Real-time Power Research Articles

Optimal control of grid-connected overvoltage of distributed photovoltaic power generation based on cluster division

To address the overvoltage problem caused by the reverse flow of current when a high proportion of distributed photovoltaic (PV) is connected to the distribution network, this paper proposes a grid-connected voltage regulation control strategy based on the cluster division of the Distributed Model Predictive Control (DMPC) algorithm. Firstly, the overvoltage responsibility of each node is calculated using the Shapley value method. This is combined with k-means clustering to achieve effective cluster division, enabling dynamic adjustment of the active and reactive power of photovoltaic power generation units to stabilize regional voltage. Secondly, a group grid-connected voltage control strategy is introduced. This strategy controls the active and reactive power outputs by integrating real-time power output and voltage information from PV generating units in the region with the DMPC algorithm, ensuring overall voltage stability of the grid-connected system. Finally, actual overvoltage data from a 10 kV distribution line in the Dingxi power grid, Gansu Province, is used to verify that under the proposed control strategy, PV grid-connected overvoltage nodes are maintained within 1.06 p.u. The control effect is improved by a margin of 0.05 compared to traditional control methods. This demonstrates the effectiveness of the grouped grid-connected voltage regulation control strategy, achieving smoother voltage regulation performance in distributed PV grid-connected systems.

Evaluating Adaptive Droop Control for Steady-State Power Balancing in DC Microgrids Using Controller Hardware-in-the-Loop

DC microgrids (DCMGs) have been gaining attention due to their advantages over AC microgrids. The most commonly used control technique for DCMGs is droop control. Despite its benefits, droop control has drawbacks, such as power mismatch and deviations in DC bus voltage, often caused by differences in line resistance among grid-forming power electronics converters. To address these issues, the article proposes an adaptive droop control technique to correct steady-state power imbalances between grid-forming units in the DCMG. Additionally, a hierarchical voltage level is introduced to regulate the DC bus voltage. The analyzed DCMG includes two energy storage units, electronic loads, and a renewable energy source, each with its respective power electronic converter. The proposed technique uses real-time output power measurements from the energy storage system to calculate line resistance differences, incorporating these into the adaptive droop calculation. Several operating conditions are tested using a controller hardware-in-the-loop. The results validate the proposed technique and design guidelines.

Adaptive wireless-powered network based on CNN near-field positioning by a dual-band metasurface

With the improvement of industry, the connectivity of electronic devices gradually shift from wired to wireless. As a solution for power delivery, the non-contact power transfer holds promising ways to charge for moving terminals, enabling battery-free sensing, processing, and communication. Based on a dual-band metasurface, this study proposes an adaptive wireless-powered network (AWPN) to realize the simultaneous wireless localization and non-contact power supply. It first achieves localization with 3 cm resolution on a single-input single-output (SISO) system, by combining space-time-coding (STC) and convolutional neural network (CNN). With precise position information, AWPN real-time aligns power beams to the terminals for stable energy transmission. Then, battery-free terminals enable to perceive the environmental data and uploads the results. From the measurement results, AWPN gets more than 98% CNN classification accuracy and can tolerate certain environmental changes. Thus, being adaptive and contactless, our study will propel the advancement in Internet of Things (IoT), intelligent metasurface, and the robot industry.

Active sensitivity coefficient-guided reinforcement learning for power grid real-time dispatching

The integration of renewable energy sources into power systems and the expansion of grid scale have introduced a higher degree of operational unpredictability, thereby increasing the risk of safety hazards such as transmission line overloads. Existing reinforcement learning algorithms that incorporate expert knowledge aim to facilitate secure dispatching of the power grid. However, these algorithms, which adopt strategies akin to imitation learning during the learning process, do not effectively address systemic constraints such as line limits and power balance. In response to this issue, this study introduces a novel active sensitivity coefficient-guided reinforcement learning method for real-time power grid dispatch (SRL-Dispatching). Firstly, a sensitivity-based reinforcement learning framework is proposed using the Soft Actor-Critic (SAC) algorithm. Secondly, a sensitivity coefficient-based method for compressing the action space boundary is proposed to address the security implications associated with transmission line overloads. Subsequently, a feasible domain projection approach is introduced to ensure that dispatch operations adhere to safety constraints. By employing sensitivity factors to guide the learning process of the agent in managing line overloads and ensuring safe operation, the precision and robustness of dispatch strategies in the power system environment are significantly enhanced. Simulation results using the SG-126 power grid simulator indicate that SRL-Dispatching accelerates training by a factor of 9.8 compared to state-of-the-art RL methods, with comparable decision-making times. Across various load levels, SRL-Dispatching demonstrates superior performance in terms of renewable energy integration, line overload management, and power balance control.

