In this paper,a model free volt/var control(VVC)algorithm is developed by using deep reinforcement learning(DRL).We transform the VVC problem of distribution networks into the network framework of PPO algorithm,in ord...In this paper,a model free volt/var control(VVC)algorithm is developed by using deep reinforcement learning(DRL).We transform the VVC problem of distribution networks into the network framework of PPO algorithm,in order to avoid directly solving a large-scale nonlinear optimization problem.We select photovoltaic inverters as agents to adjust system voltage in a distribution network,taking the reactive power output of inverters as action variables.An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment.OPENDSS is used to output system node voltage and network loss.This method realizes the goal of optimal VVC in distribution network.The IEEE 13-bus three phase unbalanced distribution system is used to verify the effectiveness of the proposed algorithm.Simulation results demonstrate that the proposed method has excellent performance in voltage and reactive power regulation of a distribution network.展开更多
Public buildings present substantial demand re sponse(DR)potential,which can participate in the power sys tem operation.However,most public buildings exhibit a high degree of uncertainties due to incomplete informatio...Public buildings present substantial demand re sponse(DR)potential,which can participate in the power sys tem operation.However,most public buildings exhibit a high degree of uncertainties due to incomplete information,varying thermal parameters,and stochastic user behaviors,which hin ders incorporating the public buildings into power system oper ation.To address the problem,this paper proposes an interval DR potential evaluation method and a risk dispatch model to integrate public buildings with uncertainties into power system operation.Firstly,the DR evaluation is developed based on the equivalent thermal parameter(ETP)model,actual outdoor tem perature data,and air conditioning(AC)consumption data.To quantify the uncertainties of public buildings,the interval evalu ation is given employing the linear regression method consider ing the confidence bound.Utilizing the evaluation results,the risk dispatch model is proposed to allocate public building re serve based on the chance constrained programming(CCP).Fi nally,the proposed risk dispatch model is reformulated to a mixed-integer second-order cone programming(MISOCP)for its solution.The proposed evaluation method and the risk dis patch model are validated based on the modified IEEE 39-bus system and actual building data obtained from a southern city in China.展开更多
The wide utilization of gas-fired generation and the rapid development of power-to-gas technologies have led to the intensified integration of electricity and gas systems.The random failures of components in either el...The wide utilization of gas-fired generation and the rapid development of power-to-gas technologies have led to the intensified integration of electricity and gas systems.The random failures of components in either electricity or gas system may have a considerable impact on the reliabilities of both systems.Therefore,it is necessary to evaluate the reliabilities of electricity and gas systems considering their integration.In this paper,a novel reliability evaluation method for integrated electricity-gas systems(IEGSs)is proposed.First,reliability network equivalents are utilized to represent reliability models of gas-fired generating units,gas sources(GSs),power-to-gas facilities,and other conventional generating units in IEGS.A contingency management schema is then developed considering the coupling between electricity and gas systems based on an optimal power flow technique.Finally,the time-sequential Monte Carlo simulation approach is used to model the chronological characteristics of the corresponding reliability network equivalents.The proposed method is capable to evaluate customers’reliabilities in IEGS,which is illustrated on an integrated IEEE Reliability Test System and Belgium gas transmission system.展开更多
With various components and complex topologies,the applications of high-voltage direct current(HVDC)links bring new challenges to the interconnected power systems in the aspect of frequency security,which further infl...With various components and complex topologies,the applications of high-voltage direct current(HVDC)links bring new challenges to the interconnected power systems in the aspect of frequency security,which further influence their reliability performances.Consequently,this paper presents an approach to evaluate the impacts of the HVDC link outage on the reliability of interconnected power system considering the frequency regulation process during system contingencies.Firstly,a multi-state model of an HVDC link with different available loading rates(ALRs)is established based on its reliability network.Then,dynamic frequency response models of the interconnected power system are presented and integrated with a novel frequency regulation scheme enabled by the HVDC link.The proposed scheme exploits the temporary overload capability of normal converters to compensate for the imbalanced power during system contingencies.Moreover,it offers frequency support that enables the frequency regulation reserves of the sending-end and receiving-end power systems to be mutually available.Several indices are established to measure the system reliability based on the given models in terms of abnormal frequency duration,frequency deviation,and energy losses of the frequency regulation process during system contingencies.Finally,a modified two-area reliability test system(RTS)with an HVDC link is adopted to verify the proposed approach.展开更多
Due to the stochasticity of charging behaviors of electric vehicles(EVs),it is difficult to anticipate when charging load demand will be densely concentrated.If massive charging loads and the system peak profile appea...Due to the stochasticity of charging behaviors of electric vehicles(EVs),it is difficult to anticipate when charging load demand will be densely concentrated.If massive charging loads and the system peak profile appear at the same time,it may pose a risk to the reliable operation of power grids.For a system integrated with renewable energies,this risk can be much higher because of their unsteady power output.With load measurements more widely collected,this paper presents a data-driven framework to assess the reliability of a power grid considering charging EVs.Specifically,the diffusion estimator is firstly applied to estimate the probability density function of EV charging loads,which possesses both regional adaptivity and good boundary estimation performance.Then,charging load samples are produced through slice sampling.It is capable of sampling from irregularly-shaped distributions with high accuracy.The proposed approach is verified by the numerical results from the simulations on a modified IEEE 30-bus test system based on real measurement data.展开更多
