Time-varying Power Research Articles

Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator

In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications.

CO2 emissions of fuel-cell battery hybrid system for large ships

ABSTRACT The needs of solid oxide fuel cells (SOFC) and batteries in large ships are analyzed by estimating the amount of fuel consumption and CO2 emissions. Three types of systems are proposed: 1) fuel cells-battery-generator engine system, 2) battery-generator engine system, and 3) generator engine system. This study is based on the time-varying electric power demand and adopted the sequential quadratic programming (SQP) method to find the optimal power distribution of mixed power sources. CO2 emissions in the SOFC hybrid system are 11.6% lower than in the conventional generator engine system, and those in the battery-generator engine system are 5.1% lower than in the conventional system. The reduction of CO2 emissions come from three factors: the operation of the generator engine at a relatively high load rate, the battery sharing the operation of the generator engine, and the high efficiency of the SOFC running. The SOFC hybrid system can be a sustainable technology in large vessels.

Dynamic sum-based event-triggered H∞ filtering for networked T-S fuzzy wind turbine systems with deception attacks

This article is concerned with the dynamic sum-based event-triggered H∞ filtering issue for networked wind turbine systems subject to deception attacks and communication delays. By considering the time-varying wind power case rather than the existing maximum power case, a more general T-S fuzzy system with two premise variables is modeled for nonlinear wind turbine system. In order to save the communication cost, a novel dynamic sum-based event-triggered scheme is proposed, which has the following three merits. First, some past sampled measurements are utilized to reduce redundant transmissions. Second, an auxiliary dynamic variable based on the past sampled measurements is introduced in the triggering condition to further enlarge the triggering intervals. Third, a dynamic triggering threshold is designed, which can be regulated adaptively along with the system evolution. With the help of Lyapunov method and linear matrix inequality technique, some sufficient co-design conditions of H∞ filter and triggering matrices are derived for the T-S fuzzy filtering error system with deception attacks and communication delays. Lastly, some simulations are carried out to illustrate the advantages of the proposed strategy.

Global Prescribed-Time Control for Switched Nonlinear Systems With Parametric Uncertainty and Time-Varying Powers

Global Prescribed-Time Control for Switched Nonlinear Systems With Parametric Uncertainty and Time-Varying Powers

Introducing storage operators for coordinating residential batteries in distribution networks under time-of-use tariffs and adaptive power limits

The transition towards Time of Use (ToU) tariffs has become a promising solution for addressing power system challenges resulting from increased installations of renewable energy systems. ToU tariffs encourage residential Battery Energy Storage System (BESS) adoption to reduce customers' bills through maximizing energy storage during low-price intervals (e.g., middle of the day). However, simultaneous BESS charging affects diversity of load, which may lead to the violation of distribution networks constraints. Traditional network management with conservative fixed and static power limits leads to inefficient network capacity use since they do not consider changes in network operating conditions and status of BESS facilities. Specially, these approaches do not allow higher import limits when proportion of BESS facilities are in idling state. To better allocate the capacity of distribution networks to active BESS facilities (charging/discharging), this work introduces an independent storage operator to coordinate BESS control actions by employing time-varying and adaptive power limits. For this purpose, a Mixed Integer Linear Programming (MILP) algorithm is proposed for storage operator to manage BESS facilities while respecting network constraints and customers' desired bills. At each time step, the algorithm decides power limits for active BESS facilities based on predefined linear functions. These functions are generated offline by using Optimal Power Flow (OPF) to establish relationships between power limits and number of active BESS. The application of the algorithm using a real Jordanian distribution network demonstrates its effectiveness to allow a larger number of customers achieving their desired bills compared to using fixed power limits.

Validation of induction/steering reserve-boosting active power control by a wind tunnel experiment with dynamic wind direction changes

This paper presents an experimental validation of an active power control methodology that combines induction control and wake steering to maximize the power reserve of the wind turbines in a farm. The experiments are carried out on a cluster of three scaled wind turbines in a boundary-layer wind tunnel, by dynamically varying the wind direction, and by considering a time-varying power tracking signal that mimics secondary frequency control of the grid. The methodology is compared with three active power control algorithms sourced from the literature. Results indicate that the instantaneous power reserves are indeed improved by an appropriate handling of wake effects, achieving a more accurate tracking of the power signal.

