The hybrid series-parallel microgrid attracts more attention by combining the advantages of both the series-stacked voltage and parallel-expanded capacity.Low-voltage distributed generations(DGs)are connected in serie...The hybrid series-parallel microgrid attracts more attention by combining the advantages of both the series-stacked voltage and parallel-expanded capacity.Low-voltage distributed generations(DGs)are connected in series to form the intra-string,and then multiple strings are interconnected in parallel.For the existing control strategies,both intra-string and inter-string depend on the centralized or distributed control with high communication reliance.It has limited scalability and redundancy under abnormal conditions.Alternatively,in this study,an intra-string distributed and inter-string decentralized control framework is proposed.Within the string,a few DGs close to the AC bus are the leaders to get the string power information and the rest DGs are the followers to acquire the synchronization information through the droop-based distributed consistency.Specifically,the output of the entire string has the active power−angular frequency(ω-P)droop characteristic,and the decentralized control among strings can be autonomously guaranteed.Moreover,the secondary control is designed to realize multi-mode objectives,including on/off-grid mode switching,grid-connected power interactive management,and off-grid voltage quality regulation.As a result,the proposed method has the ability of plug-and-play capabilities,single-point failure redundancy,and seamless mode-switching.Experimental results are provided to verify the effectiveness of the proposed practical solution.展开更多
In practical microgrids,current saturation of inverters and power interaction coupling of different forms of DERs complicate the system's transient behaviors.Existing methods of online transient stability predicti...In practical microgrids,current saturation of inverters and power interaction coupling of different forms of DERs complicate the system's transient behaviors.Existing methods of online transient stability prediction(TSP)are suitable for power systems consisting of homogeneous distributed energy resources(DERs),thus showing limited accuracy for stability prediction of microgrids.This paper develops a deep-learning-based TSP method for accurate online prediction of microgrids consisting of diverse forms of DERs under current saturation.First,a general key input feature selection method for microgrid TSP is systematically designed to ensure prediction accuracy.It is derived from a comprehensive mechanism analysis of the influence of DER's intrinsic and interaction characteristics under current saturation.Besides,impacts of load fluctuation and fault change are also considered to improve robust prediction performance.Second,to further improve prediction accuracy,an online TSP model based on deep learning is developed by effectively using the powerful nonlinear mapping capability of the deep belief network(DBN).Then,by combining feature selection method and deep-learning-based TSP model,an online TSP method is derived.Test results show the proposed method greatly improves accuracy of microgrid TSP under complex operating conditions.Furthermore,the method effectively avoids feature redundancy and the curse of dimensionality.Numbers of input features are independent of the scale of microgrids.展开更多
This paper proposes a novel framework based on the Stackelberg game and deep reinforcement learning for multi-microgrids(MGs)in achieving peer-to-peer(P2P)energy trading.A multi-leaders,multi-followers Stackelberg gam...This paper proposes a novel framework based on the Stackelberg game and deep reinforcement learning for multi-microgrids(MGs)in achieving peer-to-peer(P2P)energy trading.A multi-leaders,multi-followers Stackelberg game is utilized to model the P2P energy trading process.Stackelberg equilibrium(SE)is regarded as a P2P optimal trading strategy.A two-stage privacy protection solution technique combining data-driven and model-driven is developed to obtain the SE.Specifically,energy storage scheduling problem in MGs is formulated as a Markov decision process with discrete periods,and a multi-action single-observation deep deterministic policy gradient(MASO-DDPG)algorithm is proposed to tackle optimal scheduling of energy storage in the first stage.According to optimal scheduling of energy storage,the closed-form expression for SE based on model-driven is derived,and distributed SE solution technique(DSET)is developed to obtain SE in the second stage.Case studies involving a 4-Microgrid demonstrate the P2P electricity price obtained by the two-stage method,as a novel pricing mechanism,can reasonably regulate microgrid operation mode and improve microgrid income participating in the P2P market,which verifies effectiveness and superiority of the proposed P2P energy trading model and two-stage solution method.展开更多
Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are explorin...Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are exploring the potential of DC microgrids across var-ious configurations.However,despite the sustainability and accuracy offered by DC microgrids,they pose various challenges when integrated into modern power distribution systems.Among these challenges,fault diagnosis holds significant importance.Rapid fault detection in DC microgrids is essential to maintain stability and ensure an uninterrupted power supply to critical loads.A primary chal-lenge is the lack of standards and guidelines for the protection and safety of DC microgrids,including fault detection,location,and clear-ing procedures for both grid-connected and islanded modes.In response,this study presents a brief overview of various approaches for protecting DC microgrids.展开更多
The proliferation of distributed and renewable energy resources introduces additional operational challenges to power distribution systems.Transactive energy management,which allows networked neighborhood communities ...The proliferation of distributed and renewable energy resources introduces additional operational challenges to power distribution systems.Transactive energy management,which allows networked neighborhood communities and houses to trade energy,is expected to be developed as an effective method for accommodating additional uncertainties and security mandates pertaining to distributed energy resources.This paper proposes and analyzes a two-layer transactive energy market in which houses in networked neighborhood community microgrids will trade energy in respective market layers.This paper studies the blockchain applications to satisfy socioeconomic and technological concerns of secure transactive energy management in a two-level power distribution system.The numerical results for practical networked microgrids located at IllinoisTech−Bronzeville in Chicago illustrate the validity of the proposed blockchain-based transactive energy management for devising a distributed,scalable,efficient,and cybersecured power grid operation.The conclusion of the paper summarizes the prospects for blockchain applications to transactive energy management in power distribution systems.展开更多
This paper proposes a multi-agent cooperative operation optimization strategy for regional power grids considering the uncertainty of renewable energy output and flexibility of electric vehicle(EV)scheduling,which not...This paper proposes a multi-agent cooperative operation optimization strategy for regional power grids considering the uncertainty of renewable energy output and flexibility of electric vehicle(EV)scheduling,which not only improves the economy of networked microgrid(NMG)scheduling but also reduces the impact on active distribution network(ADN).EV condition matrix and model of the adjustable charge-anddischarge capacity of the EV may be built up by simulating the trip rule of an EV using the driving behavior of the vehicle model.In the day-ahead stage,by taking into account NMG operating cost,distribution network loss,and EV owners’payment cost,a multi-objective optimal scheduling model is developed,and the day-ahead scheduling contract for EV is obtained.Generative Adversarial Network(GAN)generates a significant number of intraday scenarios of photovoltaic(PV),load,and EV based on historical scheduling data as training data for the intra-day scheduling model multi-agent PPO(MAPPO).In the intra-day scheduling stage,intra-day ultra-short-term forecast data is input into the intra-day scheduling model,and the trained multi-agent model realizes NMG distributed real-time optimal scheduling.Finally,the economy and effectiveness of the proposed strategy are verified by Day-after optimal scheduling results.展开更多
This paper presents a novel machine learning(ML)enhanced energy management framework for residential microgrids.It dynamically integrates solar photovoltaics(PV),wind turbines,lithium-ion battery energy storage system...This paper presents a novel machine learning(ML)enhanced energy management framework for residential microgrids.It dynamically integrates solar photovoltaics(PV),wind turbines,lithium-ion battery energy storage systems(BESS),and bidirectional electric vehicle(EV)charging.The proposed architecture addresses the limitations of traditional rule-based controls by incorporating ConvLSTM for real-time forecasting,a Twin Delayed Deep Deterministic Policy Gradient(TD3)reinforcement learning agent for optimal BESS scheduling,and federated learning for EV charging prediction—ensuring both privacy and efficiency.Simulated in a high-fidelity MATLAB/Simulink environment,the system achieves 98.7%solar/wind forecast accuracy and 98.2% Maximum Power Point Tracking(MPPT)tracking efficiency,while reducing torque oscillations by 41% and peak demand by 22%.Compared to baseline methods,the solution improves voltage and frequency stability(maintaining 400V±2%,50Hz±0.015Hz)and achieves a 70% reduction in battery State of Charge(SOC)management error.The EV scheduler,informed by data from over 500 households,reduces charging costs by 31% with rapid failover to critical loads during outages.The architecture is validated using ISO 8528-8 transient tests,demonstrating 99.98% uptime.These results confirm the feasibility of transitioningmicrogrids fromreactive systems to adaptive,cognitive infrastructures capable of self-optimization under highly variable renewable generation and EV behaviors.展开更多