With the booming of electric vehicles(EVs) across the world, their increasing charging demands pose challenges to urban distribution networks. Particularly, due to the further implementation of time-of-use prices, the...With the booming of electric vehicles(EVs) across the world, their increasing charging demands pose challenges to urban distribution networks. Particularly, due to the further implementation of time-of-use prices, the charging behaviors of household EVs are concentrated on low-cost periods, thus generating new load peaks and affecting the secure operation of the medium-and low-voltage grids. This problem is particularly acute in many old communities with relatively poor electricity infrastructure. In this paper, a novel two-stage charging scheduling scheme based on deep reinforcement learning is proposed to improve the power quality and achieve optimal charging scheduling of household EVs simultaneously in active distribution network(ADN) during valley period. In the first stage, the optimal charging profiles of charging stations are determined by solving the optimal power flow with the objective of eliminating peak-valley load differences. In the second stage, an intelligent agent based on proximal policy optimization algorithm is developed to dispatch the household EVs sequentially within the low-cost period considering their discrete nature of arrival. Through powerful approximation of neural network, the challenge of imperfect knowledge is tackled effectively during the charging scheduling process. Finally, numerical results demonstrate that the proposed scheme exhibits great improvement in relieving peak-valley differences as well as improving voltage quality in the ADN.展开更多
With the increasing interactions between natural gas systems(NGS)and power systems,component failures in one system may propagate to the other one,threatening reliable operation of the whole system.Due to neglect of s...With the increasing interactions between natural gas systems(NGS)and power systems,component failures in one system may propagate to the other one,threatening reliable operation of the whole system.Due to neglect of such cross-sectorial failure propagation in integrated electricity-gas systems(IEGSs),traditional economy-oriented reserve expansion models may lead to unreasonable planning results.In order to address this,an innovative reserve expansion model is proposed to determine the allocation of energy production components through the harmonization between costs and reliability.First,novel multifactor-influenced reliability indices are defined con-sidering synthetic effects of multiple uncertainties,including failure propagation,load uncertainties and generation failures.In reliability index formulation,contribution of failure propagation on system reliability is analytically expressed.To avoid high computational complexity,the fuzzy set theory is combined with conventional methods,e.g.,Monte-Carlo simulation technique to reduce numerous contingency states.Sampled contingency states are aggregated into several clusters represented by a fuzzy number.To effectively solve the planning model,a decomposition approach is introduced and applied to decompose the original problem into a master problem and two correlated reliability sub-problems.Numerical studies show the proposed model can plan reasonable reserves to guarantee reliability levels of IEGSs considering failure propagation.展开更多
The essential task of integrated electricity-heat systems(IEHSs)is to provide customers with reliable electric and heating services.From the perspective of customers,it is reasonable to analyze the reliabilities of IE...The essential task of integrated electricity-heat systems(IEHSs)is to provide customers with reliable electric and heating services.From the perspective of customers,it is reasonable to analyze the reliabilities of IEHSs based on the ability to provide energy services with a reasonable assurance of continuity and quality,which are termed as service-based reliabilities.Due to the thermal inertia existing in IEHSs,the heating service performances can present slow dynamic characteristics,which has a great impact on the service satisfaction of customers.The neglect of such thermal dynamics will bring about inaccurate service-based reliability measurement,which can lead to the inefficient dispatch decisions of system operators.Therefore,it is necessary to provide a tool which can analyze the servicebased reliabilities of IEHSs considering the impacts of thermal dynamics.This paper firstly models the energy service performance of IEHSs in contingency states.Specifically,the nodal energy supplies are obtained from the optimal power and heat flow model under both variable hydraulic and thermal conditions,in which the transmission-side thermal dynamics are formulated.On this basis,the energy service performances for customers are further determined with the formulation of demandside thermal dynamics.Moreover,a service-based reliability analysis framework for the IEHSs is proposed utilizing the timesequential Monte Carlo simulation(TSMCS)technique with the embedded decomposition algorithm.Furthermore,the indices for quantifying service-based reliabilities are defined based on the traditional reliability indices,where dynamic service performances and service satisfactions of customers are both considered.Numerical simulations are carried out with a test system to validate the effectiveness of the proposed framework.展开更多
基金supported by the Science and Technology Project of State Grid Zhejiang Electric Power Co.,Ltd.under Grant B311JY21000A。
文摘In this paper,a model free volt/var control(VVC)algorithm is developed by using deep reinforcement learning(DRL).We transform the VVC problem of distribution networks into the network framework of PPO algorithm,in order to avoid directly solving a large-scale nonlinear optimization problem.We select photovoltaic inverters as agents to adjust system voltage in a distribution network,taking the reactive power output of inverters as action variables.An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment.OPENDSS is used to output system node voltage and network loss.This method realizes the goal of optimal VVC in distribution network.The IEEE 13-bus three phase unbalanced distribution system is used to verify the effectiveness of the proposed algorithm.Simulation results demonstrate that the proposed method has excellent performance in voltage and reactive power regulation of a distribution network.
