The increase in hydrogen production to support the energy transition in different sectors, such as the steel industry, leads to the utilization of large scale electrolyzers. These electrolyzers have the ability to become a fundamental tool for grid stability providing grid services, especially frequency regulation, for power grids with a high share of renewable energy sources. Alkaline electrolyzers (AELs) have low cost and long lifetime, but their slow dynamics make them unsuitable for fast frequency regulation, especially in case of contingencies. Proton Exchange Membrane electrolyzers (PEMELs) have fast dynamic response to provide grid services, but they have higher costs. This paper proposes a dynamic power allocation control strategy for hybrid electrolyzer systems to provide frequency regulation with reduced cost, making use of advantages of AELs and PEMELs. Simulations and experiments are conducted to verify the proposed control strategy.
Hydrogen electrolyzers are promising tools for frequency regulation of future power systems with high penetration of renewable energies and low inertia. This is due to both the increasing demand for hydrogen and their flexibility as controllable load. The two main electrolyzer technologies are Alkaline Electrolyzers (AELs) and Proton Exchange Membrane Electrolyzers (PEMELs). However, they have trade-offs: dynamic response speed for AELs, and cost for PEMELs. This paper proposes the combination of both technologies into a Hybrid Hydrogen Electrolyzer System (HHES) to obtain a fast response for frequency regulation with reduced costs. A decentralized dynamic power sharing control strategy is proposed where PEMELs respond to the fast component of the frequency deviation, and AELs respond to the slow component, without the requirement of communication. The proposed decentralized approach facilitates a high reliability and scalability of the system, what is essential for expansion of hydrogen production. The effectiveness of the proposed strategy is validated in simulations and experimental results.
The energy system, including the electrical power system, is currently undergoing major changes to meet increased demands and climate target plans, and to stand against potential malicious activities and all sorts of disruptions. Specifically, the electrical power system is drastically changing with regards to consumption, production, transmission, control, monitoring, markets, and digitalization. Such a change, however, makes the power system an attractive and vulnerable target to all kinds of disruptive events and social-cyber-physical attacks since the system is crucial for the functioning of the society and economy. In this work, to act against such events and to study the future power system's susceptibility and resilience towards social-cyber-physical attacks, the Resilient Digital Sustainable Energy Transition (REDISET) project has shown the need for a new model that is able to describe the future electrical power system in a way that reflects the future reality. In this paper, existing power system models, the changing landscape of power systems, the drivers for a new model, the suggested model that comprises 7 building blocks instead of today's 3, and finally a direction of future related work are presented.
With the increase in penetration of power electronic converters in the power systems, a demand for overcurrent/ overloading capability has risen for the fault clearance duration. This article gives an overview of the limiting factors and the recent technologies for the overcurrent performance of SiC power modules in power electronics converters. It presents the limitations produced at the power module level by packaging materials, which include semiconductor chips, substrates, metallization, bonding techniques, die attach, and encapsulation materials. Specifically, technologies for overcurrent related temperatures in excess of 200 degrees C are discussed. This article also discusses potential technologies, which have been proven or may be potential candidates for improving the safe operating area. The discussed technologies are use of phase-change materials below the semiconductor chip, Peltier elements, new layouts of the power modules, control and modulation techniques for converters. Special attention has been given to an overview of various potential phase-change materials, which can be considered for high-temperature operations.
An increasing share of fluctuating and intermittent renewable energy sources can cause over-currents (OCs) in the power system. The heat generated during OCs increases the junction temperature of semiconductor devices and could even lead to thermal runaway if thermal limits are reached. In order to keep the junction temperature within the thermal limit of the semiconductor, the power module structure with heat-absorbing material below the chip is investigated through COMSOL Multiphysics simulations. The upper limits of the junction temperature for Silicon (Si) and Silicon Carbide (SiC) are assumed to be 175 and 250 ∘∘C, respectively. The heat-absorbing materials considered for analysis are a copper block and a copper block with phase change materials (PCMs). Two times, three times, and four times of OCs would be discussed for durations of a few hundred milliseconds and seconds. This article also discusses the thermal performance of a copper block and a copper block with PCMs. PCMs used for Si and SiC are LM108 and Lithium, respectively. It is concluded that the copper block just below the semiconductor chip would enable OC capability in Si and SiC devices and would be more convenient to manufacture as compared to the copper block with PCM.
This article introduces a novel self-supervised method that leverages incoherence detection for video representation learning. It stems from the observation that the visual system of human beings can easily identify video incoherence based on their comprehensive understanding of videos. Specifically, we construct the incoherent clip by multiple subclips hierarchically sampled from the same raw video with various lengths of incoherence. The network is trained to learn the high-level representation by predicting the location and length of incoherence given the incoherent clip as input. Additionally, we introduce intravideo contrastive learning to maximize the mutual information between incoherent clips from the same raw video. We evaluate our proposed method through extensive experiments on action recognition and video retrieval using various backbone networks. Experiments show that our proposed method achieves remarkable performance across different backbone networks and different datasets compared to previous coherence-based methods.