Given the rapid development of advanced information systems,microgrids(MGs)suffer from more potential attacks that affect their operational performance.Conventional distributed secondary control with a small,fixed sam...Given the rapid development of advanced information systems,microgrids(MGs)suffer from more potential attacks that affect their operational performance.Conventional distributed secondary control with a small,fixed sampling time period inevitably causes the wasteful use of communication resources.This paper proposes a self-triggered secondary control scheme under perturbations from false data injection(FDI)attacks.We designed a linear clock for each DG to trigger its controller at aperiodic and intermittent instants.Sub-sequently,a hash-based defense mechanism(HDM)is designed for detecting and eliminating malicious data infiltrated in the MGs.With the aid of HDM,a self-triggered control scheme achieves the secondary control objectives even in the presence of FDI attacks.Rigorous theoretical analyses and simulation results indicate that the introduced secondary control scheme significantly reduces communication costs and enhances the resilience of MGs under FDI attacks.展开更多
A regional electricity-heating integrated energy system(REH-IES)can make extensive use of renewable energy sources,realize complementary and coordinated operation of multiple energy sources,reduce carbon emissions and...A regional electricity-heating integrated energy system(REH-IES)can make extensive use of renewable energy sources,realize complementary and coordinated operation of multiple energy sources,reduce carbon emissions and promote the development of zero/low carbon systems.This paper proposes a risk-averse stochastic optimal scheduling model for REHIES.An energy flow framework for the REH-IES is proposed considering energy interaction between the electric-heating microgrids(EHMs)and electricity distribution network and the heating network.Then,considering the uncertainties of power output of renewable energy sources,dynamic characteristics of pipelines in the heating network,and thermal inertia of smart buildings,a stochastic optimal scheduling model for the REH-IES is established.Uncertainties of renewable energy sources bring financial risks to optimal scheduling of the REH-IES.Therefore,conditional value-at-risk(CVaR)theory is adopted to measure the risk and to limit the risk within an acceptable range,to achieve minimum expected scheduling cost of the REH-IES.The stochastic programming-based problem is transformed into a second-order cone programming(SOCP)model through secondorder cone relaxation method.Case studies verify the stochastic optimal scheduling model can reduce expected scheduling cost of the REH-IES,promote consumption of renewable energy sources and reduce carbon emissions.展开更多
The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage...The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage systems is proposed in this study.Off-grid microgrids are self-sufficient electrical networks that are capable of effectively resolving electricity access problems in remote areas by providing stable and reliable power to local residents.A comprehensive review of the design,control strategies,energy management,and optimization of off-grid microgrids based on domestic and international research is presented in this study.It also explores the critical role of energy stor-age systems in enhancing microgrid stability and economic efficiency.Additionally,the capacity configurations of energy storage systems within off-grid networks are analyzed.Energy storage systems not only mitigate the intermittency and volatility of renewable energy gen-eration but also supply power support during peak demand periods,thereby improving grid stability and reliability.By comparing different energy storage technologies,such as lithium-ion batteries,pumped hydro storage,and compressed air energy storage,the optimal energy storage capacity configurations tailored to various application scenarios are proposed in this study.Finally,using a typical micro-grid as a case study,an empirical analysis of off-grid microgrids and energy storage integration has been conducted.The optimal con-figuration of energy storage systems is determined,and the impact of wind and solar power integration under various scenarios on grid balance is explored.It has been found that a rational configuration of energy storage systems can significantly enhance the utilization rate of renewable energy,reduce system operating costs,and strengthen grid resilience under extreme conditions.This study provides essential theoretical support and practical guidance for the design and implementation of off-grid microgrids in remote areas.展开更多
The dynamic average consensus(DAC)algorithm is to enable a group of networked agents to track the average of their time-varying reference signals.For most existing DAC algorithms,a necessary assumption is that the upp...The dynamic average consensus(DAC)algorithm is to enable a group of networked agents to track the average of their time-varying reference signals.For most existing DAC algorithms,a necessary assumption is that the upper bounds of the reference signals and their derivatives are known in advance,thereby posing significant challenges in practical scenarios.Introducing adaptive gains in DAC algorithms provides a remedy by relaxing this assumption.However,the current adaptive gains used in this type of DAC algorithms are non-decreasing and may increase to infinity if persist disturbance exists.In order to overcome this defect,this paper presents a novel DAC algorithm with modified adaptive gains.This approach obviates the necessity for prior knowledge concerning the upper bounds of the reference signals and their derivatives.Moreover,the adaptive gains are able to remain bounded even in the presence of external disturbances.Furthermore,the proposed adaptive DAC algorithm is employed to address the distributed secondary control problem of DC microgrids.Comparative case studies are provided to verify the superiority of the proposed DAC algorithm.展开更多