Time-frequency analysis of nonlinear structures under nonstationary excitation by directly solving structural dynamic equation through absolute displacement

The time-frequency evolution of seismic ground motion energy under strong earthquakes, which can be described by the evolutionary power spectral density (EPSD) matrix, will amplify the most unfavorable responses of members in nonlinear structural systems. However, the existing general finite element software fails to reduce the dimension of the EPSD matrix and then decouple time from frequency in frequency domain analysis. This failure greatly limits the simulation and calculation of multi-dimensional nonlinear structures with multiple degrees of freedom by the stochastic vibration theory. In this study, the pseudo excitation method (PEM), which can direct solve structural dynamic equations using absolute displacement, is used to derive the self-spectral and cross-spectral densities of seismic ground motions in one-dimensional and multi-dimensional cases. In MATLAB, the EPSD matrix is transformed into a four-dimensional uniform modulation excitation matrix independent of the time variable. Nonlinear iteration is conducted and the precise integration method (PIM) is employed through a transient analysis module. Furthermore, a simple one-dimensional example is presented to verify the correctness of the proposed algorithm herein. Finally, as an example, we select important parameters of the coherence model at different supports of a half-through three-span concrete-filled steel tubular (CFST) tied-arch bridge in view of real engineering conditions, and calculate the time-varying power spectrum and variance of the responses of arch ribs, arch feet and the bridge deck system. The results show that the theoretical derivation and the algorithm constructed in this paper can simulate the time-delay effect of the nonlinear structural system. This proves the feasibility of general finite element software in solving and analyzing random responses to multi-dimensional and multi-support non-stationary excitation of nonlinear structures.

Research on targeted modulation of elastic wave bandgap in cantilever-structured piezoelectric Phononic crystals

Piezoelectric phononic crystals (PPCs) have the advantages of simplicity and efficiency in controlling elastic wave bandgaps, which can be utilized to solve NVH problems with varying frequency features, such as the low-frequency vibrations caused by the time-varying power source (i.e., motor) in the subframe and underbody of new energy vehicles. However, most of the previous studies on PPCs bandgap control are qualitative, lacking the knowledge of bandgap targeted modulation. This paper analyzes the locally resonant (LR) bandgap characteristics of PPCs with shunting circuits, and aims to achieve precise control of bandgap position by circuit parameters. A theoretical research model of cantilever-structured PPCs is established firstly, and its accuracy is verified through finite element analysis and experiments. Subsequently, the formation mechanism of the LR bandgap is analyzed, and a bandgap characteristic frequency estimation method is proposed, along with an analysis of the influence of electrical parameters using this method. Finally, from the perspective of bandgap practicality, the variable capacitance method is proposed, which can quickly calculate the capacitance value through an estimation formula under the arbitrary designation of target bandgap in the low-frequency range of 200 Hz∼500 Hz. The results show that the actual bandgap position is very close to the target bandgap position, and the bandgap always keeps enough bandwidth and attenuation amount. This method has broad prospects in the field of vibration attenuation where frequency characteristics are variable.

Fatigue Damage Analysis of Steel Truss Suspension Bridge Under Non-Stationary and Non-Gaussian Buffeting

The wind field environment in mountainous zones is complex. If the wind field characteristics are regarded as stationary and Gaussian, the inaccurate response results of the structure will be obtained. In this paper, a fatigue evaluation method of steel truss suspension bridge members under non-stationary and non-Gaussian buffeting is proposed. This approach can solve the fatigue evaluation problem of steel truss suspension bridge members under mountain wind. Firstly, the normal wind speed (stationary and Gaussian) is obtained by the harmonic wave synthesis method. The wind speed considering non-stationary characteristics is obtained by adopting time-varying power spectrum. Secondly, through non-linear translation, the wind speed considering non-stationary characteristics is transformed into wind speed considering both non-Gaussian and non-stationary characteristics. Finally, combined with the Palmgren–Miner rule, the effects of three different buffeting responses on fatigue damage of suspension bridge members are studied. They are traditional buffeting response, buffeting response considering non-stationary characteristics and buffeting response considering both non-stationary and non-Gaussian characteristics. The results indicate that the fatigue damage of the main cable of the suspension bridge is the biggest at the mid-span. Under the same wind speed, considering two characteristics, the component fatigue damage will further increase.

