Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems

In this paper,a class of time-varying output group formation containment control problem of general linear hetero-geneous multiagent systems(MASs)is investigated under directed topology.The MAS is composed of a number of tracking leaders,formation leaders and followers,where two different types of leaders are used to provide reference trajectories for movement and to achieve certain formations,respectively.Firstly,compen-sators are designed whose states are estimations of tracking lead-ers,based on which,a controller is developed for each formation leader to accomplish the expected formation.Secondly,two event-triggered compensators are proposed for each follower to evalu-ate the state and formation information of the formation leaders in the same group,respectively.Subsequently,a control protocol is designed for each follower,utilizing the output information,to guide the output towards the convex hull generated by the forma-tion leaders within the group.Next,the triggering sequence in this paper is decomposed into two sequences,and the inter-event intervals of these two triggering conditions are provided to rule out the Zeno behavior.Finally,a numerical simulation is intro-duced to confirm the validity of the proposed results.

Distributed Optimal Co-multi-microgrids Energy Management for Energy Internet

Unlike conventional power systems, the upcoming energy internet U+0028 EI U+0029 emphasizes comprehensive utilization of energy in the whole power system by coordinating multi-microgrids, which also brings new challenge for the energy management. To address this issue, this paper proposes a novel consensus-based distributed approach based on multi-agent framework to solve the energy management problem of the energy internet, which only requires local information exchange among neighboring agents. Correspondingly, two consensus algorithms are presented, one of which drives the incremental cost of each distributed generator U+0028 DG U+0029 converge to the state of the leader agent-energy router, and the other one is used to estimate the global power mismatch, which is a first-order average consensus algorithm modified by a correction term. In addition, in order to meet the supply-demand balance, an effective control strategy for the energy router is proposed to accurately calculate the power exchange between the microgrid and the main grid. Finally, simulation results within a 7-bus test system are provided to illustrate the effectiveness of the proposed approach. © 2014 Chinese Association of Automation.

The Initial Guess Estimation Newton Method for Power Flow in Distribution Systems

With the increasing integration of distributed generations U+0028 DGs U+0029, there is a demand for DGs to play a more important role on the voltage regulation. Meanwhile, the high penetration of DGs could raise a technical problem that the distribution system may operate with bi-directional power flow, leading to the inadequacy of the traditional power flow. Considering this new scenario in distribution system power flow, the convergence theorem is proposed, which contributes to develop a novel selection method of the initial guess closed to the convergent solution. Moreover, to ensure the fast rate of power flow convergence, the theorem of the maximum iterations estimation is also proposed. Based on the two proposed theorems, an Initial Guess Estimation Newton method is proposed, considering different operational status of DGs and initial guess sensitivity simultaneously. Based on the standard node systems, Tongliao grid, and 69 system of USA, three simulation examples are provided to illustrate the effectiveness of the proposed method. © 2017 Chinese Association of Automation.

Containment-Based Multiple PCC Voltage Regulation Strategy for Communication Link and Sensor Faults

The distributed AC microgrid(MG) voltage restoration problem has been extensively studied. Still, many existing secondary voltage control strategies neglect the co-regulation of the voltage at the point of common coupling(PCC) in the AC multi-MG system(MMS). When an MMS consists of sub-MGs connected in series, power flow between the sub-MGs is not possible if the PCC voltage regulation relies on traditional consensus control objectives. In addition, communication faults and sensor faults are inevitable in the MMS. Therefore, a resilient voltage regulation strategy based on containment control is proposed.First, the feedback linearization technique allows us to deal with the nonlinear distributed generation(DG) dynamics, where the PCC regulation problem of an AC MG is transformed into an output feedback tracking problem for a linear multi-agent system(MAS) containing nonlinear dynamics. This process is an indispensable pre-processing in control algorithm design. Moreover, considering the unavailability of full-state measurements and the potential faults present in the sensors, a novel follower observer is designed to handle communication faults. Based on this, a controller based on containment control is designed to achieve voltage regulation. In regulating multiple PCC voltages to a reasonable upper and lower limit, a voltage difference exists between sub-MGs to achieve power flow. In addition, the secondary control algorithm avoids using global information of directed communication network and fault boundaries for communication link and sensor faults. Finally, the simulation results verify the performance of the proposed strategy.

