Search Results (71)

An increase in penetration of photovoltaic (PV) systems in a distribution system causes voltage regulation issues that create serious problems for the On-Load Tap Changer (OLTC) of the power transformer, leading to higher tap-changing frequency and reduced transformer life. Traditional voltage control methods are ineffective when PV penetration exceeds load demand, and more sophisticated control methods are needed. This paper combines a systematic literature review conducted in accordance with the PRISMA 2020 guidelines with a case study on operational issues of OLTC transformers under both normal and non-normal operating conditions. It entails a detailed examination of the effect of PV integration on the operating characteristics of OLTC in a systematic approach and also dwells upon coordination processes between OLTC and Flexible AC Transmission Systems (FACTS) devices, such as Distribution Static Synchronous Compensator (D-STATCOM) or Static VAR Compensator (SVC), which are highly effective in reducing tap operations. The future directions covered in the review include the operation of hybrid systems, cost-effective implementations, weather effects, predictive analytics, adaptive control techniques, etc. The case study included online monitoring of OLTC performance in two scenarios at the cement factory. First, under supply changes and load changes. Second, including PV penetration. The results show that OLTC increases the average daily tapping frequency (90 taps/day) by about 60%, with full PV penetration. It is concluded that this can’t be applied without coordinated control among OLTC, D-STATCOM, and PV inverters to maintain transformer life, improve reliability, and provide stable voltage profiles even under highly variable PV generation conditions. These results aim to provide a comprehensive resource for academics and practitioners, facilitating the advancement of advanced voltage control methods to support the transition to sustainable energy systems. Full article

Self-excited induction generators (SEIGs) used in wind energy systems suffer from poor voltage and frequency regulation due to varying active/reactive power demands of nonlinear and unbalanced loads. The distribution static compensator (DSTATCOM) provides an effective solution through reactive power support and harmonic mitigation. However, its performance strongly depends on the robustness of the control algorithm against harmonics, load imbalance, and sensor-induced measurement errors such as DC offset, which degrade reference current generation. This study proposes an Advanced Dual Fourth-Order Generalized Integrator (ADFOGI)-based control algorithm to improve voltage and frequency regulation of SEIG–DSTATCOM systems under such adverse conditions. The proposed method inherently rejects DC offset components and enables accurate reference current generation even under severe harmonic distortion, load imbalance, and transient disturbances. The effectiveness of the approach is validated on an OPAL-RT real-time platform under three scenarios: nonlinear load, unbalanced nonlinear load, and one-phase open-circuit condition, where DC offset is intentionally introduced to emulate sensor errors. Under the most severe case, where load current THD reaches 16.23%, SEIG current THD is reduced to 3.71% and voltage THD to 1.66%. In all scenarios, harmonic levels remain below the IEEE-519-2022 limit of 5%, confirming the robustness and effectiveness of the proposed control strategy. Full article

This paper addresses the optimal management of photovoltaic (PV) systems and distribution static synchronous compensators (D-STATCOMs) in modern electrical distribution networks. A mixed-integer nonlinear programming (MINLP) model is formulated which co-optimizes device placement, sizing, and multi-period dispatch to minimize the total annualized system costs while satisfying AC power flow and operational constraints. To solve this challenging problem, a decomposition methodology is proposed, wherein the binary location decisions for the PVs and D-STATCOMs are treated as predefined inputs, upon the basis of site selections commonly reported in the literature. With the integer variables fixed, the problem is reduced to a continuous nonlinear programming (NLP) subproblem for optimal capacity sizing and operational scheduling, which is solved using the interior point optimizer (IPOPT) via the Julia/JuMP environment. The core contribution of this work lies in its comprehensive demonstration of the economic superiority of variable reactive power injection over conventional fixed compensation schemes. Through numerical validation on standard 33- and 69-bus test systems, it is shown that a variable D-STATCOM operation yields substantial and consistent economic gains. Compared to optimized fixed-injection solutions, variable injection provides additional annual savings averaging USD 120,516 (33-bus feeder) and USD 125,620 (69-bus grid), corresponding to a further 3.4% reduction in total costs. These benefits prove robust across different device location sets identified by various metaheuristic algorithms, and they scale effectively to larger network topologies. The results demonstrate that transitioning to variable power injection is not merely an incremental improvement but a fundamental advancement for achieving techno-economic optimality in distribution system planning. The proposed methodology provides utilities with a computationally efficient framework for determining near-optimal PV and D-STATCOM management strategies by first fixing deployment locations based on established planning insights and then rigorously optimizing sizing and dispatch, in order to maximize economic returns while ensuring reliable network operation. 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

