A microwave rectifier at 5.8 GHz without any capacitors is presented, which owns a measured MW-to-DC conversion efficiency of 68.1%. A harmonic rejection filter and a DC pass filter, which replace lumped capacitors in conventional microwave rectifiers, are applied to suppressing the harmonics produced by an HSMS-286 Schottky diode during rectifying. At the fundamental frequency, a microstrip impedance transformer which contains a shunt λg/8 short-ended microstrip transmission line and two short series microstrip transmission lines are applied to compensating the imaginary impedance of the diode and matching the input impedance of the rectifier. The measured MW-to-DC conversion efficiency agrees well to the simulated results. The novel rectifier without any lumped passive elements may be applied for power transmission system at higher microwave frequencies.
Mario D. Baquedano-Aguilar, Sean Meyn, Arturo Bretas
This paper presents a methodology for reducing the complexity of large-scale power network models using spectral clustering, aggregation of electrical components, and cost function approximation. Two approaches are explored using unconstrained and constrained spectral clustering to determine areas for effective system reduction. Once the system areas are determined, both loads and generators by type are aggregated, and their new cost function is approximated through polynomial curve-fitting or statistical methods. The performance of reduced networks is evaluated in terms of their ability to follow the true daily cost of the original system over a 24-hour period considering a set of several days. Two test systems are taken as test beds. Application of the methodology to a modified version of the IEEE 39-bus system reduces it from 17 generators to a 4-bus system and 9 generators with about 93% of accuracy. Similarly, the IEEE 118-bus system is reduced from 19 generators to a 3-bus system with three aggregated units achieving over 99% of accuracy. These findings address scalability challenges and enhance accuracy for high and mid-loading level conditions, and by aggregating thermal units with similar cost functions.
Distribution or transmission of electric power, Production of electric energy or power. Powerplants. Central stations
Homopolar AC Machines (HAMs) are of interest because of low rotor loss and the ability to operate at high speeds. These machines are frequently utilized in flywheel energy storage systems but are dominated by permanent magnet or induction machines in other contexts such as vehicle traction. The aim of this work is to explore a new type of homopolar machine. The Dual Rotor Homopolar AC Machine (DHAM) is proposed herein. The fundamental operating principles of the DHAM are explained, and its torque production and terminal characteristics are outlined. The permanent magnet version of the machine is shown to achieve an extended constant power speed range without impacting the PM field intensity, allowing the use of magnet materials with modest values of intrinsic coercive force. The machine includes a modular sectionalized stator, which is easy to wind and cool. The DHAM relies on sinusoidal airgap reluctances, and so the necessary rotor geometry is derived. A prototype machine is used to validate the operating principle.
Distribution or transmission of electric power, Production of electric energy or power. Powerplants. Central stations
Integration of renewable energy is increasingly prevalent, yet its stochasticity may compromise the stability of the power system. In this paper, a high-voltage dc (HVDC) link model based on the modular multilevel converter with embedded energy storage (MMC-EES) is presented and, utilizing the massively parallel computing feature of the graphics processing unit (GPU), its efficacy in compensating a varying wind energy generation is studied. Constant power is oriented in the inverter control by incorporating a DC-DC converter with EES into its submodules. High-fidelity electromagnetic transient modeling is conducted for insights into converter control and energy management. A fully iterative solution is carried out for the nonlinear model for high accuracy. Since the sequential data processing manner of the central processing unit (CPU) is prone to an extremely long simulation following an increase of component quantity with even one order of magnitude, the massively concurrent threading of the GPU is exploited. The computational challenges posed by the complexity of the MMC circuit are effectively tackled by circuit partitioning which separates nonlinearities. In the meantime, components of an identical attribute are designed as one kernel despite inhomogeneity. The proposed modeling and computing method is applied to a multi-terminal DC system with wind farms, and significant speedups over CPU-based simulation are achieved, with the accuracy validated by the offline simulation tool PSCAD.