On-demand beamforming and wide dynamic power range for WPT and EH applications

Abstract This work delves into advancements in wireless power transfer (WPT) and radiofrequency (RF) energy harvesting (EH), focusing on on-demand beamforming and wide-dynamic power range technologies. These innovations promise significant improvements in efficiency and adaptability for wireless energy systems. For transmitting RF power, on-demand beamforming enhances power delivery precision by accurately targeting specific devices, minimizing energy waste, and maximizing received power. This technology is particularly useful in dynamic environments with constantly changing device positions, ensuring stable power levels and effective real-time power transfer, such as for mobile device charging. For receiving RF power, wide-dynamic power range implementation allows EH and WPT systems to adjust power output across a broad spectrum, optimizing energy use and extending device lifespan. This capability supports scalability, accommodating devices with varied power needs, also enabling new applications in consumer electronics, healthcare, smart homes, and cities, and enhancing energy management in smart infrastructures. Additionally, this study explores three-dimensional (3D)-printable antennas and RF circuitries for battery-free applications. The versatility of 3D printing allows the creation of complex, efficient, and customizable RF components, fostering innovative battery-free solutions. Integrating on-demand beamforming and wide-dynamic power range technologies in EH systems promise improved energy transfer efficiencies, reduced losses, and sustainable, cost-effective wireless power systems.

Transient Stability-Based Fast Power System Contingency Screening and Ranking

Today’s power systems are operated closer to their stability limits due to the continuously growing load demands, interface to open markets, and integration of more renewable energies. In order to provide operators with clear insight on the current system situation, near real-time power systems dynamic security assessment tools are required. One of the core elements of near real-time dynamic security assessment tools is contingency screening and ranking. Most of the commercially available tools screen and rank contingencies by using the traditional numerical integration or Transient Energy Functions (TEFs) or hybrid methods. The traditional numerical integration method is accurate but computationally intensive and has a slow assessment speed which makes it difficult to identify any insecure contingency before it happens. Despite the TEF method of transient stability analysis being relatively fast, it develops less accurate results due to models simplification and assumptions. This paper introduces transient stability based on fast and robust contingency screening and ranking using an Adaptive step-size Differential Transformation (AsDTM) method. Based on the most current snapshot from Supervisory Control and Data Accusation (SCADA) data, the proposed method triggers AsDTM-based transient stability simulation for each credible contingency and evaluates Transient Stability Indices (TSI) as the normalized weighted sum of squares of errors derived from state variables and complex bus voltages at every simulation time step. Finally, contingencies are ranked based on these TSI and the worst contingency is identified for the next detail assessment. The method is tested on IEEE 9 bus and 39 bus test systems. Test results reveal that the proposed method is faster, robust, and can be used in near real-time dynamic security assessment sessions.

Energy management strategy for fuel cell hybrid tractor considering demand power frequency characteristic compensation

The application of fuel cell tractors is expected to drive technological upgrades and sustainable development in agricultural machinery. However, fuel cell hybrid systems face issues such as slow dynamic response, low efficiency, and short lifespan. This paper proposes an energy management strategy based on signal reconstruction methods, including Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to achieve optimal energy utilization and system efficiency based on frequency response characteristics. First, we collected data on the tractor’s traction force and operating speed, and calculated the required traction power using a full-machine dynamics model built in MATLAB software. We conducted frequency response characteristic analysis of the fuel cell hybrid system based on EMD and VMD, establishing an energy management controller to sequentially meet the average power demand of the fuel cell under plowing load operations, the instantaneous acceleration power demand of the power battery, and the real-time compensation power demand of the supercapacitor. The results show that the EMD strategy exhibits good stability, while the VMD strategy performs better in terms of hydrogen consumption. Under the VMD strategy, the hybrid system achieves a maximum output efficiency of 55.0% with a total hydrogen consumption of 750 g. Compared to the EMD strategy, the maximum efficiency of the system increases by 27.31%, and hydrogen consumption decreases by 3.49%. This study provides a new theoretical foundation and technological route for the application of fuel cell hybrid systems in the field of agricultural machinery.