The reinforcement learning (RL) control approach with application to power electronics systems has become an emerging topic, while the sim-to-real issue remains a challenging problem as very few results can be referred to in the literature. Indeed, due to the inevitable mismatch between simulation models and real-life systems, offline-trained RL control strategies may sustain unexpected hurdles in practical implementation during the transfer procedure. In this article, a transfer methodology via a delicately designed duty ratio mapping is proposed for a dc-dc buck converter. Then, a detailed sim-to-real process is presented to enable the implementation of a model-free deep reinforcement learning controller. As the main contribution of this article, the proposed methodology is able to endow the control system to achieve: 1) voltage regulation and 2) adaptability and optimization abilities in the presence of uncertain circuit parameters and various working conditions. The feasibility and efficacy of the proposed methodology are demonstrated by comparative experimental studies.
Currently, fuel cell and hydrogen technology are attracting more and more attention as a kind of green and clean energy technology in the context of the increasingly stringent carbon emission requirements of the shipping industry. However, there are still many obstacles to their maritime application due to high costs and a lack of infrastructure. This paper conducts a literature survey of fuel cell maritime applications from four aspects: key technologies of fuel cell and hydrogen maritime applications, cost and standards. It can be concluded that ships powered by hydrogen fuel cells will have a broad application prospect with the maturity of the hydrogen industry chain and the improvement of standards and regulations. Two main contributions of this paper are to fully understand a whole-process perspective of hydrogen and fuel cell maritime application, and to provide the problems and future direction in this field, which can help relevant research institutions and scholars evaluate the development status of the industrial chain and find new valuable research topics.
This paper presents a new distributed control scheme to achieve both accurate voltage restoration and precise current sharing for islanded dc microgrid (MG) system only with limited noncontinuous communication among the distributed generators (DGs). A two-layer multiagent framework is employed for this MG system, which consists of a physical layer and a cyber layer. A distributed voltage restoration control scheme is proposed in the cyber layer, where no overall system information is required and only dc bus voltage feedback is needed. Furthermore, by employing the idea of event-triggered communication, our proposed approach only relies on limited aperiodic communication, which greatly reduces the communication cost in the cyber layer. The stability of proposed method is analyzed through a Lyapunov function based approach and we also demonstrate that the Zeno behavior can be excluded if a proper event-triggered condition is established. Our proposed method is validated in an islanded dc MG test system built in the Simulink environment and an experimental prototype consisting of three DGs simultaneously.
The increasing penetration of distributed renewable energy resources brings a great challenge for real-time voltage security of distribution grids. The paper proposes a safe multi-agent deep reinforcement learning (MADRL) algorithm for real-time control of inverter-based Volt-Var control (VVC) in distribution grids considering communication delay to minimize the network power loss, while maintaining the nodal voltages in a safe range. The multi-agent VVC is modeled as a constrained Markov game, which is solved by the MADRL algorithm. In the training stage, the safety projection is added to the combined policy to analytically solve an action correction formulation to promote more efficient and safe exploration. In the real-time decision-making stage, a state synchronization block is designed to impute the data under the latest timestamp as the input of the agents deployed in a distributed manner, to avoid instability caused by communication delay. The simulation results show that the proposed algorithm performs well in safe exploration, and also achieves better performance under communication delay.
This guest editorial summarizes the topics and the papers selected for the Special Issue on Flexible and Resilient Urban Energy Systems. After rigorous reviewing process, 21 papers are accepted for publication. These 21 accepted papers cover various aspects of urban energy systems and are distributed as following: situational awareness of urban energy systems (2 papers), quantification metrics of flexibility and resilience of urban energy systems (3 papers), vulnerability modeling of urban energy systems under various extreme events (3 papers), planning of flexible and resilient urban energy systems (4 papers), robust and resilient operation and control of urban energy systems (4 papers), recovery and restoration strategy of urban energy systems (2 papers), and coordination and interoperability of interconnected energy systems (3 papers). The Guest Editorial Board hopes this Special Issue can provide a valuable information for future research and advancements in the field of flexible and resilient urban energy systems.
Demand-side users are practical and economical resources for participating in voltage regulation of distribution networks, whose regulation effect is closely related to the price. This paper proposes a novel voltage-price coupling (VPC) mechanism to construct a fair voltage-based nodal pricing method for distribution networks to encourage demand-side users to participate in voltage regulation to improve voltage quality. The voltage impact factor is established to fairly characterize the impact of each nodal power on the voltage deviation in the distribution network. Further, the VPC mechanism is constructed to highly couple the nodal price with the voltage in both spatial and time dimensions by establishing distribution rules of prices at different nodes and different horizons. The VPC mechanism forms a fair and adaptable voltage-based nodal pricing method for distribution networks, which also considers the impact on the existing pricing mechanism, and the balance between the voltage regulation effect and user experience. Then, a price-driven voltage regulation (PVR) model is proposed for the distribution network to reduce voltage deviations and user costs, which integrates the demand-side resource management, voltage regulation, and voltage-based nodal pricing. Case studies verify the effectiveness of the VPC mechanism and the PVR model.
