Multi-energy microgrids(MEMG)play an important role in promoting carbon neutrality and achieving sustainable development.This study investigates an effective energy management strategy(EMS)for MEMG.First,an energy man...Multi-energy microgrids(MEMG)play an important role in promoting carbon neutrality and achieving sustainable development.This study investigates an effective energy management strategy(EMS)for MEMG.First,an energy management system model that allows for intra-microgrid energy conversion is developed,and the corresponding Markov decision process(MDP)problem is formulated.Subsequently,an improved double deep Q network(iDDQN)algorithm is proposed to enhance the exploration ability by modifying the calculation of the Q value,and a prioritized experience replay(PER)is introduced into the iDDQN to improve the training speed and effectiveness.Finally,taking advantage of the federated learning(FL)and iDDQN algorithms,a federated iDDQN is proposed to design an MEMG energy management strategy to enable each microgrid to share its experiences in the form of local neural network(NN)parameters with the federation layer,thus ensuring the privacy and security of data.The simulation results validate the superior performance of the proposed energy management strategy in minimizing the economic costs of the MEMG while reducing CO_2 emissions and protecting data privacy.展开更多
To predict renewable energy sources such as solar power in microgrids more accurately,a hybrid power prediction method is presented in this paper.First,the self-attention mechanism is introduced based on a bidirection...To predict renewable energy sources such as solar power in microgrids more accurately,a hybrid power prediction method is presented in this paper.First,the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network(BiGRU)to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results.Subsequently,an improved quantum particle swarm optimization(QPSO)algorithm is proposed to optimize the hyperparameters of the combined prediction model.The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively.In addition,considering the coordinated utilization of various energy sources such as electricity,hydrogen,and renewable energy,a multi-objective optimization model that considers both economic and environmental costs was constructed.A two-stage adaptive multi-objective quantum particle swarm optimization algorithm aided by a Lévy flight,named MO-LQPSO,was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system.This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems.The effectiveness and superiority of the proposed scheme are verified through comparative simulations.展开更多
Liquefied natural gas (LNG),recognized as the primary form for natural gas transportation,can release substantial cold energy during gasification.To make efficient use of this cold energy,this paper proposes a data-dr...Liquefied natural gas (LNG),recognized as the primary form for natural gas transportation,can release substantial cold energy during gasification.To make efficient use of this cold energy,this paper proposes a data-driven stochastic robust (DDSR) energy management method for the multi-stage cascade utilization of LNG cold energy in a multi-energy microgrid (MEMG) of an LNG receiving terminal.Firstly,a general scheduling model considering the flexible coupling between adjacent stages,energy losses,and electric power consumption for the cascade utilization of LNG cold energy is introduced.This model is applied to carbon capture,cryogenic power generation,and direct cooling,which are sequentially associated with the deep,medium,and shallow cooling zones of LNG cold energy,respectively.Moreover,a two-stage energy management framework is proposed to coordinate the cascade utilization of LNG cold energy with other energy resources in the MEMG.To tackle the uncertainties of renewable energy generation and various loads,a DDSR-based solution method is developed,aiming to achieve both economic benefits and solution robustness by identifying the worst-case scenarios and the corresponding worst-case probability.Accordingly,a Benders decomposition-based solution algorithm is proposed to divide the original problem into a master problem and a slave problem,which are solved iteratively.The simulation results verify the effectiveness and high efficiency of the proposed DDSR energy management method for multi-stage cascade utilization of LNG cold energy.展开更多
Multi-energy microgrid(MEMG)offers an effective approach to deal with energy demand diversification and new energy consumption on the consumer side.In MEMG,it is critical to deploy an energy management system(EMS)to e...Multi-energy microgrid(MEMG)offers an effective approach to deal with energy demand diversification and new energy consumption on the consumer side.In MEMG,it is critical to deploy an energy management system(EMS)to efficiently utilize energy and ensure reliable system operation.To help EMS formulate optimal dispatching schemes,a deep reinforcement learning(DRL)-based MEMG energy management scheme with renewable energy source(RES)uncertainty is proposed in this paper.To accurately describe the operating state of the MEMG,the off-design performance model of energy conversion devices is considered in scheduling.The nonlinear optimal dispatching model is expressed as a Markov decision process(MDP)and is then addressed by the twin delayed deep deterministic policy gradient(TD3)algorithm.In addition,to accurately describe the uncertainty of RES,the conditional-least squares generative adversarial networks(C-LSGANs)method based on RES forecast power is proposed to construct the scenario set of RES power generation.The generated data of RES is used to schedule the acquisition of caps and floors for the purchase of electricity and natural gas.Based on this,the superior energy supply sector can formulate solutions in advance to tackle the uncertainty of RES.Finally,the simulation analysis demonstrates the validity and superiority of the method.展开更多
