Aug 2026· AIP Advances· Vol 16· 0 citations· 37 references
TL;DR
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.
Abstract
With the high penetration of distributed generation, the operational state of active distribution networks exhibits strong uncertainty and fast time-varying characteristics. Power quality issues such as line losses and harmonics have become major factors affecting distribution network optimization. To address this, this paper proposes a joint active and reactive power optimization strategy for active distribution networks based on dynamic community partition. First, causal correlation weights among network nodes are extracted, and the Louvain algorithm is employed for initial community partition. Furthermore, driven by physical constraints, online adaptive updating of community boundaries is achieved. Second, within each community, a multi-objective optimization model that minimizes active power loss and voltage deviation is established, and harmonic constraints are incorporated to actively suppress harmonic pollution while reducing losses and regulating voltage. To solve this problem, 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. Simulation results demonstrate that the proposed strategy effectively reduces system losses and voltage deviations, validating its effectiveness.
The rapid integration of Distributed Generation (DG) units is transforming traditional passive radial distribution networks into active grids with bidirectional power flow. Determining the optimal allocation and physical hosting capacity of DG units is essential for maintaining grid stability while maximizing technical gains. This study presents a multi-objective optimization framework using empirical peak-load field data from an active medium-voltage distribution network in Türkiye. A custom Particle Swarm Optimization (PSO) algorithm was developed in MATLAB to simultaneously minimize active and reactive power losses while improving the Voltage Deviation Index (VDI). The baseline network model and load flow calculations established in DIgSILENT PowerFactory were rigorously cross-validated with MATPOWER, demonstrating complete mathematical agreement with a negligible deviation (<0.001%). Simulation results under both capacity-constrained (10 MW limit) and unconstrained Hosting Capacity Discovery scenarios demonstrate that single-objective optimization causes negative trade-offs across other operational metrics. Conversely, multi-objective hybrid approaches provide a balanced, globally common optimum operating point best suited to the grid’s natural electrical structure, consistently identifying a reliable ultimate hosting capacity threshold of 21.78 MW at Busbar 4. These findings offer a concrete, empirical decision-support framework for active distribution network planning and modernization.
In microgrid systems, multiple modular distributed generation (DG) units are utilized to deliver power to connected loads. However, variations in feeder line impedances among DG units linked to a common coupling point can result in uneven reactive power distribution. This research introduces an innovative control strategy that integrates consensus-based coordination with adaptive virtual impedance techniques to achieve accurate reactive power sharing and restore DG output voltages. Consensus Technique enables low-bandwidth communication between adjacent DG units, which is sufficient to meet the control objectives. Importantly, this approach does not rely on prior knowledge of line impedance values. Moreover, the control parameters are fine-tuned using Multi-Objective Particle Swarm Optimization (MOPSO) to simultaneously optimize voltage regulation, reactive power sharing accuracy, and dynamic performance, thereby enhancing overall system performance. Distinct from existing consensus-based control methods with fixed or heuristically tuned parameters, the proposed framework formulates a structured multi-objective optimization problem solved using MOPSO to jointly tune adaptive virtual impedance and consensus gains, explicitly minimizing voltage deviation, improving active and reactive power sharing accuracy, and enhancing dynamic response under varying load and network conditions. Simulation results validated the method’s effectiveness in ensuring balanced active and reactive power sharing.
Erhab Youssef, Ahmed M.Abdalgawad, Adel El Samahy et al.· Discover Applied Sciences· 0 citations
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.
Marina Dubravac, D. Topić, Goran Knežević et al.· IEEE Access· 0 citations
The large-scale integration of photovoltaic generation into distribution grids has introduced significant operational challenges, including voltage excursions, reverse power flows, and increased variability. Battery energy storage systems (BESSs) offer a versatile solution by providing coordinated active- and reactive-power support. However, their scheduling in active distribution networks is challenging because of the non-convex alternating-current (AC) power-flow equations, the nondifferentiability of battery-degradation modeling, and uncertainty in renewable generation and demand. This paper proposes a two-stage methodology for the day-ahead operation of BESSs in ADNs. In the first stage, parallel particle swarm optimization (PPSO) determines the hourly active- and reactive-power schedules of the BESS units. In the second stage, a matrix-based multi-period AC power flow based on successive approximations evaluates the schedules and verifies voltage, thermal, converter-capability, and state-of-charge (SoC) constraints. A rainflow-counting degradation model is incorporated into the objective function to account for cycling and calendar aging costs. The methodology is assessed through ablation analyses comparing active-power-only and coordinated P–Q dispatches, degradation-unaware and degradation-aware scheduling, and serial and parallel PSO implementations. It is validated on modified 33-, 69-, and 136-node systems under deterministic and uncertainty-based operating conditions, including 100 demand and PV-generation scenarios. PPSO is compared with parallel versions of the adaptive Jaya algorithm (AJAYA), genetic algorithm (GA), multi-verse optimizer (MVO), salp swarm algorithm (SSA), grey wolf optimizer (GWO), and vortex search algorithm (VSA), using operating-cost reduction, computational time, solution variability, feasibility indicators, BESS lifetime, and weekly cost analysis. Additionally, exact one-sided Wilcoxon signed-rank tests with Holm adjustment are used to assess the statistical significance of the economic differences between PPSO and the benchmark methods. Results show that PPSO provides the lowest or most competitive operating costs and the shortest computational time in the evaluated cases, while all network and storage constraints remain satisfied.
L. Grisales-Noreña, F. Machuca‐Martínez, O. D. Montoya· The Scientist· 0 citations
To address issues such as active power tracking errors, voltage overshoot, and disturbance propagation among resources that commonly occur at the distribution network's edge following the integration of distributed PV (Photovoltaic), energy storage, and controllable loads, this paper proposes a method for active-reactive power coordinated self-optimizing control of distributed resources based on an improved Adam-MPC(Adaptive Moment Estimation-Model Predictive Control). This method establishes a linearized DistFlow voltage-sensitivity model within the feeder coordination controller, embedding node-voltage constraints into the MPC (model predictive control) rolling optimization using a "hard constraints first, relaxation variables as a fallback" approach. Simultaneously, it utilizes Adam's first-order momentum and second-order variance-estimated momentum to identify measurement errors, reconstruct state tracking weights online, and achieve flexible weight reduction for disturbed resources and coordinated support for undisturbed resources. Simulations on a 10 kV radial distribution network demonstrate that, under a 45% sudden drop in end-of-line PV generation, the proposed method can control the total active power response error of the feeder within 3.8%, maintain the minimum end-of-line voltage near 0.985 p.u. (per-unit), and meet the real-time requirements of 1-second rolling control.
Chuan Wei, Xiangyu Kong, Delong Zhang et al.· 2026 IEEE 27th China Confere...· 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