Most developing countries continue to face challenges in accessing sustainable energy.This study investigates a solar panel and battery-powered system for an urban off-grid microgrid in Nigeria,where demand-sideflexib...Most developing countries continue to face challenges in accessing sustainable energy.This study investigates a solar panel and battery-powered system for an urban off-grid microgrid in Nigeria,where demand-sideflexibility and strategic interactions between households and utilities can optimize system sizing.A nonlinear programming model is built using bilevel problem formulation that incorporates both the households’willingness to reduce their energy consumption and the utility’s agreement to provide price rebates.The results show that,for an energy community of 10 households with annual energy demand of 7.8 MWh,an oversized solar-storage system is required(12 kWp of photovoltaic solar panels and 26 kWh of battery storage).The calculated average cost of 0.31€/kWh is three times higher than the current tariff,making it unaffordable for most Nigerian households.To address this,the utility company could implement Demand Response programs with direct load control that delay the use of certain appliances,such as fans,irons and air conditioners.If these measures reduce total demand by 5%,both the required system size and overall costs could decrease significantly,by approximately one-third.This adjustment leads to a reduced tariffof 0.20€/kWh.When Demand Response is imple-mented through negotiation between the utility and households,the amount of load-shaving achieved is lower.This is because house-holds experience discomfort from curtailment and are generally less willing to provideflexibility.However,negotiation allows for greaterflexibility than direct control,due to dynamic interactions and more active consumer participation in the energy transition.Nonetheless,tariffs remain higher than current market prices.Off-grid contracts could become competitive iffinancial support is pro-vided,such as low-interest loans and capital grants covering up to 75%of the upfront cost.展开更多
Interconnection planning involving bi-directional converters(BdCs)is crucial for enhancing the reliability and robustness of hybrid alternating current(AC)/direct current(DC)microgrid clusters with high penetrations o...Interconnection planning involving bi-directional converters(BdCs)is crucial for enhancing the reliability and robustness of hybrid alternating current(AC)/direct current(DC)microgrid clusters with high penetrations of renewable energy resources(RESs).However,challenges such as the non-convex nature of BdC efficiency and renewable energy uncertainty complicate the planning process.To address these issues,this paper proposes a tri-level BdC-based planning framework that incorporates dynamic BdC efficiency and a data-correlated uncertainty set(DcUS)derived from historical data patterns.The proposed framework employs a least-squares approximation to linearize BdC efficiency and constructs the DcUS to balance computational efficiency and solution robustness.Additionally,a fully parallel column and constraint generation algorithm is developed to solve the model efficiently.Numerical simulations on a practical hybrid AC/DC microgrid system demonstrate that the proposed method reduces interconnection costs by up to 21.8%compared to conventional uncertainty sets while ensuring robust operation under all considered scenarios.These results highlight the computational efficiency,robustness,and practicality of the proposed approach,making it a promising solution for modern power systems.展开更多
针对新能源可靠供电中的供需平衡问题,提出一种计及局部阴影工况的光储直流微电网能量管理。首先,针对光伏阵列在局部阴影等弱光照工况下,传统最大功率点跟踪方法在功率捕捉能力与能源利用效率方面的不足,利用蝠鲼优化算法(manta ray fo...针对新能源可靠供电中的供需平衡问题,提出一种计及局部阴影工况的光储直流微电网能量管理。首先,针对光伏阵列在局部阴影等弱光照工况下,传统最大功率点跟踪方法在功率捕捉能力与能源利用效率方面的不足,利用蝠鲼优化算法(manta ray foraging optimization,MRFO)的全局与局部搜索优势,提出一种结合MRFO的自适应电导增量法(manta ray foraging optimization-adaptive variable step-incremental conductance,MRFO-AVS-INC),以在特殊工况下实现对光伏出力的有效跟踪;其次,针对系统集成中存在的多源扰动问题,在储能系统的能量管理与控制方面,提出一种考虑蓄电池工作状态的改进下垂控制方法,通过引入蓄电池的荷电状态(state of charge,SOC)和健康状态(state of health,SOH)等参数,实现下垂系数的自适应调节,从而合理分配各蓄电池的输出功率;最后,在不同光照条件下对光储系统进行仿真验证。仿真结果表明,与基于粒子群算法的自适应变步长电导增量法(particle swarm optimization-adaptive variable stepincremental conductance,PSO-AVS-INC)及传统INC相比,MRFO-AVS-INC方法在收敛速度与跟踪稳定性方面表现更优;改进的自适应下垂控制有效实现了储能单元之间的状态均衡,延长了储能系统的整体使用寿命。展开更多
基金supported by the Smart Grid-National Science and Technology Major Project(2025ZD0804500)the National Natural Science Foundation of China under Grant 52307232the Hunan Provincial Natural Science Foundation of China under Grant 2024JJ4055.