The number of smart inverters in active distribution networks is growing rapidly, making it challenging to realize a fast, distributed Volt/Var control (VVC). This work proposes a machine learning-assisted distributed algorithm to accelerate the solution of the VVC strategy. We first observe the convergence process of the Alternating Direction Method of Multipliers (ADMM)-based VVC problem and explore the potential relationships between the convergence and time-series regression. Then, the long short-term memory (LSTM) technique is applied to learn the convergence process and regress the converged values of the dual and global variables with previous ADMM observations. After that, the LSTM-assisted ADMM algorithm is proposed, where the regressions are used for ADMM parameter updates. In this algorithm, the inputs of the LSTM-model are carefully designed since the complementary conditions implied in the conventional ADMM should be considered. Unlike existing methods, the proposed method does not use the LSTM to determine the VVC strategy directly, indicating that it is non-intrusive and can satisfy all safety constraints during operations. The proof of its optimality and convergence is also given. The numerical simulations on the 33-bus distribution system demonstrate the effectiveness and efficiency of the proposed method.
Recently, renewables equipped with smart inverters are being integrated into distribution networks on a large scale. To address the problem that renewables are too scattered and hard to dispatch uniformly, virtual power plant (VPP) technique has been developed. In this work, we proposed an optimization model to coordinate different system functions and allocate them to various devices in a VPP agent. Peak shaving, congestion management, frequency and voltage regulation are all considered. Then, this scenario-based model is solved with the scenario selection method. Two types of scenarios are carefully selected to form the model, aiming at reducing the computation burden and increasing the strategy's robustness. The model is tested on a VPP agent modified from the 33-bus system. The simulation demonstrates the effectiveness and efficiency of our work.
In this article, we consider the resilience problem in the presence of communication faults encountered in distributed secondary voltage and frequency control of an islanded alternating current microgrid. Such faults include the partial failure of communication links and some classes of data manipulation attacks. This practical and important yet challenging issue has been taken into limited consideration by existing approaches, which commonly assume that the measurement or communication between the distributed generations (DGs) is ideal or satisfies some restrictive assumptions. To achieve communication resilience, a novel adaptive observer is first proposed for each individual DG to estimate the desired reference voltage and frequency under unknown communication faults. Then, to guarantee the stability of the closed-loop system, voltage and frequency restoration, and accurate power sharing regardless of unknown communication faults, sufficient conditions are derived. Some simulation results are presented to verify the effectiveness of the proposed secondary control approach.
In this article, we investigate the adaptive resilient secondary voltage and frequency control problem for islanded ac microgrids (MG) in the presence of sensor faults. Sensor faults or data attacks have a great impact on the stability and quality of MG. Existing methods commonly assume that the sensing information from distributed generations (DGs) is healthy or satisfies some ideal conditions. To achieve resilient secondary voltage restoration, a novel adaptive fault tolerant control scheme is proposed. Since the bounds of the time-varying faults are unknown, sufficient conditions are first derived to guarantee the voltage tracking errors to be uniformly ultimately bounded. It is also theoretically shown that the frequency restoration and power sharing can be ensured stable of the overall ac MG system by using the same control scheme. Compared with the existing distributed control methods for secondary control of MG, the considered problem with unknown boundaries sensor faults is more challenging and still not well explored.
In this paper, we consider the distributed reactive power sharing control problem for an autonomous inverter-based microgrid with resilience for communication faults, which may be caused by partial communication link failures or some channel manipulation attacks. Under the standard decoupling approximation for bus angle differences, the reactive power flow of each inverter can be controlled by manipulating the voltage amplitudes of itself and its neighbour inverters. By designing an adaptive resilient cooperative control scheme, accurate reactive power sharing can be guaranteed even in the presence of communication faults. Some simulation results are presented to verify the superiority of the proposed control approach compared with the existing methods.
This paper proposes a resilient controller for DC microgrid to achieve current sharing and voltage restoration under discrete-time false data injection (FDI) and denial-of-service (DoS) attacks. Switching and impulsive signals are used to model the dynamic system of DC microgrid under DoS and FDI. To deal with the cyber attacks, a combined error of current and voltage is proposed and a switching secondary controller is designed. Based on the stability analysis method on hybrid systems, we establish a sufficient condition for selecting control parameters in relation to the average dwell time of FDI attack and the normal communication rate under DoS attack. Furthermore, an adaptive gain based control scheme is proposed to relax the requirement on knowledge of the cyber attacks in control parameter design. The utility of the results is illustrated through case studies on a tested DC microgrid.
Power imbalances between generation and consumption will cause frequency deviations in the grid. In modern power systems, intermittent renewable energy sources (RES) have resulted in more frequent frequency violations, as traditional power plants cannot compensate for power gaps timely. Electric vehicles (EVs) can participate in load frequency control (LFC) through aggregators and are capable of reacting faster to control commands than conventional frequency control reserves (FCR) in generators. Thus EVs hold great promise in assisting with LFC. This paper proposes a composite control scheme that fully utilizes EVs for LFC in both normal scenarios and contingencies. The designed droop control can greatly reduce instantaneous frequency deviations (IFD) in emergencies, while the tube model predictive control (Tube MPC) can ensure smooth frequency trajectories during normal operations. Based on realistic models, simulation results illustrate the effectiveness of the proposed method.