The isolated hybrid AC/DC multi-energy microgrid(IH-MEMG)offers an effective solution for meeting the electrical,heating,and cooling energy demands of remote and off-grid areas.For an IH-MEMG,system transient dynamics...The isolated hybrid AC/DC multi-energy microgrid(IH-MEMG)offers an effective solution for meeting the electrical,heating,and cooling energy demands of remote and off-grid areas.For an IH-MEMG,system transient dynamics(i.e.,frequency or voltage of the electricity network)and economics are critical aspects that pose the greatest concern to operators.However,these aspects are generally investigated separately owing to their different time scales.To integrate these aspects from the scope of real-time control,this study proposes a bi-layer coordinated power regulation strategy considering system dynamics and economics for the IH-MEMG.First,coupling relationships among multiple sub-networks are analyzed,and a frequency-voltage coupling model between the AC and DC sides is established.Then,a bi-layer coordinated power regulation strategy is developed for the IH-MEMG with output characteristics of different components involved:the primary layer includes a multi-entity power support mechanism used to improve the dynamics of the electricity network,wherein a cooperation principle of the combined cooling,heating,and power(CCHP)unit and energy storage unit(ESU)is designed in detailed;meanwhile,the secondary layer includes a real-time economics-oriented optimization framework used to adjust the power references of multiple units generated from the primary layer for cost efficiency improvement(notably,the primary layer can work independently).Finally,the effectiveness of the proposed strategy is verified through comprehensive simulations under varying operation scenarios.展开更多
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
This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity,gas,and heating networks.Introducing a cutting-edge stochastic gradient-enhanced...This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity,gas,and heating networks.Introducing a cutting-edge stochastic gradient-enhanced distributionally robust optimization approach,this study integrates deep learning models,especially generative adversarial networks,to adeptly handle the inherent variability and uncertainties of renewable energy and fluctuating consumer demands.The effectiveness of this framework is rigorously tested through detailed simulations mirroring real-world urban energy consumption,renewable energy production,and market price fluctuations over an annual period.The results reveal substantial improvements in the resilience and efficiency of the grid,achieving a reduction in power distribution losses by 15%and enhancing voltage stability by 20%,markedly outperforming conventional systems.Additionally,the framework facilitates up to 25%in cost reductions during peak demand periods,significantly lowering operational costs.The adoption of stochastic gradients further refines the framework’s ability to continually adjust to real-time changes in environmental and market conditions,ensuring stable grid operations and fostering active consumer engagement in demand-side management.This strategy not only aligns with contem-porary sustainable energy practices but also provides scalable and robust solutions to pressing challenges in modern power network management.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
The latest generation of aero engines has set higher standards for thrust-to-weight ratio and energy conversion efficiency,making it imperative to address the challenge of efficiently and accurately machining film coo...The latest generation of aero engines has set higher standards for thrust-to-weight ratio and energy conversion efficiency,making it imperative to address the challenge of efficiently and accurately machining film cooling holes.It has been demonstrated that conventional long-pulse lasers are incapable of meeting the elevated quality surface finish requirements for these holes,a consequence of the severe thermal defects.The employment of backside water-assisted laser drilling technology confers a number of distinct advantages in terms of mitigating laser thermal damage,thus representing a highly promising solution to this challenge.However,significant accumulation of bubbles and machining products during the backside water-assisted laser drilling process has been demonstrated to have a detrimental effect on laser transmission and machining stability,thereby reducing machining quality.In order to surmount these challenges,a novel method has been proposed,namely an ultrasonic shock water flow-assisted picosecond laser drilling technique.Numerical models for ultrasonic acoustic streaming and particle tracking for machining product transport have been established to investigate the mechanism.The simulation results demonstrated that the majority of the machining products could rapidly move away from the machining area because of the action of acoustic streaming,thereby avoiding the accumulation of bubbles and products.Subsequent analysis,comparing the process performance in micro-hole machining,confirmed that the ultrasonic field could effectively eliminate bubble and chip accumulation,thus significantly improving micro-hole quality.Furthermore,the impact of ultrasonic and laser parameters on micro-hole quality under varying machining methods was thoroughly investigated.The findings demonstrated that the novel methodology outlined in this study yielded superior-quality micro-holes at elevated ultrasonic and laser power levels,in conjunction with reduced laser frequency and scanning velocity.The taper of the micro-holes produced by the new method was reduced by more than 25%compared with the other conventional methods.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