文摘The hybrid series-parallel microgrid attracts more attention by combining the advantages of both the series-stacked voltage and parallel-expanded capacity.Low-voltage distributed generations(DGs)are connected in series to form the intra-string,and then multiple strings are interconnected in parallel.For the existing control strategies,both intra-string and inter-string depend on the centralized or distributed control with high communication reliance.It has limited scalability and redundancy under abnormal conditions.Alternatively,in this study,an intra-string distributed and inter-string decentralized control framework is proposed.Within the string,a few DGs close to the AC bus are the leaders to get the string power information and the rest DGs are the followers to acquire the synchronization information through the droop-based distributed consistency.Specifically,the output of the entire string has the active power−angular frequency(ω-P)droop characteristic,and the decentralized control among strings can be autonomously guaranteed.Moreover,the secondary control is designed to realize multi-mode objectives,including on/off-grid mode switching,grid-connected power interactive management,and off-grid voltage quality regulation.As a result,the proposed method has the ability of plug-and-play capabilities,single-point failure redundancy,and seamless mode-switching.Experimental results are provided to verify the effectiveness of the proposed practical solution.
基金supported in part by the National Key RD Program of China under Grant 2023YFB4204400,and in part by the National Natural Science Foundation of China under Grant 52125705.
文摘In practical microgrids,current saturation of inverters and power interaction coupling of different forms of DERs complicate the system's transient behaviors.Existing methods of online transient stability prediction(TSP)are suitable for power systems consisting of homogeneous distributed energy resources(DERs),thus showing limited accuracy for stability prediction of microgrids.This paper develops a deep-learning-based TSP method for accurate online prediction of microgrids consisting of diverse forms of DERs under current saturation.First,a general key input feature selection method for microgrid TSP is systematically designed to ensure prediction accuracy.It is derived from a comprehensive mechanism analysis of the influence of DER's intrinsic and interaction characteristics under current saturation.Besides,impacts of load fluctuation and fault change are also considered to improve robust prediction performance.Second,to further improve prediction accuracy,an online TSP model based on deep learning is developed by effectively using the powerful nonlinear mapping capability of the deep belief network(DBN).Then,by combining feature selection method and deep-learning-based TSP model,an online TSP method is derived.Test results show the proposed method greatly improves accuracy of microgrid TSP under complex operating conditions.Furthermore,the method effectively avoids feature redundancy and the curse of dimensionality.Numbers of input features are independent of the scale of microgrids.
基金supported in part by the Fundamental Research Funds for the Central Universities(No.2020YJS162).
文摘This paper proposes a novel framework based on the Stackelberg game and deep reinforcement learning for multi-microgrids(MGs)in achieving peer-to-peer(P2P)energy trading.A multi-leaders,multi-followers Stackelberg game is utilized to model the P2P energy trading process.Stackelberg equilibrium(SE)is regarded as a P2P optimal trading strategy.A two-stage privacy protection solution technique combining data-driven and model-driven is developed to obtain the SE.Specifically,energy storage scheduling problem in MGs is formulated as a Markov decision process with discrete periods,and a multi-action single-observation deep deterministic policy gradient(MASO-DDPG)algorithm is proposed to tackle optimal scheduling of energy storage in the first stage.According to optimal scheduling of energy storage,the closed-form expression for SE based on model-driven is derived,and distributed SE solution technique(DSET)is developed to obtain SE in the second stage.Case studies involving a 4-Microgrid demonstrate the P2P electricity price obtained by the two-stage method,as a novel pricing mechanism,can reasonably regulate microgrid operation mode and improve microgrid income participating in the P2P market,which verifies effectiveness and superiority of the proposed P2P energy trading model and two-stage solution method.
文摘Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are exploring the potential of DC microgrids across var-ious configurations.However,despite the sustainability and accuracy offered by DC microgrids,they pose various challenges when integrated into modern power distribution systems.Among these challenges,fault diagnosis holds significant importance.Rapid fault detection in DC microgrids is essential to maintain stability and ensure an uninterrupted power supply to critical loads.A primary chal-lenge is the lack of standards and guidelines for the protection and safety of DC microgrids,including fault detection,location,and clear-ing procedures for both grid-connected and islanded modes.In response,this study presents a brief overview of various approaches for protecting DC microgrids.