The growing penetration of renewable energy sources (RES) in modern grids may result in severe voltage violation problems due to high stochastic features. Conventional centralized approaches could provide optimal solutions for voltage regulation while with great communication burdens. Control methods based on local information usually have non-optimal results and cannot always guarantee voltage security. This paper proposes a neural network-based decentralized strategy for volt/var control using inverter reactive power capacity. Learning from optimal power flow (OPF) results of historical data, the developed controller can provide optimal results approximate to centralized solutions and outperform local control methods in minimizing the power loss. The proposed method is tested on the IEEE 33-bus system and simulation results illustrate the effectiveness in voltage regulation and loss minimization.
This paper proposes a neural network-based digital twin for online health monitoring of vulnerable components in converters. The proposed digital twin consists of a physics-informed model with uncertain parameters, and a neural network (NN) for real-time model updating and health monitoring of components. This method is noninvasive, without extra circuits, and can identify parameters in real-time with high efficiency. Simulation and experiment are conducted to validate the effectiveness of the proposed method in accurate parameter identification and degradation monitoring of capacitor and MOSFET.
Data manipulation attacks have become one of the main threats to cyber-physical direct current (DC) microgrids, but how to ensure voltage and current restoration under cyber attacks has not been well explored. In this paper, the event-based attack detection and mitigation problem for DC microgrids is considered. Specifically, an attack detection mechanism is designed to detect whether an attack has occurred. Then the proposed resilient secondary control strategy is only activated when the detection mechanism generates an attack event. For unknown types of attacks that aim at tampering with the information transmitted in the communication network, an adaptive linear quadratic regulator (LQR) based control strategy is designed to mitigate the effects such that the voltage and current restoration is achieved. Finally, the effectiveness of the proposed strategy is verified through simulationthis.
Growing penetration of renewables comes with increased cyber security threat due to inherent low inertia characteristic and sophisticated control and communication networks of power electronics. This paper proposes a data-driven cyberattack detection strategy for grid-connected photovoltaic (PV) inverters. Ideas of long short term memory (LSTM) and convolutional neural network (CNN) as the core of detection achieve time series classification to diagnose the target and mode of cyberattack. Input de-redundancy and hyperparameter selection are conducted to optimize the detection. Meanwhile, well-designed cyberattack toolboxes of false data injection (FDI), denial-of-service (DoS) and delay are applied upon the communication of both sampled signals and issued commands in a grid-connected inverter model. By observing system performance via electrical measurements, this case study evaluates the LSTM, CNN-LSTM and convolutional LSTM based detection and obtains stable high quality of classification.
The stability of dual active bridge converter (DAB) is threatened when feeding the constant power loads (CPLs). This article proposes a deep reinforcement learning-based backstepping control strategy to solve this problem. First, a nonlinear disturbance observer is adopted to estimate the large-signal nonlinear disturbance. Then, a backstepping controller is used to stabilize the voltage response of the DAB under the large-signal disturbance. Finally, a compensation method based on deep reinforcement learning is developed to intelligently minimize output voltage tracking error and improve the operating efficiency of the system. The proposed controller can guarantee system stability under the large-signal disturbance of the CPL and achieve a fast dynamic response with accurate voltage tracking; it is more adaptive by using the deep reinforcement learning technique through the learning of its neural networks. The effectiveness of the proposed controller is verified by experiments.
With the growth of energy and transportation demand, the integrated energy dispatching of ship power grid has become the focus of researchers. The optimization technique is used to reduce the total energy consumption and pollutant emissions of ships, optimizing the ship power generation planning. The purpose is to achieve environmental protection and energy saving while ensuring the continuous and reliable power supply of ships. However, heterogeneous ship microgrid poses new challenges to integrated energy dispatch. This paper proposes an integrated energy scheduling scheme that integrates photovoltaic, wind power, diesel engine, gas turbine, and battery for a heterogeneous multienergy ship microgrid. Under the system constraints, a multi-objective optimal scheduling model including operating costs and pollutant emissions is established, then the gravity search algorithm is applied to solve such an issue. The simulation results show that the scheme can effectively reduce the cost of energy consumption and pollutant emissions of ships, improving the economy, reliability and energy conservation, which verify the advantages of the proposed scheme.
Hybrid energy storage system (HESS) is effective to compensate for fluctuation power in renewables and fast fluctuation loads in DC microgrids. To regulate DC bus voltage, a power management strategy is an essential issue. In the meantime, the increasing integration of constant power loads (CPLs) in DC microgrids brings great challenges to stable operation due to their negative incremental impedance. In this paper, a fast composite backstepping control (FBC) method is proposed for the HESS to achieve faster dynamics, smaller voltage variations, and large-signal stabilization. In the FBC method, a higher order sliding mode observer (HOSMO) is adopted to estimate the coupled disturbances. Furthermore, the FBC method is integrated with the droop control; so that the FBC-based decentralized power allocation (FBC-DPA) strategy for HESS in DC microgrids is developed. The proposed FBC method is designed based on the Lyapunov function to ensure its stability. Moreover, the design guidelines are provided to facilitate the application of the proposed method. Both simulation and experimental studies under different operating scenarios show that the proposed method achieves faster voltage recovery and smaller voltage variations than the conventional backstepping control method.
