Wind Powered Thermal Energy Systems (WTES) are the entirety of all conceivable combinations that consist of wind energy converters and thermal energy storage facilities. Although there is still a pressing demand for innovative technological solutions that allow the decarbonization of power and especially heat supply, comparative costs assessments that include the direct conversion of wind energy into heat are pending. In this paper, we conduct such an analysis for the first time. In particular, a techno-economic analysis based on the calculation of levelized costs of heat supply (LCOE) is presented. The novelty of this study is the comparison of five specific WTES concepts which either make use of electric boilers, hydro-dynamic retarders or heat pumps. The spectrum of applications considered ranges from heat supply for individual buildings to small villages and cities. The results show that LCOE below 5 c€/kWh can be reached. This indicates already competitiveness compared to conventional space heating technologies. In this means, we provide a systematic framework for future studies to evaluate the particular economic potentials of WTES in the energy market.
AMIRIS is an agent-based model (ABM) to simulate electricity markets. The focus of this bottom-up model is on the business-oriented decisions of actors in the energy system. These actors are represented as prototypical agents in the model, each with own complex decisionmaking strategies. Inter alia, the bidding decisions are based on the assessment of electricity market prices and generation forecasts (Nitsch, Deissenroth-Uhrig, et al., 2021), and diverse actors deciding on different time scales may be modelled. In particular, the agents' behavior does not only reflect marginal prices, but can also consider effects of support instruments like market premia, uncertainties and limited information, or market power (Frey et al., 2020). This allows assessing which policy or market design is best suited to an economic and effective energy system (Torralba-Díaz et al., 2020). The simulations generate results on the dispatch of power plants and flexibility options, technology-specific market values, development of system costs or CO2 emissions. One important output of the model are simulated market prices (Deissenroth et al., 2017).
The comprehensive evaluation of strategies for decarbonizing large‐scale energy systems requires insights from many different perspectives. In energy systems analysis, optimization models are widely used for this purpose. However, they are limited in incorporating all crucial aspects of such a complex system to be sustainably transformed. Hence, they differ in terms of their spatial, temporal, technological, and economic perspective and either have a narrow focus with high resolution or a broad scope with little detail. Against this background, we introduce the so‐called granularity gaps and discuss two possibilities to address them: increasing the resolutions of the established optimization models, and the different kinds of model coupling. After laying out open challenges, we propose a novel framework to design power systems in particular. Our exemplary concept exploits the capabilities of power system optimization, transmission network simulation, distribution grid planning, and agent‐based simulation. This integrated framework can serve to study the energy transition with greater comprehensibility and may be a blueprint for similar multimodel analyses.
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