Search Results (10)

This paper presents an optimal scheduling framework for a multi-energy hub (EH) that integrates electricity, natural gas, wind energy, energy storage systems, and demand response (DR) programs. The EH incorporates key system components including transformers, converters, boilers, combined heat and power (CHP) units, and both thermal and electrical energy storage. A novel aspect of this work is the joint coordination of multi-carrier energy flows with DR flexibility, enabling consumers to actively shift or reduce loads in response to pricing signals while leveraging storage and renewable resources. The optimisation problem is formulated as a mixed-integer linear programming (MILP) model and solved using the CPLEX solver in GAMS. To evaluate system performance, five case studies are investigated under varying natural gas price conditions and hub configurations, including scenarios with and without DR and CHP. Results demonstrate that DR participation significantly reduces total operating costs (up to 6%), enhances renewable utilisation, and decreases peak demand (by around 6%), leading to a flatter demand curve and improved system reliability. The findings highlight the potential of integrated EHs with DR as a cost-effective and flexible solution for future low-carbon energy systems. Furthermore, the study provides insights into practical deployment challenges, including storage efficiency, communication infrastructure, and real-time scheduling requirements, paving the way for hardware-in-the-loop and pilot-scale validations. Full article

Energy management for hybrid electric commercial vehicles, involving continuous power output and discrete gear shifting, constitutes a typical mixed-integer programming (MIP) problem, presenting significant challenges for real-time performance and computational efficiency. To address this, this paper proposes a physics-informed neural network-optimized model predictive control (PINN-MPC) strategy. On one hand, this strategy simultaneously optimizes continuous and discrete states within the MPC framework to achieve the integrated objectives of minimizing fuel consumption, tracking speed, and managing battery state-of-charge (SOC). On the other hand, to overcome the prohibitively long solving time of the MIP-MPC, a physics-informed neural network (PINN) optimizer is designed. This optimizer employs the soft-argmax function to handle discrete gear variables and embeds system dynamics constraints using an augmented Lagrangian approach. Validated via hardware-in-the-loop (HIL) testing under two distinct real-world driving cycles, the results demonstrate that, compared to the open-source solver BONMIN, PINN-MPC significantly reduces computation time—dramatically decreasing the average solving time from approximately 10 s to about 5 ms—without sacrificing the combined vehicle dynamic and economic performance. Full article

The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine schedule loads and prices. Throughout this manuscript, a novel framework for energy trading among prosumers is introduced. Rather than solving the problem in a centralized manner, the proposed orchestrator relies on a distributed game theory to determine optimal bids. Experimental results validate the efficiency of proposed solution, since it achieves average energy cost reduction of 2×, as compared to the associated cost from the main grid. Additionally, the hardware implementation of the introduced framework onto a low-cost embedded device achieves near real-time operation with comparable performance to state-of-the-art computational intensive solvers. Full article

The model presented by Gabriel Kron in 1945 is an example of an analog computer simulating quantum phenomena on a hardware level. It uses passive RLC elements to construct a hardware solver for the problem of quantum particles confined by rectangular or other classes of potential. The analytical and numerical validation of Kron’s second model is conducted for different shapes of particle-confining potentials in the one-dimensional case using an LTspice simulator. Thus, there remains potential for obtaining solutions in two- and three-dimensional cases. Here, a circuit model representing a linearized Ginzburg–Landau equation is given. Kron’s second model is generalized by the introduction of linear and non-linear resistive elements. This transforms the deformed Schrödinger equation into a linear dissipative Schrödinger equation and its non-linear form. The quantum mechanical roton problem is the main result of this work and is formulated by means of classical physical states naturally present in the LC classical circular electrical transmission line. The experimental verification of Kron’s model is confirmed. Full article

The Modular Multilevel Converter (MMC) is a promising converter for medium-/high voltage applications due to its various features. The waveform quality could be enhanced further by expanding the number of generated voltage levels, which increases the number of submodules (SMs); however, this improvement enlarges the size and cost of the converter, posing a persistent challenge. Hence, there exists a trade-off between power quality and the size and complexity of the converter. To verify the performance of such a complex converter and to validate the effectiveness of the control system, especially in the absence of a physical system, Real-Time (RT) simulation becomes crucial. However, the large number of components of a MMC creates important numerical challenges and computational difficulties in RT simulation. This paper proposes a grid-connected MMC employing a Z Packed U-Cell converter as a SM to generate a higher number of voltage levels while minimizing the required number of SMs. The ZPUC-MMC is implemented on an FPGA-based RT simulation platform using Electric Hardware Solver to reduce computational burden and simulation time, while improving the accuracy of the obtained results. Conventional controllers of MMCs are applied to assess the effectiveness and robustness of the proposed system during steady-state and dynamic operations. Full article