DC microgrids have emerged as a promising solution for efficient and reliable electricity distribution. In DC microgrids, when power electronic loads and motor drives are tightly regulated, they behave as constant power loads (CPLs) and may lead to the instability issue. In this paper, a novel output-constrained controller for the DC/DC buck converter feeding CPLs is proposed. By introducing the output-constrained technique into the backstepping method, the proposed control scheme can keep the DC bus working within the pre-specific boundary even when large-signal disturbances happen. Relevant theoretical analyses are conducted by employing Laypunov stability theorem. Simulations in Matlab/Simulink are presented to verify the proposed controller.
The proliferation of electric vehicles (EVs) has resulted in new charging infrastructure at all levels, including domestically. These new domestic EVs can potentially provide vehicle to home (V2H) services where EVs are used as energy storage systems (ESSs) for the home when they are not in use. Energy management systems (EMSs) can control these EVs to minimize the electricity cost to the owner but must satisfy constraints. Uncertainty in EV availability and the microgrid environment is also a challenge and can be addressed through real-time operation. Hence this paper formulates the EV charge/discharge scheduling problem as a Markov Decision Process (MDP). A safe implementation of Proximal Policy Optimization (PPO) is proposed for real-time optimization and compared to a day-ahead Mixed Integer Linear Programming (MILP) benchmark. The resulting PPO agent is able to minimize RA and SD costs for a typical EV user 3% better than the MILP solution. It obtains a 39% higher electricity cost than MILP, but unlike MILP does not require accurate forecasting data and operates in real-time.
Dynamic average consensus (DAC) has found applications in various systems. The existing event-triggered DAC algorithms have not well addressed the issue of key communication link failures that lead to the separation of the initial communication topology. This article presents a modified event-triggered DAC algorithm that is independent of its initial conditions. As a result, it is robust against key communication link failures. In this algorithm, each agent decides locally when to transmit signals to its neighbors. In this way, the communication burden among the neighboring agents is reduced. A numerical example is provided to illustrate the effectiveness of the proposed algorithm. Moreover, the proposed algorithm is applied to a state-of-charge balance control problem of batteries in energy systems, and both simulations and hardware-in-the-loop tests are provided to demonstrate the control performance.
The distributed control of DC microgrid is becoming increasingly important in modern power systems. One important control objective is to ensure DC bus voltage stability and proper current sharing with a reduced communication burden. This paper presents a new event-triggered distributed secondary control strategy for single-bus DC microgrid. Through this strategy, both current sharing and bus voltage regulation can be guaranteed. Moreover, through the event-triggering mechanism, each converter can decide locally when to transmit signals to its neighbours. In this way, the communication burden among converters is significantly reduced. Compared to existing results, the proposed strategy also enables various types of loads, including both linear and nonlinear loads, to be connected to the DC microgrid. Simulation and experiment results illustrate the effectiveness of the proposed strategy.
In this chapter, the stability analysis methods for power electronic-based power systems are reviewed. First, modeling methods and small signal stability analysis methods, i.e., eigenvalue method and impedance-based method, are illustrated with detailed procedures. Next, large-signal stability analysis tools are discussed. Then two case studies are presented with small-signal stability analysis and large-signal stability analysis. Finally, conclusions are drawn.
Virtual synchronous generator (VSG) is a promising solution for inertia support of the future electricity grid to deal with the frequency stability issues caused by the high penetration of renewable generations. However, the power variation in power electronic interface converters caused by VSG emulation increases the stress on power semiconductor devices and hence has a negative impact on their reliability. Unlike existing works that only consider stability for VSG control design, this article proposes a double-artificial neural network (ANN)-based method for designing VSG inertia parameter considering simultaneously the reliability and stability. First, a representative frequency profile is generated to extract various VSG power injection profiles under different inertia values through detailed simulations. Next, a functional relationship between inertia parameter (H) and lifetime consumption (LC) of VSG is established by the proposed double-ANN reliability model: ANN t provides fast and accurate modeling of thermal stress in the semiconductor devices from a given operating profile; with the aid of ANN t , ANN LC is built for fast and accurate estimation of LC for different inertia parameters in the next step. The proposed approach not only provides a guideline for parameter design given a certain LC requirement, but can also be used for optimal design of VSG parameter considering reliability and other factors (e.g., inertia support in this article). The proposed technique is applied to a grid-connected VSG system as a demonstration example.
With the penetration of renewable generation and tightly regulated power electronic loads, the power quality and stability of modern dc microgrids are greatly challenged. To solve this problem, energy storage systems (ESSs) are widely proposed. Unfortunately, most of the existing control methods for ESSs can only ensure the stability of dc microgrids with small signal disturbances, which may not be satisfying for real-time applications where large signal disturbances exist. Moreover, they are usually designed for low boost ratio and low power converters, negating their suitability for grid-scale applications. To ensure the large signal stability of dc microgrids using high boost ratio interleaved converter interfaced ESSs, this article proposes a new backstepping control strategy with finite-time disturbance observers. Its effectiveness is verified by simulation and experiments carried out on an interleaved double dual boost converter.
