Search Results (85)

This paper presents a comprehensive life-cycle analysis of distribution transformers, based on realized measurements of the increased power losses as a result of their long-term service under real-world conditions. The study is based on aggregated measured data from extensive fleets of oil-immersed distribution transformers characterized by diverse designs, manufacturing vintages, and service lives. The evolution of no-load losses and short-circuit losses is analyzed as a function of operational duration, structural characteristics, and the specific technologies employed for windings and magnetic core construction. Statistical models describing the variation in these losses are presented, highlighting the limitations of the static assumptions commonly utilized in power distribution network planning. On this basis, an approximation of the time evolution of the transformer’s total power and energy losses is proposed as appropriate for implementation in a life-cycle analysis model. Furthermore, the impacts of thermal loading and abnormal operating conditions—such as unbalanced loads, frequent short circuits, and repeated overheating of the transformer oil—are analyzed as drivers of accelerated transformer aging. These effects are integrated into a unified life-cycle framework, enabling the quantitative assessment of loss variations and their associated operational expenditures (OPEX). A numerical example is provided to evaluate the cost-effectiveness of “repair vs. replacement” scenarios, utilizing a discounted cash flow analysis that incorporates a carbon component. The findings establish a methodological foundation for a broader assessment of technical condition and energy performance, identifying the optimal intervention point for repair or replacement to support decision-making for Distribution System Operators (DSOs) amidst increasing requirements for efficiency and decarbonization. Full article

The expansion and ongoing refinement of control solutions for three-phase microgrids are key enablers in the transition from conventional distribution networks to smart microgrids. By integrating distributed generation, a microgrid can operate in either grid-connected or island mode. One of the major technical challenges in microgrid operation is mitigating or eliminating phase power unbalances. Unbalanced single-phase loads, combined with unbalanced and intermittent single-phase generation, can produce adverse effects on both energy efficiency and power quality. Unlike conventional distribution networks, microgrids may exhibit bidirectional power flows, which can occur simultaneously on all phases or differ from phase to phase. This paper introduces new analytical expressions for sizing a balancing capacitive compensator (BCC) for three-phase four-wire systems and derives a simplified sizing algorithm. The approach is validated through a numerical study using a Matlab/Simulink model of a low-voltage three-phase microgrid with high penetration of single-phase loads and single-phase distributed sources. The BCC is installed at the point of common coupling (PCC) between the microgrid and the main grid. Three operating regimes (cases) of the microgrid were analyzed, considering three compensation scenarios (sub-cases) for each: 1—without compensation, 2—with balanced capacitive compensation (classical), and 3—with unbalanced capacitive compensation (with BCC). For each of the three regimes (cases), the use of the BCC determines, at the PCC, in addition to the cancellation of the reactive component of the positive sequence current, the cancellation of the negative- and zero-sequence currents. In other words, the BCC–microgrid assembly is seen from the main grid either as a perfectly balanced active power load or as a perfectly balanced active power source. Thus, the BCC prevents the propagation of the unbalance disturbance in the main grid; in the considered case study, this also results from the cancellation of the negative- and zero-sequence components of the phase voltages measured at the PCC. The results show that the load-balancing capability of the BCC can be extended to power-flow balancing in any network section, including cases where the phase power directions differ. Implemented as a BCC-type SVC or as an automatically adjustable variant (ABCC), the proposed unbalanced shunt capacitive compensation method is effective for mitigating or eliminating bidirectional phase power-flow unbalances. Full article

This paper investigates post-disaster restoration and stable operation of three-phase unbalanced distribution networks with high penetration of distributed generation and electric-vehicle integration. A two-stage hierarchical restoration and coordinated dispatch strategy is developed to improve restoration feasibility and reliability while reducing post-fault restoration cost. In Stage I, island partitioning and load prioritization are carried out using DFS and BFS to maximize the restoration of critical loads. In Stage II, without reducing the Stage I restoration level, branch switch statuses and coordinated outputs of DG-EV-BESS are jointly optimized under three-phase unbalanced power flow, radiality, and other operational constraints. The objective minimizes the total restoration cost, including network losses, switching operations, three-phase voltage-unbalance penalties, and customer compensation. Simulation results on a modified IEEE 33-bus system demonstrate that the proposed method reduces restoration cost and improves the system restoration ratio. In addition, phase-wise EV dispatch provides targeted compensation for weak-phase power deficits and improves post-fault inter-phase operating quality. The results validate the effectiveness of the proposed strategy. Full article