Assessing Energy Availability and Glucose Dynamics in Adolescent Cyclists: Implications for Nutritional Interventions During the Competitive Season.

The risk of developing a state of low energy availability (LEA) (<30 kcals/kg free-fat mass) in endurance athletes is known and recommendations for nutrition are available. However, information on male adolescent cyclists and the influence of hot temperatures is limited. The aim of this study was to investigate the impact on energy availability of two 4-day nutritional intervention strategies: (1) supplementary carbohydrate (CHO) intake during exercise and (2) designing and implementing individual nutritional interventions. Each intervention was preceded by a 4-day basal assessment. Eight competitive male junior road cyclists (aged 16-17 years) were investigated using a 4-day diet and activity records, alongside bioelectric impedance analysis. Their real-time power output, interstitial glucose, and temperature were recorded via sensors and a bike computer. Their energy intake (EI) was estimated from daily, self-reported food diaries. Overall, 100% and 71% of the cyclists were in a state of LEA during the baseline assessment of the supplementary CHO and nutritional interventions, respectively. LEA prevalence, not modified by supplementary CHO intake alone (from 100% to 87%, ns), was markedly reduced by the individual nutritional intervention (from 71% to 14%, p < 0.05). When considering all the data as a whole, LEA was positively influenced by the training load (OR 1.06; 95% Cl 1.03 to 1.09) and free-fat mass (OR 1.46; 1.04 to 2.04) and was negatively affected by EI (OR 0.994; 0.991 to 0.997). A hot environment (air temperature) failed to influence the LEA or glucose dynamics. the nutritional intervention, but not the supplementary CHO intake, markedly reduced the prevalence of LEA in adolescents, who often fail to match their energy expenditure with their energy intake during the competitive season. Nutritional education is essential for adolescent endurance cycling teams.

MPPT Solar Controller: A Theoretical Design and Prototype Development

This paper discusses the theoretical design and ongoing prototype development of a Maximum Power Point Tracking (MPPT) solar controller system aimed at efficient energy harvesting and smart load management. The system is designed to optimize energy extraction from a solar panel using MPPT algorithms, with smart load management for real-time power distribution. The controller incorporates cloud-based monitoring, AI optimization, and a user-friendly interface. The system is expected to deliver robust, efficient, and safe operation under varying environmental conditions

A novel digital-twin approach based on transformer for photovoltaic power prediction

The prediction of photovoltaic (PV) system performance has been intensively studied as it plays an important role in the context of sustainability and renewable energy generation. In this paper, a digital twin (DT) model based on a domain-matched transformer is proposed using convolutional neural network (CNN) for domain-invariant feature extraction, transformer for PV performance prediction, and domain adaptation neural network (DANN) for domain adaptation. The effectiveness of the proposed framework is validated using a PV power prediction dataset. The results indicate an accuracy improvement of up to 39.99% in model performance. Additionally, experiments with varying numbers of timestamps demonstrate enhanced PV power prediction performance as parameters are continuously updated within the DT framework, offering a reliable solution for real-time and adaptive PV power forecasting.

Locally Distributed Digital Real-Time Power System Co-Simulation via Multi-Phase Distributed Transmission Line Model

Locally Distributed Digital Real-Time Power System Co-Simulation via Multi-Phase Distributed Transmission Line Model

Multi-Objective Reliable Optimization Dispatch of High-proportion Power System Based on Improved Particle Swarm Algorithm

Abstract Energy storage technology is pivotal for managing high-proportion power system consumption and maintaining real-time power balance. The optimal configuration of energy storage is influenced by factors such as capacity, wind power loads, and output power. This paper utilizes dynamic programming to evaluate the operational efficiency of conventional units and wind turbines within the power system, followed by the application of an enhanced particle swarm optimization algorithm to tackle optimization challenges. A BP neural network is employed to predict the impact of different units on the power balance of the micro-grid, aiming to achieve maximum comprehensive benefits and minimize investment costs. A reliability model is proposed to balance power storage and load release, thereby improving system stability and reliability. Simulation results demonstrate that dynamic programming combined with enhanced particle swarm optimization can reduce the operating costs of the micro-grid, highlighting the practical advantages of power system energy storage technology.