In view of the problem of low self-service capability of the microgrid due to the high operating cost and low capacity of the traditional battery energy storage system.In this paper,an electrothermal hybrid energy sto...In view of the problem of low self-service capability of the microgrid due to the high operating cost and low capacity of the traditional battery energy storage system.In this paper,an electrothermal hybrid energy storage model based on electricity,hydrogen and thermal energy conversion and storage is introduced,and a microgrid autonomous operational strategy is proposed.First,the addition of the power to hydrogen transfer equipment in the traditional combined heat and power(TCHP)system without battery energy storage is studied,and a micro gas turbine,electric to hydrogen transfer equipment and electric boiler based electrothermal energy storage system(ETSS)model is established.Aiming at the lowest comprehen-sive operating cost of multiple energy sources in a microgrid and maximizing the consumption of curtailed wind,the multi-objective scheduling model of an electrothermal hybrid energy storage system is established,then the multi-energy autonomous operational strategy of a microgrid is proposed.Lastly,the simulation of a multi-energy microgrid in Northeast China is taken as an example.The results of the simulation showed that compared with a combined heat and power microgrid system considering conventiona battery energy storage,a multi-energy microgrid system using electrothermal hybrid energy storage has better flexibility and economy,and can improve wind power accommodation.展开更多
The coordinated operation and comprehensive utilization of multi-energy sources require systematic research.A multi-energy microgrid(MEMG)is a coupling system with multiple inputs and outputs.In this paper,a system mo...The coordinated operation and comprehensive utilization of multi-energy sources require systematic research.A multi-energy microgrid(MEMG)is a coupling system with multiple inputs and outputs.In this paper,a system model based on unified energy flows is proposed to describe the static relationship,and an analogue energy storage model is proposed to represent the time-dependency characteristics of energy transfer processes.Then,the optimal dispatching model of an MEMG is established as a mixed-integer linear programming(MILP)problem using piecewise linear approximation and convex relaxation.Finally,the system model and optimal dispatching method are validated in an MEMG,including district electricity,natural gas and heat supply,and renewable generation.The proposed model and method provide an effective way for the energy flow analysis and optimization of MEMGs.展开更多
An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy complementation and the reliability of e...An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy complementation and the reliability of energy supply in extreme scenarios.First,based on the historical meteorological data,the typical meteorological clusters and extreme temperature types are obtained.Then,to reflect the uncertainty of energy consumption and renewable energy output in different weather types,a deep joint generation model using a radiation-electric load-temperature scenario based on a denoising variational autoencoder is established for each weather module.At the same time,to cover the potential high energy consumption scenarios with extreme temperatures,the extreme scenarios with fewer data samples are expanded.Then,the scenarios are reduced by clustering analysis.The normal days of different typical scenarios and extreme temperature scenarios are determined,and the cooling and heating loads are determined by temperature.Finally,the optimal configuration of a multi-energy microgrid system is carried out.Experiments show that the optimal configuration based on the extreme scenarios and typical scenarios can improve the power supply reliability of the system.The proposed method can accurately capture the complementary potential of energy sources.And the economy of the system configuration is improved by 14.56%.展开更多
To enhance the flexible interactions among multiple energy carriers,i.e.,electricity,thermal power and gas,a coordinated programming method for multi-energy microgrid(MEMG)system is proposed.Various energy requirement...To enhance the flexible interactions among multiple energy carriers,i.e.,electricity,thermal power and gas,a coordinated programming method for multi-energy microgrid(MEMG)system is proposed.Various energy requirements for both residential and parking loads are managed simultaneously,i.e.,electric and thermal loads for residence,and charging power and gas filling requirements for parking vehicles.The proposed model is formulated as a two-stage joint chance-constrained programming,where the first stage is a day-ahead operation problem that provides the hourly generation,conversion,and storage towards the minimization of operation cost considering the forecasting error of photovoltaic output and load demand.Meanwhile,the second stage is an on-line scheduling which adjusts the energy scheme in hourly time-scale considering the uncertainty.Simulations have demonstrated the validity of the proposed method,i.e.,collecting the flexibilities of thermal system,gas system,and parking vehicles to facilitate the operation of electrical networks.Sensitivity analysis shows that the proposed scheme can achieve a compromise between the operation efficiency and service quality.展开更多
