With the increase in the amount of tasks offtoaded to the network edge, the energy supply of edge devices has become a challenge worthy of attention. It is a feasible way to use renewable energy to supply energy for e...With the increase in the amount of tasks offtoaded to the network edge, the energy supply of edge devices has become a challenge worthy of attention. It is a feasible way to use renewable energy to supply energy for edge devices, but production of renewable energy has certain uncertainty and stochasticity. In order to provide sufficient energy to ensure stable operation of edge devices, energy Internet (EI) provides an idea, that is, different edge devices are connected with distributed small energy supply and storage systems. As the core equipment of energy Internet, energy router (ER) plays an important role in information transmission, energy transmission and system control. In this paper, the concept of edge energy router is proposed, which has the ability of task computing and scheduling similar to edge computing server, as well as the ability of energy transmission and system control of energy router. Each edge energy router is connected with loads, photovoltaic panel (PV), micro turbine (MT) and battery energy storage (BES) to form a self-sufficient microgrid (MG) system. However, there exists a delay in energy transmission and task scheduling between different ERs. Moreover, the DC bus voltage stability of each edge energy router system is negatively affected by internal uncertainty, stochasticity and external interference. Therefore, the system is modeled by Markov jump ODEs with time delay, and robust control of DC bus voltage deviation is discussed in this paper. The linear matrix inequality (LMI) method is used to solve this Markov jump control problem. Finally, numerical simulations show the effectiveness of the proposed method.展开更多
Photovoltaic(PV)inverter,as a promising voltage/var control(VVC)resource,can supply flexible reactive power to reduce microgrid power loss and regulate bus voltage.Meanwhile,active power plays a significant role in mi...Photovoltaic(PV)inverter,as a promising voltage/var control(VVC)resource,can supply flexible reactive power to reduce microgrid power loss and regulate bus voltage.Meanwhile,active power plays a significant role in microgrid voltage profile.Price-based demand response(PBDR)can shift load demand via determining time-varying prices,which can be regarded as an effective means for active power shifting.However,due to the different characteristics,PBDR and inverter-based VVC lack systematic coordination.Thus,this paper proposes a PBDR-supported three-stage hierarchically coordinated voltage control method,including day-ahead PBDR price scheduling,hour-ahead reactive power dispatch of PV inverters,and realtime local droop control of PV inverters.Considering their mutual influence,a stochastic optimization method is utilized to centrally or hierarchically coordinate adjacent two stages.To solve the bilinear constraints of droop control function,the problem is reformulated into a second-order cone programming relaxation model.Then,the concave constraints are convexified,forming a penalty convex-concave model for feasible solution recovery.Lastly,a convex-concave procedure-based solution algorithm is proposed to iteratively solve the penalty model.The proposed method is tested on 33-bus and IEEE 123-bus distribution networks and compared with other methods.The results verify the high efficiency of the proposed method to achieve power loss reduction and voltage regulation.展开更多
In the park-level integrated energy system(PIES)trading market involving various heterogeneous energy sources,the traditional vertically integrated market trading structure struggles to reveal the interactions and col...In the park-level integrated energy system(PIES)trading market involving various heterogeneous energy sources,the traditional vertically integrated market trading structure struggles to reveal the interactions and collaborative relationships between energy stations and users,posing challenges to the economic and low-carbon operation of the system.To address this issue,a dual-layer optimization strategy for energy station-user,taking into account the demand response for electricity and thermal,is proposed in this paper.The upper layer,represented by energy stations,makes decisions on variables such as the electricity and heat prices sold to users,as well as the output plans of energy supply equipment and the operational status of battery energy storage.The lower layer,comprising users,determines their own electricity and heat demand through demand response.Subsequently,a combination of differential evolution and quadratic programming(DE-QP)is employed to solve the interactive strategies between energy stations and users.The simulation results indicate that,compared to the traditional vertically integrated structure,the strategy proposed in this paper increases the revenue of energy stations and the consumer surplus of users by 5.09%and 2.46%,respectively.展开更多
In this paper,the problem of mixed optimization for energy sharing and frequency regulation in a typical energy network scenario where energy routers(ERs)interconnected AC microgrids(MGs)is investigated.Continuous-tim...In this paper,the problem of mixed optimization for energy sharing and frequency regulation in a typical energy network scenario where energy routers(ERs)interconnected AC microgrids(MGs)is investigated.Continuous-time Markov chains are introduced to describe the switching paths in the power dynamics of MGs.Such that the modelling of considered energy network system could be closer to the real-world engineering practice.Advanced parameter estimation techniques are integrated into the proposed method to achieve better modelling accuracy and controlling performance.Based on the parameters of MG power dynamics,the mixed H_(2)/H_(∞) controllers are obtained via stochastic control theory.The feasibility and efficacy of the proposed approach are evaluated in numerical examples.展开更多