This paper presents an intelligent operational strategy that performs the coordinated dispatch of active and reactive power from PV distributed generators (PV DGs) and Distributed Static Compensators (D-STATCOMs) to support secure and economical operation of active distribution networks. The problem is formulated as a nonlinear optimization problem that explicitly represents the P and Q control capabilities of Distributed Energy Resources (DER), encompassing small-scale generation and compensation units connected at the distribution level, such as PV generators and D-STATCOM devices, adjusting their reference power setpoints to minimize daily operating costs, including energy purchasing and DER maintenance, while satisfying device power limits and the voltage and current constraints of the grid. To solve this problem efficiently, a parallel version of the Population Continuous Genetic Algorithm (CGA) is implemented, enabling simultaneous evaluation of candidate solutions and significantly reducing computational time. The strategy is assessed on the 33- and 69-node benchmark systems under deterministic and uncertainty scenarios derived from real demand and solar-generation profiles from a Colombian region. In all cases, the proposed approach achieved the lowest operating cost, outperforming state-of-the-art metaheuristics such as Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA), while maintaining power limits, voltages and line currents within secure ranges, exhibiting excellent repeatability with standard deviations close to 0.0090%, and reducing execution time by more than 68% compared with its sequential counterpart. The main contributions of this work are: a unified optimization model for joint P–Q control in PV and D–STATCOM units, a robust codification mechanism that ensures stable convergence under variability, and a parallel evolutionary framework that delivers optimal, repeatable, and computationally efficient energy management in distribution networks subject to realistic operating uncertainty. Full article

The interaction between the Photovoltaic station and the weak grid can easily trigger sub- or super-synchronous oscillation (SSO). In this article, the equivalent impedance model of the photovoltaic grid-connected system is built, and the mechanism of SSO is analyzed based on the global admittance criterion (GA). To mitigate the SSO, a Distribution Static Synchronous Compensator (D-STATCOM) supplementary damping control (SDC) strategy is proposed, which uses a three-parameter notch filter to extract the sub- or super-synchronous harmonic component without a phase shift. The component is superimposed on the modulated wave of the D-STATCOM through the gain link to obtain the modulation instruction. At the sub- or super-synchronous frequency, the D-STATCOM can be equivalent to the parallel impedance in the system and play a role in suppressing the sub- or super-synchronous oscillation. Compared to the complex combination filters in the traditional SDC, which require phase compensation and have poor adaptability, the three-parameter notch filter used in this SDC does not need a phase compensation stage and can effectively cope with the presence of oscillation frequencies on both sides of the fundamental frequency with a simpler design. Simulation results prove that the proposed scheme effectively improves the stability of photovoltaic generation under different short-circuit ratios, irradiance levels, and fault conditions. The proposed solution can be applied to photovoltaic generation equipped with D-STATCOM. Full article

Voltage instability and power quality degradation represent critical barriers to the reliable operation of modern wind farm-based microgrids. As the share of distributed wind generation continues to grow, fluctuating wind speeds and variable reactive power demands increasingly challenge grid stability. This study proposes an adaptive decentralized framework integrating a Dynamic Distribution Static Compensator (DSTATCOM) with an Artificial Neuro-Fuzzy Inference System (ANFIS)-based control strategy to enhance dynamic voltage and frequency stability in wind farm microgrids. Unlike conventional centralized STATCOM configurations, the proposed system employs parallel wind turbine modules that can be selectively switched based on voltage feedback to maintain optimal grid conditions. Each turbine is connected to a capacitive circuit for real-time voltage monitoring, while the ANFIS controller adaptively adjusts compensation signals to ensure minimal voltage deviation and reduced harmonic distortion. The framework was modeled and validated in the MATLAB/Simulink R2023a environment using the Simscape Power Systems toolbox. Simulation results demonstrated superior transient response, voltage recovery, and power factor correction compared with traditional PI and fuzzy-based controllers, achieving a total harmonic distortion below 2.5% and settling times under 0.5 s. The findings confirm that the proposed decentralized DSTATCOM–ANFIS approach provides an effective, scalable, and cost-efficient solution for maintaining dynamic stability and high power quality in wind farm based microgrids. Full article

