Adaptive Electromechanical Drive with Internal Inertial Energy Exchange and Energy-Based Control

Internal energy exchanges and chaotic dynamics in an intrinsically coupled system

The recent use of hybrid renewable energy systems (HRESs) is considered one of the most reliable ways to improve energy access to decentralized communities because of their techno-economic and environmental benefits. Many distant locales, such as camps in war-torn nations, lack basic necessities like power. This study proposes a remedy for power outages in these areas; by designing an HRES and a control system for monitoring, distributing, and managing the electrical power from sustainable energy sources to supply the load. Hence, providing affordable, reliable, and clean energy for all (Sustainable Development Goal 7). In this study, the feasibility and techno-economic performance of an HRES for a refugee camp was evaluated under load following (LF), cycle charging (CC), and predictive control strategy (PS). The optimization results revealed that the PS was the most suitable, as it had the lowest cost and was more eco-friendly and energy-efficient. The predictive control strategy had a 48-h foresight of the load demand and resource potential and hence could effectively manage the HRES. The total net present cost (NPC) for the electrification of this refugee camp was $3,809,822.54, and the cost of electricity generated for every kWh is $0.2018. Additionally, 991,240.32 kg of emissions can be avoided annually through the hybridization of the diesel generator under the PS.

Energy management of cooperative microgrids: A distributed optimization approach

A long-horizon hybrid data-model predictive direct power control (HD-MPDPC) scheme is proposed. The hybrid control scheme is for grid-side power electronic converters (PEC) in grid-connected renewable energy sources (RES). Computational burden of conventional MPDPC is alleviated by reducing the number of candidate voltage vectors to be examined in the cost function. This is achieved by employing data-driven forecast of RES output power to identify and eliminate the voltage vectors that do not counteract active and reactive power errors appropriately. RES power is forecasted using recurrent neural networks. The method for eliminating voltage vector candidates is inspired from direct power control, where output of active and reactive power hysteresis controllers along with the sector in which grid voltage vector lies are used to determine the switching states of the grid-side PEC. Thanks to reduced computational complexity, the hybrid predictive control scheme dispatches RES power more reliably over long horizons. This in turn enables RES as a regulating reserves service provider in power systems. Hardware-in-the-loop studies of a grid-connected wave energy conversion system verify reduced computational complexity of HD-MPDPC and its effectiveness in controlling RES output power over long horizons.

This paper investigates the effect of environmental factors on user performance in a dual-user haptic guidance system. The system under study allows for interaction between both users, the trainee and the trainer, to collaboratively perform a common task in a shared virtual environment. User studies are carried out to experimentally evaluate the users' performance while following square and circular trajectories with two viewpoints of the environment (top view and front view), while the virtual manipulator tool moves in free motion or against forbidden-region virtual fixtures. The performance is measured and statistically evaluated against task completion time, tracking accuracy, and user energy exchange. The studies revealed that changing the environment geometry from a square to a circle results in reduced task completion time and tracking error. Changing the environment viewpoint from top to front decreases the task completion time in both geometries. Forbidden-region virtual fixtures increase energy exchange by both users and decrease task completion time while compromising the tracking performance in the square-following task. However, when visual feedback is removed in the presence of the fixtures, the square tracking performance improves. The results also indicate a strong relationship between user dominance and tracking error only when the experiment is time-limited.

When underactuated surface vessels (USVs) sail on the sea, strong disturbances from wind, wave and current probably lead to large roll motion, which severely affects the stability of path following. The roll motion, produced by steering, can offset the roll motion generated by waves, and then the rudder roll stabilization can be realized. However, the path following performance is affected by strong coupling between the control of heading and rudder roll stabilization. In order to handle with the condition, a robust controller with roll constraints is designed to follow the straight path based on the predictive control strategy, and then the coordinate control of heading and rudder roll stabilization can be realized. By defining the error dynamics of path following in the Serret-Frenet frame and dividing the rudder angle envelope, the switched affine model is established combing with heading and roll dynamics. The disturbance observer is used to estimate the disturbances and unmodeled errors, and the real time feedforward compensation is designed to improve the system robustness. Meanwhile, the non-minimum phase characteristic of the USV system is analyzed, and it is transferred into a new minimum phase system based on the output re-definition method, which means reselecting the weighted matrix of the objective function in the predictive control. The analytical predictive control law is obtained using the state space prediction and receding horizon, and the linear programming is used to ensure the roll angle in the specified range. The simulation results have shown a good performance of the proposed control law. The coordinate convergences of heading and heading angle errors are ensured with the satisfaction of roll constraints.

A large number of wind turbine generators in the large-scale offshore wind farm system poses a challenge to the wind farm control system due to the computational burden in the central control methods or the complexity of the communication network in the decentralized control strategies. A hybrid control method based on the distributed consensus control and the central model predictive control is proposed in this study to overcome the problem. Typically, the wind turbine generators in the large-scale offshore wind farm system are clustered into several groups. The consensus-based distributed reactive power coordination control is proposed to each cluster and the centralized predictive voltage control is used to manage the total reactive powers of all clusters and regulate the voltage at the point of common coupling. The gradient-descent algorithm for the optimal design of the consensus-based cluster control is presented firstly. Based on the convergence property of the consensus control, the equivalent model of the total reactive power response of each cluster is identified, which is used for the design of the centralized predictive voltage control. Eigenvalue analysis of the proposed predictive control strategy is carried out to verify the stability of the distributed and predictive control systems. The robustness of the proposed predictive controller is evaluated in the conditions of significant model errors due to the communication delay in each cluster. A comparison study with the full distributed control based on consensus algorithm is presented to demonstrate the effectiveness of the proposed control method. The feasibility of the proposed predictive controller is evaluated by the control-hardware-in-the-loop simulation using OPAL-RT Technologies. An additional comparison study in term of computation time with the central control method is also carried out. Real-time simulation results show the superior performance of the proposed hybrid method compared to the full distributed consensus controller or the central control strategies.

