Search Results (83)

High penetration of distributed photovoltaic (PV) generation has intensified voltage violations and stochastic voltage fluctuations in distribution networks, while existing voltage–var control methods still have limitations in terms of communication dependence, scalability, and edge deployment. To address these issues, this paper proposes a stability-constrained voltage–var self-optimizing control method for distribution networks based on the Bandit-Guided Online Self-Tuning Group Relative Policy Optimization (BOST-GRPO) algorithm. First, based on the LinDistFlow linearized power-flow model, a communication-free, decentralized, and locally observable reinforcement learning control environment is constructed, enabling each node to independently generate reactive power regulation commands using only local voltage measurements. Second, a contraction-mapping-based stability constraint is embedded into the policy output layer, theoretically guaranteeing the local exponential convergence of nodal voltage deviations around the equilibrium point and reducing the risk of voltage instability caused by overly aggressive policy actions. Meanwhile, device capacity constraints are incorporated into the policy output through a tanh-based action mapping, ensuring the physical feasibility of control commands. On this basis, BOST-GRPO realizes the online self-tuning of key hyperparameters within a single training process through a Bandit-guided mechanism, thereby avoiding the repeated training overhead caused by traditional offline hyperparameter tuning. Simulation results on the IEEE 33-bus system show that the proposed method outperforms benchmark reinforcement learning algorithms in final test cost, voltage deviation suppression, steady-state error, and regulation speed. Further tests under sensitivity matrix mismatch, different initial voltage disturbance intensities, and the extended IEEE 69-bus system demonstrate that the proposed method achieves good robustness and scalability. Full article

The high penetration of distributed photovoltaic (PV) systems introduces significant voltage fluctuations in distribution networks due to the stochastic nature of PV generation. To address the limitations of conventional volt–var regulation, this paper proposes a novel two-layer hierarchical framework driven by two core innovations: a robust globally scheduled magnetically controlled reactor (MCR) and an autonomous adaptive control strategy for local PV inverters. At the local layer, an adaptive five-region weighting strategy enables PV inverters to rapidly mitigate minor voltage fluctuations without relying on communication networks. At the global layer, an improved particle swarm optimization (IPSO) algorithm is employed to coordinate MCR reactive power scheduling, thereby mitigating severe voltage violations and reducing active power losses. The proposed framework is validated on a modified IEEE 33-bus distribution system. Simulation results show that the adaptive local control of PV inverters effectively reduces node voltage deviations compared with conventional control methods. Furthermore, the two-layer coordinated optimization significantly improves overall system performance by reducing both the objective function value and the maximum voltage deviation compared with single-layer control strategies. Compared with other optimization algorithms, IPSO demonstrates strong robustness and stable convergence in the proposed optimization problem. Overall, the proposed hierarchical framework provides a reliable, scalable, and cost-effective solution for real-time voltage regulation in modern active distribution networks. Full article

To address the issues of prediction failure and energy storage regulation saturation caused by drastic source-load variations under extreme scenarios, a novel voltage optimization strategy based on the coordination of a Volt/Var Regulator and an Energy Router is proposed. Firstly, the mechanism by which the accumulation of prediction errors leads to integral saturation of energy storage and subsequent failure in voltage regulation is elucidated. Subsequently, by constructing a refined model, the proposed approach integrates the series voltage regulation capability of the VVR—which alters the electrical distance—with the cross-node load transfer capability of the ER, achieving an organic synergy between discrete coarse adjustment and continuous fine-tuning. Simulations based on the IEEE 33-node system demonstrate that the proposed method exhibits excellent robustness under extreme operating conditions such as photovoltaic surges and abrupt load changes. It does not rely on prediction accuracy and effectively overcomes the regulation blind spots caused by prediction failures, thereby significantly mitigating voltage violations and effectively maintaining the system voltage strictly within safe operational limits. Full article