The highway service area,with facilities for electricity-hydrogen charging,includes multi-energy load energy demands and domestic waste process demands.Based on these needs,a fully renewable energy based multi-energy ...The highway service area,with facilities for electricity-hydrogen charging,includes multi-energy load energy demands and domestic waste process demands.Based on these needs,a fully renewable energy based multi-energy microgrid with electricity-hydrogen charging services and waste process capacity is proposed.This paper studies the energy input and output characteristics of multi-energy conversion and storage devices,and establishes the model for electricity-hydrogen charging microgrid(EH-CMG).The multi-energy conversion,storage characteristics and multi-energy flow coordination in the EHCMG are then studied.An optimization model and its algorithm solution,based on constraints such as the charging time of vehicles,the reliability of multi-energy load energy supply and the available grid regulation performance in the EH-CMG,are established.The proposed optimization of EH-CMG is illustrated with the actual multi-energy operation data of a highway service area in northwest China.The results demonstrate that the proposed EH-CMG and its optimization method can achieve economic benefits for a multi-energy system with the ability of waste process,electricity-hydrogen charging,and also provide better regulation characteristics for the power grid.展开更多
With increasing interdependence among electricity,district heating,and natural gas systems in economy and physics,this paper focuses on the optimal bidding problem of a dominant gas-fired CHP unit in synchronized elec...With increasing interdependence among electricity,district heating,and natural gas systems in economy and physics,this paper focuses on the optimal bidding problem of a dominant gas-fired CHP unit in synchronized electricity-heat-gas markets with real-life step-wise energy offer format.Gas-fired CHP generators act as price makers and submit price-quantity offering curves in independently cleared electricity and district heating markets.A novel loss-embedded power flow model is proposed for market clearing which accounts for active power loss,congestion,reactive power flow,and voltage constraints.Adding penalty terms into the objective function eliminates additional binary variables,which eases computation burden.A two-stage trading mechanism is designed for gas-fired CHP generators to simultaneously participate in the multi-energy market.Based on a mathematical program with equilibrium constraints,an optimal bidding model is established in which the bilinear terms are eliminated by applying the binary expansion method.A diagonalization algorithm can be nested in the proposed trading mechanism if we intend to study the Nash equilibrium of the Nperson Cournot oligopoly market.Numerical tests with different scales are carried out to validate the proposed methodology in detail.展开更多
As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy o...As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy output limit its profitability in the electricity market and hinder its market-based integration.This paper first constructs a wind-solar-thermalmulti-energy complementary system,analyzes its external game relationships,and develops a bi-level market optimization model.Then,it considers the contribution levels of internal participants to establish a comprehensive internal distribution evaluation index system.Finally,simulation studies using the IEEE 30-bus system demonstrate that the multi-energy complementary system stabilizes nodal outputs,enhances the profitability of market participants,and promotes the market-based integration of renewable energy.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
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
With the direct rise of the social demand for renewable energy,as a new type of energy supply model in the new era,the operation control and optimization of microgrid play an important role in solving the problem of r...With the direct rise of the social demand for renewable energy,as a new type of energy supply model in the new era,the operation control and optimization of microgrid play an important role in solving the problem of resource sharing.Microgrid can realize the flexibility of distributed power supply and the application of high efficiency,solving the problem of a large number and variety of forms of the power grid.Based on this,this paper will discuss the operation control strategy of microgrid based on a new energy grid connection,and provide constructive ideas for high-quality operation of microgrid.展开更多
基金supported by the Research and Development of Key Technologies of the Regional Energy Internet based on Multi-Energy Complementary and Collaborative Optimization(BE2020081)。
文摘Multi-energy microgrids(MEMG)play an important role in promoting carbon neutrality and achieving sustainable development.This study investigates an effective energy management strategy(EMS)for MEMG.First,an energy management system model that allows for intra-microgrid energy conversion is developed,and the corresponding Markov decision process(MDP)problem is formulated.Subsequently,an improved double deep Q network(iDDQN)algorithm is proposed to enhance the exploration ability by modifying the calculation of the Q value,and a prioritized experience replay(PER)is introduced into the iDDQN to improve the training speed and effectiveness.Finally,taking advantage of the federated learning(FL)and iDDQN algorithms,a federated iDDQN is proposed to design an MEMG energy management strategy to enable each microgrid to share its experiences in the form of local neural network(NN)parameters with the federation layer,thus ensuring the privacy and security of data.The simulation results validate the superior performance of the proposed energy management strategy in minimizing the economic costs of the MEMG while reducing CO_2 emissions and protecting data privacy.