The planning of unbalanced three-phase distribution networks increasingly requires the coordinated integration of distributed energy resources, such as photovoltaic (PV) generators and static compensators (D-STATCOMs), to enhance system performance and reduce costs. This planning task is inherently challenging, as it leads to a mixed-integer nonlinear optimization problem driven by nonconvex voltage–current–power relationships, phase unbalances, and the temporal variability of demand and solar irradiance. This work proposes an exact Mixed-Integer Nonlinear Programming (MINLP) framework for the joint siting and sizing of PV units and D-STATCOM devices, with an objective function based on the minimization of the equivalent annual cost of energy purchases and investments. The methodology is applied to 25- and 37-bus unbalanced test systems and benchmarked against four state-of-the-art metaheuristic algorithms. The results show that the exact MINLP consistently attains the global optimum, yielding reductions in equivalent annual cost of USD 392,855 (14.36%) and USD 436,361 (14.90%) for the respective test systems, whereas the metaheuristics provide near-optimal but slightly dispersed solutions. These findings highlight the potential of exact optimization as a robust and economically sound tool for long-term distribution network planning, combining technical reliability with guaranteed global optimality. Full article

This study aims to provide an efficient framework for the coordinated integration of AC and DC chargers, intermittent solar Photovoltaic (PV) Distributed Generation (DG) units, and a Distribution Static Compensator (DSTATCOM) across residential, commercial, and industrial zones of a Radial Distribution Network (RDN) considering the benefits of various stakeholders: Electric Vehicle (EV) charging station owners, EV owners, and distribution network operators. The model uses a multi-zone planning method and healthy-bus strategy to allocate Electric Vehicle Charging Stations (EVCSs), Photovoltaic Distributed Generation (PVDG) units, and DSTATCOMs. The proposed framework optimally determines the numbers of EVCSs, PVDG units, and DSTATCOMs using Harris Hawk Optimization, considering the maximization of techno-economic benefits while satisfying all the security constraints. Further, to showcase the benefits from the perspective of EV owners, an EV waiting-time evaluation is performed. The simulation results show that integrating EVCSs (with both AC and DC chargers) with solar PVDG units and DSTATCOMs in the existing RDN improves the voltage profile, reduces power losses, and enhances cost-effectiveness compared to the system with only EVCSs. Furthermore, the zonal division ensures that charging infrastructure is distributed across the network increasing accessibility to the EV users. It is also observed that combining AC and DC chargers across the network provides overall benefits in terms of voltage profile, line loss, and waiting time as compared to a system with only AC or DC chargers. The proposed framework improves EV owners’ access and reduces waiting time, while supporting distribution network operators through enhanced grid stability and efficient integration of EV loads, PV generation, and DSTATCOM. Full article

