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This paper investigates a novel adaptive voltage control over a three-phase grid-forming (GFM) inverter. The proposed voltage controller includes two function parts: power control input and signal control input. The former improves dynamic performance by taking advantage of state feedback, load current feedforward, and reference feedforward, while the latter, a time-variant signal in reference feedforward, considers uncertainties of system parameters and load disturbances. In order to compensate for harmonic distortion on loads, a multi-resonant control is added to the signal control input. Then, a multi-resonant based reference feedforward adaptive control strategy is put forward. Thanks to its linearization, the stability of the strategy is ensured by the mature linear control theory instead of the Lyapunov stability theory. Then, the principles of parameter selection based on the root locus method are introduced to improve the dynamic performance of this technique. Compared with classical PI/PR methods or other adaptive ones, this newly proposed adaptive control method synthesizes high robustness, excellent voltage regulation performance, and low total harmonic distortion under nonlinear loads in island mode. The experiments verify its effectiveness on a GFM inverter using a TI TMS320F28335 DSP.

With the popularization of power electronic equipment, the problem of harmonic pollution has become more and more prominent, posing a serious threat to the safe operation of power systems. In order to improve the stability and efficiency of the power system, the broadband measurement of harmonics has become a key direction of research. In this paper, a novel broadband harmonic measurement method is proposed, which combines the electric field sensing technique and the TCW add-window discrete Fourier transform technique. The amplification gain coefficient deviation of the measurement system is less than 3 dB in both steady-state and transient excitation signal test experiments. The measurement system can maintain a stable and continuous output in a wide frequency band within the range of MHz, which can effectively detect the harmonic components and provide an effective technical means for the monitoring of harmonics in the power system.

With the rapid development of global energy transformation and new power system, ensuring the stability of distributed energy grid connections is the key to maintaining the reliable operation of the whole power system. This paper constructs detailed impedance models of grid-following (GFL) and grid-forming (GFM) inverters using a harmonic linearization method and thoroughly investigates the mechanisms of resonance when inverters are connected to the grid, as well as the impact of model parameters on the stability of the grid system. This paper also briefly analyzes the scenario where distributed energy and electric vehicles are integrated into the grid simultaneously, demonstrating that grid system stability can be ensured in complex grid situations through reasonable parameter design. Lastly, the accuracy of the proposed impedance models and analysis is verified through MATLAB/Simulink simulations.

Synchronous rectification (SR) technology has been a critical technology for LLC converters to achieve high efficiency and power density. However, conventional SR driving methods face challenges in terms of light-load condition, module size, switching accuracy, and circuit complexity. This paper proposes an SR driving strategy based on resonant inductor voltage (RLV) to address those issues. This RLV-SR driving strategy does not require current sensors and is insensitive to rectifier parasitic parameters. In addition, the RLV-SR driving strategy can be applied in a relatively wide operating frequency range and load conditions. Experimental results based on a 100-W/24-V LLC converter are presented to verify the effectiveness of the proposed RLV-SR driving strategy. Furthermore, the error of turn-on time caused by stray inductance is significantly reduced compared with the conventional VDS-ON sensing method, which improves the power converter’s efficiency.

Introduction: This paper proposes a sigmoid-based adaptive inertia control strategy for grid-forming (GFM) inverter to enhance frequency stability.Methods: Firstly, the frequency response characteristics under different disturbances are analyzed theoretically. Then, to solve the problem that fixed inertia leads to a contradiction between the maximum frequency deviation and the setting time, a non-linear inertia regulator is investigated. This non-linear inertia regulator, which is based on an improved sigmoid function, is applied to achieve real-time inertia by the frequency deviation. Moreover, the full-order small-signal model of the system containing the nonlinear inertia regulator is established and thus the stability of the proposed strategy is analyzed.Result and Discussion: The proposed control strategy is implemented without derivative action, which may suffer from high-frequency noises. Finally, the hardware-inthe-loop (HIL) results verify the remarkable performance of the proposed control strategy.

As the penetration of renewable energy resources increases, their intrinsic uncertainty and randomness raise significant concerns about the grid’s reliability. With the development of information technologies, residential air-conditioners serve increasing services, including secondary frequency regulation, peak load shaving, and capacity reserve. However, these resources can hardly be allocated economically and reliably. On the one hand, it is unrealistic to provide advanced and expensive control modes for all AC units due to the limited investment cost. On the other hand, the uncertainty of residents’ behavior varying with control modes affects the reliability. Few previous works effectively make a trade-off between economics and reliability, which restricts the value of residential air-conditioners. Thus, this paper proposed an economical and reliable allocation method based on a real survey dataset and the online learning multi-armed bandits framework. This method explores the inherent relationship between the allocation plan’s economics and reliability. We also propose an allocation model which enables system operators to design an economic and reliable plan for each residential area considering ancillary services requirements. A case study in a modified IEEE 9 nodes system illustrates the effectiveness of the proposed method.

