Cooperative Operation Optimization of Flexible Interconnected Distribution Networks Considering Demand Response

The integration of renewable energy into distribution networks has led to voltage violations and increased network losses. Traditional control devices, with slow response, struggle to precisely control power flow in active distribution networks (ADNs). Optimizing from both supply and demand sides, a...

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Bibliographic Details
Published in:Processes Vol. 13; no. 9; p. 2809
Main Authors: Yao, Yinzhou, Wan, Ziruo, Yang, Ting, Yang, Zeyu, Xu, Haoting, Rong, Fei
Format: Journal Article
Language:English
Published: Basel MDPI AG 02.09.2025
Subjects:
Algorithms
Alternative energy sources
Consumption
Control equipment
Costs
Deviation
Elasticity of demand
Electric potential
Electric power demand
Electricity
Energy distribution
Genetic algorithms
Load
Load fluctuation
Networks
Operating costs
Optimization
Pareto optimum
Power flow
Prices
Renewable resources
Sorting algorithms
Supply & demand
Time of use
User satisfaction
Voltage
Iran
ISSN:2227-9717, 2227-9717
Online Access:Get full text
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Summary:The integration of renewable energy into distribution networks has led to voltage violations and increased network losses. Traditional control devices, with slow response, struggle to precisely control power flow in active distribution networks (ADNs). Optimizing from both supply and demand sides, an ADN power flow optimization method is proposed for accurate and dynamic power flow regulation to address these issues. On the demand side, the peak, valley, and flat periods are divided by the fuzzy transitive closure method. Balancing user satisfaction maximization and load fluctuation minimization, time-of-use (TOU) prices are solved by the non-dominated sorting genetic algorithm II (NSGA-II). On the supply side, operating cost and voltage deviation minimization are objectives, with a proposed optimization method coordinating precise continuous regulation devices and low-cost discrete ones. After second-order cone programming and linearization, the multi-objective model is solved via the normalized normal constraint (NNC) algorithm to get a solution set, from which the optimal solution is selected using Entropy Weight and Technique for Order Preference by Similarity to an Ideal Solution (EW-TOPSIS). The results indicate that, in comparison with the proposed method, ADN not implementing demand-side TOU pricing strategies exhibits an increase in operating costs by 13.83% and a rise in voltage deviation by 4.14%. Meanwhile, ADN utilizing only traditional discrete control devices demonstrates more significant increments, with operating costs increasing by 182.40% and voltage deviation rising by 113.02%.
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr13092809