Nowadays,power quality problems are affecting people’s daily life and production activities.With an aim to improve disturbance detection accuracy,a novel analysis approach,based on multiple impact factors,is proposed...Nowadays,power quality problems are affecting people’s daily life and production activities.With an aim to improve disturbance detection accuracy,a novel analysis approach,based on multiple impact factors,is proposed in this paper.First,a multiple impact factors analysis is implemented in which two perspectives,i.e.,the wavelet analysis and disturbance features are simultaneously considered.Five key factors,including wavelet function,wavelet decomposition level,redundant algorithm,event type and disturbance intensity,and start and end moment of disturbance,have been considered.Next,an impact factor based accuracy analysis algorithm is proposed,through which each factor’s potential impact on disturbance location accuracy is investigated.Three transforms,i.e.,the classic wavelet,lifting wavelet and redundant lifting wavelet are employed,and their superiority on disturbance location accuracy is investigated.Finally,simulations are conducted for verification.Through the proposed method,the wavelet based parameters can be validly selected in order to accurately detect power quality disturbance.展开更多
This paper focuses on solving the modeling issues of monitoring system service performance based on the network calculus theory.First,we formulate the service model of the smart grid monitoring system.Then,we derive t...This paper focuses on solving the modeling issues of monitoring system service performance based on the network calculus theory.First,we formulate the service model of the smart grid monitoring system.Then,we derive the flow arrival curve based on the incremental process related functions.Next,we develop flow arrival curves for the case of the incremental process being a fractional Gaussian process,and then we obtain the generalized Cauchy process.Three technical theorems related to network calculus are presented as our main results.Mathematically,the variance of arrival flow for the continuous time case is derived.Assuming that the incremental process of network flow is a Gaussian stationary process,and given the auto-correlation function of the incremental process with violation probability,the formula of the arrival curve is derived.In addition,the overall flow variance under the discrete time case is explicitly derived.The theoretical results are evaluated in smart grid applications.Simulations indicate that the generalized Cauchy process outperforms the fractional Gaussian process for our considered problem.展开更多
Electrical power network analysis and computation play an important role in the planning and operation of the power grid,and they are modeled mathematically as differential equations and network algebraic equations.Th...Electrical power network analysis and computation play an important role in the planning and operation of the power grid,and they are modeled mathematically as differential equations and network algebraic equations.The direct method based on Gaussian elimination theory can obtain analytical results.Two factors affect computing efficiency:the number of nonzero element fillings and the length of elimination tree.This article constructs mapping correspondence between eliminated tree nodes and quotient graph nodes through graph and quotient graph theories.The Approximate Minimum Degree(AMD)of quotient graph nodes and the length of the elimination tree nodes are composed to build an Approximate Minimum Degree and Minimum Length(AMDML)model.The quotient graph node with the minimum degree,which is also the minimum length of elimination tree node,is selected as the next ordering vector.Compared with AMD ordering method and other common methods,the proposed method further reduces the length of elimination tree without increasing the number of nonzero fillings;the length was decreased by about 10%compared with the AMD method.A testbed for experiment was built.The efficiency of the proposed method was evaluated based on different sizes of coefficient matrices of power flow cases.展开更多
Consensus has been widely used in distributed control,where distributed individuals need to share their states with their neighbors through communication links to achieve a common goal.However,the objectives of existi...Consensus has been widely used in distributed control,where distributed individuals need to share their states with their neighbors through communication links to achieve a common goal.However,the objectives of existing consensus-based control strategies for energy systems seldom address battery degradation cost,which is an important performance indicator to assess the performance and sustainability of battery energy storage(BES)systems.In this paper,we propose a consensusbased optimal control strategy for multi-microgrid systems,aiming at multiple control objectives including minimizing battery degradation cost.Distributed consensus is used to synchronize the ratio of BES output power to BES state-of-charge(SoC)among all microgrids while each microgrid is trying to reach its individual optimality.In order to reduce the pressure of communication links and prevent excessive exposure of local information,this ratio is the only state variable shared between microgrids.Since our complex nonlinear problem might be difficult to solve by traditional methods,we design a compressive sensing-based gradient descent(CSGD)method to solve the control problem.Numerical simulation results show that our control strategy results in a 74.24%reduction in battery degradation cost on average compared to the control method without considering battery degradation.In addition,the compressive sensing method causes an 89.12%reduction in computation time cost compared to the traditional Monte Carlo(MC)method in solving the control problem.展开更多
基金supported by the BNRist project(No.BNR2024TD03003).