Enhancing solar furnace thermal stress testing using an adaptive model and nonlinear predictive control

This paper presents an innovative predictive current control (PCC) strategy based on virtual voltage vectors with optimal amplitude and phase for six-phase asymmetrical permanent magnet synchronous machines (PMSMs) with isolated neutrals. This strategy combines two virtual vectors and two zero vectors over a sampling period in order to reduce the torque ripple and enhance the tracking of references in the fundamental subspace, which are responsible for the production of flux and torque. Several simulation results are provided in order to compare the performance of the six-phase PMSM drive under the proposed control strategy with other finite control set model predictive control strategies available in the literature.

This paper presents a combination between a fuzzy logic control (FLC) and a predictive direct power control for multifunctional grid connected photovoltaic (PV) system, to solve the oscillation problem in the DC link voltage of the in-verter caused by the fast irradiation changing. The whole system consists of a PV system which interface a DC-AC inverter, a FLC maximum power point tracking (MPPT) algorithm has been adopted to operate the DC-DC converter at the MPP. The predictive control strategy is applied to the DC-AC inverter with FLC in its voltage control loop to improve the power exchange between the grid and the PV system. Simulation results have been verified through MATLAB/Simulink software for the purpose of giving the effectiveness of the suggested control against existed controllers.

Conventional finite control set model predictive direct power control for grid-connected inverter causes high current harmonics and power fluctuations, during per control cycle, only one voltage vector is used. To address this issue, this paper introduces a hybrid vector model predictive direct power control (HV-MPDPC) strategy. A power prediction model is established in accordance with the instantaneous power theory. Then, eighteen alternative hybrid voltage vector combinations are formed by integrating double-vector and triple-vector combinations. Through cost function-driven selection of the optimal voltage vector combination, the power control performance and power quality are enhanced. The proposed strategy's effectiveness is validated by simulation results.

This paper presents a two-phase framework for the optimal operation of renewable energy communities that balances operational efficiency with equitable benefit distribution. We propose a two-phase stochastic programming framework: the first phase maximizes internal energy exchange, and the second ensures fair cost-benefit allocation. This integrated approach addresses both operational efficiency and equity under uncertainty. Our methodology addresses uncertainty in renewable generation and demand through stochastic programming, incorporating flexible loads and energy storage to enhance system performance. A key innovation is the fairness mechanism that guarantees minimum benefits for each participant, promoting community cohesion and long-term viability. Computational experiments, carried out on a realistic test case, validate the advantages of our approach, demonstrating improved efficiency through uncertainty management and equitable distribution of community benefits. The results show that our framework successfully navigates the technical and social dimensions of community energy systems, offering a practical solution for sustainable energy communities development that aligns individual interests with collective goals. • Novel stochastic program optimizes Renewable Energy Community (REC) operations fairly. • Two-phase methodology: novel energy exchange first, then fair cost-benefit split. • Models uncertainty via stochastic scenarios for robust, novel REC decisions. • Integration of flexible loads & energy storage to boost efficiency & sustainability. • Fair pricing mechanism to promote inclusivity and long-term community trust.

Prosumers as active market participants: A systematic review of evolution of opportunities, models and challenges

Various striking nonlinear evolution dynamics have been observed in mode-locked fiber lasers owing to the high peak power of the solitons. Herein, we numerically demonstrate the dynamical generation of dispersion-dependent pure-quartic solitons (PQS) molecules in a fiber laser. We discover the evolution from a single-pulse initial condition to different types of pulsating PQS molecules with enhanced net fourth-order dispersion, indicating that the internal motion and energy exchange can be both quasi-periodical and periodical. Furthermore, we reveal that the generation of these two types of pulsating PQS molecules is associated with a gain competition effect between the two sub pulses during mode-locking. Finally, we propose a new method that can achieve the transition between a loose PQS molecule and a tight PQS molecule. Enlightened by the numerical results, we speculate that more internal motion within the PQS molecule will be discovered, which will promote a deep insight into the physical mechanism behind.

The increasing adoption of electric vehicles has introduced challenges in maintaining grid stability, energy efficiency, and economic optimization. Advanced control strategies are required to ensure seamless integration while enhancing system reliability. This study systematically reviews predictive control applications in energy systems, particularly in electric vehicle integration and bidirectional energy exchange. Using the PRISMA 2020 methodology, 101 high-quality studies were selected from an initial dataset of 5150 records from Scopus and Web of Science. The findings demonstrate that predictive control strategies can significantly enhance energy system performance, achieving up to 35% reduction in frequency deviations, 20–30% mitigation of harmonic distortion, and a 15–20% extension of battery lifespan. Additionally, hybrid approaches combining predictive control with adaptive learning techniques improve system responsiveness by 25% under uncertain conditions, making them more suitable for dynamic and decentralized networks. Despite these advantages, major barriers remain, including high computational demands, limited scalability for large-scale electric vehicle integration, and the absence of standardized communication frameworks. Future research should focus on integrating digital modeling, real-time optimization, and machine learning techniques to improve predictive accuracy and operational resilience. Additionally, the development of collaborative platforms and regulatory frameworks is crucial for large-scale implementation.