A Distributed Self-Consistent Control Method for Electric Vehicles to Coordinate Low-Carbon Transportation and Energy

Dear Editor,In this letter, a distributed self-consistent control method to coordinate low-carbon transportation and energy is proposed to address the efficient utilization of regional transportation energy and renewable energy. Specifically, taking into account the coordinated development of transportation, power grids, and renewable energy, transportation energy self-consistent, including instant self-consistent rate and power self-consistent rate。

Adaptive Dead-Time Modulation Scheme for Bidirectional LLC Resonant Converter in Energy Router

Although the dead-time optimization design of resonant converters has been widely researched,classical design methods focus more on achieving zero-voltage switching(ZVS)operation.The body diode loss is always ignored,which results in low-efficiency of the converter,especially,in energy router(ER).To deal with this problem,this paper proposes an adaptive deadtime modulation scheme for bidirectional LLC resonant converters in ER.First,the power loss of the MOSFET is analyzed based on the dead-time.Then,a novel dead-time optimization modulation principle is proposed.It can eliminate the body diode loss of MOSFET compared with existing literature.Based on the optimization modulation principle,this paper proposes an adaptive dead-time modulation scheme.To this end,the converter adopting the scheme no longer needs to calculate dead-time,which simplifies the parameter design process.Meanwhile,this scheme enables dead-time to dynamically change with working conditions according to the dead-time optimization modulation principle.With these effects,the ZVS operation is achieved,and the body diode loss of MOSFET is also eliminated.Furthermore,a digital implementation method is designed to make the proposed modulation scheme have fast-transient response.Finally,experimental results show that the proposed dead-time modulation scheme enables converters to achieve ZVS operation in all working conditions,and has higher efficiency than classical dead-time design methods.

Real-Time Energy Management for Net-Zero Power Systems Based on Shared Energy Storage

Battery energy storage systems(BESSs)serve a crucial role in balancing energy fluctuations and reducing carbon emissions in net-zero power systems.However,the efficiency and cost performance have remained significant challenges,which hinders the widespread adoption and development of BESSs.To address these challenges,this paper proposes a real-time energy management scheme that considers the involvement of prosumers to support net-zero power systems.The scheme is based on two shared energy storage models,referred to as energy storage sale model and power line lease model.The energy storage sale model balances real-time power deviations by energy interaction with the goal of minimizing system costs while generating revenue for shared energy storage providers(ESPs).Additionally,power line lease model supports peer-to-peer(P2P)power trading among prosumers through the power lines laid by ESPs to connect each prosumer.This model allows ESP to earn profits from the use of power lines while balancing power deviations and better consuming renewable energy.Experimental results validate the effectiveness of the proposed scheme,ensuring stable power supply for net-zero power systems and providing benefits for both the ESP and prosumers.

A Multi-slack Bus Model for Bi-directional Energy Flow Analysis of Integrated Power-gas Systems

The bi-directional energy conversion components such as gas-fired generators(GfG)and power-to-gas(P2G)have enhanced the interactions between power and gas systems.This paper focuses on the steady-state energy flow analysis of an integrated power-gas system(IPGS)with bi-directional energy conversion components.Considering the shortcomings of adjusting active power balance only by single GfG unit and the capacity limitation of slack bus,a multi-slack bus(MSB)model is proposed for integrated power-gas systems,by combining the advantages of bi-directional energy conversion components in adjusting active power.The components are modeled as participating units through iterative participation factors solved by the power sensitivity method,which embeds the effect of system conditions.On this basis,the impact of the mixed problem of multi-type gas supply sources(such as hydrogen and methane generated by P2G)on integrated system is considered,and the gas characteristics-specific gravity(SG)and gross calorific value(GCV)are modeled as state variables to obtain a more accurate operational results.Finally,a bi-directional energy flow solver with iterative SG,GCV and participation factors is developed to assess the steady-state equilibrium point of IPGS based on Newton-Raphson method.The applicability of proposed methodology is demonstrated by analyzing an integrated IEEE 14-bus power system and a Belgian 20-node gas system.