基金funded in part by Grant No.RG-15-135-43 from the Deanship of Scientific Research(DSR)at King Abdulaziz University in Saudi Arabia.
文摘The proliferation of distributed and renewable energy resources introduces additional operational challenges to power distribution systems.Transactive energy management,which allows networked neighborhood communities and houses to trade energy,is expected to be developed as an effective method for accommodating additional uncertainties and security mandates pertaining to distributed energy resources.This paper proposes and analyzes a two-layer transactive energy market in which houses in networked neighborhood community microgrids will trade energy in respective market layers.This paper studies the blockchain applications to satisfy socioeconomic and technological concerns of secure transactive energy management in a two-level power distribution system.The numerical results for practical networked microgrids located at IllinoisTech−Bronzeville in Chicago illustrate the validity of the proposed blockchain-based transactive energy management for devising a distributed,scalable,efficient,and cybersecured power grid operation.The conclusion of the paper summarizes the prospects for blockchain applications to transactive energy management in power distribution systems.
基金supported by the Science and Technology Project of State Grid Corporation of China(5100-202155320A-0-0-00).
文摘This paper proposes a multi-agent cooperative operation optimization strategy for regional power grids considering the uncertainty of renewable energy output and flexibility of electric vehicle(EV)scheduling,which not only improves the economy of networked microgrid(NMG)scheduling but also reduces the impact on active distribution network(ADN).EV condition matrix and model of the adjustable charge-anddischarge capacity of the EV may be built up by simulating the trip rule of an EV using the driving behavior of the vehicle model.In the day-ahead stage,by taking into account NMG operating cost,distribution network loss,and EV owners’payment cost,a multi-objective optimal scheduling model is developed,and the day-ahead scheduling contract for EV is obtained.Generative Adversarial Network(GAN)generates a significant number of intraday scenarios of photovoltaic(PV),load,and EV based on historical scheduling data as training data for the intra-day scheduling model multi-agent PPO(MAPPO).In the intra-day scheduling stage,intra-day ultra-short-term forecast data is input into the intra-day scheduling model,and the trained multi-agent model realizes NMG distributed real-time optimal scheduling.Finally,the economy and effectiveness of the proposed strategy are verified by Day-after optimal scheduling results.
基金supported by a grant fromtheUniversity of Tabuk,SaudiArabia(GrantNo.UT-2024-CIT-0527)Additional funding was provided by the Saudi Arabian Ministry of Education through the Scientific Research Support Program.
文摘This paper presents a novel machine learning(ML)enhanced energy management framework for residential microgrids.It dynamically integrates solar photovoltaics(PV),wind turbines,lithium-ion battery energy storage systems(BESS),and bidirectional electric vehicle(EV)charging.The proposed architecture addresses the limitations of traditional rule-based controls by incorporating ConvLSTM for real-time forecasting,a Twin Delayed Deep Deterministic Policy Gradient(TD3)reinforcement learning agent for optimal BESS scheduling,and federated learning for EV charging prediction—ensuring both privacy and efficiency.Simulated in a high-fidelity MATLAB/Simulink environment,the system achieves 98.7%solar/wind forecast accuracy and 98.2% Maximum Power Point Tracking(MPPT)tracking efficiency,while reducing torque oscillations by 41% and peak demand by 22%.Compared to baseline methods,the solution improves voltage and frequency stability(maintaining 400V±2%,50Hz±0.015Hz)and achieves a 70% reduction in battery State of Charge(SOC)management error.The EV scheduler,informed by data from over 500 households,reduces charging costs by 31% with rapid failover to critical loads during outages.The architecture is validated using ISO 8528-8 transient tests,demonstrating 99.98% uptime.These results confirm the feasibility of transitioningmicrogrids fromreactive systems to adaptive,cognitive infrastructures capable of self-optimization under highly variable renewable generation and EV behaviors.
基金supported by Hainan Provincial Natural Science Foundation of China(No.524RC532)Research Startup Funding from Hainan Institute of Zhejiang University(No.0210-6602-A12202)Project of Sanya Yazhou Bay Science and Technology City(No.SKJC-2022-PTDX-009/010/011).