Modern utility systems are being heavily strained by rising energy consumption and dynamic load variations, which have an impact on the quality and reliability of the supply. Harmonic injection and reactive power imbalance are caused by the widespread divergence. Power quality (PQ) issues are mostly caused by renewable energy powered by power electronic converters that are integrated into the utility grid, despite the fact that a range of industries require high-quality power to function properly at all times. Several solutions have been created, but continuing efforts and newly improved solutions are needed to solve these problems by operating according to various international standards. Distributed Static Compensator (DSTATCOM) was created in the proposed model to enhance PQ in a standard bus system. A standard bus system using the DSTATCOM model was initially developed. A real-time dataset was gathered while applying various PQ disturbance conditions. A deep learning controller was created using this generated dataset, which examined the bus voltages to generate the DSTATCOM pulse signal. Two case studies, the IEEE 13 bus and the IEEE 33 bus system, were used to analyze the proposed work. Performance of the proposed deep learning controller was verified in various situations, including interruption, swell, harmonics, and sag. The outcome of THD in the IEEE 13 bus is 0.09% at the sag period, 0.08% at the swell period, 0.01% at the interruption period, and in the IEEE 33 bus was 1.99% at the sag period, 0.44% at the swell period, and 0.01% at the interruption period. Also, the effectiveness of the proposed deep learning controller was examined and contrasted with current methods like K-Nearest Neighbor (KNN) and Feed Forward Neural Network (FFNN). The validated results show that the suggested method provides an efficient mitigation mechanism, making it suitable for all cases involving PQ issues. Full article

With the increasing penetration of distributed generation and the diversification of electrical equipment, distribution networks face issues like three-phase unbalance and harmonic currents, while the voltage stability and inertia of the grid-connected system also decrease. A certain amount of energy storage is needed in a Distribution Static Synchronous Compensator (DSTATCOM) to manage power quality and actively support voltage and inertia in the network. This paper first addresses the limitations of traditional $dq0$ compensation algorithms in effectively filtering out negative-sequence twice-frequency components. An improved $dq0$ compensation algorithm is proposed to reduce errors in detecting positive-sequence fundamental current under unbalanced three-phase conditions. Second, considering the impedance ratio characteristics of the distribution network, while reactive power voltage regulation is common, active power regulation is more effective in high-resistance distribution networks. A grid-forming model-based active and reactive power coordinated voltage regulation method is proposed. This method uses synchronous control to establish a virtual three-phase voltage internal electromotive force, forming a comprehensive compensation strategy that combines power quality improvement and active voltage support, exploring the potential of energy storage DSTATCOM applications in distribution networks. Finally, simulation and experimental results demonstrate the effectiveness of the proposed control method. Full article

In this paper, we address the problem of intelligent operation of Distribution Static Synchronous Compensators (D-STATCOMs) in power distribution systems to reduce energy losses and CO₂ emissions while improving system operating conditions. In addition, we consider the entire set of constraints inherent in the operation of such networks in an environment with D-STATCOMs. To solve such a problem, we used three master–slave methodologies based on sequential programming methods. In the proposed methodologies, the master stage solves the problem of intelligent D-STATCOM operation using the continuous versions of the Monte Carlo (MC) method, the population-based genetic algorithm (PGA), and the Particle Swarm Optimizer (PSO). The slave stage, for its part, evaluates the solutions proposed by the algorithms to determine their impact on the objective functions and constraints representing the problem. This is accomplished by running an Hourly Power Flow (HPF) based on the method of successive approximations. As test scenarios, we employed the 33- and 69-node radial test systems, considering data on power demand and CO₂ emissions reported for the city of Medellín in Colombia (as documented in the literature). Furthermore, a test system was adapted in this work to the demand characteristics of a feeder located in the city of Talca in Chile. This adaptation involved adjusting the conductors and voltage limits to include a test system with variations in power demand due to seasonal changes throughout the year (spring, winter, autumn, and summer). Demand curves were obtained by analyzing data reported by the local network operator, i.e., Compañía General de Electricidad. To assess the robustness and performance of the proposed optimization approach, each scenario was simulated 100 times. The evaluation metrics included average solution quality, standard deviation, and repeatability. Across all scenarios, the PGA consistently outperformed the other methods tested. Specifically, in the 33-node system, the PGA achieved a 24.646% reduction in energy losses and a 0.9109% reduction in CO₂ emissions compared to the base case. In the 69-node system, reductions reached 26.0823% in energy losses and 0.9784% in CO₂ emissions compared to the base case. Notably, in the case of the Talca feeder—particularly during summer, the most demanding season—the PGA yielded the most significant improvements, reducing energy losses by 33.4902% and CO₂ emissions by 1.2805%. Additionally, an uncertainty analysis was conducted to validate the effectiveness and robustness of the proposed optimization methodology under realistic operating variability. A total of 100 randomized demand profiles for both active and reactive power were evaluated. The results demonstrated the scalability and consistent performance of the proposed strategy, confirming its effectiveness under diverse and practical operating conditions. Full article

