Search Results (45)

This paper presents a novel cyber-resilient control strategy for enhancing the operational security of future smart distribution systems (SDSs) against compromised control setpoints originating from higher-level controllers. The proposed framework addresses the structure, control architecture, and cyber vulnerabilities of SDSs by embedding an anomaly detection and autonomous response mechanism within each control layer. An artificial neural network (ANN)-based detector is employed to identify non-implementable or malicious control commands based on local measurements and grid location data. Upon detecting a cyber anomaly, the controller avoids disconnection and enables droop-based autonomous operation, ensuring continued grid support. The proposed strategy was validated using MATLAB/Simulink R2022a under various dynamic test scenarios, demonstrating its ability to maintain system stability. Unlike prior studies that rely on offline anomaly detection, this study presents a real-time capable closed-loop control solution that detects anomalies during simulation runtime. The proposed method rejects erroneous commands arising from both cyber intrusions and human errors, thereby enhancing the cyber-resilience and reliability of SDS operations. Full article

Effective communication among distributed energy sources (DESs) is essential for optimizing energy allocation across power sources, loads, and storage devices in integrated renewable energy and energy management systems. This paper proposes a novel communication and energy management strategy to address challenges related to communication interference and inefficiencies in energy management. The proposed strategy employs the DC bus as a communication medium, enabling module coordination via pulsed voltage signals. Controller modules regulate the bus voltage based on the energy state of the bus and control the supplementary or absorptive energy flow from slave modules to maintain voltage stability. Simultaneously, communication between the master and slave modules is achieved through pulsed voltage signals of varying pulse widths, and power sharing is realized via droop control. Experimental results demonstrate that the proposed method effectively distributes energy across different voltage levels, enabling the controller modules to precisely regulate the voltage of the slave modules under varying load conditions. Full article

Hybrid energy storage systems (HESSs), with superior transient response characteristics compared to conventional battery (BAT) systems, have emerged as an effective solution for power balance. However, the high penetration of constant power loads (CPLs) introduces destabilization risks to the system. To address this challenge, this paper proposes a novel hierarchical control strategy to achieve voltage stabilization and accurate current sharing. First, this paper proposes an improved P–V² controller as the primary controller. It utilizes virtual conductance to replace the fixed coefficients of traditional droop controllers to achieve automatic power allocation between supercapacitors (SCs) and BATs, while eliminating the effects of CPLs on the voltage–current relationship. Second, based on traditional distributed control, the secondary control layer integrates a dynamic event-triggered communication mechanism, which reduces communication bandwidth requirements while maintaining precise current sharing across distributed buses. Finally, simulation and experimental results validate the effectiveness and robustness of the proposed control strategy. Full article

Unlike diesel generators or energy storage systems, photovoltaic (PV) arrays lack inherent rotational inertia and have output limitations due to their operational environmental dependencies. These characteristics restrict their suitability as primary power system backbone components. This study proposes a grid-forming (GF) control strategy for PV inverters in low voltage grid (LVG) using a model predictive control (MPC) approach. The proposed method introduces a novel predictive model accounting for capacitor dynamics to precisely regulate both AC-side output voltage and DC-side voltage. Furthermore, in this paper, P-V droop control replaces the traditional frequency regulation, achieving the real-time balance of DC/AC power and seamless sharing of multiple photovoltaic power sources. By integrating a modified cost function, the controller can flexibly switch between maximum power point tracking (MPPT) mode and power reserve mode according to varying output demands. The proposed strategy can provide advanced frequency stability, MPPT accuracy, and fast dynamic response under rapidly changing solar irradiance and load conditions. Simulation and experimental tests are carried out to validate the effectiveness of the proposed strategy. Full article

This paper addresses the challenges of efficient electric vehicle (EV) charging integration in Direct Current (DC) microgrids (MGs), particularly the impact of intermittent EV loads on power sharing and voltage regulation. Traditional droop control methods suffer from inherent trade-offs between performance indices of parallel distributed energy resources (DERs), which in turn results in improper source utilization. We propose a novel decentralized piece-wise droop control (PDC) approach with voltage compensation for EV charging to overcome this limitation and to minimize the unequal cable resistance effect on power sharing. This strategy dynamically optimises the droop characteristics based on EV charging load profiles, partitioning the droop curve to optimize power sharing accuracy and voltage stability considering the constraints of maximum allowable voltage deviation and loading. Simulation and experimental results demonstrate significant improvements in power sharing, enhanced DER utilization, and voltage deviations consistently within 2.5% when compared with traditional strategies. PDC offers a robust solution for enabling efficient and reliable EV charging in MGs, as it is not sensitive for EV load prediction errors and measurement noise. Full article