Cost-Effective Power Management for Smart Homes: Innovative Scheduling Techniques and Integrating Battery Optimization in 6G Networks

This paper presents an Optimal Power Management System (OPMS) for smart homes in 6G environments, which are designed to enhance the sustainability of Green Internet of Everything (GIoT) applications. The system employs a brute-force search using an exact solution to identify the optimal decision for adapting power consumption to renewable power availability. Key techniques, including priority-based allocation, time-shifting, quality degradation, battery utilization and service rejection, will be adopted. Given the NP-hard nature of this problem, the brute-force approach is feasible for smaller scenarios but sets the stage for future heuristic methods in large-scale applications like smart cities. The OPMS, deployed on Multi-Access Edge Computing (MEC) nodes, integrates a novel demand response (DR) strategy to manage real-time power use effectively. Synthetic data tests achieved a 100% acceptance rate with zero reliance on non-renewable power, while real-world tests reduced non-renewable power consumption by over 90%, demonstrating the system’s flexibility. These results provide a foundation for further AI-based heuristics optimization techniques to improve scalability and power efficiency in broader smart city deployments.

Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control

This paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. Existing charging stations often encounter issues such as unstable PV power generation and dependence on grid stability, which can interrupt the EV charging process during grid faults. Additionally, PV arrays are typically designed to extract maximum power, leading to over-current or over-voltage situations that compromise the safety of the charging infrastructure and the EV. Furthermore, these systems often require multiple power electronics converters, increasing complexity and costs while reducing overall system efficiency. To overcome these limitations, the proposed algorithm dynamically switches between on-grid and off-grid modes based on real-time weather conditions, grid availability, and the state of charge of the battery electric vehicle (BEV). This approach maximizes PV power utilization, minimizes grid dependency, and enhances BEV charging performance while prioritizing EV safety and ensuring an uninterrupted power supply. It also provides flexibility in BEV power sizing, optimizing the use of power electronics converters to reduce costs and complexity. Two distinct operating modes, adaptive charging and fast charging, are introduced, each integrated with dedicated model predictive controllers (MPC) to achieve specific control objectives. Semi-experimental simulations using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335 demonstrate that the station manager significantly improves performance over conventional methods under various irradiance levels. Moreover, numerical results demonstrate the superiority of the proposed MPC-based approach over traditional controllers such as Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) in different charging modes. For instance, the MPC controller achieved an Integral Absolute Error (IAE) of 11.45%, lower than that of the PID controller, and 4.3% lower than the SMC controller.

A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties

The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.

Energy management with adaptive moving average filter and deep deterministic policy gradient reinforcement learning for fuel cell hybrid electric vehicles

Fuel cell hybrid electric vehicles (FCHEV) with battery (BAT) and supercapacitor (SC) advance in flexible configuration and high energy efficiency. However, the complex coupling relationship among various power sources poses a severe challenge to the design of the energy management system (EMS), including multi-degrees of freedom power allocation, fuel economy, and power sources lifespan of the FCHEV. This paper proposes an EMS based on a dual-layer power distribution structure. In the upper layer, adaptive moving average filter (AMAF) is designed to separate different frequency power, where the energy supply of the SC is managed to attenuate fluctuating power and simplifies the optimization problem and reduces computational costs. The lower layer is constructed by the deep deterministic policy gradient (DDPG) algorithm, where fuel cell system (FCS) hydrogen consumption and degradation rewards are designed to simultaneously enhance fuel efficiency and degradation performance by regulating the FCS real-time power variation. The proposed strategy has been evaluated regarding FCHEV fuel economy and FCS durability under combined driving cycle simulation, which shows AMAF + DDPG strategy reduces fuel consumption by 7.24 % and 1.3 %, also the degradation reduces by 0.04 % and 0.02 % compared with different EMS. Simulation results demonstrate that AMAF + DDPG optimizes the output characteristics of power sources.

