  Renewable energy optimisation poses computationally-intensive challenges.
Yet, often the continuous nature of the decision space precludes the use of
many emerging, non-von-Neumann computing platforms such as quantum annealing,
which are limited to discrete problems. We propose Quantum Annealing Continuous
Optimisation (QuAnCO), a Trust Region (TR)-based algorithm, where the TR Newton
sub-problem is transformed into Quadratic Unconstrained Binary Optimisation
(QUBO), thereby allowing the use of Ising solvers such as D-Wave's quantum
annealer. This transformation to QUBO is done by 1) using a hyper-rectangular
shape for the TR, 2) discrete representation of each continuous dimension using
an interval-bounded integer, and 3) binary encoding of the resulting bounded
integers. We tackle a real-world challenge of optimising the biomass mix
selection for Nature Energy, the largest biogas producer in Europe, thus
providing evidence of feasibility and performance advantage in using QuAnCO in
green energy production, and beyond.