To address the frequency regulation requirements of hybrid energy storage (HES) in renewable-dominated power grids, this paper proposes an asymmetric droop control strategy based on an improved backpropagation (BP) neural network. First, a simulation model of HES (comprising supercapacitors for the power support and batteries for the energy balance) participating in primary frequency regulation is established, with a stepwise frequency regulation dead zone designed to optimize multi-device coordination. Second, an enhanced Sigmoid activation function (with controllable parameters a, b, m, and n) is introduced to dynamically adjust the power regulation coefficients of energy storage units, achieving co-optimization of frequency stability and State of Charge (SOC). Simulation results demonstrate that under a step load disturbance (0.05 p.u.), the proposed strategy reduces the maximum frequency deviation by 79.47% compared to scenarios without energy storage (from 1.7587 × 10⁻³ to 0.0555 × 10⁻³) and outperforms fixed-droop strategies by 44.33%. During 6-min continuous random disturbances, the root mean square (RMS) of system frequency deviations decreases by 4.91% compared to conventional methods, while SOC fluctuations of supercapacitors and batteries are reduced by 49.28% and 45.49%, respectively. The parameterized asymmetric regulation mechanism significantly extends the lifespan of energy storage devices, offering a novel solution for real-time frequency control in high-renewable penetration grids. Full article

The emergence of highly efficient and cost-effective power converters, coupled with the growing diversity of DC loads, has elevated the importance of DC microgrids to a level comparable with AC microgrids in the modern power industry. DC microgrids are free from synchronization and reactive power dynamics, making them more reliable and cost-effective. In autonomous mode, achieving effective voltage regulation and satisfactory power sharing is critical to ensuring the overall stability of the microgrid. As the common DC bus of the microgrid connects distributed generators (DGs), storage devices, and loads through power electronic converters (PECs), the controllers of these PECs must regulate the bus voltage effectively, track reference signals to meet power demands, and enable satisfactory load sharing. In this work, a real time decentralized droop controller is implemented for an islanded DC microgrid to enhance the voltage regulation at the DC bus and current sharing efficacy between the sources subject to load transients. A novel control strategy is presented in which the conventional droop control is modified considering the load dynamics. The performance of the proposed control strategy is compared with the conventional voltage droop control strategy. The fluctuations in the DC bus voltage, which is the major cause of voltage instability of the DC microgrid is effectively reduced by the proposed strategy. The proposed strategy is validated by comparing it with the conventional fixed droop control method on the MATLAB Simulink platform. The variable droop control strategy outperforms the fixed droop method by addressing sudden voltage fluctuations in the DC bus, which occur due to the inherent load current dependency of the fixed droop approach. This technique achieves enhanced voltage regulation, which is crucial for microgrid stability. 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

Reactive power sharing in distributed generators (DGs) is one of the key issues in the control technologies of greenship microgrids. Reactive power imbalance in ship microgrids can cause instability and potential equipment damage. In order to improve the poor performance of the traditional adaptive droop control methods used in microgrids under high-load conditions and influenced by virtual impedance parameters, this paper proposes a novel strategy based on the deep reinforcement learning DQN-VI, in which a deep Q network (DQN) is combined with the virtual impedance (VI) method. Unlike traditional methods which may use static or heuristically adjusted VI parameters, the DQN-VI strategy employs deep reinforcement learning to dynamically optimize these parameters, enhancing the microgrid’s performance under varying conditions. The proposed DQN-VI strategy considers the current situation in greenships, wherein microgrids are generally equipped with cables of different lengths and measuring the impedance of each cable is challenging due to the lack of space. By modeling the control process as a Markov decision process, the observation space, action space, and reward function are designed. In addition, a deep neural network is used to estimate the Q function that describes the relationship between the state and the action. During the training of the DQN agent, the process is optimized step-by-step by observing the state and rewards of the system, thereby effectively improving the performance of the microgrids. The comparative simulation experiments verify the effectiveness and superiority of the proposed strategy. Full article

Conventional droop control (a decentralized method to regulate power sharing by adjusting voltage–current slopes) in DC microgrids faces challenges in balancing precise current distribution, bus voltage regulation, and communication pressure, especially in distributed energy management scenarios. To address these limitations, this paper proposes an adaptive control strategy combining three layers: (1) Primary control achieves power sharing and voltage stabilization via U-I droop characteristics for distributed energy resources (DERs); (2) Secondary control corrects voltage deviations and droop coefficient imbalances through multi-agent consensus algorithms, ensuring global equilibrium; (3) Event-triggered consensus control minimizes communication pressure via a novel protocol with time-varying coupling weights and a hybrid trigger function combining state variables and time-decaying terms rigorously proven to exclude Zeno behavior (i.e., infinite triggering in finite time) using Lyapunov stability theory. Full article