Real-time power optimization based on Q-learning algorithm for direct methanol fuel cell system

Efficient real-time power optimization of direct methanol fuel cell (DMFC) system is crucial for enhancing its performance and reliability. The power of DMFC system is mainly affected by stack temperature and circulating methanol concentration. However, the methanol concentration cannot be directly measured using reliable sensors, which poses a challenge for the real-time power optimization. To address this issue, this paper investigates the operating mechanism of DMFC system and establishes a system power model. Based on the established model, reinforcement learning using Q-learning algorithm is proposed to control methanol supply to optimize DMFC system power under varying operating conditions. This algorithm is simple, easy to implement, and does not rely on methanol concentration measurements. To validate the effectiveness of the proposed algorithm, simulation comparisons between the proposed method and the traditional perturbation and observation (P&O) algorithm are implemented under different operating conditions. The results show that proposed power optimization based on Q-learning algorithm improves net power by 1% and eliminates the fluctuation of methanol supply caused by P&O. For practical implementation considerations and real-time requirements of the algorithm, hardware-in-the-loop (HIL) experiments are conducted. The experiment results demonstrate that the proposed methods optimize net power under different operating conditions. Additionally, in terms of model accuracy, the experimental results are well matched with the simulation. Moreover, under varying load condition, compared with P&O, proposed power optimization based on Q-learning algorithm reduces root mean square error (RMSE) from 7.271% to 2.996% and mean absolute error (MAE) from 5.036% to 0.331%.

Directed energy deposition of Ti6Al4V wire with continuous layer varied laser power

The laser-based directed energy deposition (DED) additive manufacturing enables the efficient fabrication of metallic parts. The layer-wise performance in DED-built metallic parts is challenging to remain consistent due to the intricate heat accumulation. As such, it is of significance to control the printing quality by adjusting the process settings with layers. In this work, we used DED to achieve Ti6Al4V wire deposition with continuously layer-varied laser power. Three laser power settings were selected to investigate the variation in defects, microstructure, and mechanical properties with layers. Our findings suggested that optimizing real-time laser power is critical for achieving desirable manufacturing efficiency and resultant performance in the DED of metallic materials. This research provides valuable guidelines for parameter optimization in DED processes, aiming to enhance the production quality and performance of high-strength metallic components, particularly in sectors demanding high-performance materials.

Application of Improved Classification Algorithm with Binary Tree Support Vector Machine in Power Grid Prediction

Abstract To further improve the accuracy of the power grid prediction, the paper proposes an improved binary tree classification algorithm with the SVM and predicts the real time power grid. Based on the basic principles of SVM, the common multi-classifier classification algorithm and its characteristics are summarized. Incorporating the advantages of existing classification algorithms, an improved binary tree classification algorithm was used, addressing the limitations of conventional methods. From the simulation results, it can be seen that the improved binary tree algorithm can shorten the test time from the original 0.285 seconds to 0.267 seconds, and the measured category can also be increased from the original 94.42 to 95.27. This shows that the improved binary tree can not only improve the practicability of the support vector machine algorithm, but also better improve the ability of real-time prediction of power grid.

Sliding mode-based direct power control of unified power quality conditioner

The Unified Power Quality Conditioner (UPQC) is a promising solution for mitigating multiple Power Quality(PQ) issues in distribution systems, including harmonics, poor power factor, voltage sag/swell and voltage imbalance. The conventional Sliding Mode Controller (SMC) in UPQCs suffers from wide switching frequency variations, chattering problems, and inherent active and reactive power coupling. This study proposes a nonlinear control method, Sliding Mode-based Direct Power Control (SMC-DPC), for the simultaneous regulation of the shunt and series compensators in a UPQC. By optimizing voltage vector selection based on real-time power errors, the proposed method effectively mitigates chattering, and reduces switching frequency variations, and ensures precise tracking of instantaneous active and reactive powers even in the presence of coupling effects. The proposed approach simplifies the system design and improves steady state and dynamic power tracking. The simulation results in MATLAB Simulink® on a 20 kVA system demonstrate that the proposed method achieves a source current THD of 1.52%, compared to 2.31% for conventional SMC and 5.11% for linear controller. Furthermore, the method is robust to grid parameter variations and demonstrates satisfactory performance in both strong and weak grids. In weak grids, the proposed method reduces the line losses by 13.71% and 14.54% compared to SMC and linear controller respectively. The reported results comply with international standards such as IEEE-519 and IEC 61000-3-12, confirming effectiveness of SMC-DPC for enhancing PQ in distribution system.