With the rapid growing penetration of solar power, extreme weather conditions may cause disturbance to power grids. Previous works have studied the impact of extreme weather conditions on active power balance at transmission level, while few studies investigated the potential voltage issue in distribution systems. As most small-scale rooftop photovoltaics are invisible to system operators and cannot participate in voltage regulation in practice, the sudden increase of the unknown amount of reactive power demand caused by extreme weather conditions may lead to a severe voltage fluctuation in the distribution systems with high penetration of rooftop photovoltaics. Hence, this paper investigates this potential voltage issue. We first explicitly modelled the solar power output during extreme weather conditions, and demonstrated the voltage issue in IEEE 33-bus system. Then, we formulated a model for optimal deployment of smart inverters to empower the rooftop photovoltaics at selected nodes to mitigate the voltage issue with minimum cost. With the optimal and efficient allocation plan generated by the proposed model, the distribution systems, with high penetration of solar power, are expected to remain stable and robust to extreme weather conditions.

In demand response programs, the load aggregator would send requests to customers to confirm whether they are willing to participate in the demand response events later, making sure that there is sufficient capacity to meet the load adjustment requirement of the power grid in real-time. However, sending requests frequently to the customers would lead to customer fatigue effect, which dampens customers’ enthusiasm for demand response. Motivated by this dilemma, an optimization problem of scheduling customers considering fatigue based on the MAB framework is proposed, and the online learning and ranking method: fatigue-aware MAB is presented to solve it. Finally, the numerical simulation demonstrates that the proposed method outperforms the traditional method under different parameter settings. Detailed analysis of how those settings affect the performance of our proposed method is also provided.

As the penetration of distributed renewable generation increases, their intrinsic uncertainty and random raise neutral concerns on the reliability of microgrid. Demand side resources are considered helpful in facilitating the operation of microgrid. In recent years, the development of Internet of Things and electric vehicle enables the capability of residential demands serving as flexible grid assets to provide various ancillary services and enhance operation reliability. For the residential flexible resource, their control modes and subsidy prices greatly affect the performance and operation cost, and different resources, such as air conditioner and electric vehicle (distributed energy storage), have considerably different flexible capability, capacity and quantity. Thus, this paper provides an economic-based resource allocation model for residential flexible resources. The proposed model finds the cost-effective plan about how to allocate residential flexible resources to enhance reliability of microgrid. Case studies are conducted on a modified IEEE 9-node network to illustrate the effectiveness of the proposed model.

Abstract In systems with high penetration of renewables, demand side resources have been aggregated to facilitate system operation. However, the natural uncertainty and randomness of users' behaviour may deteriorate the demand aggregation performance, including a large mismatch from the expected aggregation target and unnecessary cost while executing aggregation. Here, the most fast‐growing demand side resource, electric vehicle is targeted, and an algorithm based on a multi‐armed bandit approach is proposed to aggregate those electric vehicle demands. In the proposed multi‐armed bandit model, each electric vehicle user's behaviour is viewed as two arms. Then, a combinatorial upper confidence bound mixed sorting algorithm, which selects the optimal set of users participating in demand aggregation, is developed. The case studies show that the proposed method can reduce the demand aggregation mismatch and eliminate the unnecessary cost. Additionally, it can be observed that the user experience is also improved.

In grid-tied power converters, the grid parameters such as the frequency, phase angle of the fundamental component, and the harmonics are essential to the control of power converters, the fast and accurate estimation of the grid parameters is a challenge especially for high-order harmonics. This paper proposes a one-step-prediction (OSP) discrete observer (OSPDO) to observe the fundamental and harmonic sequence components of the grid voltage. Based on the proposed OSPDO, a frequency locked loop (FLL) is designed. The proposed OSPDO-FLL has a good robustness to sampling frequency on dynamic response and high precision for frequency estimation. The proposed algorithm can estimate up to maximum 50th harmonic with a low sampling frequency thanks to the robust properties. Finally, the good characteristics of the proposed synchronization algorithm are verified by simulations and laboratory experiments.

Modular multilevel converters (MMCs), as one of the core components of hybrid AC/DC power grids, become the preferred converter topology and show good developments. Urgently, a general MMC modeling scheme with good model accuracy needs to be developed to realize small-signal analyses and designs for the large-scale AC/DC power grids easily. This paper proposes a unified modeling scheme (UMS) for MMC systems in a synchronous rotating (dq) reference frame. Based on the dynamic phasor theory and with the proposed modular decouple modeling (MDM), the nonlinear state-space model of the overall MMC system can be obtained by configuring and connecting the input and output of the state-space model of each subsystem. Besides, the unified controller, modeling different control modes, normalizes the MMC systems modeling. Simultaneously, with the proposal of UMS, linearization and splicing could be uesd to develop a small-signal model of the overall MMC system directly. Therefore, the proposed model is suitable for simulating the large-scale hybrid AC/DC power grids and analyzing the stability of small-signal. Finally, the simulation results verify the accuracy and effectiveness of the proposed modeling method.