Distribution or transmission of electric power, Production of electric energy or power. Powerplants. Central stations
Conventional state-space-based stability assessment method of voltage-source converters (VSCs) can be hindered by the black-box feature. Black box-based state-space model identification method using the terminal admittance/impedance frequency responses has thus been drawing increasing research attentions recently. However, the estimation of out-of-band modes commonly suffers from narrow bandwidth of frequency responses. This article presents, for the first time, the potential identification of several critical out-of-band modes of an artificially created rational function and a grid-connected VSC. This identification is achieved through their band-limited frequency responses using the vector fitting (VF) algorithm. On its basis, a sensitivity index of a partial fraction term is derived to explain the out-of-band modal identification behavior of the VF. The effects of the pole, residue, and fitting frequency interval width on the sensitivity index are further investigated and demonstrated. The numerical analysis shows that, with the help of the proposed sensitivity index, the extrapolation behavior of the VF can be explained, and several invisible out-of-band modes can further be identified or synthesized from a band-limited frequency response. This extrapolation feature may strengthen the curve fitting capability of the VF, i.e., compared to the VSC’s band-limited frequency responses, more modal information can be obtained and further used for eigenvalue-based stability analysis.
Distribution or transmission of electric power, Production of electric energy or power. Powerplants. Central stations
This paper applies the Q-learning method, a reinforcement learning (RL) technique, to a quadrotor for finding a low-noise trajectory while avoiding obstacles. The proposed method introduces a novel Q-value function, resulting in obtaining a 2D surface that preserves the features of the environment. Therefore, the path-finding problem in a 3D space is simplified into a 2D space problem. Since the data is obtained based on the pre-calculated 2D surface, online path planning in the presence of unpredictable environmental changes is handled with markedly reduced computational complexities, effectively resolving a significant challenge in this area. The Q-learning algorithm is developed by defining two cost functions to avoid obstacles and reduce the observers' perceived noise level. To find the noise Sound Pressure Level (SPL), the perceived noise model is derived through the Gutin equation. In addition, the Octomap 3D optimizer is used to map the obstacles. Compared to the related works, noise observers are used vertically and horizontally, leading to more accurate environmental details. Moreover, the proposed algorithm leads to global optimal paths and avoids local minimum points commonly produced by similar optimization approaches. Finally, the performance of the proposed methodology in path finding and noise reduction is further demonstrated via a practical example of a quadrotor.
Applications of electric power, Distribution or transmission of electric power
Abstract In future modern power systems, reliability and resilience could be an extreme challenge caused by the stability issues of the bidirectional power converters (BPCs). The non‐linear dynamics of DC link voltage (DCLV) of BPCs in interaction with the existing linear control schemes may decrease the stability margin and cause operating‐point‐dependent instability issues. Existing approaches may solve this issue by reducing the DCLV control loop bandwidth, which considerably degrades the system performance. To tackle this issue, first, the root cause of the instability challenge is analytically investigated, and then, a non‐linear stabilizer control scheme based on Lyapunov theorem is proposed. Considering the non‐linear dynamic of the BPCs and the interaction between dynamics of DC link voltage and AC currents in the proposed stabilizer, it guarantees the stability of the converter in both directions of power flow and the full range of loading conditions. The performance of the proposed scheme is verified through simulation of the system under various operating conditions, considering uncertainties, disturbances, and short‐circuit events, and comparing it with that of prevalent controllers.
Distribution or transmission of electric power, Production of electric energy or power. Powerplants. Central stations
Abstract Resilience is one of the main features of smart distribution networks, and a microgrid (MG) access to the distribution network provides an effective way to improve resilience. MG and distribution network belong to different interests, so it is necessary that MGs and flexible resources are actively guided through price leverage. In this way, MGs take part in the post‐disaster restoration and enhance its resilience. Firstly, this paper proposes a dynamic restoration electricity price response mechanism after extreme disasters and constructs a power response model for loads and electric vehicles within the MGs. Secondly, the optimal scheduling model of the distribution network with multiple‐microgrids (MMG) is proposed to improve the restoration rate of critical loads (RRCL). Single microgrid achieves the largest microgrid revenue and restoration contribution, and MMG uses the power headroom index to optimize the dynamic restoration electricity price to achieve the smallest power purchase cost of distribution network. Finally, the optimal scheduling method for resilience enhancement of distribution networks with MMG considering dynamic restoration electricity price response mechanism is validated by dual microgrid access to an IEEE 33‐node distribution system. The simulation results show that the proposed optimization method effectively improves the RRCL of distribution network.
