Incentive-based demand response program with phase unbalance mitigation: A bilevel approach

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2025-03-06 DOI:10.1016/j.segan.2025.101671
Abhishek Tiwari , Bablesh K. Jha , Naran M. Pindoriya
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Abstract

This article proposes an adaptable incentive framework for an incentive-based demand response (IBDR) program. The framework is based on changes in demand from end-consumers using the bilevel approach to optimize the scheduling of flexible loads. The distribution system operator (DSO) acts as a leader with a multi-objective optimization problem. The objective is to maximize profit while minimizing network energy loss and peak load at the point of common coupling. The DSO’s strategy involves changing demand-based adaptive incentive offers to enhance end-consumers participation in the DR program. Furthermore, the DSO aimed to mitigate phase unbalancing as an objective to address power quality issues caused by imbalances in phase voltage and power. Aggregators are regarded as followers in the bilevel approach, aiming to maximize incentives for mitigating the discomfort caused by scheduling flexible energy resources in the IBDR program. By utilizing Karush-Kuhn–Tucker conditions, the previously mentioned bilevel problem transformed into a single-level optimization problem. This work examined two case studies to determine the effectiveness of the proposed adaptable IBDR model. The efficacy of the proposed framework was assessed on a modified IEEE 25 bus unbalanced distribution system. The evaluation reveals that adaptive IBDR confers advantages to all participants, including DSO and end-consumers.
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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