The increasing penetration of inverter-interfaced photovoltaic (PV) generation in active distribution networks (ADNs) intensifies fast voltage violations and makes real-time Volt-VAR control (VVC) challenging, especially when each inverter has only partial and noisy measurements and communication is limited. Existing local droop-type strategies lack coordination, while fully centralized optimization/learning is often impractical for online deployment. To address these gaps, an attention-enhanced multi-agent deep reinforcement learning (MADRL) framework is developed for inverter-based VVC under the centralized training and decentralized execution (CTDE) paradigm. First, the voltage regulation problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP) to explicitly account for system stochasticity and temporal variability under partial observability. To solve this complex game, an attention-enhanced MADRL architecture is employed, where an agent-level attention mechanism is integrated into the centralized critic. Unlike traditional methods that treat all neighbor information equally, the proposed mechanism enables each inverter agent to dynamically prioritize and selectively focus on the most influential states from other agents, effectively capturing complex intercorrelations while enhancing training stability and learning efficiency. Operating under the CTDE paradigm, the framework realizes coordinated reactive power support using only local measurements, ensuring high scalability and practical implementability in communication-constrained environments. Simulations on the IEEE 33-bus system with six PV inverters show that the proposed method reduces the average voltage deviation on the test set from 0.0117 p.u. (droop control) and 0.0112 p.u. (MADDPG) to 0.0074 p.u., while maintaining millisecond-level execution time comparable to other MADRL baselines. Scalability tests with up to 12 agents further demonstrate robust performance of the proposed method under higher PV penetration. Full article

In this paper, we propose a data-driven distributionally robust optimization (DRO) framework that ensures the economical and robust operation of solar photovoltaic (PV)-integrated battery charging swapping stations (BCSSs) for electric vehicles (EVs) under uncertainties in active distribution systems with stand-alone PV systems. In the proposed framework, multiple inventory batteries in each BCSS are used through their charging and discharging real and/or reactive power scheduling to perform Volt/VAR control (VVC) along with stand-alone PV systems, and to reduce the BCSS operational cost via battery-to-battery (B2B)-based real power exchange and demand response (DR) while satisfying the desired EV battery swapping load. To handle the uncertainties in both PV generation outputs and DR-induced maximum demand reduction capability, the proposed framework is formulated as a data-driven DRO problem based on the Wasserstein metric using historical samples of the probability distributions of the uncertainties. Using a duality theory, the original Wasserstein-based DRO problem is reformulated into a tractable optimization problem that calculates the distributionally robust bounds of uncertainties using their support information. The effectiveness of the proposed framework was assessed on an IEEE 33-node power distribution system in terms of real power loss reduction via VVC and BCSS operational cost savings via B2B/DR capability. Full article

This paper proposes a quasi-dynamic Volt–Var control strategy for radial distribution networks based on the optimal sizing of a distribution static synchronous compensator (D-STATCOM) using a genetic algorithm (GA). The objective is to enhance voltage regulation and reduce technical energy losses under variable loading conditions while preserving nonlinear AC power flow fidelity. The IEEE 33-bus test system was modeled in DIgSILENT PowerFactory (v2021), and the D-STATCOM installation bus was selected based on a rigorous literature-supported placement criterion derived from optimization-based studies. Three representative demand scenarios—minimum, average, and maximum loading—were defined to approximate quasi-dynamic operation over a daily cycle. The GA was implemented in MATLAB (R2023b) to solve a normalized nonlinear multi-objective optimization problem that simultaneously minimizes total active power losses and the aggregate voltage deviation index. The optimized reactive power capacities obtained were 0.49 Mvar, 1.1933 Mvar, and 2.30 Mvar for the minimum, average, and maximum demand scenarios, respectively. These configurations achieved active power loss reductions of 27.5%, 24.602%, and 23.44% under the corresponding loading levels while improving voltage regulation at the critical bus (bus 18) and maintaining system voltages within the admissible 0.95–1.05 p.u. range. Through quasi-dynamic interpolation of operating points, the daily performance assessment showed a 24.11% reduction in total energy losses and a 38.28% decrease in the average voltage deviation. A statistical robustness analysis confirmed stable convergence behavior across independent executions. The results demonstrate that the proposed framework provides a computationally efficient, planning-oriented approach for reactive power compensation in distribution systems subject to demand variability. Full article