能源互联网中自能源的建模与应用

本文提出了自能源(we-energy, WE)这一新的能源互联网基本能源单元,为新能源就地消纳、各类分布式电源的即插即用和产–消–储一体化的多能互补提供了一种能源管理策略.为处理自能源在不同运行条件下的多能协调控制与优化问题,建立了基于信息–物理–能源–经济系统(information-equipment-energy-economical system, IEEES)的四元模型,提出了自能源的4种运行模式.该模型利用自能源中信息、物理、能源和经济4个子系统的耦合特性,根据能源互联网运行态势进行模式转化,建立了对应的自能源状态方程,并进行了自能源分布优化、协同控制等典型应用场景的分析.通过经济优化,获取了多个自能源在市场交易中的最优策略,实现了自能源内部能源的合理分配.通过对自能源内部设备的协同控制,实现了在功率波动情况下并网点系统参数及能源交换的稳定.

基于能控性的微电网控制器的优化配置

本文提出了一种超越结构能控性的研究微电网能控性的方法,证明了系统低通滤波器的截止频率是影响控制微电网所需的最少控制器数目的一大因素,该方法适用于实际微电网.此外,基于微电网特征矩阵的列初等变换,识别微电网中需要独立和非独立控制的节点,进而提出一种通用的系统控制矩阵构造方法,实现完全控制微电网的状态.进一步,根据节点状态误差曲线,证明所构造的控制矩阵可以保证微电网的能控性.最后以含7个分布式电源的微电网为例进行仿真验证,仿真结果验证了本文所提方法的有效性.

Non-intrusive modeling for integrated energy system based on two-stage GAN

Generally,an accurate model can describe the operating states of a system more effectively and provide a more reliable theoretical basis for the system optimization and control.Different from the traditional intrusive modeling,a non-intrusive modeling method based on two-stage generative adversarial network(TS-GAN)is proposed for integrated energy system(IES).By using this method,non-intrusive modeling for the IES including photovoltaic,wind power,energy storage,and energy coupling equipment can be carried out.First,the characteristics of IES are analyzed and extracted based on the meteorological data,energy output,and energy price,and then the characteristic database is established.Meanwhile,the loads are classified as uncontrollable loads and schedulable loads based on frequency domain decomposition to facilitate energy management.Furthermore,TS-GAN algorithm based on the Stackelberg game is designed.In the TS-GAN,the first-stage GAN is used to generate the operating data of each equipment identified by non-invasive monitoring,and the second-stage GAN distinguishes the accumulated data generated by first-stage GAN and further modifies the generator models of the first-stage GAN.Finally,the effectiveness and accuracy of the proposed method are verified by the simulation of an energy region.

Emission Trading Based Optimal Scheduling Strategy of Energy Hub with Energy Storage and Integrated Electric Vehicles

This paper focuses on the optimal scheduling of the district energy system with multiple energy supply modes and flexible loads.For multi-energy system(MES),the energy hub(EH)model including energy storage system and integrated electric vehicle(EV)is established.Based on the model,the influence of pollutant trading market on total operation cost is analyzed,and the optimal scheduling strategy is further put forward to realize the minimum purchase cost and emission tax cost of the MES.Finally,this paper compares the economic benefit of the fixed mode and the response mode,and discusses the contribution of the energy storage device and the multi-energy complementary mode to energy utilization efficiency.The simulation results indicate that optimal scheduling strategy of the EH can coordinate various energy complementary modes reasonably.Meanwhile,the proposed strategy is able to improve the operation economy of the EH,and ensure the better response effect of the demand side.The sensitivity analysis demonstrates the impact of pollutant emission price change on emission reduction.

