Results indicate that low-voltage distribution network parameters have a strong impact on control mechanism effectiveness, independent of the selected objective function, and notably favor the active power curtailment.
Abstract
This paper proposes a co-simulation model for the operation of an active distribution network scheduling, incorporating three objective functions: voltage deviation minimization, losses minimization and the voltage unbalance factor minimization. The developed model accounts for the available operational control mechanisms in an active low-voltage distribution network, including conventional transformer tap changer and the prosumers’ inverter capabilities with emphasis on active power curtailment and reactive power control. The proposed model is formulated within a co-simulation environment, which enables the application of computational intelligence methods to obtain near-optimal solutions. In this paper, particle swarm optimization is used as a computational intelligence method. The evaluation of the proposed model is assessed on a real-world low-voltage distribution network. To analyse the impact of the various control mechanisms across all three objective functions, 45 case studies are carried out. The impact of each control mechanism on achieving a particular objective function is analyzed, assuming the remaining two variables defining objective functions are treated as a state. The results indicate that low-voltage distribution network parameters have a strong impact on control mechanism effectiveness, independent of the selected objective function, and notably favor the active power curtailment. Under the proposed control, energy losses decreased from 164.98 kWh to 6.09–14.33 kWh (92.70-96.31%) for different objective functions. Voltage limits are maintained within a 0.92–1.09 p.u. band (compared to 0.85–1.19 p.u. in the reference case), and the VUF is reduced from 2.41% to 0.46%–1.52%. Considering existing regulations and technical limits on reactive power, reactive power control is insufficient to handle significant deviations. Finally, voltage deviation minimization and voltage unbalance factor minimization each have notable limitations in their operation scheduling performance.
The distribution network faces significant operational challenges in maintaining acceptable voltage levels under dynamic load conditions. Rapid load fluctuations often degrade the performance of localized voltage control schemes, leading to voltage instability and increased power losses. To address this issue, this paper proposes a centralized, optimization-based Volt-VAR control strategy that integrates Particle Swarm Optimization (PSO) with Conservation Voltage Reduction (CVR) for enhanced voltage regulation and energy efficiency in distribution systems. The proposed approach determines optimal tap positions of Automatic Voltage Regulators and On-Load Tap Changers using a system-wide optimization framework implemented in OpenDSS. A voltage-dependent load model is employed to realistically capture the impact of voltage variations on active and reactive power consumption. Simulation studies conducted on the IEEE-123 bus distribution system demonstrate that the PSO-based centralized control improves voltage regulation. It also reduces active power losses compared with conventional localized control. Additionally, peak load reduction is observed as a secondary benefit of CVR implementation. The proposed PSO-based control reduces active power losses from 4.72% to 4.59% and improves the minimum bus voltage from 0.8714 p.u. to 0.9459 p.u., ensuring voltage profiles remain within acceptable limits while enabling effective load reduction under CVR operation. The proposed framework provides a scalable and practical solution for advanced distribution management systems to improve voltage quality and energy efficiency.
Chandra Prakash Prajapati, S. Chanana· Electrica· 0 citations
The increasing penetration of distributed energy resources has introduced substantial operational uncertainties into active distribution networks, posing significant challenges to stable electromagnetic energy transmission and intelligent power dispatch. This study proposes a fuzzy logic-based power balance scheduling optimization algorithm that dynamically adjusts daily dispatch plans through a multi-input single-output fuzzy inference system. A three-input fuzzy controller is first established using net load deviation, energy storage state-of-charge deviation, and transmission line power fluctuation as input variables, while the output represents the power adjustment of dispatchable resources. To enhance adaptability under varying operating conditions, a variable-domain mechanism is incorporated to overcome the limitations of fixed membership functions. Historical operational data are further classified through fuzzy clustering, enabling scenario-oriented rule-base optimization. Simulation results on the IEEE 33-node active distribution network demonstrate that, compared with fixed-domain fuzzy control, the proposed method reduces the cumulative daily average absolute power deviation by 8.4%, decreases energy storage charge-discharge cycles by 1.3%, and improves tie-line power fluctuation variance by 6.4%. Relative to model predictive control (MPC), it achieves comparable control performance within 1.2% while requiring only 6.6% of the computational time. Robustness evaluations under communication latency and measurement noise further verify its practical applicability. The proposed algorithm provides an efficient and reliable solution for uncertainty-aware dispatching in active distribution networks and offers valuable support for intelligent electromagnetic energy management and modern power transmission systems.
