  Demand Side Response (DSR) is a strategy that enables consumers to actively
participate in managing electricity demand. It aims to alleviate strain on the
grid during high demand and promote a more balanced and efficient use of
(renewable) electricity resources. We implement DSR through discount
scheduling, which involves offering discrete price incentives to consumers to
adjust their electricity consumption patterns to times when their local energy
mix consists of more renewable energy. Since we tailor the discounts to
individual customers' consumption, the Discount Scheduling Problem (DSP)
becomes a large combinatorial optimization task. Consequently, we adopt a
hybrid quantum computing approach, using D-Wave's Leap Hybrid Cloud. We
benchmark Leap against Gurobi, a classical Mixed Integer optimizer in terms of
solution quality at fixed runtime and fairness in terms of discount allocation.
Furthermore, we propose a large-scale decomposition algorithm/heuristic for the
DSP, applied with either quantum or classical computers running the
subroutines, which significantly reduces the problem size while maintaining
solution quality. Using synthetic data generated from real-world data, we
observe that the classical decomposition method obtains the best overall
\newp{solution quality for problem sizes up to 3200 consumers, however, the
hybrid quantum approach provides more evenly distributed discounts across
consumers.
