Multiple time grids in operational optimisation of energy systems with short- and long-term thermal energy storage. (15th August 2017)

• Record Type: Journal Article
• Authors: Renaldi, Renaldi; Friedrich, Daniel
• Is Part Of: Energy. Volume 133(2017)
• Page Start: 784
• Page End: 795
• Publication Date: 2017-08-15
• Subjects: Thermal energy storage -- Seasonal storage -- Optimisation -- District heating -- Mixed integer linear programming; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2017.05.120
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: As a vital part of future low carbon energy systems, storage technologies need to be included in the overall optimisation of energy systems. However, this comes with a price of increasing complexity and computational cost. The increase in complexity can be limited by using simplified time series formulations in the optimisation process, e.g. typical days or multiple time grids. This in turn will affect the computational cost and quality of the optimisation results. The trade-off between these two aspects has to be quantified in order to appropriately use the simplification method. This paper investigates the implementation of the multiple time grids approach in the optimisation of a solar district heating system with short- and long-term thermal energy storage. The multiple time grids can improve the optimisation computational time by over an order of magnitude. Nevertheless, this is not a general rule since it is shown that there is a possibility for the computational time to increase with time step size. Furthermore, the benefits of multiple time grids become more evident in optimisation with a longer time horizon, reaching almost two order of magnitude improvement in computational time for the case with 6 years time horizon and 5% MIP gap. Highlights: Implementation of the multiple time grids method in energy system optimisation. Optimisation of a solar district heating with thermal energy storage. Improved computational time and good level of accuracy. Time step size should be carefully determined to avoid computational time increase. Significant reduction in computational time in optimisation with multi-year time horizon.