DC microgrids encounter the challenges of constant power loads (CPLs) and pulsed power loads (PPLs), which impose the requirements of fast dynamics, large stability margin, high robustness that cannot be easily addressed by conventional linear control methods. This necessitates the implementation of advanced control technologies in order to significantly improve the robustness, dynamic performance, stability and flexibility of the system. This article presents an overview of advanced control technologies for bidirectional dc/dc converters in dc microgrids. First, the stability issue caused by CPLs and the power balance issue caused by PPLs are discussed, which motivate the utilization of advanced control technologies for addressing these issues. Next, typical advanced control technologies including model predictive control, backstepping control, sliding-mode control, passivity-based control, disturbance estimation techniques, intelligent control, and nonlinear modeling approaches are reviewed. Then the applications of advanced control technologies in bidirectional dc/dc converters are presented for the stabilization of CPLs and accommodation of PPLs. Finally, advanced control techniques are explored in other high-gain nonisolated (e.g., interleaved, multilevel, cascaded) and isolated converters (e.g., dual active bridge) for high-power applications.
Hybrid energy storage systems (HESSs) with batteries and supercapacitors (SCs) provide an effective solution to compensate fluctuations of renewable resources and fast loads in DC microgrid. This paper proposes a distributed power management strategy for HESSs. In primary level, a virtual resistance/capacitance droop control strategy is implemented to achieve decentralized low-and high-frequency power sharing of batteries and supercapacitors (SCs). Then a distributed finite-time secondary control strategy is proposed to address the issues of DC bus deviation, state-of-charge (SoC) balancing of batteries and SoC recovery of SCs. With the proposed finite-time controller, DC bus voltage is restored to the nominal value and SoC balancing of batteries is achieved in finite time with information from neighbouring batteries; SoCs of SCs are restored to their initial values autonomously. The proposed power management strategy only requires limited communication among batteries and does not require communication among SCs. Simulations are conducted to verify the effectiveness of the proposed method.
The high penetration of power electronic converter loads in dc microgrid causes system stability issue, or also known as constant power load issue, due to their negative impedance characteristics. The stability concern will be more complicated for a self-disciplined microgrid that allows plug and play of various distributed generations (DGs). This article proposes a robust droop-based controller for decentralized power sharing in a dc microgrid considering large-signal stability. For each DG interface converter subsystem, the interactions with other DG interface converters and loads are estimated by a nonlinear disturbance observer (NDO) utilizing the subsystem's own information to achieve decentralized power sharing and fast voltage regulation. With the uncertainties of circuit parameters modeled as a lumped disturbance term and compensated by an NDO, the proposed controller can significantly enhance the robustness against the uncertainties of circuit parameters. The large-signal stability of the whole interconnected system is proved by the backstepping algorithm and Lyapunov theorem. The efficacy and large-signal stability of the proposed approach are verified by both simulations and experiments.
In the existing works of microgrid clusters, operation and real-time control are normally designed separately in a hierarchical architecture, with the real-time control in the primary and secondary levels, and operation in the tertiary level. This article proposes a hierarchically coordinated control scheme for DC MG clusters under uncertainty. In each MG, the tertiary level controller optimizes the operating cost in the MG by taking into account the real-time uncertainties of renewable generations and loads deviated from the forecasting data; and the primary controller responds to the real-time power fluctuations through an optimised droop curve. The hierarchically coordinated optimization problem is formulated to optimize the power set points and droop curve coefficients simultaneously under uncertainties using an adjustable robust optimization model. For the MG cluster, the energy sharing of each MG in the cluster is optimized to minimize the total operating cost and the transmission loss. The overall optimization problem is solved in a distributed manner by alternating direction method of multipliers (ADMM) where each MG entity only exchanges boundary information (i.e. the power exchange of MG entity with the MG cluster), thus information privacy and plug-and-play feature of each MG are guaranteed. The proposed approach optimally coordinates the operation and real-time control layers of a DC MG cluster with uncertainties; it achieves decentralized power sharing at the real-time control layer and distributed optimization at the operation layer, featuring high scalability, reliability and economy. Case studies of a DC MG cluster are conducted in Matlab/Simulink in order to demonstrate the effectiveness of the proposed approach.
The high penetration of power electronic converters into dc microgrids may cause the constant power load stability issues, which could lead to large voltage oscillations or even system collapse. On the other hand, dynamic performance should be satisfied in the control of power electronic converter systems with small overshoot, less oscillations, and smooth transient performance. This article proposes an offset-free model predictive controller for a dc/dc buck converter feeding constant power loads with guaranteed dynamic performance and stability. First, a receding horizon optimization problem is formulated for optimal voltage tracking. To deal with the unknown load variation and system uncertainties, a higher order sliding mode observer is designed and integrated into the optimization problem. Then an explicit closed-loop solution is obtained by solving the receding horizon optimization problem offline. A rigorous stability analysis is performed to ensure the system large signal stability. The proposed controller achieves optimized transient dynamics and accurate tracking with simple implementation. The effectiveness of the proposed controller is validated by simulation and experimental results.
The hybrid fuel cell/supercapacitor (FC/SC) system is a promising onboard power supply system for more electric aircraft (MEA), where system stability is a critical issue due to the high penetration of constant power loads (CPLs) in MEA. This article proposes a composite finite-time controller for decentralized power sharing and stabilization of the hybrid FC/SC system with CPLs. It consists of an integral droop (ID) + finite-time controller for the SC converter and a proportional droop (PD) + finite-time controller for the FC converter. First, the coordination of PD and ID achieves decentralized power sharing between FC and SC such that SC only compensates fast fluctuations and FC provides smooth power at the steady state. Then, a finite-time observer is designed to provide feedforward compensation for the disturbances and enables accurate tracking with fast dynamics. Finally, a composite finite-time controller is constructed following a nonrecursive synthesis procedure with a rigorous large signal stability analysis. The proposed controller guarantees finite-time convergence even under large signal variations and can be easily implemented with a practical gain tuning procedure. Simulations and experiments are conducted to verify the proposed technique.