L. Chen, P. Zhang, J. Wang et al.· Advanced Electromagnetics· 0 citations
A hybrid Tabu Search-Adaptive Particle Swarm Optimization is proposed and embedded into the Alternating Direction Method of Multipliers framework, enabling consistent coordination of boundary variables among communities and distributed parallel solving.
: Because power–quality mitigation devices in distribution networks often lack a system–wide optimal regulation strategy, this paper proposes an integrated optimization and control strategy based on a genetically enhanced multi–objective particle swarm optimization (GA–MOPSO) algorithm to coordinate the control schemes of reactive–power devices. Crossover–based genetic operations are incorporated into the multi–objective computation, enabling MOPSO to explore a broader search space and further accelerate convergence. A 10 kV simulation model is built on the IEEE 33–bus system, with dispersed integration of loads exhibiting reactive–power deficiency to emulate power–quality issues. Simulation results verify the feasibility and high efficiency of the proposed optimization and control strategy.
This paper proposes a dynamic intelligent method for voltage violation management in high-renewable-penetration distribution networks. The method employs a dual-agent architecture: DERMS_Agent coordinates task scheduling, data management, and computational resource allocation, while Solution_Agent performs three-phase unbalanced power flow calculation and MIQP-based voltage violation joint optimization. Four key technical contributions are presented. (i) An asymmetric nodal admittance matrix is developed to incorporate transformer tap-phase-shift and capacitor branches within a unified formulation. (ii) Five categories of analytical sensitivities are systematically derived, covering transformer tap, phase shift, and capacitor compensation effects for both voltage regulation and harmonic suppression. (iii) A three-parameter MIQP joint optimization model is constructed with voltage deviation minimization as the objective and three-phase unbalance and resonance avoidance as constraints. (iv) A two-stage hybrid solution strategy combining Ipopt continuous relaxation with Gurobi neighborhood enumeration is designed to achieve real-time solvability. Validation on a real 10 kV feeder with 91 transformer areas over 768 time sections (8 days) demonstrates a 95.6% voltage violation resolution rate within the first three polling cycles and an average single-section solution time of 0.83 s, satisfying the real-time requirements of 15 min operational control cycles.
Hua Zhang, C. Long, Xueneng Su et al.· Processes· 0 citations
The high rate of replacement of synchronous machines by inverter-based resources (IBRs) has increased the role of Dynamic Voltage Support (DVS) during grid disturbances, especially on weak and failed grid conditions. Traditional reactive-current-based grid-support rules can have a poor performance with large R/X ratios, deep voltage sags, and hard inverter current or power constraints. To this end, much research has been done on the development of the best control measures that can help in maximizing positive- sequence voltage, improving low-voltage ride-through capability, and reducing synchronization instability. The review gives a cohesive and thorough evaluation of the best DVS strategies, including analytical global-optimality models, model-driven optimization, sensitivity-based methods, active/reactive power allocation models, and newly developed model-free and real-time optimum-seeking controllers. The review identifies the importance of current limits, active power availability, and synchronization stability limitations in determining inverter behavior, and contrasts the outputs of various approaches to these problems in the presence of varying grid strengths. Aspects in practical implementation of photovoltaic, wind and storage inverters are discussed and also robustness in the face of parameter uncertainties. Future directions in research (such as adaptive optimization, controllers based on learning, multi-inverter coordination, and grid-forming DVS) are determined. This review summarizes the latest knowledge and defines the directions of resilient, optimal, and grid-code-conforming voltage support of next- generation power electronic inverters.
Prashant Kumar, Y. Singh· 2026 International Conferenc...· 0 citations
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