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

Zhe Luo, SeungHo Hong and YueMin Ding

Applied Energy, 2019, vol. 239, issue C, 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.

Keywords: Virtual power plant; Data mining; Incentive-based demand response; Incentive rate strategy (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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DOI: 10.1016/j.apenergy.2019.01.142

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