文摘With the increase in the amount of tasks offtoaded to the network edge, the energy supply of edge devices has become a challenge worthy of attention. It is a feasible way to use renewable energy to supply energy for edge devices, but production of renewable energy has certain uncertainty and stochasticity. In order to provide sufficient energy to ensure stable operation of edge devices, energy Internet (EI) provides an idea, that is, different edge devices are connected with distributed small energy supply and storage systems. As the core equipment of energy Internet, energy router (ER) plays an important role in information transmission, energy transmission and system control. In this paper, the concept of edge energy router is proposed, which has the ability of task computing and scheduling similar to edge computing server, as well as the ability of energy transmission and system control of energy router. Each edge energy router is connected with loads, photovoltaic panel (PV), micro turbine (MT) and battery energy storage (BES) to form a self-sufficient microgrid (MG) system. However, there exists a delay in energy transmission and task scheduling between different ERs. Moreover, the DC bus voltage stability of each edge energy router system is negatively affected by internal uncertainty, stochasticity and external interference. Therefore, the system is modeled by Markov jump ODEs with time delay, and robust control of DC bus voltage deviation is discussed in this paper. The linear matrix inequality (LMI) method is used to solve this Markov jump control problem. Finally, numerical simulations show the effectiveness of the proposed method.
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)+1 种基金in part by the China Postdoctoral Science Foundation(No.2023M740976)in part by the Start Up Grant of City University of Hong Kong(No.9380163)。
文摘Photovoltaic(PV)inverter,as a promising voltage/var control(VVC)resource,can supply flexible reactive power to reduce microgrid power loss and regulate bus voltage.Meanwhile,active power plays a significant role in microgrid voltage profile.Price-based demand response(PBDR)can shift load demand via determining time-varying prices,which can be regarded as an effective means for active power shifting.However,due to the different characteristics,PBDR and inverter-based VVC lack systematic coordination.Thus,this paper proposes a PBDR-supported three-stage hierarchically coordinated voltage control method,including day-ahead PBDR price scheduling,hour-ahead reactive power dispatch of PV inverters,and realtime local droop control of PV inverters.Considering their mutual influence,a stochastic optimization method is utilized to centrally or hierarchically coordinate adjacent two stages.To solve the bilinear constraints of droop control function,the problem is reformulated into a second-order cone programming relaxation model.Then,the concave constraints are convexified,forming a penalty convex-concave model for feasible solution recovery.Lastly,a convex-concave procedure-based solution algorithm is proposed to iteratively solve the penalty model.The proposed method is tested on 33-bus and IEEE 123-bus distribution networks and compared with other methods.The results verify the high efficiency of the proposed method to achieve power loss reduction and voltage regulation.
基金supported by the National Natural Science Foundation of China(Grant Nos.U22B20112 and 51925605).
文摘In the park-level integrated energy system(PIES)trading market involving various heterogeneous energy sources,the traditional vertically integrated market trading structure struggles to reveal the interactions and collaborative relationships between energy stations and users,posing challenges to the economic and low-carbon operation of the system.To address this issue,a dual-layer optimization strategy for energy station-user,taking into account the demand response for electricity and thermal,is proposed in this paper.The upper layer,represented by energy stations,makes decisions on variables such as the electricity and heat prices sold to users,as well as the output plans of energy supply equipment and the operational status of battery energy storage.The lower layer,comprising users,determines their own electricity and heat demand through demand response.Subsequently,a combination of differential evolution and quadratic programming(DE-QP)is employed to solve the interactive strategies between energy stations and users.The simulation results indicate that,compared to the traditional vertically integrated structure,the strategy proposed in this paper increases the revenue of energy stations and the consumer surplus of users by 5.09%and 2.46%,respectively.
基金supported in part by National Key Research and Development Program of China(Grant No.2017YFE0132100)the BNRist Program under(Grant No.BNR2019TD01009)Fundamental Research Funds for the Central Universities of China(B200201071)。
文摘In this paper,the problem of mixed optimization for energy sharing and frequency regulation in a typical energy network scenario where energy routers(ERs)interconnected AC microgrids(MGs)is investigated.Continuous-time Markov chains are introduced to describe the switching paths in the power dynamics of MGs.Such that the modelling of considered energy network system could be closer to the real-world engineering practice.Advanced parameter estimation techniques are integrated into the proposed method to achieve better modelling accuracy and controlling performance.Based on the parameters of MG power dynamics,the mixed H_(2)/H_(∞) controllers are obtained via stochastic control theory.The feasibility and efficacy of the proposed approach are evaluated in numerical examples.
