Abstract
• Analysis of geothermal energy production and storage, from daily to seasonal. • Description of non-linear behavior of geothermal fields and thermal networks. • Simultaneous supply of heating and cooling demands via geothermal network. • Optimization of real-world system deployed at ETH Zurich university campus. • Definition of rationale for design and operation of geothermal fields and network. We investigate the optimal operation of multi-energy systems deploying geothermal energy storage to deal with the seasonal variability of heating and cooling demands. We do this by developing an optimization model that improves on the state-of-the-art by accounting for the nonlinearities of the physical system, and by capturing both the short- and long-term dynamics of energy conversion, storage and consumption. The algorithm aims at minimizing the CO 2 emissions of the system while satisfying the heating and cooling demands of given end-users, and it determines the optimal operation of the system, i.e. the mass flow rate and temperature of the water circulating through the network, accounting for the time evolution of the temperature of the geothermal fields. This optimization model is developed with reference to a real-world application, namely the Anergy Grid installed at ETH Zurich, in Switzerland. Here, centralized heating and cooling provision based on fossil fuels is complemented by a dynamic underground network connecting geothermal fields, acting as energy source and storage, and demand end-users requiring heating and cooling energy. The proposed optimization algorithm allows reducing the CO 2 emissions of the university campus by up to 87% with respect to the use of a conventional system based on centralized heating and cooling. This improves on the 72% emissions reduction achieved with the current operation strategies. Furthermore, the analysis of the system allows to derive design guidelines and to explain the rationale behind the operation of the system. The study highlights the importance of coupling daily and seasonal energy storage towards the achievement of low-carbon energy systems.
Highlights
The evidence of climate change clearly indicates the necessity of new routes for energy supply, entailing zero-carbon emissions around 2050 and limiting global warming at 1.5 °C [1]
We do this by developing an optimization model that improves on the state-of-the-art by accounting for the nonlinearities of the physical system, and by capturing both the short- and long-term dynamics of energy conversion, storage and consumption
This paper investigates the optimal operation of multi-energy systems (MES) deploying geothermal energy storage to cope with the seasonal variability of heating and cooling demands
Summary
The evidence of climate change clearly indicates the necessity of new routes for energy supply, entailing zero-carbon emissions around 2050 and limiting global warming at 1.5 °C [1]. The full potential of the system can only be exploited by adopting an optimization-based EMS able to (i) describe the underground network structure, (ii) capture the short- and long-term dynamics of energy pro duction, storage and consumption, (iii) account for the different tempera ture levels at which heat and cold are required during the year, (iv) model the time evolution of the geothermal fields, (v) model the scheduling of the conversion technologies installed in the demand clusters. We tackle them by formulating a mixed-integer nonlinear program (MINLP) that accurately describes the physical be havior of the system, and by reducing it to a mixed-integer linear program (MILP) that is able to capture the most relevant aspects and features a reasonable computational complexity This optimization al gorithm aims at minimizing the CO2 emissions of the multi-energy system while satisfying the heating and cooling demands of end-users.
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