Data-oriented distributed demand response optimization with global inequality constraints based on multi-agent system
•The data-driven inverse optimization corrects the DR execution online.•The proposed strategy is realized in a distributed manner and free of initialization.•This paper provides rigorous stability proof for the proposed optimization.•D-PPDS algorithm solves the DR optimization with global inequality...
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| Published in: | International journal of electrical power & energy systems Vol. 133; p. 107025 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.12.2021
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| ISSN: | 0142-0615 |
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| Abstract | •The data-driven inverse optimization corrects the DR execution online.•The proposed strategy is realized in a distributed manner and free of initialization.•This paper provides rigorous stability proof for the proposed optimization.•D-PPDS algorithm solves the DR optimization with global inequality constrain.
This paper provides an insight into the demand response (DR) optimization in distribution markets consisting of a retailer and multiple demand response aggregators (DRA), where a retailer determines DR incentives based on power consumption profile. Conventional DR optimization with global states and constraints is intractable to be implemented in a distributed framework, which restricts the application feasibility and the potential profit of DR. To handle these limitations, we design a multi-agent architecture for distributed demand response (DDR). An online data-mining method is developed to identify the characteristics of DR. A leader–follower structure decomposes the original problem into a leader problem with global variables and aggregators of sub-problems, where discrete singular consensus is designed to broadcast the leader’s strategy to followers in real-time. The distributed perturbation primal–dual sub-gradient (D-PPDS) algorithm is proposed to solve the DDR problem with global inequality constraints in a completely distributed fashion. The proposed DDR strategy is tested by an actual case. The simulation results demonstrate that the asynchronous D-PPDS algorithm can obtain the near-optimal solution of the problem with global inequality constraints, and is robust against delay or plug-and-play. |
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| AbstractList | •The data-driven inverse optimization corrects the DR execution online.•The proposed strategy is realized in a distributed manner and free of initialization.•This paper provides rigorous stability proof for the proposed optimization.•D-PPDS algorithm solves the DR optimization with global inequality constrain.
This paper provides an insight into the demand response (DR) optimization in distribution markets consisting of a retailer and multiple demand response aggregators (DRA), where a retailer determines DR incentives based on power consumption profile. Conventional DR optimization with global states and constraints is intractable to be implemented in a distributed framework, which restricts the application feasibility and the potential profit of DR. To handle these limitations, we design a multi-agent architecture for distributed demand response (DDR). An online data-mining method is developed to identify the characteristics of DR. A leader–follower structure decomposes the original problem into a leader problem with global variables and aggregators of sub-problems, where discrete singular consensus is designed to broadcast the leader’s strategy to followers in real-time. The distributed perturbation primal–dual sub-gradient (D-PPDS) algorithm is proposed to solve the DDR problem with global inequality constraints in a completely distributed fashion. The proposed DDR strategy is tested by an actual case. The simulation results demonstrate that the asynchronous D-PPDS algorithm can obtain the near-optimal solution of the problem with global inequality constraints, and is robust against delay or plug-and-play. |
| ArticleNumber | 107025 |
| Author | He, Hongyin Ai, Qian Hao, Ran Lu, Tianguang |
| Author_xml | – sequence: 1 givenname: Ran surname: Hao fullname: Hao, Ran organization: National Power Dispatching & Control Center, State Grid Corporation of China, Beijing 100031, China – sequence: 2 givenname: Tianguang surname: Lu fullname: Lu, Tianguang email: tlu@seas.harvard.edu organization: School of Electrical Engineering, Shandong University, Jinan 250061, China – sequence: 3 givenname: Qian surname: Ai fullname: Ai, Qian organization: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China – sequence: 4 givenname: Hongyin surname: He fullname: He, Hongyin organization: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China |
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