The increasing penetration of rooftop photovoltaic (PV) systems has introduced significant challenges to voltage regulation and power quality within low voltage (LV) distribution networks. Reverse power flows during periods of high solar generation and low local demand can lead to overvoltage issues, voltage unbalance, and increased neutral-to-ground potential. This paper presents a comprehensive review of voltage regulation challenges and mitigation strategies for PV-rich distribution networks. The review consolidates findings from recent literature, focusing on traditional methods such as on-load tap changers and reactive power compensation, as well as modern techniques including smart inverter functionalities, community energy storage, static compensators, and advanced coordinated control schemes. A detailed examination of the suitability and limitations of these approaches in the Australian regulatory and network context is provided. The literature review demonstrates that previous work has mainly considered generic LV regulation issues without explicit four-wire MEN modelling or detailed LV–MV time series impact analysis. As a response to the lack of detailed practical analysis, a detailed three-phase four-wire LV–MV modelling and case study analysis, which illustrates the technical implications of high PV penetration on a representative Australian LV feeder, has been completed. The network is modelled using a three-phase four-wire unbalanced load flow formulation, explicitly incorporating the neutral conductor and multiple earthed neutral (MEN) system configuration. Results demonstrate pronounced voltage rise and unbalance during midday generation periods, highlighting the need for distributed and adaptive voltage-management solutions. The paper concludes by identifying key research gaps and future directions for voltage regulation in Australian distribution networks, emphasizing the importance of low voltage visibility, coordinated control architectures, and the integration of emerging distributed energy resources. The novelty of this work lies in combining a focused review of state-of-the-art with respect to management of voltage regulation in the presence of high penetration of distributed PV generation with a detailed three-phase four-wire LV–MV modelling framework and time-series case study of a representative Australian residential feeder, which illustrates the practical implications of increasing PV penetration. Full article

As the oil price has skyrocketed, the attraction towards electric vehicles has gone up. This scenario has also increased the demand for charging infrastructure. This paper proposes a novel charging infrastructure for electric vehicles which is energized by a solar photovoltaic unit, integrated with a distribution static compensator. The output of the photovoltaic array is regulated by a DC–DC converter, which uses maximum power point tracking to support optimal solar energy conversion. The compensator is integrated into the grid through a zigzag-star transformer, which helps with neutral current compensation, promoting balanced and distortion-free operation. The control algorithm is designed to ensure superior power quality during grid synchronization and sustainable energy management. This novel architecture ensures bidirectional power flow, enabling the charge–discharge dynamics of the electric vehicles, which can be termed Grid-to-Vehicle and Vehicle-to-Grid modes. Better grid flexibility and resilience are ensured by this dynamic power exchange. The control strategy based on the Linear Kalman Filter provides reactive power balance and maintains steady voltage at the point of common coupling, and it ensures enhanced power quality during power flow, resulting in efficient and reliable grid operations. The effectiveness of the control algorithm is tested and validated under Grid-to-Vehicle, Vehicle-to-Grid, nonlinear, unbalanced, and isolated solar conditions. Analytical tuning of the gains in the controller, by using the conventional methods, is not efficient under dynamic conditions and nonlinear loads. An optimization technique is used to estimate the proportional–integral control gains, which avoids the difficulty of tuning the controllers. Simulation of the system is carried out using MATLAB 2022b/SIMULINK. Simulation results under diverse operating scenarios confirm the system’s capability to sustain superior power quality, maintain grid stability, and support a robust and reliable charging infrastructure. By enabling regulated bidirectional energy exchange and autonomous operation during grid disturbances, the charger operates as a resilient grid-support asset rather than as a passive electrical load. Full article

