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Abstract
The Ecovat is a seasonal thermal storage solution consisting of a large underground water tank divided into a number of virtual segments that can be individually charged and discharged. The goal of the Ecovat is to supply heat demand to a neighborhood throughout the entire year. In this work, we extend an integer linear programming model to describe the charging and discharging of such an Ecovat buffer by adding a long-term (yearly) planning step to the model. We compare the results from the model using this extension to previously obtained results and show significant improvements when looking at the combination of costs and the energy content of the buffer at the end of the optimization. Furthermore, we show that the model is very robust against prediction errors. For this, we compare two different cases: one case in which we assume perfect predictions are available and one case in which we assume no predictions are available. The largest observed difference in costs between these two cases is less than 2%.
Highlights
In an effort to reduce green house gas emissions, as well as the dependency on the finite supply of fossil fuels, we see an increasing trend towards the use of renewable alternatives
By solving the optimization problem (2) using the method described in Section 3, we obtain a target for the useful energy content of the Ecovat buffer at the end of every day within the optimization horizon
We wish to investigate whether the choice we made for the no predictions available (NP) case, namely that we distribute the amount of intervals in which storing energy is required evenly throughout the year, leads to target values that are close to those obtained from the perfect predictions (PP) case
Summary
In an effort to reduce green house gas emissions, as well as the dependency on the finite supply of fossil fuels, we see an increasing trend towards the use of renewable alternatives. The buffer was not sufficiently pre-charged, meaning that the energy content of the buffer stayed very low for a large part of the simulated year leading to higher costs for meeting the heat demand. We propose adding such a long-term planning step for a complete year before solving the ILP model in a rolling horizon approach In this long-term planning step, first, daily targets for the energy content of the buffer are generated based on the predicted energy prices and heat demand over the optimization period. We are interested in simulations over a complete year to determine whether the Ecovat system is able to supply the heat demand of a neighborhood of houses during the entire year This is useful for example when determining the size of the buffer needed for a given neighborhood.
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