Bilevel programming approach to demand response management with day-ahead tariff
This paper introduces a bilevel programming approach to electricity tariff optimization for the purpose of demand response management (DRM) in smart grids. In the multi-follower Stackelberg game model, the leader is the profit-maximizing electricity retailer, who must set a time-of-use variable ener...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8982268/ |
| _version_ | 1819076892889710592 |
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| author | Andras Kovacs |
| author_facet | Andras Kovacs |
| author_sort | Andras Kovacs |
| collection | DOAJ |
| description | This paper introduces a bilevel programming approach to electricity tariff optimization for the purpose of demand response management (DRM) in smart grids. In the multi-follower Stackelberg game model, the leader is the profit-maximizing electricity retailer, who must set a time-of-use variable energy tariff in the grid. Followers correspond to groups of prosumers (simultaneous producers and consumers of the electricity. They response to the observed tariff, schedule controllable loads and determine the charging/discharging policy of their batteries to minimize the cost of electricity and to maximize the utility at the same time. A bilevel programming formulation of the problem is defined, and its fundamental properties are proven. The primal-dual reformulation is proposed in this paper to convert the bilevel optimization problem into a single-level quadratically constrained quadratic program (QCQP), and a successive linear programming (SLP) algorithm is applied to solve it. It is demonstrated in computational experiments that the proposed approach outperforms typical earlier methods based on the Karush–Kuhn–Tucker (KKT) reformulation regarding both solution quality and computational efficiency on practically relevant problem sizes. Besides, it also offers more flexible modeling capabilities. |
| first_indexed | 2024-12-21T18:48:32Z |
| format | Article |
| id | doaj.art-ce9e456905444244a71d77d08ef82de6 |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-12-21T18:48:32Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-ce9e456905444244a71d77d08ef82de62022-12-21T18:53:49ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202019-01-01761632164310.1007/s40565-019-0569-78982268Bilevel programming approach to demand response management with day-ahead tariffAndras Kovacs0EPIC Center of Excellence in Production Informatics and Control, Institute for Computer Science and Control, Hungarian Academy of Sciences,Budapest,HungaryThis paper introduces a bilevel programming approach to electricity tariff optimization for the purpose of demand response management (DRM) in smart grids. In the multi-follower Stackelberg game model, the leader is the profit-maximizing electricity retailer, who must set a time-of-use variable energy tariff in the grid. Followers correspond to groups of prosumers (simultaneous producers and consumers of the electricity. They response to the observed tariff, schedule controllable loads and determine the charging/discharging policy of their batteries to minimize the cost of electricity and to maximize the utility at the same time. A bilevel programming formulation of the problem is defined, and its fundamental properties are proven. The primal-dual reformulation is proposed in this paper to convert the bilevel optimization problem into a single-level quadratically constrained quadratic program (QCQP), and a successive linear programming (SLP) algorithm is applied to solve it. It is demonstrated in computational experiments that the proposed approach outperforms typical earlier methods based on the Karush–Kuhn–Tucker (KKT) reformulation regarding both solution quality and computational efficiency on practically relevant problem sizes. Besides, it also offers more flexible modeling capabilities.https://ieeexplore.ieee.org/document/8982268/Demand response management (DRM)Smart gridGame theoryOptimizationBilevel programming |
| spellingShingle | Andras Kovacs Bilevel programming approach to demand response management with day-ahead tariff Journal of Modern Power Systems and Clean Energy Demand response management (DRM) Smart grid Game theory Optimization Bilevel programming |
| title | Bilevel programming approach to demand response management with day-ahead tariff |
| title_full | Bilevel programming approach to demand response management with day-ahead tariff |
| title_fullStr | Bilevel programming approach to demand response management with day-ahead tariff |
| title_full_unstemmed | Bilevel programming approach to demand response management with day-ahead tariff |
| title_short | Bilevel programming approach to demand response management with day-ahead tariff |
| title_sort | bilevel programming approach to demand response management with day ahead tariff |
| topic | Demand response management (DRM) Smart grid Game theory Optimization Bilevel programming |
| url | https://ieeexplore.ieee.org/document/8982268/ |
| work_keys_str_mv | AT andraskovacs bilevelprogrammingapproachtodemandresponsemanagementwithdayaheadtariff |