Power quality is a continuous concern in industry, especially in steel mills operating with nonlinear, fluctuating, and unbalanced loads. This paper investigates the influence of a Static Var Compensator (SVC) on power quality parameters in a hot metallurgical rolling mill. The study includes experimental measurements performed under real operating conditions, both with and without reactive power compensation. The measurements included voltage and current waveforms, active and reactive power, total harmonic distortion (THD), and the power factor, recorded both with and without a Static Var Compensator (SVC) in operation. The results show that in the presence of an SVC, voltage stability was improved and reactive power flow was reduced, although the current harmonic distortion remained above the recommended limits (ATHD > 40%). Despite these limitations, the voltage THD (VTHD) remained within acceptable ranges, as the upstream network maintained voltage levels despite the harmonic injection. The results obtained emphasize the effectiveness of SVCs in reducing reactive power while highlighting the need for complementary strategies to mitigate harmonics due to the presence of converters. These conclusions are particularly relevant for industrial installations that want to improve power quality and energy efficiency through specific compensation technologies. Full article

This study investigates the effects of high levels of photovoltaic (PV) generation on the unbalanced distribution network using the quasi-dynamic simulation method on DIgSILENT PowerFactory. We are motivated by the need to diversify the national energy matrix, following the power blackout that occurred in Ecuador in 2024 and the energy limitations characterized by the use of fossil fuels. For this purpose, we deployed the simulation of the PJM 13-Node Test Feeder, which is a low-voltage distribution network and mimics the U.S. system, and represents a realist distribution network with residential and commercial load profiles. We simulated realistic PV generation dynamics for a typical day, capturing stochastic solar irradiance, ambient temperature variation, and the impacts of cloud cover. In those conditions, PV generation reached 31.6% of the system total load. We found that during peak irradiance hours, the voltage levels on certain nodes, predominantly low-load buses, exceed nominal levels. The average power factor is noted to diminish by 0.90 p.u to 0.82 p.u at the feeder bus, and further drops to 0.35 p.u at the most PV-penetrated site. While distributed PV generation can effectively reduce line loading and improve energy efficiency, without reactive power compensation, the highest penetration PV generation scenario could result in deterioration of voltage stability and power quality. The prescribed quasi-dynamic framework is practical and computationally feasible, allowing for the assessment of operational performance of distribution networks with high renewables penetration. Full article

The increasing penetration of distributed energy resources (DERs) in unbalanced distribution networks presents significant challenges for optimal operation, particularly concerning power loss minimization and voltage regulation. This paper proposes a comprehensive Optimal Power Flow (OPF) model that coordinates various assets, including on-load tap changers (OLTCs), reactive power compensators, and controllable electric vehicles (EVs). To solve this complex and non-convex optimization problem, we developed the Shrinking Net Algorithm (SNA), a novel metaheuristic with mathematically proven convergence. The proposed framework was validated using the standard IEEE 123-bus test system. The results demonstrate significant operational improvements: total active power loss was reduced by 32.1%, from 96.103 kW to 65.208 kW. Furthermore, all node voltage violations were eliminated, with the minimum system voltage improving from 0.937 p.u. to a compliant 0.973 p.u. The findings confirm that the proposed SNA is an effective and robust tool for this application, highlighting the substantial economic and technical benefits of coordinated asset control for modern distribution system operators. Full article

Addressing the problems of wind power’s anti-peak regulation characteristics, increasing system peak regulation difficulty, and wind power uncertainty causing frequency deviation leading to power imbalance, this paper considers the peak shaving and valley filling function and frequency regulation characteristics of energy storage, establishing a day-ahead and intraday coordinated two-stage optimization scheduling model for research. Stage 1 establishes a deterministic wind power prediction model based on time series Autoregressive Integrated Moving Average (ARIMA), adopts dynamic peak-valley identification method to divide energy storage operation periods, designs energy storage peak regulation working interval and reserves frequency regulation capacity, and establishes a day-ahead 24 h optimization model with minimum cost as the objective to determine the basic output of each power source and the charging and discharging plan of energy storage participating in peak regulation. Stage 2 still takes the minimum cost as the objective, based on the output of each power source determined in Stage 1, adopts Monte Carlo scenario generation and improved scenario reduction technology to model wind power uncertainty. On one hand, it considers how energy storage improves wind power system inertia support to ensure the initial rate of change of frequency meets requirements. On the other hand, considering energy storage reserve capacity responding to frequency deviation, it introduces dynamic power flow theory, where wind, thermal, load, and storage resources share unbalanced power proportionally based on their frequency characteristic coefficients, establishing an intraday real-time scheduling scheme that satisfies the initial rate of change of frequency and steady-state frequency deviation constraints. The study employs improved chaotic mapping and an adaptive weight Particle Swarm Optimization (PSO) algorithm to solve the two-stage optimization model and finally takes the improved IEEE 14-node system as an example to verify the proposed scheme through simulation. Results demonstrate that the proposed method improves the system net load peak-valley difference by 35.9%, controls frequency deviation within ±0.2 Hz range, and reduces generation cost by 7.2%. The proposed optimization scheduling model has high engineering application value. Full article