With more renewables being integrated into distribution grids, the problem of voltage fluctuation has become prominent. Effective Volt/VAR regulation is a commonly used method to ensure the safe operation of distribution networks. Model-based approaches tend to work well only if detailed network parameters are available, while data-driven approaches can suffer from overfitting and may not generalize well. We created the PHY-GAT-SAC framework to address these issues. Physics-regularized reinforcement learning uses bidirectional graph attention, which combines a physics-informed model with a safety projection method that relies on sensitivity matrices. This makes it so that the voltage regulation is practical, interpretable, and secure. The framework works with two combined branches. One branch takes care of the nonlinear mapping from power injections to voltage states using a forward graph encoder and a reverse consistency constraint. At the same time, another branch extracts features directly from the voltages to improve the perception of system violation risk. The framework has a sensitivity-based safety layer as well. This layer projects every control action into a feasible area formed by linearized voltage restrictions, thus securing operation safety. Experiments on an IEEE 33-node system show that the framework works well. A safety layer guarantees a safe operating range without exact impedance values. And PHY-GAT-SAC greatly lowers voltage violations compared to multi-agent deep reinforcement learning. By successfully combining physics with learning, this study gives a unified framework for merging graph neural networks and reinforcement learning within intricate grid management. Full article

With the increasing penetration of inverter-based photovoltaic (PV) generation, utility-scale grid-connected PV plants are frequently exposed to voltage regulation and voltage stability challenges driven by intermittent irradiance and limited reactive power flexibility under operating constraints. Conventional static Volt/VAR control schemes are typically designed for quasi-steady conditions and therefore struggle to respond to fast variations in PV output and network states. This paper presents a digital twin (DT)-enabled framework for dynamic Volt/VAR optimization in large PV plants. A four-layer DT architecture is developed to achieve real-time cyber-physical synchronization through multi-source data acquisition, secure transmission, fusion, and quality control. To balance model fidelity and computational efficiency, a hybrid physics–data-driven model is constructed, and a local voltage stability L-index is incorporated as an explicit security constraint. A multi-objective optimization problem is formulated to minimize node voltage deviations and reactive power losses while maximizing the static voltage stability margin. The problem is solved using an adaptive parameter particle swarm optimization (AP-PSO) algorithm with dynamic inertia and learning coefficients. Case studies on modified IEEE 33-bus and 53-bus systems demonstrate that the proposed method reduces the voltage profile index by up to 68.9%, improves the static voltage stability margin by 76.5%, and shortens optimization time by up to 30.3% compared with conventional control and representative meta-heuristic or learning-based baselines. The framework further shows good scalability and robustness under practical uncertainties, including irradiance forecast errors and measurement noise. Overall, the proposed approach provides a feasible pathway to enhance operational security and efficiency of grid-connected PV plants under high-penetration scenarios. Full article

Low-voltage grids are undergoing rapid change as rooftop photovoltaics, electric vehicles and other distributed energy resources increase their share of demand. Without new local control, these trends risk more frequent voltage problems and costly reinforcement, which can slow affordable and just energy transitions. This article proposes a MATLAB/Simulink methodology for designing and comparing PID and Mamdani fuzzy volt–var controllers implemented at a single PV inverter in a radial low-voltage feeder. The feeder model aggregates residential demand, two PV units, a small wind unit, battery storage and an EV charging event; controller performance is assessed using time-domain simulations and scalar indices of overshoot, undershoot, settling time, time outside a ±5% voltage band, and reactive power usage. In the studied high-PV scenario, both controllers maintain acceptable voltage quality with limited overshoot and short settling times, while the fuzzy controller yields smoother transients at the expense of slightly higher but still modest reactive power adjustments. The results illustrate how accessible digital tools can help system operators and regulators explore local volt–var strategies that increase renewable hosting capacity and power quality compliance without immediate grid reinforcement, thereby supporting sustainable electrification in the context of the fourth industrial revolution. Full article