From Independence to Interconnection--A Review of AI Technology Applied in Energy Systems

The development of diversified energy structures,distributed energy scheduling models and active participation ability of users,leads to a rapid movement toward energy system in which different energy carriers and systems interact together in a synergistic way.This energy development will face many challenges with the requirements of big data processing capability,professional skill,distributed collaboration and realtime monitoring for the energy system that demands an intelligent and flexible tool to realize the smart energy.Artificial intelligence(AI)technology has become a focus because of its better performance.This paper proposed a classification method that incorporates the intelligence of an independent energy unit(IEU)and the intelligence among interconnected energy units(IEUS)to review the development of AI technology in energy systems.The dominant structures of IEU can be considered from three aspects including perception,decision and implementation to study the optimal strategy for AI methods utilized in IEU.And considering the interaction relationship of IEUS,the AI applied for it can be described by the coordinated relationship and adversarial relationship problems to achieve consensus.By discussing the AI technologies and the potentials of AI in the energy system,some suggestions are presented to improve intelligent technologies for sustainable energy systems in the future.

Exponential-function-based droop control for islanded microgrids

An exponential-function-based droop control strategy for the distributed energy resources(DERs)is proposed to reduce the reactive power-sharing deviation,limit the minimum value of frequency/voltage,whilst improving the utilization rate of renewable energy.Both DERs and loads are interconnected to achieve a power exchange by converters,where the power management system should accurately share the active/reactive power demand.However,the proportional reactive power sharing often deteriorates due to its dependence on the line impedances.Thus,an exponential-function-based droop control is proposed to(1)prevent voltage and frequency from falling to the lower restraint,(2)achieve accurate reactive power sharing,(3)eliminate communication and improve the usage ratio of renewable energy.Furthermore,its stability is analyzed,and the application in islanded AC/DC hybrid microgrids is investigated to achieve the bidirectional power flow.The simulation and experimental results show that the reactive power sharing deviation can be reduced,and the utilization rate of renewable energy is improved by using the proposed method.Moreover,the simulation results illustrate that the system can maintain stable operation when the microgrid is switched from one supplied energy operation condition to another absorbed one.

Adaptive Bidirectional Droop Control for Electric Vehicles Parking with Vehicle-to-grid Service in Microgrid

With the popularity of electric vehicles(EVs),a large number of EVs will become a burden to the future grid with arbitrary charging management.It is of vital significance to the control of the EVs charging and discharging state appropriately to enable the EVs to become friendly to the grid.Therefore,considering the potential for EVs seen as energy storage devices,this paper proposes a multiport DC-DC solid state transformer topology for bidirectional photovoltaic/battery-assisted EV parking lot with vehicle-to-grid service(V2G-PVBP).Relying on the energy storage function of EVs,V2G-PVBP is able to not only satisfy the normal requirements of EVs’owner,but also provide the function of load shifting and load regulation to the microgrid.In this paper,EVs are categorized into limited EV and freedom EV.Limited EVs are always kept in charging state and freedom EVs can take part in the load regulation of the microgrid.The proposed adaptive bidirectional droop control is designed for freedom EVs to make them autonomously charge or discharge with certain power which according to each EV’s state of charge,battery capacity,leaving time,and other factors to maintain the stability of the future microgrid.Eventually,the simulation and experiment of the adaptive bidirectional droop control based V2G-PVBP are provided to prove the availability of V2G-PVBP.

Steady-state Stability Assessment of AC-busbar Plug-in Electric Vehicle Charging Station with Photovoltaic

Although the deployment of alternating current(AC)-busbar plug-in electric vehicle(PEV)charging station with photovoltaic(PV)is a promising alternative,the interaction among subsystems always causes the instability problem.Meanwhile,the conventional generalized Nyquist criterion(GNC)is complex,and it is not suitable for the design of the AC system.Therefore,this paper proposes a modified infinityone-norm(MION)stability criterion based on the impedance method to assess the stability of the foresaid charging station.Firstly,the typical structure and operation modes of the charging station are studied.Furthermore,each subsystem impedance matrix is built by small-signal method,and the MION stability criterion based on impedance method is proposed to assess the charging station stability.Compared with the previous simplified stability criteria based on the norm,the proposed criterion has lower conservatism.Furthermore,the design regulation for the controller parameters is provided,and the stability recovery way is provided by connecting the doubly-fed line and energy storage equipment,which are selected based on intermediate variable,i.e.,short-circuit ratio(SCR).Finally,the effectiveness and conservatism of the proposed stability criterion are validated through simulation and experimental results.