This study addresses the issues of unbalanced capacity allocation and rigid train formations in urban metro systems under tidal passenger flow conditions. By integrating temporal–spatial passenger demand with real-time dynamic train formation, we propose a virtual formation optimization method driven by carriage load factors. This method enhances the flexibility of train formation strategies by coordinating virtual coupling and decoupling operations between trains traveling in opposite directions. A mixed-integer linear programming (MILP) model is developed, with train unit allocation and turnover scheduling as the main decision variables. The model aims to minimize total passenger waiting time and system operating costs, while incorporating constraints related to unit allocation, turnover, and passenger assignment. The model can be efficiently solved using commercial solvers such as CPLEX. To evaluate the proposed method, a case study is conducted on a metro line in a major city. Numerical experiments demonstrate that, compared with a fixed 6-car formation scheme, the proposed method reduces total passenger waiting time by approximately 4.2% and operating costs by 11.6%. When compared to a fixed 8-car formation scheme, it achieves a 48.8% reduction in operating costs with only a 4.3% increase in passenger waiting time. These results highlight the potential of the proposed virtual formation strategy to enhance operational efficiency and resource utilization in urban metro systems, offering both practical value and implementation feasibility. Full article

This paper presents a heuristic methodology for the optimal expansion of unbalanced three-phase distribution systems in rural areas, simultaneously addressing feeder routing and conductor sizing to minimize the total annualized cost—defined as the sum of investments in conductors and operational energy losses. The planning strategy explores two radial topological models: the Minimum Spanning Tree (MST) and the Steiner Tree (ST). The latter incorporates auxiliary nodes to reduce the total line length. For each topology, an initial conductor sizing is performed based on three-phase power flow calculations using Broyden’s method, capturing the unbalanced nature of the rural networks. These initial solutions are refined via four metaheuristic algorithms—the Chu–Beasley Genetic Algorithm (CBGA), Particle Swarm Optimization (PSO), the Sine–Cosine Algorithm (SCA), and the Grey Wolf Optimizer (GWO)—under a master–slave optimization framework. Numerical experiments on 15-, 25- and 50-node rural test systems show that the ST combined with GWO consistently achieves the lowest total costs—reducing expenditures by up to 70.63% compared to MST configurations—and exhibits superior robustness across all performance metrics, including best-, average-, and worst-case solutions, as well as standard deviation. Beyond its technical contributions, the proposed methodology supports the United Nations Sustainable Development Goals by promoting universal energy access (SDG 7), fostering cost-effective rural infrastructure (SDG 9), and contributing to reductions in urban–rural inequalities in electricity access (SDG 10). All simulations were implemented in MATLAB 2024a, demonstrating the practical viability and scalability of the method for planning rural distribution networks under unbalanced load conditions. Full article

The strong coupling of the six-degree-of-freedom (6-DoF) electro-hydraulic Stewart parallel mechanism manifests as adjusting the elongation of one actuator potentially inducing motion in multiple degrees of freedom of the platform, i.e., a change in pose; this pose change leads to time-varying and unbalanced load forces (disturbance inputs) on the six hydraulic actuators; unbalanced load forces exacerbate the time-varying nature of the acceleration and velocity of the six hydraulic actuators, causing instantaneous changes in the pressure and flow rate of the electro-hydraulic system, thereby enhancing the pressure–flow nonlinearity of the hydraulic actuators. Considering the advantage of artificial intelligence in learning hidden patterns within complex environments (strong coupling and strong nonlinearity), this paper proposes a reinforcement learning motion control algorithm based on deep deterministic policy gradient (DDPG). Firstly, the static/dynamic coordinate system transformation matrix of the electro-hydraulic Stewart parallel mechanism is established, and the inverse kinematic model and inverse dynamic model are derived. Secondly, a DDPG algorithm framework incorporating an Actor–Critic network structure is constructed, designing the agent’s state observation space, action space, and a position-error-based reward function, while employing experience replay and target network mechanisms to optimize the training process. Finally, a simulation model is built on the MATLAB 2024b platform, applying variable-amplitude variable-frequency sinusoidal input signals to all 6 degrees of freedom for dynamic characteristic analysis and performance evaluation under the strong coupling and strong nonlinear operating conditions of the electro-hydraulic Stewart parallel mechanism; the DDPG agent dynamically adjusts the proportional, integral, and derivative gains of six PID controllers through interactive trial-and-error learning. Simulation results indicate that compared to the traditional PID control algorithm, the DDPG-PID control algorithm significantly improves the tracking accuracy of all six hydraulic cylinders, with the maximum position error reduced by over 40.00%, achieving high-precision tracking control of variable-amplitude variable-frequency trajectories in all 6 degrees of freedom for the electro-hydraulic Stewart parallel mechanism. Full article