基金This study is supported by the National Natural Science Foundation of China(Grant No.61501040)Beijing Key Laboratory of Digital Printing Equipment,Fundamental Research Funds for the Central Universities of China(Grant No.B200201071)+1 种基金National Key Research and Development Program of China(Grant No.2017YFE0132100)BNRist Program(Grant No.BNR2020TD01009).
文摘Nowadays,power quality problems are affecting people’s daily life and production activities.With an aim to improve disturbance detection accuracy,a novel analysis approach,based on multiple impact factors,is proposed in this paper.First,a multiple impact factors analysis is implemented in which two perspectives,i.e.,the wavelet analysis and disturbance features are simultaneously considered.Five key factors,including wavelet function,wavelet decomposition level,redundant algorithm,event type and disturbance intensity,and start and end moment of disturbance,have been considered.Next,an impact factor based accuracy analysis algorithm is proposed,through which each factor’s potential impact on disturbance location accuracy is investigated.Three transforms,i.e.,the classic wavelet,lifting wavelet and redundant lifting wavelet are employed,and their superiority on disturbance location accuracy is investigated.Finally,simulations are conducted for verification.Through the proposed method,the wavelet based parameters can be validly selected in order to accurately detect power quality disturbance.
基金This work was funded in part by the National Key Research and Development Program of China(Grant No.2017YFE0132100)Tsinghua-Toyota Joint Research Institute Cross-discipline Program,and the BNRist Program(Grant No.BNR2020TD01009).
文摘This paper focuses on solving the modeling issues of monitoring system service performance based on the network calculus theory.First,we formulate the service model of the smart grid monitoring system.Then,we derive the flow arrival curve based on the incremental process related functions.Next,we develop flow arrival curves for the case of the incremental process being a fractional Gaussian process,and then we obtain the generalized Cauchy process.Three technical theorems related to network calculus are presented as our main results.Mathematically,the variance of arrival flow for the continuous time case is derived.Assuming that the incremental process of network flow is a Gaussian stationary process,and given the auto-correlation function of the incremental process with violation probability,the formula of the arrival curve is derived.In addition,the overall flow variance under the discrete time case is explicitly derived.The theoretical results are evaluated in smart grid applications.Simulations indicate that the generalized Cauchy process outperforms the fractional Gaussian process for our considered problem.
基金supported in part by the National Key Basic Research and Development Program of China(No.2017YFE0132100)the Tsinghua-Toyota Research Fund(No.20203910016)the BNRist Program(No.BNR2020TD01009)。
文摘Electrical power network analysis and computation play an important role in the planning and operation of the power grid,and they are modeled mathematically as differential equations and network algebraic equations.The direct method based on Gaussian elimination theory can obtain analytical results.Two factors affect computing efficiency:the number of nonzero element fillings and the length of elimination tree.This article constructs mapping correspondence between eliminated tree nodes and quotient graph nodes through graph and quotient graph theories.The Approximate Minimum Degree(AMD)of quotient graph nodes and the length of the elimination tree nodes are composed to build an Approximate Minimum Degree and Minimum Length(AMDML)model.The quotient graph node with the minimum degree,which is also the minimum length of elimination tree node,is selected as the next ordering vector.Compared with AMD ordering method and other common methods,the proposed method further reduces the length of elimination tree without increasing the number of nonzero fillings;the length was decreased by about 10%compared with the AMD method.A testbed for experiment was built.The efficiency of the proposed method was evaluated based on different sizes of coefficient matrices of power flow cases.
基金supported by the BNRist Program(No.BNR 2021TD01009)Fundamental Research Funds for the Central Universities of China(Grant No.B200201071).
文摘Consensus has been widely used in distributed control,where distributed individuals need to share their states with their neighbors through communication links to achieve a common goal.However,the objectives of existing consensus-based control strategies for energy systems seldom address battery degradation cost,which is an important performance indicator to assess the performance and sustainability of battery energy storage(BES)systems.In this paper,we propose a consensusbased optimal control strategy for multi-microgrid systems,aiming at multiple control objectives including minimizing battery degradation cost.Distributed consensus is used to synchronize the ratio of BES output power to BES state-of-charge(SoC)among all microgrids while each microgrid is trying to reach its individual optimality.In order to reduce the pressure of communication links and prevent excessive exposure of local information,this ratio is the only state variable shared between microgrids.Since our complex nonlinear problem might be difficult to solve by traditional methods,we design a compressive sensing-based gradient descent(CSGD)method to solve the control problem.Numerical simulation results show that our control strategy results in a 74.24%reduction in battery degradation cost on average compared to the control method without considering battery degradation.In addition,the compressive sensing method causes an 89.12%reduction in computation time cost compared to the traditional Monte Carlo(MC)method in solving the control problem.