A Smart Charging Algorithm Based on a Fast Charging Station Without Energy Storage System

With the growing popularity of electric vehicles(EV),there is an urgent demand to solve the stress placed on grids caused by the irregular and frequent access of EVs.The traditional direct current(DC)fast charging station(FCS)based on a photovoltaic(PV)system can effectively alleviate the stress of the grid and carbon emission,but the high cost of the energy storage system(ESS)and the under utilization of the grid-connected interlinking converters(GIC)are not very well addressed.In this paper,the DC FCS architecture based on a PV system and ESS-free is first proposed and employed to reduce the cost.Moreover,the proposed smart charging algorithm(SCA)can fully coordinate the source/load properties of the grid and EVs to achieve the maximum power output of the PV system and high utilization rate of GICs in the absence of ESS support for FCS.SCA contains a self-regulated algorithm(SRA)for EVs and a grid-regulated algorithm(GRA)for GICs.While the DC bus voltage change caused by power fluctuations does not exceed the set threshold,SRA readjusts the charging power of each EV through the status of the charging(SOC)feedback of the EV,which can ensure the power rebalancing of the FCS.The GRA would participate in the adjustment process once the DC bus voltage is beyond the set threshold range.Under the condition of ensuring the charging power of all EVs,a GRA based on adaptive droop control can improve the utilization rate of GICs.At last,the simulation and experimental results are provided to verify the effectiveness of the proposed SCA.

Proportional Current Sharing Based on Periodic Dynamic Event-driven H_(∞) Consensus in DC Microgrids with Power Coupling

Although the proportional current sharing has been widely studied,the heterogeneous characteristic of the different interfaced converters and power coupling terms among distributed generators(DGs)are rarely considered.Therefore,this paper proposes a secondary H_(∞)consensus method with a periodic dynamic event-driven scheme for dc microgrids with power coupling to accomplish the precise proportional current-sharing.It is useful for reducing carbon.First,a generalized converter is constructed through equivalent transformation between rectifier and boost converter.Moreover,the heterogeneous characteristic of the interfaced converters regarding different DGs,such as wind and solar generators,is embedded into controller design.Furthermore,the standard linear heterogeneous multi-agent system with power coupling term is built.On this basis,the problem of proportional current sharing is modified into the output consistency problem of multi-agent systems.Furthermore,the H_(∞)consensus approach is proposed to accomplish the precise proportional current sharing.Meanwhile,to shorten communication bandwidth,the periodic dynamic event-driven communication strategy is designed.Compared with traditional event-driven communication schemes,a lower communication frequency has been obtained through the proposed communication scheme.In addition,this communication scheme not only avoids Zeno-behavior,but also acquires the smallest sampling time interval.Finally,effectiveness of the proposed approach is verified by two test systems.

ADP-based intelligent frequency control via adaptive virtual inertia emulation

This paper proposes a novel virtual inertia controller for converters in power systems,whichcan solve the system’s nonlinearity for frequency support.First,the system dynamics are formulatedas a nonlinear state-space,in which the reciprocal of inertia is modeled as controlinput.Correspondingly,a cost function is defined by considering frequency deviation andrate of change of the frequency,which can preserve a tradeoff between critical frequencylimits and respective control energy.Following,the optimal frequency regulation problemis solved by using an online adaptive dynamic programming method,where the actor andcritic neural networks are constructed to approximate the optimal control input and optimalcost function,respectively.After that,the small-signal analysis is provided to identify the stabilityof the converter under the proposed controller.Finally,simulation results verify thatthe frequency response of the system is significantly improved,while retaining more DC sideenergy.

Digital Twin Empowered PV Power Prediction

The accurate prediction of photovoltaic(PV)power generation is significant to ensure the economic and safe operation of power systems.To this end,the paper establishes a new digital twin(DT)empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction.With this framework,considering potential data contamination in the collected PV data,a generative adversarial network is employed to restore the historical dataset,which offers a prerequisite to ensure accurate mapping from the physical space to the digital space.Further,a new DT-empowered PV power prediction method is proposed.Therein,we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model(i.e.,a parallel network of convolution and bidirectional long short-term memory model)for capturing the hidden spatiotemporal features.The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model,resulting in enhanced prediction accuracy.Finally,a real dataset is conducted to assess the effectiveness of the proposed method.