Hybrid AC/DC microgrids (MGs) provide efficient integration of renewable sources into grids and the interconnection of multiple MGs can improve system reliability, efficiency and economy by energy sharing. In this paper, a distributed and robust energy management system is proposed for networked hybrid AC/DC MGs. For each individual MG, an adjustable robust optimization model is proposed to optimize its individual operational cost considering the uncertainty of the renewable generation and load demand. For the networked-MGs system, the energy sharing information of each MG is coordinated by the DC network to minimize the power transmission loss with network constraints. The overall optimization model is formulated, exactly convexified and solved in a distributed manner by the alternating direction method of multipliers (ADMM), where only limited information is required from each MG entity (i.e., the power injection to the network) and thus information privacy is guaranteed. Simulations of the networked hybrid AC/DC MGs are conducted to demonstrate the effectiveness of the proposed energy management system.
WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access. Recently, besides network services, WiFi sensing is appealing in smart homes since it is device-free, cost-effective and privacy-preserving. Though numerous WiFi sensing methods have been developed, most of them only consider single smart home scenario. Without the connection of powerful cloud server and massive users, large-scale WiFi sensing is still difficult. In this paper, we firstly analyze and summarize these obstacles, and propose an efficient large-scale WiFi sensing framework, namely EfficientFi. The EfficientFi works with edge computing at WiFi APs and cloud computing at center servers. It consists of a novel deep neural network that can compress fine-grained WiFi Channel State Information (CSI) at edge, restore CSI at cloud, and perform sensing tasks simultaneously. A quantized auto-encoder and a joint classifier are designed to achieve these goals in an end-to-end fashion. To the best of our knowledge, the EfficientFi is the first IoT-cloud-enabled WiFi sensing framework that significantly reduces communication overhead while realizing sensing tasks accurately. We utilized human activity recognition and identification via WiFi sensing as two case studies, and conduct extensive experiments to evaluate the EfficientFi. The results show that it compresses CSI data from 1.368Mb/s to 0.768Kb/s with extremely low error of data reconstruction and achieves over 98% accuracy for human activity recognition.
In this paper, the real-time energy trading problem between the energy provider and the consumers in a smart grid system is studied. The problem is formulated as a hierarchical game, where the energy provider acts as a leader who determines the pricing strategy that maximizes its profits, while the consumers act as followers who react by adjusting their energy demand to save their energy costs and enhance their energy consumption utility. In particular, the energy provider employs a pricing strategy that depends on the aggregated amount of energy requested by the consumers, which suits a commodity-limited market. With this price setting, the consumers' energy demand response strategies are designed under a non-cooperative game framework, where a unique generalized Nash equilibrium point is shown to exist. As an extension, the consumers are assumed to be unaware of their future energy consumption behaviors due to uncertain personal needs. To address this issue, an online distributed energy trading framework is proposed, where the energy provider and the consumers can design their strategies only based on the historical knowledge of consumers' energy consumption behavior at each bidding stage. Besides, the proposed framework can be implemented in a distributed manner such that the consumers can design their demand responses by only exchanging information with their neighboring consumers, which requires much fewer communication resources and would thus be more suitable for the practical operation of the grid. As a theoretical guarantee, the proposed framework is further proved to asymptotically achieve the same performance as the offline solution for both energy provider and consumers' optimization problems. The performance of practical designs of the proposed online distributed energy trading framework is finally illustrated in numerical experiments.
The alternating direction method of multipliers (ADMM) has been recently recognized as well-suited for solving distributed optimization problems among multiple agents. Nonetheless, there remains a scarcity of research exploring ADMM's communication costs. Especially for large-scale multi-agent systems, the impact of communication costs becomes more significant. On the other hand, it is well-known that the convergence property of ADMM is significantly influenced by the different parameters while tuning these parameters arbitrarily would disrupt the convergence of ADMM. To this end, inspired by the preliminary works on incremental ADMM, we propose a fast incremental ADMM algorithm that can solve large-scale multi-agent optimization problems with enhanced communication efficiency and fast convergence speed. The proposed algorithm can improve the convergence speed by introducing an extra adjustable parameter to modify the penalty parameter ? in both primal and dual updates of incremental ADMM. With several mild assumptions, we provide the convergence analysis of our proposed algorithm. Finally, the numerical experiments demonstrate the superiority of the proposed fast incremental ADMM algorithm compared to the other incremental ADMM-type methods.