As the penetration of renewable energy continues to increase, stochastic and intermittent generation resources gradually replace the conventional generators, bringing significant challenges in stabilizing power system frequency. Thus, aggregating demand-side resources for frequency regulation attracts attentions from both academia and industry. However, in practice, conventional aggregation approaches suffer from random and uncertain behaviors of the users such as opting out control signals. The risk-averse multi-armed bandit learning approach is adopted to learn the behaviors of the users and a novel aggregation strategy is developed for residential heating, ventilation, and air conditioning (HVAC) to provide reliable secondary frequency regulation. Compared with the conventional approach, the simulation results show that the risk-averse multi-armed bandit learning approach performs better in secondary frequency regulation with fewer users being selected and opting out of the control. Besides, the proposed approach is more robust to random and changing behaviors of the users.

With the wide application of distributed generations (DGs) and microgrids (MGs), the inverter control becomes a hot research topic. For the inverter control in MG applications, first, a complex variable state-feedback-based switch control frame is proposed. In the proposed control frame, the state feedback leads to a generalized control objective (GCO), and then the instantaneous voltage and current controls are designed based on the GCO. Finally, a complex variable frequency-locked loop (FLL) is adopted to realize the voltage and current reference computation. The control system is integrated by complex variables to alleviate the seamless switch. The effectiveness of the proposed control method is validated by experimental results.

This paper presents a novel distributed voltage control strategy to maintain the voltage of active distribution networks containing multiple microgrids. Local voltage regulation characteristics, such as power reserve, adjustment cost, and regulating speed, are identified first. According to a neighbors' voltage regulation characteristics, the microgrid coordinates neighborhood resources and asks them for coordination as soon as voltage problems occur. Microgrids schedule local resources based on a cooperative distributed model predictive control algorithm scheme with neighbor-to-neighbor communication. Tests are performed in an active distribution network with three microgrids. The numerical studies have shown that the proposed strategy mitigates the voltage fluctuation rapidly and efficiently with minimum operating cost.

Inverter voltage control is an important task in the operation of a DC/AC microgrid system. To improve the inverter voltage control dynamics, traditional approaches attempt to measure and feedforward the load current, which, however, needs remote measurement with communications in a microgrid system with distributed loads. In this paper, a load current observer (LCO) based control strategy, which does not need remote measurement, is proposed for sinusoidal signals tracking control of a three-phase inverter of the microgrid. With LCO, the load current is estimated precisely, acting as the feedforward of the dual-loop control, which can effectively enlarge the stability margin of the control system and improve the dynamic response to load disturbance. Furthermore, multiple PR regulators are applied in this strategy conducted in a stationary frame to suppress the transient fluctuations and the total harmonic distortion (THD) of the output voltage and achieve faster transient performance compared with traditional dual-loop control in a rotating dq0 frame under instantaneous change of various types of load (i.e., balanced load, unbalanced load, and nonlinear load). The parameters of multiple PR regulators are analyzed and selected through the root locus method and the stability of the whole control system is evaluated and analyzed. Finally, the validity of the proposed approach is verified through simulations and a three-phase prototype test system with a TMS320F28335 DSP.

Operation control is a vital and complex issue for microgrids. The objective of this paper is to explore the practical means of applying decentralized control by using a multi agent system in actual microgrids and devices. This paper presents a hierarchical control framework (HCF) consisting of local reaction control (LRC) level, local decision control (LDC) level, horizontal cooperation control (HCC) level and vertical cooperation control (VCC) level to meet different control requirements of a microgrid. Then, a hybrid multi-agent control model (HAM) is proposed to implement HCF, and the properties, functionalities and operating rules of HAM are described. Furthermore, the paper elaborates on the implementation of HAM based on the IEC 61850 Standard, and proposes some new implementation methods, such as extended information models of IEC 61850 with agent communication language and bidirectional interaction mechanism of generic object oriented substation event (GOOSE) communication. A hardware design and software system are proposed and the results of simulation and laboratory tests verify the effectiveness of the proposed strategies, models and implementations.

Ultracapacitors (UCs), with their features of high power density and high current charge-discharge, have become the best choice for dynamic power compensation to improve the stability of microgrids and are increasingly being applied in microgrids. This paper presents the control of an energy storage system (ESS) based on ultracapacitors in the context of grid-connected microgrids. The ESS is composed of DC/AC and DC/DC converters tied by a dc link. An improved dynamic model for the ESS is proposed. Based on the proposed model a Proportional-Integral-Resonant (PIR) DC link voltage controller is proposed to maintain the DC link voltage through the charging-discharging control of ultracapacitors, capable of working properly under all operating conditions. An extra double frequency component is injected into the UC current by a R controller to dynamically compensate for DC instantaneous power and double frequency AC instantaneous power due to unbalanced grid conditions and disturbances. This feature maintains the DC link voltage constant under unbalanced conditions and increases the degrees of freedom of the DC/AC converter and thus facilitates the application of UCs in microgrids. Simulation and experimental results verify the effectiveness of the proposed control strategy.