Distribution or transmission of electric power, Production of electric energy or power. Powerplants. Central stations
This paper presents the concept and implementation of participatory living labs in the context of the unIT-e² project, which aims to integrate electromobility into the energy system efficiently. The article focuses on two field trials involving private and corporate users of electric vehicles and charging infrastructure. The paper describes the theoretical foundations of participatory aspects in living labs and proposes a participation pyramid model to structure the different levels and methods of participation. The paper also reports on the practical experiences and lessons from applying the participation pyramid model in the context unIT-e². The paper offers general recommendations for future participatory living labs in the energy and mobility transition context, such as developing an interaction strategy before participant acquisition, ensuring early onboarding and continuous information provision, balancing online and offline events, and gathering and listening to participant feedback. The paper concludes that professional, proactive, and regular interaction with the participants is crucial for the success of living labs and the knowledge gained for civil society and science.
Ghulam Mohy-ud-din, Rahmat Heidari, Frederik Geth
et al.
This paper addresses the challenges of embedding common droop control characteristics in ac-dc power system steady-state simulation and optimization problems. We propose a smooth approximation methodology to construct differentiable functions that encode the attributes of piecewise linear droop control with saturation. We transform the nonsmooth droop curves into smooth nonlinear equality constraints, solvable with Newton methods and interior point solvers. These constraints are then added to power flow, optimal power flow, and security-constrained optimal power flow problems in ac-dc power systems. The results demonstrate significant improvements in accuracy in terms of power sharing response, voltage regulation, and system efficiency, while outperforming existing mixed-integer formulations in computational efficiency.
Using electric field induced second harmonic generation (E-FISH), we performed direction-resolved absolute electric field measurements on single-channel streamer discharges in 70 mbar (7 kPa) air with 0.2 mm and 2 ns resolutions. In order to obtain the absolute (local) electric field, we developed a deconvolution method taking into account the phase variations of E-FISH. The acquired field distribution shows good agreement with the simulation results under the same conditions, in direction, magnitude and in shape. This is the first time that E-FISH is applied to streamers of this size (> 0.5cm radius), crossing a large gap. Achieving these high resolution electric field measurements benefits further understanding of streamer discharges and enables future use of E-FISH on cylindrically symmetric (transient) electric field distributions.
Abstract The virtual synchronous generator (VSG) is a good solution for stabilizing the power system with high penetration of renewable energy. However, in case of serious unbalanced voltage disturbance/fault, the conventional VSG may lose voltage, inertia, and damping support characteristics to the grid and even can cause disconnection of renewable energy. This paper proposes an improved fault control strategy for VSG with the coordination of STATCOM. The proposed method can provide sufficient voltage support while keeping continuous system inertia and damping support under severe unbalanced fault. In the paper, an improved VSG and STATCOM control topology based on positive and negative sequence current control are first proposed so as to keep the damping and inertia support to grid during the grid fault. Secondly, the voltage support control method for the VSG with improved topology during unbalanced fault is introduced, which can achieve multiple control objectives, in terms of voltage support, current limitation, and active power output simultaneously. Then a coordination control scheme of improved VSG and STATCOM is developed so as to optimize the maximum control objectives in all possible scenarios, especially in the case of severe unbalanced fault. Finally, the effectiveness of the method is verified by using the MATLAB/SIMULINK simulation platform.
Distribution or transmission of electric power, Production of electric energy or power. Powerplants. Central stations
Seyed Hamed Jalalzad, Mohammad Reza Salehizadeh, Rouzbeh Haghighi
The idea of this paper is to provide a framework for simultaneous energy cost optimization and congestion management by using shared energy storage. As a case study, this paper addresses this aim by proposing the integration of a community energy storage (CES) within a distribution system, connected to four microgrids (MGs). The shared storage system enables the MGs to reduce their energy costs by optimizing the operation of the battery using a Heuristic optimization algorithm, specifically the Teaching-Learning-Based Optimization (TLBO) algorithm. Simultaneously, the distribution system operator (DSO) leverages the shared storage to alleviate congestion by purchasing charged power from the storage manager. Interestingly, the DSO is willing to pay a premium price for the charged power from the shared storage, surpassing the prevailing electricity price during congested hours. Moreover, to account for uncertainties arising from load variations and intermittent renewable energy resources (RES), Monte Carlo simulation is employed in this study. Through comprehensive simulations and analyses, the proposed approach demonstrates the potential of CES as an effective tool for congestion relief and operational cost optimization in distribution systems, and providing economic benefits to both the MGs and the DSO.