基金supported by the National Natural Science Foundation of China under Grant 51977004the Beijing Natural Science Foundation under Grant 4212042.
文摘To predict renewable energy sources such as solar power in microgrids more accurately,a hybrid power prediction method is presented in this paper.First,the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network(BiGRU)to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results.Subsequently,an improved quantum particle swarm optimization(QPSO)algorithm is proposed to optimize the hyperparameters of the combined prediction model.The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively.In addition,considering the coordinated utilization of various energy sources such as electricity,hydrogen,and renewable energy,a multi-objective optimization model that considers both economic and environmental costs was constructed.A two-stage adaptive multi-objective quantum particle swarm optimization algorithm aided by a Lévy flight,named MO-LQPSO,was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system.This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems.The effectiveness and superiority of the proposed scheme are verified through comparative simulations.
基金supported in part by the National Natural Science Foundation of China(No.52307091)in part by the Natural Science Foundation of Jiangsu Province(No.BK20230952)in part by the China Postdoctoral Science Foundation(No.2023M740976).
文摘Liquefied natural gas (LNG),recognized as the primary form for natural gas transportation,can release substantial cold energy during gasification.To make efficient use of this cold energy,this paper proposes a data-driven stochastic robust (DDSR) energy management method for the multi-stage cascade utilization of LNG cold energy in a multi-energy microgrid (MEMG) of an LNG receiving terminal.Firstly,a general scheduling model considering the flexible coupling between adjacent stages,energy losses,and electric power consumption for the cascade utilization of LNG cold energy is introduced.This model is applied to carbon capture,cryogenic power generation,and direct cooling,which are sequentially associated with the deep,medium,and shallow cooling zones of LNG cold energy,respectively.Moreover,a two-stage energy management framework is proposed to coordinate the cascade utilization of LNG cold energy with other energy resources in the MEMG.To tackle the uncertainties of renewable energy generation and various loads,a DDSR-based solution method is developed,aiming to achieve both economic benefits and solution robustness by identifying the worst-case scenarios and the corresponding worst-case probability.Accordingly,a Benders decomposition-based solution algorithm is proposed to divide the original problem into a master problem and a slave problem,which are solved iteratively.The simulation results verify the effectiveness and high efficiency of the proposed DDSR energy management method for multi-stage cascade utilization of LNG cold energy.
基金supported by National Natural Science Foundation of China(51777027)。
文摘Multi-energy microgrid(MEMG)offers an effective approach to deal with energy demand diversification and new energy consumption on the consumer side.In MEMG,it is critical to deploy an energy management system(EMS)to efficiently utilize energy and ensure reliable system operation.To help EMS formulate optimal dispatching schemes,a deep reinforcement learning(DRL)-based MEMG energy management scheme with renewable energy source(RES)uncertainty is proposed in this paper.To accurately describe the operating state of the MEMG,the off-design performance model of energy conversion devices is considered in scheduling.The nonlinear optimal dispatching model is expressed as a Markov decision process(MDP)and is then addressed by the twin delayed deep deterministic policy gradient(TD3)algorithm.In addition,to accurately describe the uncertainty of RES,the conditional-least squares generative adversarial networks(C-LSGANs)method based on RES forecast power is proposed to construct the scenario set of RES power generation.The generated data of RES is used to schedule the acquisition of caps and floors for the purchase of electricity and natural gas.Based on this,the superior energy supply sector can formulate solutions in advance to tackle the uncertainty of RES.Finally,the simulation analysis demonstrates the validity and superiority of the method.
