and traceability of the analysis of future energy systems