Online Decentralized Tracking for Nonlinear Time-Varying Optimal Power Flow of Coupled Transmission–Distribution Grids

Online Decentralized Tracking for Nonlinear Time-Varying Optimal Power Flow of Coupled Transmission–Distribution Grids

Daily optimization of maintenance routing and scheduling in a large-scale photovoltaic power plant with time-varying output power

Photovoltaic (PV) power generation is an environmentally friendly and clean energy source with extensive and widespread installations worldwide. Consequently, the maintenance planning for PV power plants has become exceedingly significant. Owing to the time-varying output power of PV power plants, the optimization of daily maintenance planning for PV power plants should encompass not only the optimization of maintenance routes and task assignments, but also the consideration of reducing downtime costs resulting from failures and maintenance activities. To find the optimal route that minimizes the total cost, including travel, technician, downtime, and penalty costs, a PV power plant maintenance routing problem and its solution approach were investigated in this study. First, considering the influence of daily variations in solar radiation and temperature on output power, this study proposed an optimization problem for maintenance routing in a PV power plant. Next, linearization techniques with controllable error precision were developed to construct a mixed-integer linear programming model. Then, to efficiently solve large-scale problems, a heuristic algorithm was customized based on an adaptive large neighborhood search approach. Finally, the proposed approach was implemented at the Talatan PV power plant in Qinghai Province, China. Compared to the traditional shortest route model, which neglects the time-varying output power, the proposed method significantly reduces the total cost.

Fractional-Order Sliding Mode Load Frequency Control and Stability Analysis for Interconnected Power Systems With Time-Varying Delay

The universal use of open communication networks in interconnected power systems has changed the constant delay in the formerly dedicated channels into time-varying delay. The delay will deteriorate the dynamic performance of the load frequency control and lead to system frequency instability. This paper presents a fractional-order sliding mode controller for interconnected power systems with time-varying delay and performs stability analysis based on the Lyapunov direct method. Firstly, fractional-order integral sliding mode control is used to improve the stability of interconnected power systems containing time-varying delay. This expands the delay margin by taking advantage of the scalability of the fractional-order to the sliding mode surface. Then, the Riemann-Liouville fractional-order Lyapunov functional is introduced to stability analysis. This reduced the conservatism of the stability analysis by adjusting the upper limit of the functional integral. Lastly, combining Lyapunov's theory and integral inequality to obtain the stability conditions of the time-varying delay power system described by linear matrix inequality, and bring the stability margin of the system. The simulation results show that the designed controller can guarantee that the maximum delay time of the system is less than a predefined upper limit. The proposed control strategy obtains a larger stability margin than the other four scenarios. Additional simulation study was also conducted to verify its robustness to load variations and wind fluctuations.

Stochastic Dynamics of Fusion Low-to-High Confinement Mode (L-H) Transition: Correlation and Causal Analyses Using Information Geometry.

We investigate the stochastic dynamics of the prey-predator model of the Low-to-High confinement mode (L-H) transition in magnetically confined fusion plasmas. By considering stochastic noise in the turbulence and zonal flows as well as constant and time-varying input power Q, we perform multiple stochastic simulations of over a million trajectories using GPU computing. Due to stochastic noise, some trajectories undergo the L-H transition while others do not, leading to a mixture of H-mode and dithering at a given time and/or input power. One of the consequences of this is that H-mode characteristics appear at a smaller input power Q<Qc (where Qc is the critical value for the L-H transition in the deterministic system) as a secondary peak of a probability density function (PDF) while dithering characteristics persists beyond the power threshold for Q>Qc as a second peak. The coexisting H-mode and dithering near Q=Qc leads to a prominent bimodal PDF with a gradual L-H transition rather than a sudden transition at Q=Qc and uncertainty in the input power. Also, a time-dependent input power leads to increased variability (dispersion) in stochastic trajectories and a more prominent bimodal PDF. We provide an interpretation of the results using information geometry to elucidate self-regulation between zonal flows, turbulence, and information causality rate to unravel causal relations involved in the L-H transition.

