Journal
IEEE SYSTEMS JOURNAL
Volume 16, Issue 3, Pages 3522-3530Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2021.3106397
Keywords
Reactive power; Mathematical model; Distribution networks; Load modeling; Indexes; Generators; Wind speed; Active distribution network (ADN); demand response (DR); distribution network optimal power flow (DN-OPF); network transformation; real-time pricing
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Funding
- Department of Science and Technology (DST) India
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This article introduces a demand response scheme for an active distribution network utilizing unbalanced feeder line and consumer loads, coupled with day-ahead dynamic pricing and active distribution network constraints to prevent new peak formation. The use of symmetrical domain components helps tackle the complexities of unbalanced networks and integration of distributed generation (DG). Additionally, consideration of power electronically interfaced DG reactive power component ensures constant power and voltage mode of operation while optimizing distribution network efficiency with modified optimal power flow.
Demand response works as a bridge to enable activities between end customer and distribution network operators. Previously, most of the demand response work is concerned about maintaining supply-demand balance and reducing peak load demand without acknowledging distribution network behavior. This article presents a demand response scheme for an active distribution network via the operation of an unbalanced feeder line and consumer loads. The day-ahead dynamic price is coupled with active distribution network constraints to prevent the formation of new peaks. To tackle up the complexities of unbalanced network and distributed generation (DG) integration, the merits of symmetrical domain components are utilized. Power electronically interfaced DG reactive power component is also considered for constant power and voltage mode of operation. For optimizing the distribution network, optimal power flow is modified to achieve social welfare with distribution network constraints. The performance and computational efficiency of the proposed algorithm are verified through extensive case studies.
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