基金supported by the International Science and Technology Cooperation Program of China(Grant No.2022YFE0129300)the National Natural Science Foundation of China(Grant Nos.U22B20104,52277090,52207097)+2 种基金the Science and Technology Innovation Program of Hunan Province(Grant No.2023RC3102)the Excellent Innovation Youth Program of Changsha of China(Grant No.kq2209010)the Key Research and Development Program of Hunan Province(Grant No.2023GK2007)。
文摘The isolated hybrid AC/DC multi-energy microgrid(IH-MEMG)offers an effective solution for meeting the electrical,heating,and cooling energy demands of remote and off-grid areas.For an IH-MEMG,system transient dynamics(i.e.,frequency or voltage of the electricity network)and economics are critical aspects that pose the greatest concern to operators.However,these aspects are generally investigated separately owing to their different time scales.To integrate these aspects from the scope of real-time control,this study proposes a bi-layer coordinated power regulation strategy considering system dynamics and economics for the IH-MEMG.First,coupling relationships among multiple sub-networks are analyzed,and a frequency-voltage coupling model between the AC and DC sides is established.Then,a bi-layer coordinated power regulation strategy is developed for the IH-MEMG with output characteristics of different components involved:the primary layer includes a multi-entity power support mechanism used to improve the dynamics of the electricity network,wherein a cooperation principle of the combined cooling,heating,and power(CCHP)unit and energy storage unit(ESU)is designed in detailed;meanwhile,the secondary layer includes a real-time economics-oriented optimization framework used to adjust the power references of multiple units generated from the primary layer for cost efficiency improvement(notably,the primary layer can work independently).Finally,the effectiveness of the proposed strategy is verified through comprehensive simulations under varying operation scenarios.
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 by the National Key R&D Program of China(No.2021ZD0112700).
文摘This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity,gas,and heating networks.Introducing a cutting-edge stochastic gradient-enhanced distributionally robust optimization approach,this study integrates deep learning models,especially generative adversarial networks,to adeptly handle the inherent variability and uncertainties of renewable energy and fluctuating consumer demands.The effectiveness of this framework is rigorously tested through detailed simulations mirroring real-world urban energy consumption,renewable energy production,and market price fluctuations over an annual period.The results reveal substantial improvements in the resilience and efficiency of the grid,achieving a reduction in power distribution losses by 15%and enhancing voltage stability by 20%,markedly outperforming conventional systems.Additionally,the framework facilitates up to 25%in cost reductions during peak demand periods,significantly lowering operational costs.The adoption of stochastic gradients further refines the framework’s ability to continually adjust to real-time changes in environmental and market conditions,ensuring stable grid operations and fostering active consumer engagement in demand-side management.This strategy not only aligns with contem-porary sustainable energy practices but also provides scalable and robust solutions to pressing challenges in modern power network management.
