Incentivizing Renewable Energy Utilization via Demand Side Response using Quantum Optimization

Integrating renewable energy sources has become crucial in today's sustainable energy transition. However, the inherent variability of renewables presents challenges for grid stability and efficiency. Demand Side Response emerges as a pivotal strategy in aligning consumption patterns with renewable energy generation. By incentivizing consumers to adjust their electricity usage in sync with renewable energy availability, DSR optimizes the utilization of clean energy resources while minimizing waste and grid strain. 

Simple time-of-use tariffs are the cornerstone for executing DSR, yet they do not account for individual consumer behavior. We address this in our use case by offering tailored discounts to each customer at every investigated timestep. Because these discounts are customized for each consumer, solving the resulting Discount Scheduling Problem (DSP) becomes a complex combinatorial optimization task. 

Consequently, we adopt a hybrid quantum computing approach using D-Wave's Leap Hybrid Cloud to investigate the applicability of Quantum Optimization. We benchmark Leap against classical Mixed-Integer optimization tools on solution quality and fairness on synthetic data generated based on real-world consumption profiles. 

Furthermore, we develop a large-scale decomposition algorithm for the DSP. Since it drastically reduces the size of combinatorial sub-problems, decomposition allows direct utilization of D-Wave's Quantum Annealing chip. Still, decomposition remains an excellent solution quality, and in conjunction with classical solvers, it provided the best-seen results.