Applications of electric power, Distribution or transmission of electric power
Abstract Future distribution networks (DN) are subject to rapid load changes and high penetration of variable distributed energy resources (DER). Due to this, the DN operators face several operational challenges, especially voltage violations. Optimal power flow (OPF)‐based reactive power control (RPC) from the smart converter (SC) is one of the viable solutions to address such violations. However, sufficient communication and monitoring infrastructures are not available for OPF‐based RPC. With the development of the latest information communication technology in SC, cyber‐physical co‐simulation (CPCS) has been extensively used for real‐time monitoring and control. Moreover, deploying OPF‐based RPC using CPCS considering the controller design of SC for a realistic DN is still a big challenge. Hence, this paper aims to mitigate voltage violations by using OPF‐based RPC in a real‐time CPCS framework with multiple SCs in a realistic DN. The OPF‐based RPC is achieved by performing the CPCS framework developed in this study. The CIGRE medium‐voltage DN is considered as a test system. Real‐time optimization and signal processing are achieved by Python‐based programs using a model‐based toolchain of a real‐time DN solver and simulator. Real‐time simulation studies showed that the proposed method is capable of handling uncertain voltage violations in real time.
Distribution or transmission of electric power, Production of electric energy or power. Powerplants. Central stations
This paper investigates the energy efficiency of a multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) system, in which one multi-antenna base station (BS) transmits unified ISAC signals to a multi-antenna communication user (CU) and at the same time use the echo signals to estimate an extended target. We focus on one particular ISAC transmission block and take into account the practical on-off non-transmission power at the BS. Under this setup, we minimize the energy consumption at the BS while ensuring a minimum average data rate requirement for communication and a maximum Cramér-Rao bound (CRB) requirement for target estimation, by jointly optimizing the transmit covariance matrix and the ``on'' duration for active transmission. We obtain the optimal solution to the rate-and-CRB-constrained energy minimization problem in a semi-closed form. Interestingly, the obtained optimal solution is shown to unify the spectrum-efficient and energy-efficient communications and sensing designs. In particular, for the special MIMO sensing case with rate constraint inactive, the optimal solution follows the isotropic transmission with shortest ``on'' duration, in which the BS radiates the required sensing energy by using sufficiently high power over the shortest duration. For the general ISAC case, the optimal transmit covariance solution is of full rank and follows the eigenmode transmission based on the communication channel, while the optimal ``on'' duration is determined based on both the rate and CRB constraints. Numerical results show that the proposed ISAC design achieves significantly reduced energy consumption as compared to the benchmark schemes based on isotropic transmission, always-on transmission, and sensing or communications only designs, especially when the rate and CRB constraints become stringent.
Abstract This paper presents a novel stochastic planning framework for the integration of renewable distributed energy resources (DERs) into existing power systems without relying on new investments in the transmission networks. The upper‐level problem of the proposed model aims at minimizing the total expected social cost of supplying demand that includes the expected cost of getting energy from conventional generating units and DERs, the congestion cost of transmission networks, and the greenhouse gas (GHG) emission cost, while each of the privately invested DER satisfies a specified rate of return. The lower‐level problem clears the electricity market to find locational marginal prices (LMPs) and operation status of the system. The proposed framework is formulated as a bi‐level optimization problem that is recast as a single‐level problem using the duality technique. The non‐linear terms are then linearized using the “bigM” method and the complementary slackness conditions. The uncertainties in the power production of renewable resources and the future electric loads are captured using the scenario technique in which the copula method is employed for considering the correlation between uncertainties. Finally, the effectiveness and applicability of the proposed method are validated on a 3‐bus test system and the modified IEEE RTS 24‐bus system.
Distribution or transmission of electric power, Production of electric energy or power. Powerplants. Central stations
Time series prediction is often complicated by distribution shift which demands adaptive models to accommodate time-varying distributions. We frame time series prediction under distribution shift as a weighted empirical risk minimisation problem. The weighting of previous observations in the empirical risk is determined by a forgetting mechanism which controls the trade-off between the relevancy and effective sample size that is used for the estimation of the predictive model. In contrast to previous work, we propose a gradient-based learning method for the parameters of the forgetting mechanism. This speeds up optimisation and therefore allows more expressive forgetting mechanisms.