Using representative time slices for optimization of thermal energy storage systems in low-temperature district heating systems

4 th generation district heating and cooling networks (shortly THERNETs) are often coined as a crucial technology to enable the transition towards low-carbon smart energy systems. Most importantly, they open perspectives for integration of low-grade residual heat from industry, renewable energy sources (such as geothermal heat and cold and solar thermal collectors), more efficient energy conversion units (such as collective heat pumps), while thermal energy storage (TES) systems increase system flexibility. In order to optimize design and control of such complex systems, a toolbox modesto (Multi-objective district energy systems toolbox for optimization) is under development. However, the representation of seasonal heat and cold storage systems on an annual basis requires large computational power. In an attempt to decrease computational cost, a technique with representative time slices (inspired by and combining aspects from optimization studies of electrical energy systems, unit commitment problems, thermal systems with short term energy storage and smaller scale industrial thermal systems with longer term energy storage) is developed and tested. The aim of this study is to investigate the applicability of such representative time periods to optimize seasonal TES systems in THERNETs. To this end a full year optimization is compared to one with representative time periods for a realistic case study that uses demand profiles from the city of Genk (Belgium) and energy system parameters from Marstal (Denmark). This comparative study shows that modelling with representative periods is sufficient to mimic the behaviour of a full year optimization. However, when curtailment of solar heat injection occurs, not all representations yield the same results. It was found that for the studied case, a selection of 12 representative weeks performs best, while all reduced optimizations result in a substantial reduction (speed-up of on average x4.8 to x7.7) of the calculation time

Using representative time slices for optimization of thermal energy storage systems in low-temperature district heating systems

\u3cp\u3e 4 \u3csup\u3eth\u3c/sup\u3e generation district heating and cooling networks (shortly THERNETs) are often coined as a crucial technology to enable the transition towards low-carbon smart energy systems. Most importantly, they open perspectives for integration of low-grade residual heat from industry, renewable energy sources (such as geothermal heat and cold and solar thermal collectors), more efficient energy conversion units (such as collective heat pumps), while thermal energy storage (TES) systems increase system flexibility. In order to optimize design and control of such complex systems, a toolbox modesto (Multi-objective district energy systems toolbox for optimization) is under development. However, the representation of seasonal heat and cold storage systems on an annual basis requires large computational power. In an attempt to decrease computational cost, a technique with representative time slices (inspired by and combining aspects from optimization studies of electrical energy systems, unit commitment problems, thermal systems with short term energy storage and smaller scale industrial thermal systems with longer term energy storage) is developed and tested. The aim of this study is to investigate the applicability of such representative time periods to optimize seasonal TES systems in THERNETs. To this end a full year optimization is compared to one with representative time periods for a realistic case study that uses demand profiles from the city of Genk (Belgium) and energy system parameters from Marstal (Denmark). This comparative study shows that modelling with representative periods is sufficient to mimic the behaviour of a full year optimization. However, when curtailment of solar heat injection occurs, not all representations yield the same results. It was found that for the studied case, a selection of 12 representative weeks performs best, while all reduced optimizations result in a substantial reduction (speed-up of on average x4.8 to x7.7) of the calculation time. \u3c/p\u3