The evolution of power generation brings about extensive changes in other parts of the grid, especially in the transmission and distribution components. Within the scope of the Internet of Energy (IoE), electric power flows more flexibly between different parts of the grid. DC power will play an essential role in IoE. Decentralized photovoltaic panels, energy storage, electric vehicle charging stations, and data centers are some of the significant components of future grids dealing with DC power. As a result, power transformers must be appropriately modified to manage power among the different parts of the grid. A power electronic transformer (PET), also known as a solid-state transformer (SST) or smart transformer (ST), is a solution enabling a power grid to deal with this growing complexity. ROMAtrix, as a matrix-converter-based ST, is a developing project targeting future power grids. ROMAtrix realizes the application of a medium voltage (MV) transformer using commercially available power electronic semiconductors. Due to the distinctive features of ROMAtrix and a high number of switches, the implementation of the control system using a single control board is highly demanding. This paper aims to illustrate the implementation, on a field-programmable gate array (FPGA), of pulse width modulation (SVMPWM) applied to the ROMAtrix, considering specific switching patterns. The proposed switching procedure was simulated with PLECS and validated with the hardware-in-the-loop using the OPAL-RT solver. Full article

The increasing number and functional complexity of power electronics in more electric aircraft (MEA) power systems have led to a high degree of complexity in modelling and computation, making real-time energy management a formidable challenge, and the discrete-continuous action space of the MEA system under consideration also poses a challenge to existing DRL algorithms. Therefore, this paper proposes an optimisation strategy for real-time energy management based on hybrid deep reinforcement learning (HDRL). An energy management model of the MEA power system is constructed for the analysis of generators, buses, loads and energy storage system (ESS) characteristics, and the problem is described as a multi-objective optimisation problem with integer and continuous variables. The problem is solved by combining a duelling double deep Q network (D3QN) algorithm with a deep deterministic policy gradient (DDPG) algorithm, where the D3QN algorithm deals with the discrete action space and the DDPG algorithm with the continuous action space. These two algorithms are alternately trained and interact with each other to maximize the long-term payoff of MEA. Finally, the simulation results show that the effectiveness of the method is verified under different generator operating conditions. For different time lengths T, the method always obtains smaller objective function values compared to previous DRL algorithms, is several orders of magnitude faster than commercial solvers, and is always less than 0.2 s, despite a slight shortfall in solution accuracy. In addition, the method has been validated on a hardware-in-the-loop simulation platform. Full article

Autonomous driving is a breakthrough technology in the automobile and transportation fields. The characteristics of planned trajectories and tracking accuracy affect the development of autonomous driving technology. To improve the measurement accuracy of the vehicle state and realise the online application of predictive control algorithm, an online active set-based longitudinal and lateral model predictive tracking control method of autonomous driving is proposed for electric vehicles. Integrated with the vehicle inertial measurement unit (IMU) and global positioning system (GPS) information, a vehicle state estimator is designed based on an extended Kalman filter. Based on the 3-degree-of-freedom vehicle dynamics model and the curvilinear road coordinate system, the longitudinal and lateral errors dimensionality reduction is carried out. A fast-rolling optimisation algorithm for longitudinal and lateral tracking control of autonomous vehicles is designed and implemented based on convex optimisation, online active set theory and QP solver. Finally, the performance of the proposed tracking control method is verified in the reconstructed curve road scene based on real GPS data. The hardware-in-the-loop simulation results show that the proposed MPC controller has apparent advantages compared with the PID-based controller. Full article

This paper presents a computationally efficient novel heuristic approach for solving the combined heat and power economic dispatch (CHP-ED) problem in residential buildings considering component interconnections. The proposed solution is meant as a substitute for the cutting-edge approaches, such as model predictive control, where the problem is a mixed-integer nonlinear program (MINLP), known to be computationally-intensive, and therefore requiring specialized hardware and sophisticated solvers, not suited for residential use. The proposed heuristic algorithm targets simple embedded hardware with limited computation and memory and, taking as inputs the hourly thermal and electrical demand estimated from daily load profiles, computes a dispatch of the energy vectors including the CHP. The main idea of the heuristic is to have a procedure that initially decomposes the three energy vectors’ requests: electrical, thermal, and hot water. Then, the latter are later combined and dispatched considering interconnection and operational constraints. The proposed algorithm is illustrated using series of simulations on a residential pilot with a nano-cogenerator unit and shows around 25–30% energy savings when compared with a meta-heuristic genetic algorithm approach. Full article

Dynamic simulation of turbomachinery by Hardware in the Loop (HIL) real-time systems has become an essential practice, due to the high cost of real equipment testing and the need to verify the control and diagnostic systems’ reaction to emergency situations. The authors developed a full model of a power generation Gas Turbine Plant, including liquid and gaseous auxiliaries, and the electrical generator and starter motor, integrated in a MATLAB^®/Simulink^® simulation suite: TGSim Plus™. This allows assembling models of various gas turbine (GT) architectures by customised Simulink^® library blocks and simulating steady state and transient conditions, such as complete start-up and shutdown operations as well as emergency, contingent operations and artificially injected fault scenarios. The model solver runs real-time steps at milliseconds scale. The paper describes the main modelling characteristics and typical results of steady state and transient simulations of a heavy-duty gas turbine under development by Doosan Heavy Industries and Construction (Changwon, South Korea). Comparison with benchmark design simulations obtained by a reference non real-time software shows a good match between the two environments, duly taking into account some differences in the GT models setting affecting parts of the sequence. The paper discusses also the bleed streams warm-up influence on GT performance and the start-up states trajectories dependency on control logic and on the starter helper motor torque envelope. Full article