The wide integration of inverter based renewable energy sources (RESs) in modern grids may cause severe voltage violation issues due to high stochastic fluctuations of RESs. Existing centralized approaches can achieve optimal results for voltage regulation, but they have high communication burdens; existing decentralized methods only require local information, but they cannot achieve optimal results. Deep reinforcement learning (DRL) based methods are effective to deal with uncertainties, but it is difficult to guarantee secure constraints in existing DRL training. To address the above challenges, this paper proposes a projection embedded multi-agent DRL algorithm to achieve decentralized optimal control of distribution grids with guaranteed 100% safety. The safety of the DRL training is guaranteed via an embedded safe policy projection, which could smoothly and effectively restrict the DRL agent action space, and avoid any violation of physical constraints in distribution grid operations. The multi-agent implementation of the proposed algorithm enables the optimal solution achieved in a decentralized manner that does not require real-time communication for practical deployment. The proposed method is tested in modified IEEE 33-bus distribution and compared with existing methods; the results validate the effectiveness of the proposed method in achieving decentralized optimal control with guaranteed 100% safety and without the requirement of real-time communications.
Deep reinforcement learning (DRL) is a promising solution for voltage control of distribution grids with high penetration of inverter-based renewable energy sources (RESs). Yet, when adopting the DRL-based control method, the safe and optimal operation of the system cannot be guaranteed at the same time, as the conventional DRL agent is not designed to solve the hard constraint problem. To address this challenge, this paper proposes a deep neural network (DNN) assisted projection based DRL method for safe control of distribution grids. First, a finite iteration projection algorithm is proposed to guarantee hard constraints by converting a non-convex optimization problem into a finite iteration problem. Next, a DNN assisted projection method is proposed to accelerate the calculation of projection and achieve the practical implementation of hard constraints in DRL problem. Finally, a DNN Projection embedded twin-delayed deep deterministic policy gradient (DPe-TD3) method is proposed to achieve optimal operation of distribution grids with guaranteed 100% safety of the distribution grid. The safety of the DRL training is guaranteed via the embedded Projection DNN in TD3 with participation in gradient return process, which could smoothly and effectively project the DRL agent actions into the feasible area, thus guaranteeing the safety of data driven control and the optimal operation at the same time. The case studies and comparisons are conducted in the IEEE 33 bus system to show the effectiveness of the proposed method.
Power converters and motor drives are playing a significant role in the transition towards sustainable energy systems and transportation electrification. In this context, rich diversity of new power converters and motor drive products are developed and commissioned by the industry every year. However, to achieve efficient, reliable and stable operation of power converter and drive systems, there are challenges in condition monitoring, fault diagnosis, lifecycle estimation, stability evaluation and control. Online learning is an emerging technology that can serve as a powerful remedy to these challenges. This paper aims to provide a systematic study of algorithms, implementations, and applications of online learning for control and diagnostics in the area of power converters and drives. First, online learning problems are formulated for condition monitoring, fault detection, online stability assessment, model predictive control for power converter and drive applications. Next, guidelines are provided about how to develop online learning models and algorithms for these applications. Practical case studies are presented with experimental demonstrations. Finally, challenges and future opportunities are discussed about online learning for power converter and drive applications.
Large-scale integrations of power-electronics devices have introduced the stability challenges to the conventional power system. The stability of the power-electronics-based power systems, which are modeled by a Multi-Input Multi-Output (MIMO) transfer function matrix, can be analyzed based on the Nyquist Criterion. However, since no or limited information about the internal control details, this matrix can only be obtained using the measured data. On the other hand, the elements of the matrix will change along with the operating point of each power-electronics converter, which introduces the challenge to guarantee the interaction stability of each inverter at different operating points. In this paper, a data-driven method is proposed to overcome this operating-point dependent challenge. An artificial neural network (ANN) is used to characterize the operating-point dependent model of power-electronics-based power systems. The comparison results confirm the accuracy of the impedance model obtained by this data-driven modeling method.
The renewable energy hydrogen based dc microgrid is an attractive solution for renewables integration, as the hydrogen is a clean fuel, that extra renewable energy source generation can be stored as hydrogen through electrolysis technology, and be used later through fuel cell technology. However, the efficiency of the electrolyzer and fuel cell change significantly under the wide operation ranges, and they have different degradation mechanisms that are greatly impacted by current ripples. Moreover, to achieve consistent power supply with 100% RESs, the electrolyzer and fuel cell need to be optimally coordinated. To address the issues, this paper proposes an MPC based power management method to achieve smooth power sharing and reduce the current ripple, also can guarantee the system stability under uncertainties of the renewable energy source and load. It consists of a baseline MPC for optimized transient performance and a sliding mode observer to estimate system uncertainties. Both the simulation and experiment results can validate the effectiveness of the proposed method.
With the large integration of renewables, the traditional power system becomes more sustainable and effective. Yet, the fluctuation and uncertainties of renewables have led to large challenges to the voltage stability in distribution power systems. This paper proposes a multi-agent deep reinforcement learning method to address the issue. The voltage control issue of the distribution system is modeled as the Markov Decision Process, while each grid-connected interface inverter of renewables is modeled as a deep neural network (DNN) based agent. With the designed reward function, the agents will interact with and seek for the optimal coordinated voltage-var control strategy. The offline-trained agents will execute online in a decentralized way to guarantee the voltage stability of the distribution without any extra communication. The proposed method can effectively achieve a communication-free and accurate voltage-var control of the distribution system under the uncertainties of renewables. The case study based on IEEE 33-bus system is demonstrated to validate the method.