With the focus on the growing concern of voltage instability and its inherent risks connected to blackouts, this study addresses the importance of Volt/VAR control (VVC) in maintaining voltage stability, optimizing power factor, and reducing losses. As such, this scientific article presents a review of the methodologies used to estimate the quantity of reactive power required to restore voltage in power grids. Although reviews exist on classical methods, optimization, and machine learning, a study unifying these approaches is lacking. This gap hinders an integrated comparison of methodologies and constitutes the main motivation for this study in 2025. This absence of a consolidated and up-to-date review limits both academic progress and practical decision-making in modern power systems, especially as DER penetration accelerates. This research was conducted using the Scopus database through the selection of articles that address reactive power estimation methods. The results indicate that traditional numerical and optimization methods, although accurate, demonstrate high computational costs for real-time application. In contrast, techniques such as Deep Reinforcement Learning (DRL) and hybrid models show greater potential for dealing with uncertainties and dynamic topologies. The conclusion reached is that the solution for reactive power management lies in hybrid approaches, which combine machine learning with numerical methods, supported by an intelligent and robust data infrastructure. The comparative analysis shows that numerical methods offer high precision but are computationally expensive for real-time use; optimization techniques provide good robustness but depend on detailed models that are sensitive to system conditions; and machine learning-based approaches offer greater adaptability under uncertainty, although they require large datasets and careful training. Given these complementary limitations, hybrid approaches emerge as the most promising alternative, combining the reliability of classical methods with the flexibility of intelligent models, especially in smart grids with dynamic topologies and high penetration of Distributed Energy Resources (DERs). Full article

The sustained growth of electricity demand and the global transition toward low-carbon energy systems have intensified the need for efficient, flexible, and reliable operation of electrical distribution networks. In this context, the coordinated integration of distributed renewable energy resources and demand-side flexibility has emerged as a key strategy to improve technical performance and economic efficiency. This work proposes an integrated optimization framework for active power supply in a radial, distribution-like network through the optimal siting and sizing of photovoltaic (PV) units and wind turbines (WTs), combined with a real-time pricing (RTP)-based demand-side response (DSR) program. The problem is formulated using the branch-flow (DistFlow) model, which explicitly represents voltage drops, branch power flows, and thermal limits in radial feeders. A multiobjective function is defined to jointly minimize annual operating costs, active power losses, and voltage deviations, subject to network operating constraints and inverter capability limits. Uncertainty associated with solar irradiance, wind speed, ambient temperature, load demand, and electricity prices is captured through probabilistic modeling and scenario-based analysis. To solve the resulting nonlinear and constrained optimization problem, an Improved Whale Optimization Algorithm (I-WaOA) is employed. The proposed algorithm enhances the classical Whale Optimization Algorithm by incorporating diversification and feasibility-oriented mechanisms, including Cauchy mutation, Fitness–Distance Balance (FDB), quasi-oppositional-based learning (QOBL), and quadratic penalty functions for constraint handling. These features promote robust convergence toward admissible solutions under stochastic operating conditions. The methodology is validated on a large-scale radialized network derived from the IEEE 118-bus benchmark, enabling a DistFlow-consistent assessment of technical and economic performance under realistic operating scenarios. The results demonstrate that the coordinated integration of PV, WT, and RTP-driven demand response leads to a reduction in feeder losses, an improvement in voltage profiles, and an enhanced voltage stability margin, as quantified through standard voltage deviation and fast voltage stability indices. Overall, the proposed framework provides a practical and scalable tool for supporting planning and operational decisions in modern power distribution networks with high renewable penetration and demand flexibility. Full article

The rapid proliferation of distributed solar photovoltaic systems has intensified voltage fluctuations and uncertainty in distribution networks. Traditional Volt/VAR control strategies often struggle with robustness against extreme scenarios and impose high communication overheads. To address these challenges, this paper proposes a Bayesian Evolutionary Optimization with Conditional Value at Risk (BEO-CVaR) framework for optimizing Volt/VAR control rules. This novel approach integrates Conditional Value at Risk (CVaR) into the objective function to explicitly mitigate tail risks arising from grid uncertainties. Furthermore, it employs Bayesian Evolutionary Optimization (BEO) utilizing Gaussian process surrogate modeling to efficiently solve the computationally expensive, black-box optimization problem. Validation on a standard IEEE test feeder demonstrates that BEO-CVaR achieves superior voltage regulation, strict adherence to safety standards, and significantly reduced communication requirements compared to conventional decentralized strategies. Additionally, the framework’s scalability and robustness are verified through extensive experiments across varying dimensions of decision spaces, confirming its effectiveness in complex multi-inverter coordination scenarios. Full article

