Influence of solar field size on the multi-objective techno-economic optimisation of a Carnot battery application in a parabolic trough concentrating solar power plant

This research extends our previous work by investigating the critical influence of the solar field size, quantified through the solar multiple (SM), on the multi-objective optimisation (MOO) of concentrating solar power (CSP) Carnot battery applications. The levelised costs of electricity and storage (LCOE and LCOS) and the capacity factor (CF) are our objective functions. Design variables are the thermal energy storage (TES) and heater capacities and the solar multiple (SM) for solar field size. Our main findings show that: (1) higher SMs decrease the trade-off between LCOE and LCOS; (2) For smaller SMs, Pareto-optimal TES and heater capacities have a one-to-one pairing and correlate positively. For higher SMs, one TES capacity can be paired with multiple heater capacities for Pareto optimality; (3) Regardless of the SM, higher TES capacities are paired with a single, higher heater capacity for Pareto optimality; (4) All Pareto-optimal solutions lie on the boundary of the LCOE-based graphical solution method with high accuracy, providing MOO estimates especially for lower SMs; (6) Pareto-optimal design ranges are: 0 ≤ H cap ∗ ≤ 500 MW (all SMs), 1 . 6 ≤ t TES ∗ ≤ 17 . 5 h ( SM = 2 ), 3 . 4 ≤ t TES ∗ ≤ 20 h ( SM = 2 . 6 ), 5 . 9 ≤ t TES ∗ ≤ 20 h ( SM = 3 ), 10 . 5 ≤ t TES ∗ ≤ 20 h ( SM = 4 ); (7) Utopian results are: LCOE U ( SM ∗ = 3 , t TES ∗ = 5 . 9 h , H cap ∗ = 0 MW ) = 10 . 71 ¢/kWh, LCOS U ( SM ∗ = 4 , t TES ∗ = 8 . 8 h , H cap ∗ = 119 MW ) = 20 . 57 ¢/kWh, CF U ( SM ∗ = 4 , t TES ∗ = 20 h , H cap ∗ = 500 MW ) = 92 . 8 %. • A Carnot battery application in a concentrating solar power plant is examined. • Multi-objective techno-economic optimisation is done for varying solar field size. • The influence of solar field size on Pareto-optimal designs is considered. • The Pareto-optimal TES and heater capacity relation depends on solar field size. • A graphical solution method for the full optimisation problem is presented.