As a core device in low-voltage distribution networks, the distribution zone transformer (DZT) is influenced by short circuits, overloads, and unbalanced loads, which cause thermal aging, mechanical stress, and eventually deformation of the winding, resulting in parameter deviations from nameplate values and impairing system operation. However, existing identification methods typically require synchronized high- and low-voltage data and are limited to symmetric three-phase conditions, which limits their application in practical distribution systems. To address these challenges, this paper proposes a parameter identification method for DZTs under three-phase unbalanced conditions. Firstly, based on the transformer’s T-equivalent circuit considering the load, the power flow equations are derived without involving the synchronization issue of high-voltage and low-voltage side data, and the sum of the impedances on both sides is treated as an independent parameter. Then, a novel power flow equation under three-phase unbalanced conditions is established, and an adaptive recursive least squares (ARLS) solution method is constructed using the measurement data sequence provided by the smart meter of the intelligent transformer terminal unit (TTU) to achieve online identification of the transformer winding parameters. The effectiveness and robustness of the method are verified through practical case studies. Full article

This paper presents a comprehensive study on the formulation and solution of the power flow problem in bipolar direct current (DC) distribution networks with unbalanced constant power loads. Using the nodal voltage method, a unified nonlinear model is proposed which accurately captures both monopolar and bipolar load configurations as well as the voltage coupling between conductors. The model assumes a solid grounding of the neutral conductor and known system parameters, ensuring reproducibility and physical consistency. Seven iterative algorithms are developed and compared, including three Newton–Raphson-based formulations and four quasi-Newton methods with constant Jacobian approximations. The proposed techniques are validated on two benchmark networks comprising 21 and 85 buses. Numerical results demonstrate that Newton-based methods exhibit quadratic convergence and high accuracy, while quasi-Newton approaches significantly reduce computational time, making them more suitable for large-scale systems. The findings highlight the trade-offs between convergence speed and computational efficiency, and they provide valuable insights for the planning and operation of modern bipolar DC grids. Full article

This paper discusses the operation and control of a low-voltage DC (LVDC) isolated distribution network powered by distributed generation (DG) from a variable-speed wind turbine induction generator (WTIG) to supply unbalanced AC loads. The system incorporates a DC-DC storage converter to regulate network voltages and interconnect battery energy storage with the DC network. The wind turbines are equipped with a squirrel cage induction generator (IG) to connect a DC network via individual power inverters (WTIG inverters). Loads are unbalanced ACs and are interfaced using transformerless power inverters, referred to as load inverters. The DC-DC converter is equipped with a novel control strategy, utilizing a droop regulator for the DC voltage to stabilize network operation. The control system is modeled based on Clark and Park transformations and is developed for the load inverters to provide balanced AC voltage despite unbalanced load conditions. The system employs the perturbation and observation (P&O) method for maximum power point tracking (MPPT) to optimize wind energy utilization, while blade angle controllers maintain generator performance within rated power and speed limits under high wind conditions. System operation is analyzed under two scenarios: normal operation with varying wind speeds and the effects of load variations. Simulation results using PSCAD/EMTDC demonstrate that the proposed LVDC isolated distribution network (DC) achieves a stable DC bus voltage within ±5% of the nominal value, efficiently delivers balanced AC voltages with unbalanced levels below 2%, and operates with over 90% wind energy utilization during varying wind speeds, confirming LVDC network reliability and robustness. Full article