文摘Given the rapid development of advanced information systems,microgrids(MGs)suffer from more potential attacks that affect their operational performance.Conventional distributed secondary control with a small,fixed sampling time period inevitably causes the wasteful use of communication resources.This paper proposes a self-triggered secondary control scheme under perturbations from false data injection(FDI)attacks.We designed a linear clock for each DG to trigger its controller at aperiodic and intermittent instants.Sub-sequently,a hash-based defense mechanism(HDM)is designed for detecting and eliminating malicious data infiltrated in the MGs.With the aid of HDM,a self-triggered control scheme achieves the secondary control objectives even in the presence of FDI attacks.Rigorous theoretical analyses and simulation results indicate that the introduced secondary control scheme significantly reduces communication costs and enhances the resilience of MGs under FDI attacks.
基金supported by the National Natural Science Foundation of China(52377080,U24B2078)the Department of Science and Technology of Jilin Province(20230101374JC).
文摘A regional electricity-heating integrated energy system(REH-IES)can make extensive use of renewable energy sources,realize complementary and coordinated operation of multiple energy sources,reduce carbon emissions and promote the development of zero/low carbon systems.This paper proposes a risk-averse stochastic optimal scheduling model for REHIES.An energy flow framework for the REH-IES is proposed considering energy interaction between the electric-heating microgrids(EHMs)and electricity distribution network and the heating network.Then,considering the uncertainties of power output of renewable energy sources,dynamic characteristics of pipelines in the heating network,and thermal inertia of smart buildings,a stochastic optimal scheduling model for the REH-IES is established.Uncertainties of renewable energy sources bring financial risks to optimal scheduling of the REH-IES.Therefore,conditional value-at-risk(CVaR)theory is adopted to measure the risk and to limit the risk within an acceptable range,to achieve minimum expected scheduling cost of the REH-IES.The stochastic programming-based problem is transformed into a second-order cone programming(SOCP)model through secondorder cone relaxation method.Case studies verify the stochastic optimal scheduling model can reduce expected scheduling cost of the REH-IES,promote consumption of renewable energy sources and reduce carbon emissions.
基金funded by Humanities and Social Sciences of Ministry of Education Planning Fund of China(21YJA790009)National Natural Science Foundation of China(72140001).
文摘The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage systems is proposed in this study.Off-grid microgrids are self-sufficient electrical networks that are capable of effectively resolving electricity access problems in remote areas by providing stable and reliable power to local residents.A comprehensive review of the design,control strategies,energy management,and optimization of off-grid microgrids based on domestic and international research is presented in this study.It also explores the critical role of energy stor-age systems in enhancing microgrid stability and economic efficiency.Additionally,the capacity configurations of energy storage systems within off-grid networks are analyzed.Energy storage systems not only mitigate the intermittency and volatility of renewable energy gen-eration but also supply power support during peak demand periods,thereby improving grid stability and reliability.By comparing different energy storage technologies,such as lithium-ion batteries,pumped hydro storage,and compressed air energy storage,the optimal energy storage capacity configurations tailored to various application scenarios are proposed in this study.Finally,using a typical micro-grid as a case study,an empirical analysis of off-grid microgrids and energy storage integration has been conducted.The optimal con-figuration of energy storage systems is determined,and the impact of wind and solar power integration under various scenarios on grid balance is explored.It has been found that a rational configuration of energy storage systems can significantly enhance the utilization rate of renewable energy,reduce system operating costs,and strengthen grid resilience under extreme conditions.This study provides essential theoretical support and practical guidance for the design and implementation of off-grid microgrids in remote areas.
基金supported in part by the National Natural Science Foundation of China(20221017-10,62573258,62188101)the National Natural Science Foundation of Shandong Province(ZR2024 JQ018,ZR2022MF227).
文摘The dynamic average consensus(DAC)algorithm is to enable a group of networked agents to track the average of their time-varying reference signals.For most existing DAC algorithms,a necessary assumption is that the upper bounds of the reference signals and their derivatives are known in advance,thereby posing significant challenges in practical scenarios.Introducing adaptive gains in DAC algorithms provides a remedy by relaxing this assumption.However,the current adaptive gains used in this type of DAC algorithms are non-decreasing and may increase to infinity if persist disturbance exists.In order to overcome this defect,this paper presents a novel DAC algorithm with modified adaptive gains.This approach obviates the necessity for prior knowledge concerning the upper bounds of the reference signals and their derivatives.Moreover,the adaptive gains are able to remain bounded even in the presence of external disturbances.Furthermore,the proposed adaptive DAC algorithm is employed to address the distributed secondary control problem of DC microgrids.Comparative case studies are provided to verify the superiority of the proposed DAC algorithm.