The optimal integration of photovoltaic (PV) systems and distribution static synchronous compensators (D-STATCOMs) in electrical distribution networks is important to reduce their operating costs, improve their voltage profiles, and enhance their power quality. To this effect, this paper proposes a mixed-integer convex (MI-Convex) optimization model for the optimal siting and sizing of PV systems and D-STATCOMs, with the aim of minimizing investment and operating costs in electrical distribution networks. The proposed model transforms the traditional mixed-integer nonlinear programming (MINLP) formulation into a convex model through second-order conic relaxation of the nodal voltage product. This model ensures global optimality and computational efficiency, which is not achieved using traditional heuristic-based approaches. The proposed model is validated on IEEE 33- and 69-bus test systems, showing a significant reduction in operating costs in both feeders compared to traditional heuristic-based approaches such as the vortex search algorithm (VSA), the sine-cosine algorithm (SCA), and the sech-tanh optimization algorithm (STOA). According to the results, the MI-convex model achieves cost savings of up to 38.95% in both grids, outperforming the VSA, SCA, and STOA. Full article

The proactive involvement of photovoltaic (PV) smart inverters (PVSIs) in grid management facilitates voltage regulation and enhances the integration of distributed energy resources (DERs) within distribution networks. However, to fully exploit the capabilities of PVSIs, it is essential to achieve optimal control of their operations and effective coordination with voltage-regulating devices in the distribution network. This study developed a dual strategy approach to forecast the optimal setpoints of onload tap changers (OLTCs), PVSIs, and distribution static synchronous compensators (DSTATCOMs) to improve the voltage profiles in power distribution systems. The study began by running a centralized AC optimal power flow (CACOPF) and using the hourly PV output power and the load demand to determine the optimal active and reactive power of the PVSIs, the setpoint of the DSTATCOM, and the optimal tap setting of the OLTC. Furthermore, Machine Learning (ML) models were trained as controllers to determine the reactive-power setpoints for the PVSIs and DSTATCOMs as well as the optimal OLTC tap position required for voltage stability in the network. To assess the effectiveness of the method, comprehensive evaluations were carried out on a modified IEEE 33 bus with a high penetration of PV energy. The results showed that deep neural networks (DNNs) outperformed other ML models used to mimic the coordination method based on CACOPF. Furthermore, when the DNN-based controller was tested and compared with the optimizer approach under different loading and PV conditions, the DNN-based controller was found to outperform the optimizer in terms of computational time. This approach allows predictive control in power systems, helping system operators determine the action to be initiated under uncertain PV energy and loading conditions. The approach also addresses the computational inefficiency arising from contingencies in the power system that may occur when optimal power flow (OPF) is run multiple times. Full article

This study proposes an optimized energy management strategy for alternating current microgrids, integrating wind generation, battery energy storage systems (BESSs), and distribution static synchronous compensators (D-STATCOMs). The objective is to minimize operational costs, including grid electricity purchases (grid-connected mode), diesel generation costs (islanded mode), and maintenance expenses of distributed energy resources while ensuring voltage limits, maximum line currents, and power balance. A master–slave optimization approach is employed, where the Gray Wolf Optimizer (GWO) determines the optimal dispatch of energy resources, and successive approximations (SAs) perform power flow analysis. The methodology was validated on a 33-node microgrid, considering variable wind generation and demand profiles from a Colombian region under grid-connected and islanded conditions. To assess performance, 100 independent runs per method were conducted, comparing GWO against particle swarm optimization (PSO) and genetic algorithms (GAs). Statistical analysis confirmed that GWO achieved the lowest operational costs (USD 3299.39 in grid-connected mode and USD 11,367.76 in islanded mode), the highest solution stability (0.19% standard deviation), and superior voltage regulation. The results demonstrate that GWO with SA provides the best trade-off between cost efficiency, system stability, and computational performance, making it an optimal approach for microgrid energy management. Full article