In a traction power supply system, the design of traction substations significantly influences both the system’s operational stability and investment costs, while the energy management strategy of the flexible substations affects the overall operational expenses. This study proposes a novel two-stage system optimization design method that addresses both the configuration of the system and the control parameters of traction substations. The first stage of the optimization focuses on the system configuration, including the optimal location and capacity of traction substations. In the second stage, the control parameters of the traction substations, particularly the droop rate of reversible converters, are optimized to improve regenerative braking energy utilization by applying a fuzzy logic-based adjustment strategy. The optimization process aims to minimize the total annual system cost, incorporating traction network parameters, power supply equipment costs, and electricity expenses. The parallel cheetah algorithm is employed to solve this complex optimization problem. Simulation results for Metro Line 9 show that the proposed method reduces the total annual project costs by 5.8%, demonstrating its effectiveness in both energy efficiency and cost reduction. Full article

The rapid proliferation of EVs has ushered in a transformative era for the power industry, characterized by increased demand volatility and grid frequency instability. In response to these challenges, this paper introduces a novel approach that combines fuzzy logic with adaptive inertia control to improve the frequency stability of grids amidst large-scale electric vehicle (EV) integration. The proposed methodology not only adapts to varying charging scenarios but also strikes a balance between steady-state and dynamic performance considerations. This research establishes a solid theoretical foundation for the inertia-adaptive virtual synchronous generator (VSG) concept and introduces a pioneering fuzzy inertia-based VSG methodology. Additionally, it incorporates adaptive output scaling factors to enhance the robustness and adaptability of the control strategy. These contributions offer valuable insights into the evolving landscape of adaptive VSG strategies and provide a pragmatic solution to the pressing challenges arising from the integration of large-scale EVs, ultimately fostering the resilience and sustainability of contemporary power systems. Finally, simulation results illustrate that the new proposed fuzzy adaptive inertia-based VSG method is effective and has superior advantages over the traditional VSG and droop control strategies. Specifically, the proposed method reduces the maximum frequency change by 25% during load transitions, with a peak variation of 0.15 Hz compared to 0.2 Hz for the traditional VSG. Full article

Managing parallel−connected single−phase distributed generators in low−voltage microgrids is challenging due to the volatility of renewable energy sources and fluctuating load demands. Traditional droop control struggles to maintain precise power sharing under dynamic conditions and varying line impedances, leading to inefficiency. This paper presents a novel adaptive droop control strategy integrating artificial neural networks and particle swarm optimization to enhance microgrid performance. Unlike prior methods that optimize artificial neural network parameters, the proposed approach uses particle swarm optimization offline to generate optimal dq−axis voltage references that compensate for line effects and load variations. These serve as training data for the artificial neural network, which adjusts voltage in real time based on line impedance and load variations without online optimization. This decoupling ensures computational efficiency and responsiveness, maintaining voltage and frequency stability during rapid load changes. Addressing dynamic load fluctuations and line impedance mismatches without inter−generator communication enhances reliability and reduces complexity. Simulations demonstrate that the proposed strategy maintains stability, achieves accurate power sharing with errors below 0.5%, and reduces total harmonic distortion, outperforming conventional droop control methods. These findings advance adaptive control in microgrids, supporting seamless renewable energy integration and enhancing the reliability and stability of distributed generation systems. Full article

As the proportion of renewable energy in energy use continues to increase, to solve the problem of line impedance mismatch leading to the difference in the state of charge (SOC) of each distributed energy storage unit (DESU) and the DC bus voltage drop, a distributed energy storage system control strategy considering the time-varying line impedance is proposed in this paper. By analyzing the fundamental frequency harmonic components of the pulse width modulation (PWM) signal carrier of the converter output voltage and output current, we can obtain the impedance information and, thus, compensate for the bus voltage drop. Then, a novel, droop-free cooperative controller is constructed to achieve SOC equalization, current sharing, and voltage regulation. Finally, the validity of the system is verified by a hardware-in-the-loop experimental platform. Full article

In the off-grid photovoltaic DC microgrid, traditional droop control encounters challenges in effectively adjusting the droop coefficient in response to varying power fluctuation frequencies, which can be influenced by factors such as line impedance. This paper introduces a novel Multi-strategy Harris Hawk Optimization Algorithm (MHHO) that integrates variable universe fuzzy control theory with droop control to develop an adaptive variable universe fuzzy droop control strategy. The algorithm employs Fuch mapping to evenly distribute the initial population across the solution space and incorporates logarithmic spiral and improved adaptive weight strategies during both the exploration and exploitation phases, enhancing its ability to escape local optima. A comparative analysis against five classical meta-heuristic algorithms on the CEC2017 benchmarks demonstrates the superior performance of the proposed algorithm. Ultimately, the adaptive variable universe fuzzy droop control based on MHHO dynamically optimizes the droop coefficient to mitigate the negative impact of internal system factors and achieve a balanced power distribution between the battery and super-capacitor in the DC microgrid. Through MATLAB/Simulink simulations, it is demonstrated that the proposed adaptive variable universe fuzzy droop control strategy based on MHHO can limit the fluctuation range of bus voltage within ±0.75%, enhance the robustness and stability of the system, and optimize the charge and discharge performance of the energy storage unit. Full article