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 by the National Natural Science Foundation of China(No.52205468,No.52275431,No.52375186)China Postdoctoral Science Foundation(No.2025M771349)Zhejiang Province Natural Science Foundation(No.LD22E050001)。
文摘The latest generation of aero engines has set higher standards for thrust-to-weight ratio and energy conversion efficiency,making it imperative to address the challenge of efficiently and accurately machining film cooling holes.It has been demonstrated that conventional long-pulse lasers are incapable of meeting the elevated quality surface finish requirements for these holes,a consequence of the severe thermal defects.The employment of backside water-assisted laser drilling technology confers a number of distinct advantages in terms of mitigating laser thermal damage,thus representing a highly promising solution to this challenge.However,significant accumulation of bubbles and machining products during the backside water-assisted laser drilling process has been demonstrated to have a detrimental effect on laser transmission and machining stability,thereby reducing machining quality.In order to surmount these challenges,a novel method has been proposed,namely an ultrasonic shock water flow-assisted picosecond laser drilling technique.Numerical models for ultrasonic acoustic streaming and particle tracking for machining product transport have been established to investigate the mechanism.The simulation results demonstrated that the majority of the machining products could rapidly move away from the machining area because of the action of acoustic streaming,thereby avoiding the accumulation of bubbles and products.Subsequent analysis,comparing the process performance in micro-hole machining,confirmed that the ultrasonic field could effectively eliminate bubble and chip accumulation,thus significantly improving micro-hole quality.Furthermore,the impact of ultrasonic and laser parameters on micro-hole quality under varying machining methods was thoroughly investigated.The findings demonstrated that the novel methodology outlined in this study yielded superior-quality micro-holes at elevated ultrasonic and laser power levels,in conjunction with reduced laser frequency and scanning velocity.The taper of the micro-holes produced by the new method was reduced by more than 25%compared with the other conventional methods.
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.
基金This work was supported by the National Key Research and Development Program of China(No.2017YFB0902100).
文摘In view of the problem of low self-service capability of the microgrid due to the high operating cost and low capacity of the traditional battery energy storage system.In this paper,an electrothermal hybrid energy storage model based on electricity,hydrogen and thermal energy conversion and storage is introduced,and a microgrid autonomous operational strategy is proposed.First,the addition of the power to hydrogen transfer equipment in the traditional combined heat and power(TCHP)system without battery energy storage is studied,and a micro gas turbine,electric to hydrogen transfer equipment and electric boiler based electrothermal energy storage system(ETSS)model is established.Aiming at the lowest comprehen-sive operating cost of multiple energy sources in a microgrid and maximizing the consumption of curtailed wind,the multi-objective scheduling model of an electrothermal hybrid energy storage system is established,then the multi-energy autonomous operational strategy of a microgrid is proposed.Lastly,the simulation of a multi-energy microgrid in Northeast China is taken as an example.The results of the simulation showed that compared with a combined heat and power microgrid system considering conventiona battery energy storage,a multi-energy microgrid system using electrothermal hybrid energy storage has better flexibility and economy,and can improve wind power accommodation.
基金supported by the Technology Program of State Grid Corporation of China(No.SGSDJY00GPJS1900058)
文摘The coordinated operation and comprehensive utilization of multi-energy sources require systematic research.A multi-energy microgrid(MEMG)is a coupling system with multiple inputs and outputs.In this paper,a system model based on unified energy flows is proposed to describe the static relationship,and an analogue energy storage model is proposed to represent the time-dependency characteristics of energy transfer processes.Then,the optimal dispatching model of an MEMG is established as a mixed-integer linear programming(MILP)problem using piecewise linear approximation and convex relaxation.Finally,the system model and optimal dispatching method are validated in an MEMG,including district electricity,natural gas and heat supply,and renewable generation.The proposed model and method provide an effective way for the energy flow analysis and optimization of MEMGs.
基金supported by National Key Research and Development Program of China(2019YFB1505400)Jilin Science and Technology Development Program(20160411003XH)Jilin Industrial Technology Research and Development Program(2019C058-8).
文摘An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy complementation and the reliability of energy supply in extreme scenarios.First,based on the historical meteorological data,the typical meteorological clusters and extreme temperature types are obtained.Then,to reflect the uncertainty of energy consumption and renewable energy output in different weather types,a deep joint generation model using a radiation-electric load-temperature scenario based on a denoising variational autoencoder is established for each weather module.At the same time,to cover the potential high energy consumption scenarios with extreme temperatures,the extreme scenarios with fewer data samples are expanded.Then,the scenarios are reduced by clustering analysis.The normal days of different typical scenarios and extreme temperature scenarios are determined,and the cooling and heating loads are determined by temperature.Finally,the optimal configuration of a multi-energy microgrid system is carried out.Experiments show that the optimal configuration based on the extreme scenarios and typical scenarios can improve the power supply reliability of the system.The proposed method can accurately capture the complementary potential of energy sources.And the economy of the system configuration is improved by 14.56%.
