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A data mining-driven incentive-based demand response scheme for a virtual power plant

Title
A data mining-driven incentive-based demand response scheme for a virtual power plant
Author
홍승호
Keywords
Virtual power plant; Data mining; Incentive-based demand response; Incentive rate strategy
Issue Date
2019-04
Publisher
ELSEVIER SCI LTD
Citation
APPLIED ENERGY, v. 239, Page. 549-559
Abstract
Given the increasing prevalence of smart grids, the introduction of demand-side participation and distributed energy resources (DERs) has great potential for eliminating peak loads, if incorporated within a single framework such as a virtual power plant (VPP). In this paper, we develop a data mining-driven incentive-based demand response (DM-IDR) scheme to model electricity trading between a VPP and its participants, which induces load curtailment of consumers by offering them incentives and also makes maximum utilization of DERs. As different consumers exhibit different attitudes toward incentives, it is both essential and practical to provide flexible incentive rate strategies (IRSs) for consumers, thus respecting their unique requirements. To this end, our DM-IDR scheme first employs data mining techniques (e.g., clustering and classification) to divide consumers into different categories by their bid-offers. Next, from the perspective of VPP, the proposed scheme is formulated as an optimization problem to minimize VPP operation costs as well as guarantee consumer's interests. The experimental results demonstrate that through offering different IRSs to categorized consumers, the DM-IDR scheme induces more load reductions; this mitigates critical load, further decreases VPP operation costs and improves consumer profits.
URI
https://www.sciencedirect.com/science/article/pii/S0306261919301850https://repository.hanyang.ac.kr/handle/20.500.11754/121822
ISSN
0306-2619; 1872-9118
DOI
10.1016/j.apenergy.2019.01.142
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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