Optimal placement of time-varying distributed generators by using crow search and black widow - Hybrid optimization

This paper focuses on the optimal reorganization of radial distribution system (RDS) and the superlative location of time-varying power generation distributed generators, considering time-varying loads. The electric generators, utilizing non-conventional energy sources, are directly connected to the distribution grids with lower ratings compared to conventional source-driven generators. In this study, a combination of optimization techniques, such as the black widow and crow search techniques, is employed to enhance performance in terms of optimal reorganization and superlative location of distribution generation. The reorganization problem is examined and analyzed by considering various scenarios in a standard 69-bus grid, as well as in larger-scale 119-bus and 135-bus systems, with and without the presence of distributed generation resources. The analysis of the proposed optimization techniques with the obtained results is well discussed in this paper.

Multi-physics coupling analysis of test heat pipe reactor KRUSTY based on MOOSE framework

The heat pipe reactor utilizes heat pipes for passive heat transfer, eliminating the need for any moving parts. This feature significantly enhances its safety characteristics. Given the intricate nature of the heat pipe reactor system, a comprehensive multi-physics coupling simulation is necessary to study its safety features. In this study, a high-fidelity multi-physics coupling analysis incorporating a neutronic-thermal–mechanical-heat pipe coupling model is conducted. The work is based on the multi-physics coupling framework MOOSE, which integrates the Monte Carlo code OpenMC for neutronic analysis and built-in modules for 3-dimensional thermal–mechanical analysis. Furthermore, a heat pipe simulation method called the Thermal Resistance Grid Method is implemented within the MOOSE framework and employed in the multi-physics coupling analysis. To facilitate transient analysis, the Point Kinetics method is also integrated into the MOOSE framework to account for the time-varying power factor during reactor transients. The various modules and physical fields are coupled together using either loose or tight coupling strategies. These coupling strategies are applied to assess a test heat pipe reactor known as KRUSTY. Steady-state simulations are conducted for both normal operation and single heat pipe failure conditions, enabling the calculation of fission power distribution, temperature distribution, stress and strain profiles, as well as power and temperature redistribution following a heat pipe failure. Moreover, a load-following transient of KRUSTY is simulated, demonstrating its capability for load following without reactivity control. The aforementioned calculations validate the feasibility of the coupling framework utilized in this study and highlight its ability to accurately capture local phenomena within the reactor core.

Flatbread baking process under time-varying input power in a home-scale electric oven: 3D CFD simulation with experimental validation

This study investigates the flatbread baking process in a home-scale electric oven by developing a three-dimensional transient numerical model using the finite volume method along with the discrete ordinates (DO) model, evaporation–condensation mechanism, and realizable k-ε model for the turbulent regime through the oven chamber. The validity of the proposed numerical model has been examined through a comparison with experimental data provided in this study, considering both scenarios with and without bread in the oven, where good agreement is observed between the numerical and experimental findings. Using the validated model, a qualitative and quantitative analysis is performed to study the flatbread baking process, focusing on temperature distribution within the oven and the bread as well as the degree of starch gelatinization during the baking process. The consideration of input power patterns is crucial in terms of both energy consumption and bread quality, making it imperative for electric oven designers. Consequently, under a fixed energy consumption of the oven, various time-varying input powers are employed to assess the bread baking process. For this purpose, six different scenarios are designed including constant elements power, linear/stepwise increase/decrease in elements power, and ON/OFF modes with constant power. The results demonstrate that employing time-varying input power profiles characterized by linear and stepwise decrease yields superior performance to control the baking process and quality of bread. However, preheating the oven prior to dough baking could potentially mitigate the impact of the input power profile type on the resulting final product.

