It is very important to consider the operation and scheduling of generat= ion and storage units already at design phase to determine the most conveni= ent combination (i.e. minimum objective function) of technology selection a= nd size. This is especially true when dealing with selection, sizing and un= it commitment of long-term, or seasonal, energy storage. Long-term storage = systems have recently caught much attention due to their ability to compens= ate the seasonal intermittency of renewable energy sources. However, compen= sating renewable fluctuations at the seasonal scale is particularly challen= ging: on the one hand, a few systems, such as hydro storage, hydrogen stora= ge and large thermal storage can be used to this purpose; on the other hand= , the optimization problem is complicated due to the different periodicitie= s of the involved operation cycles, i.e. from daily to yearly. This implies= long time horizons with fine resolution which, in its turn, translates int= o very large optimization problems. Furthermore, such systems often require= the integration of different energy carriers, e.g. electricity, heat and h= ydrogen. Exploiting the interaction between different energy infrastructure= , in the so-called multi-energy systems (MES), allows to improve the techni= cal, economic and environmental performance of the overall system [1].

In this framework, including the unit commitment problem already at desi= gn phase implies taking into account the expected profiles of electricity a= nd gas prices, weather conditions, and electricity and thermal demands alon= g entire years. Moreover, the technical features of conversion and storage = units should be accurately described. The resulting optimization problem ca= n be described through a mixed integer nonlinear program (MINLP), which is = often simplified in a mixed integer linear problem (MILP) due to the global= optimality guarantees and the effectiveness of the available commercial so= lvers (e.g. CPLEX, Gurobi, Mosek, etc.). In this context, integer variables= are generally implemented to describe the number of installed units for a = given unit, whereas binary variables are typically used to describe the on/= off status of a certain technology. Furthermore, decomposition approaches r= elying on meta-heuristic algorithms for unit selection and sizing have been= proposed. A comprehensive review of MINLP, MILP and decomposition approach= es for the design of MES including storage technologies has been carried ou= t by [2]. However, independently of the implemented approach, significant m= odel simplifications are required to maintain the tractability of the probl= em. Such simplifications include limiting the number of considered technolo= gies, restricting technology installation to a subset of locations, analyzi= ng entire years based on seasonal design days or weeks, or aggregating the = hours of each day into a few periods.

**References**

[1] P. Mancarella, MES (multi-energy systems): An overview o= f concepts and evaluation models, Energy. 65 (2014) 1=E2=80=9317.

[2] C. Elsido, A. Bischi, P. Silva, E. Martelli, Two-stage M= INLP algorithm for the optimal synthesis and design of networks of CHP unit= s, Energy. 121 (2017) 403=E2=80=93426.

**Contributors**:

Dr Paolo Gabrielli, ETH Z=C3=BCrich

Dr Fabrizio Lacalandra, QuanTek