基金support from Nantes Universite through the project AAP II GENOME(Ges-tion des Energies Nouvelles et Optimisation Electrique)and LEAP-RE MiDiNa project,grant N°NR-23-LERE-0002-01.
文摘Most developing countries continue to face challenges in accessing sustainable energy.This study investigates a solar panel and battery-powered system for an urban off-grid microgrid in Nigeria,where demand-sideflexibility and strategic interactions between households and utilities can optimize system sizing.A nonlinear programming model is built using bilevel problem formulation that incorporates both the households’willingness to reduce their energy consumption and the utility’s agreement to provide price rebates.The results show that,for an energy community of 10 households with annual energy demand of 7.8 MWh,an oversized solar-storage system is required(12 kWp of photovoltaic solar panels and 26 kWh of battery storage).The calculated average cost of 0.31€/kWh is three times higher than the current tariff,making it unaffordable for most Nigerian households.To address this,the utility company could implement Demand Response programs with direct load control that delay the use of certain appliances,such as fans,irons and air conditioners.If these measures reduce total demand by 5%,both the required system size and overall costs could decrease significantly,by approximately one-third.This adjustment leads to a reduced tariffof 0.20€/kWh.When Demand Response is imple-mented through negotiation between the utility and households,the amount of load-shaving achieved is lower.This is because house-holds experience discomfort from curtailment and are generally less willing to provideflexibility.However,negotiation allows for greaterflexibility than direct control,due to dynamic interactions and more active consumer participation in the energy transition.Nonetheless,tariffs remain higher than current market prices.Off-grid contracts could become competitive iffinancial support is pro-vided,such as low-interest loans and capital grants covering up to 75%of the upfront cost.
基金supported by the National Natural Science Foundation of China(72271213)the Shenzhen Science and Technology Program(JCYJ20220530143800001 and RCYX20221008092927070)+1 种基金the Guangdong Basic and Applied Basic Research Foundation(2024A1515240024)the National Key Research and Development Program of China(2022YFB2403500).
文摘Interconnection planning involving bi-directional converters(BdCs)is crucial for enhancing the reliability and robustness of hybrid alternating current(AC)/direct current(DC)microgrid clusters with high penetrations of renewable energy resources(RESs).However,challenges such as the non-convex nature of BdC efficiency and renewable energy uncertainty complicate the planning process.To address these issues,this paper proposes a tri-level BdC-based planning framework that incorporates dynamic BdC efficiency and a data-correlated uncertainty set(DcUS)derived from historical data patterns.The proposed framework employs a least-squares approximation to linearize BdC efficiency and constructs the DcUS to balance computational efficiency and solution robustness.Additionally,a fully parallel column and constraint generation algorithm is developed to solve the model efficiently.Numerical simulations on a practical hybrid AC/DC microgrid system demonstrate that the proposed method reduces interconnection costs by up to 21.8%compared to conventional uncertainty sets while ensuring robust operation under all considered scenarios.These results highlight the computational efficiency,robustness,and practicality of the proposed approach,making it a promising solution for modern power systems.
文摘针对新能源可靠供电中的供需平衡问题,提出一种计及局部阴影工况的光储直流微电网能量管理。首先,针对光伏阵列在局部阴影等弱光照工况下,传统最大功率点跟踪方法在功率捕捉能力与能源利用效率方面的不足,利用蝠鲼优化算法(manta ray foraging optimization,MRFO)的全局与局部搜索优势,提出一种结合MRFO的自适应电导增量法(manta ray foraging optimization-adaptive variable step-incremental conductance,MRFO-AVS-INC),以在特殊工况下实现对光伏出力的有效跟踪;其次,针对系统集成中存在的多源扰动问题,在储能系统的能量管理与控制方面,提出一种考虑蓄电池工作状态的改进下垂控制方法,通过引入蓄电池的荷电状态(state of charge,SOC)和健康状态(state of health,SOH)等参数,实现下垂系数的自适应调节,从而合理分配各蓄电池的输出功率;最后,在不同光照条件下对光储系统进行仿真验证。仿真结果表明,与基于粒子群算法的自适应变步长电导增量法(particle swarm optimization-adaptive variable stepincremental conductance,PSO-AVS-INC)及传统INC相比,MRFO-AVS-INC方法在收敛速度与跟踪稳定性方面表现更优;改进的自适应下垂控制有效实现了储能单元之间的状态均衡,延长了储能系统的整体使用寿命。