Online optimization and tracking control strategy for battery energy storage in DC microgrids

This paper proposes a fast time-varying optimal power flow (OPF) command tracking control algorithm for DC microgrids based on measurement feedback. The algorithm establishes a model by adding power command tracking constraints and considering the power, voltage, and current constraints of the microgrid. A forward-back generation DC power flow sensitivity calculation method is designed to calculate the power output of the energy storage at each node, and charging and discharging decisions are made based on the state of charge (SOC) of the energy storage system, current voltage, and charging and discharging scenarios previous and subsequent. By continuously issuing control decisions, the DC microgrid OPF command tracking solution is achieved. The measurement feedback control scheme used in this algorithm reduces model error and non-iterative calculation error, thus improving calculation and tracking accuracy. The algorithm is tested in a modified 28-node arithmetic system and suppresses voltage fluctuations caused by unstable PV active output effectively, while accurately tracking the power command of the upper level and meeting the voltage safety operation requirements of the microgrid.

Adaptive Control for Complex Systems with Dynamics and Time-Varying Powers

This work focuses on presenting a control algorithm to investigate nonlinear systems, which contain time-varying powers, inverse dynamics, and uncertainties. First, some appropriate transformations are introduced to obtain a new system. Then, a Lyapunov function, which covers quadratic and high-order components, is recursively constructed for control design. Subsequently, by introducing the neural networks, the uncertain functions encountered during the design are approximated. Based on the inequality techniques, the nonlinear terms are skillfully estimated. By defining the bounds of some unknown parameters and using the adaptive technique, some virtual controllers are selected in each step to dominate the nonlinear functions and guarantee that the derivative of the Lyapunov function satisfies the required form. Finally, a new adaptive controller is constructed and semiglobal practical finite time stability (SGPFS) is guaranteed. The proposed approach is verified with a numerical example.

Dynamic Modelling and Mutual Coordination of Electricity and Watershed Networks for Spatio-Temporal Operational Flexibility Enhancement Under Rainy Climates

This paper proposes a watershed-electricity nexus model to unlock the flexibility of watershed networks (WSNs) for supporting the operation of power distribution networks (PDNs) under rainy climates. The proposed model exploits the spatio-tem-poral flexibility of geographically dispersed pump clusters to pro-vide reserve services to PDNs, and a hyperbolic partial differential function derived from Saint-Venant hydrodynamic equations is formed to describe the dynamic processes of river stream flows. Be-sides, a flexibility evaluation method based on a composite sensitiv-ity matrix of water levels with respect to power injections is pre-sented to quantify the time-varying adjustable power domain of pump loads. Then, a multi-stage interactive coordinated scheduling strategy is developed for the mutual operation of WSNs and PDNs, where drainage pumps are jointly optimized to provide flexible power reserves, while an optimal PDN economic dispatch is per-formed to improve the power supply voltage of pump loads. Fur-thermore, an equivalent mixed-integer linear programming refor-mulation method is derived to cope with the original nonlinear par-tial differential optimization problem for computational tractabil-ity improvements. Comparative results have validated the effective-ness of the proposed strategy in eliminating voltage violations and shaving peak loads.

A Real-Time Adaptive Energy Optimization Method for Urban Rail Flexible Traction Power Supply System

In urban rail traction power supply systems, the optimal power flow method is mostly utilized for energy optimization. Conventional power flow optimization adopts offline calculation methods to obtain the optimal strategy under given scenarios. However, this method cannot continuously realize the ideal optimization effect for the time-varying flexible traction power supply system. For this reason, this paper proposes a real-time adaptive energy optimization method to solve the problem above. Firstly, the control objective of the optimal power flow is determined based on analyzing the system power flow. Secondly, a hierarchical control system is designed to achieve adaptive optimal control for the flexible traction power supply system. Moreover, a swarm intelligence optimization algorithm-based optimal power flow calculation method is employed to realize the real-time optimal power flow. Finally, some simulations are performed to verify the effectiveness and accuracy of the proposed real-time optimal power flow. It is demonstrated that the real-time optimal power flow method makes it possible to consider the low delay and high accuracy of control commands at the same time. Compared with the conventional offline method, the proposed real-time method can reduce the input electricity consumption by 1.47%, which is valuable in practical applications.