文摘To enhance the flexible interactions among multiple energy carriers,i.e.,electricity,thermal power and gas,a coordinated programming method for multi-energy microgrid(MEMG)system is proposed.Various energy requirements for both residential and parking loads are managed simultaneously,i.e.,electric and thermal loads for residence,and charging power and gas filling requirements for parking vehicles.The proposed model is formulated as a two-stage joint chance-constrained programming,where the first stage is a day-ahead operation problem that provides the hourly generation,conversion,and storage towards the minimization of operation cost considering the forecasting error of photovoltaic output and load demand.Meanwhile,the second stage is an on-line scheduling which adjusts the energy scheme in hourly time-scale considering the uncertainty.Simulations have demonstrated the validity of the proposed method,i.e.,collecting the flexibilities of thermal system,gas system,and parking vehicles to facilitate the operation of electrical networks.Sensitivity analysis shows that the proposed scheme can achieve a compromise between the operation efficiency and service quality.
基金This work was supported by National Key R&D Program of China under Grant 2017YFB0902100。
文摘The highway service area,with facilities for electricity-hydrogen charging,includes multi-energy load energy demands and domestic waste process demands.Based on these needs,a fully renewable energy based multi-energy microgrid with electricity-hydrogen charging services and waste process capacity is proposed.This paper studies the energy input and output characteristics of multi-energy conversion and storage devices,and establishes the model for electricity-hydrogen charging microgrid(EH-CMG).The multi-energy conversion,storage characteristics and multi-energy flow coordination in the EHCMG are then studied.An optimization model and its algorithm solution,based on constraints such as the charging time of vehicles,the reliability of multi-energy load energy supply and the available grid regulation performance in the EH-CMG,are established.The proposed optimization of EH-CMG is illustrated with the actual multi-energy operation data of a highway service area in northwest China.The results demonstrate that the proposed EH-CMG and its optimization method can achieve economic benefits for a multi-energy system with the ability of waste process,electricity-hydrogen charging,and also provide better regulation characteristics for the power grid.
基金supported in part by the National Natural Science Foundation of China(52107076)in part by the Natural Science Foundation of Jiangsu Province(BK20200013)in part by the Smart Grid Joint Fund of National Science Foundation of China&State Grid Corporation of China(U1866208).
文摘With increasing interdependence among electricity,district heating,and natural gas systems in economy and physics,this paper focuses on the optimal bidding problem of a dominant gas-fired CHP unit in synchronized electricity-heat-gas markets with real-life step-wise energy offer format.Gas-fired CHP generators act as price makers and submit price-quantity offering curves in independently cleared electricity and district heating markets.A novel loss-embedded power flow model is proposed for market clearing which accounts for active power loss,congestion,reactive power flow,and voltage constraints.Adding penalty terms into the objective function eliminates additional binary variables,which eases computation burden.A two-stage trading mechanism is designed for gas-fired CHP generators to simultaneously participate in the multi-energy market.Based on a mathematical program with equilibrium constraints,an optimal bidding model is established in which the bilinear terms are eliminated by applying the binary expansion method.A diagonalization algorithm can be nested in the proposed trading mechanism if we intend to study the Nash equilibrium of the Nperson Cournot oligopoly market.Numerical tests with different scales are carried out to validate the proposed methodology in detail.
基金funded by the National Key R&D Program of China,grant number 2019YFB1505400.
文摘As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy output limit its profitability in the electricity market and hinder its market-based integration.This paper first constructs a wind-solar-thermalmulti-energy complementary system,analyzes its external game relationships,and develops a bi-level market optimization model.Then,it considers the contribution levels of internal participants to establish a comprehensive internal distribution evaluation index system.Finally,simulation studies using the IEEE 30-bus system demonstrate that the multi-energy complementary system stabilizes nodal outputs,enhances the profitability of market participants,and promotes the market-based integration of renewable energy.
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.
文摘With the direct rise of the social demand for renewable energy,as a new type of energy supply model in the new era,the operation control and optimization of microgrid play an important role in solving the problem of resource sharing.Microgrid can realize the flexibility of distributed power supply and the application of high efficiency,solving the problem of a large number and variety of forms of the power grid.Based on this,this paper will discuss the operation control strategy of microgrid based on a new energy grid connection,and provide constructive ideas for high-quality operation of microgrid.