Power distribution systems are witnessing a growing deployment of distributed, inverter-based renewable resources such as solar generation. This poses certain challenges such as rapid voltage fluctuations due to the intermittent nature of renewables. Volt-Var control (VVC) methods have been proposed to utilize the ability of inverters to supply or consume reactive power to mitigate fast voltage fluctuations. These methods usually require a detailed power network model including topology and impedance data. However, network models may be difficult to obtain. Thus, it is desirable to develop a model-free method that obviates the need for the network model. This paper proposes a novel model-free cooperative control method to perform voltage regulation and reduce inverter aging in power distribution systems. This method assumes the existence of time-series voltage and load data, from which the relationship between voltage and nodal power injection is derived using a feedforward artificial neural network (ANN). The node voltage sensitivity versus reactive power injection can then be calculated, based on which a cooperative control approach is proposed for mitigating voltage fluctuation. The results obtained for a modified IEEE 13-bus system using the proposed method have shown its effectiveness in mitigating fast voltage variation due to PV intermittency. Moreover, a comparative analysis between model-free and model-based methods is provided to demonstrate the feasibility of the proposed method. Full article

With the increasing penetration of high-proportion renewable energy sources and large-scale integration of power electronic devices, distribution networks are evolving towards power-electronized systems. The integration of high-proportion renewable energy introduces challenges such as bidirectional power flow and voltage violations. Unlike traditional voltage regulation devices with slow and discrete adjustment characteristics, power electronic devices can continuously and rapidly respond to voltage fluctuations in distribution networks. However, the integration of power electronic devices alters the operational paradigm of distribution networks, necessitating adaptive voltage-reactive power control methods tailored to the regulation characteristics of both power electronic devices and discrete equipment. To fully exploit the real-time regulation capabilities of power electronic devices, this paper established a hierarchical coordinated control model for power-electronized distribution networks to achieve optimal voltage-reactive power control. A three-stage hierarchical coordinated control architecture is proposed based on the distinct response speeds of different devices. A variable-slope linear droop control method based on voltage boundary parameter optimization is employed for real-time adjustment of soft open point (SOP) and inverter outputs. To address uncertainties in PV generation and load demand, a rolling optimization strategy is implemented for centralized control, supplemented by probabilistic modeling to generate multiple representative scenarios for hierarchical coordinated control. Case studies demonstrate optimized operational results across centralized and local control stages, with comparative analyses against existing voltage-reactive power control methods confirming the superiority of the proposed hierarchical coordinated control framework. Full article

A coordinated control for the volt-var optimization (VVO) problem is presented using load tap changer transformers, voltage regulators, and capacitor banks with the integration of a PV-based microgrid. The harmony search (HS) algorithm, which is a metaheuristic-based optimization algorithm, was used to determine global optimum settings of related devices to operate efficiently under changing conditions. The major objectives of volt-var optimization were to reduce power losses, peak power demands, and voltage variations in the distribution circuit while maintaining voltages within the permitted range at all nodes and under all loading conditions. The problem was a mixed integer nonlinear problem with discrete integer variables; binary variables for the capacitor status on/off, voltage regulator taps as integers, and continuous variables; the current output of the microgrid; and nonlinear electric circuit equations. The simulations were verified using the IEEE 13-node test circuit. Daily load profiles of the main power system grid and the microgrid’s PV were used with a 15 min resolution. Power flow solutions were produced using the OpenDSS (version 9.5.1.1, year 2022) power distribution system solver. It can be applied to operational and planning purposes. The results showed that active power loss, peak power demand, and voltage fluctuation were significantly reduced by the coordinated control of the volt-var problem. Full article