Incentivizing Demand-Side Response Through Discount Scheduling Using Hybrid Quantum Optimization
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 sch...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Transactions on Quantum Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10542394/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832586849939357696 |
|---|---|
| author | David Bucher Jonas Nuslein Corey O'Meara Ivan Angelov Benedikt Wimmer Kumar Ghosh Giorgio Cortiana Claudia Linnhoff-Popien |
| author_facet | David Bucher Jonas Nuslein Corey O'Meara Ivan Angelov Benedikt Wimmer Kumar Ghosh Giorgio Cortiana Claudia Linnhoff-Popien |
| author_sort | David Bucher |
| collection | DOAJ |
| description | 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 solution quality for problem sizes up to 3200 consumers; however, the hybrid quantum approach provides more evenly distributed discounts across consumers. |
| format | Article |
| id | doaj-art-3b2a5ae06dc84cf3b1b20fc56bf8cd9d |
| institution | Kabale University |
| issn | 2689-1808 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-3b2a5ae06dc84cf3b1b20fc56bf8cd9d2025-01-25T00:03:33ZengIEEEIEEE Transactions on Quantum Engineering2689-18082024-01-01511510.1109/TQE.2024.340723610542394Incentivizing Demand-Side Response Through Discount Scheduling Using Hybrid Quantum OptimizationDavid Bucher0https://orcid.org/0009-0002-0764-9606Jonas Nuslein1Corey O'Meara2https://orcid.org/0000-0001-7056-7545Ivan Angelov3https://orcid.org/0009-0001-9707-1918Benedikt Wimmer4https://orcid.org/0009-0004-5481-594XKumar Ghosh5https://orcid.org/0000-0002-4628-6951Giorgio Cortiana6Claudia Linnhoff-Popien7Aqarios GmbH, Munich, GermanyMobile and Distributed Systems Chair, LMU Munich, Munich, GermanyE.ON Digital Technology GmbH, Hannover, GermanyComsysto Reply GmbH, Munich, GermanyAqarios GmbH, Munich, GermanyE.ON Digital Technology GmbH, Hannover, GermanyE.ON Digital Technology GmbH, Hannover, GermanyMobile and Distributed Systems Chair, LMU Munich, Munich, GermanyDemand-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 solution quality for problem sizes up to 3200 consumers; however, the hybrid quantum approach provides more evenly distributed discounts across consumers.https://ieeexplore.ieee.org/document/10542394/Demand-side response (DSR)problem decompositionquadratic unconstrained binary optimization (QUBO)quantum annealing (QA)quantum computing (QC)smart grids |
| spellingShingle | David Bucher Jonas Nuslein Corey O'Meara Ivan Angelov Benedikt Wimmer Kumar Ghosh Giorgio Cortiana Claudia Linnhoff-Popien Incentivizing Demand-Side Response Through Discount Scheduling Using Hybrid Quantum Optimization IEEE Transactions on Quantum Engineering Demand-side response (DSR) problem decomposition quadratic unconstrained binary optimization (QUBO) quantum annealing (QA) quantum computing (QC) smart grids |
| title | Incentivizing Demand-Side Response Through Discount Scheduling Using Hybrid Quantum Optimization |
| title_full | Incentivizing Demand-Side Response Through Discount Scheduling Using Hybrid Quantum Optimization |
| title_fullStr | Incentivizing Demand-Side Response Through Discount Scheduling Using Hybrid Quantum Optimization |
| title_full_unstemmed | Incentivizing Demand-Side Response Through Discount Scheduling Using Hybrid Quantum Optimization |
| title_short | Incentivizing Demand-Side Response Through Discount Scheduling Using Hybrid Quantum Optimization |
| title_sort | incentivizing demand side response through discount scheduling using hybrid quantum optimization |
| topic | Demand-side response (DSR) problem decomposition quadratic unconstrained binary optimization (QUBO) quantum annealing (QA) quantum computing (QC) smart grids |
| url | https://ieeexplore.ieee.org/document/10542394/ |
| work_keys_str_mv | AT davidbucher incentivizingdemandsideresponsethroughdiscountschedulingusinghybridquantumoptimization AT jonasnuslein incentivizingdemandsideresponsethroughdiscountschedulingusinghybridquantumoptimization AT coreyomeara incentivizingdemandsideresponsethroughdiscountschedulingusinghybridquantumoptimization AT ivanangelov incentivizingdemandsideresponsethroughdiscountschedulingusinghybridquantumoptimization AT benediktwimmer incentivizingdemandsideresponsethroughdiscountschedulingusinghybridquantumoptimization AT kumarghosh incentivizingdemandsideresponsethroughdiscountschedulingusinghybridquantumoptimization AT giorgiocortiana incentivizingdemandsideresponsethroughdiscountschedulingusinghybridquantumoptimization AT claudialinnhoffpopien incentivizingdemandsideresponsethroughdiscountschedulingusinghybridquantumoptimization |