Optimal design of multi-energy grid systems Finance a by: Hikmet Eliyev Optimal design of multi-energy systems with seasonal storage

Input data

The input data is time-dependent profiles for 2016 in the Altstetten Valley, near the city of Zurich, Switzerland, where the peak demand for electricity is 0.43 MW and the peak demand for thermal energy is 2 MW. Watch profiles for all input data are given in the appendix. It is assumed that the data are constant throughout the life of the system and are known without uncertainty, that is, it is assumed that the possible realization of the uncertainty and the evolution of demand profiles are represented by historical data. Indeed, optimizing MES taking into account the uncertainty of input data (for example, the price of electricity, demand for heat and electricity, renewable potential) is an important and open topic that is addressed by several researchers using various methodologies (for example, [25], [26], [27], [28], [29], [30], [31], [32]). However, such research is not within the scope of this article, which aims to provide a MILP structure for seasonal storage, regardless of the extent to which the input represents the future. Moreover, the results of this work can be expanded and implemented in optimization problems under uncertainty. In this context, solving the optimization problem at any time, that is, every hour of the year, provides an optimal solution to the problem.

The expected weather conditions, namely air temperature and solar radiation, where T indicates the length of the time horizon. Data is available at every hour of the year, therefore, T = 8760.

II.

The expected energy prices, namely the prices of imports and, accordingly, of electricity and gas and the export price of electricity, are variables for various technologies, where M denotes the number of available technologies. Although the price of imported gas is considered fixed throughout the year, the price of imported electricity is not constant. It is noteworthy that the price of electricity has a very variable profile, which is the result of complex market regulation along with a wide portfolio of electricity suppliers (i.e. based on renewable energy sources, nuclear fuel, fossil fuels). Switzerland's spot market price for electricity for 2016 with an hourly resolution is considered.

III.Expected electricity and heat consumption, demand profiles. Data is available at any time of the year.

A set of available technologies with relevant performance and cost metrics.