基金supported by the National Science Fund for Distinguished Young Scholars(No.52125702)the Key Science and Technology Project of China Southern Power Grid Corporation(No.090000KK52220020).
文摘Public buildings present substantial demand re sponse(DR)potential,which can participate in the power sys tem operation.However,most public buildings exhibit a high degree of uncertainties due to incomplete information,varying thermal parameters,and stochastic user behaviors,which hin ders incorporating the public buildings into power system oper ation.To address the problem,this paper proposes an interval DR potential evaluation method and a risk dispatch model to integrate public buildings with uncertainties into power system operation.Firstly,the DR evaluation is developed based on the equivalent thermal parameter(ETP)model,actual outdoor tem perature data,and air conditioning(AC)consumption data.To quantify the uncertainties of public buildings,the interval evalu ation is given employing the linear regression method consider ing the confidence bound.Utilizing the evaluation results,the risk dispatch model is proposed to allocate public building re serve based on the chance constrained programming(CCP).Fi nally,the proposed risk dispatch model is reformulated to a mixed-integer second-order cone programming(MISOCP)for its solution.The proposed evaluation method and the risk dis patch model are validated based on the modified IEEE 39-bus system and actual building data obtained from a southern city in China.
基金supported by National Natural Science Foundation of China(No.71871200).
文摘The wide utilization of gas-fired generation and the rapid development of power-to-gas technologies have led to the intensified integration of electricity and gas systems.The random failures of components in either electricity or gas system may have a considerable impact on the reliabilities of both systems.Therefore,it is necessary to evaluate the reliabilities of electricity and gas systems considering their integration.In this paper,a novel reliability evaluation method for integrated electricity-gas systems(IEGSs)is proposed.First,reliability network equivalents are utilized to represent reliability models of gas-fired generating units,gas sources(GSs),power-to-gas facilities,and other conventional generating units in IEGS.A contingency management schema is then developed considering the coupling between electricity and gas systems based on an optimal power flow technique.Finally,the time-sequential Monte Carlo simulation approach is used to model the chronological characteristics of the corresponding reliability network equivalents.The proposed method is capable to evaluate customers’reliabilities in IEGS,which is illustrated on an integrated IEEE Reliability Test System and Belgium gas transmission system.
基金supported by the National Science Foundation of China (No.51807173)the Foundation Research Funds for Central Universities (No.2021QNA4012)the Project of State Grid Zhejiang Electric Power Co.,Ltd. (No.2021ZK11)。
文摘With various components and complex topologies,the applications of high-voltage direct current(HVDC)links bring new challenges to the interconnected power systems in the aspect of frequency security,which further influence their reliability performances.Consequently,this paper presents an approach to evaluate the impacts of the HVDC link outage on the reliability of interconnected power system considering the frequency regulation process during system contingencies.Firstly,a multi-state model of an HVDC link with different available loading rates(ALRs)is established based on its reliability network.Then,dynamic frequency response models of the interconnected power system are presented and integrated with a novel frequency regulation scheme enabled by the HVDC link.The proposed scheme exploits the temporary overload capability of normal converters to compensate for the imbalanced power during system contingencies.Moreover,it offers frequency support that enables the frequency regulation reserves of the sending-end and receiving-end power systems to be mutually available.Several indices are established to measure the system reliability based on the given models in terms of abnormal frequency duration,frequency deviation,and energy losses of the frequency regulation process during system contingencies.Finally,a modified two-area reliability test system(RTS)with an HVDC link is adopted to verify the proposed approach.
基金supported by the National Science Foundation for Distinguished Young Scholars of China under Grant(52125702).
文摘Due to the stochasticity of charging behaviors of electric vehicles(EVs),it is difficult to anticipate when charging load demand will be densely concentrated.If massive charging loads and the system peak profile appear at the same time,it may pose a risk to the reliable operation of power grids.For a system integrated with renewable energies,this risk can be much higher because of their unsteady power output.With load measurements more widely collected,this paper presents a data-driven framework to assess the reliability of a power grid considering charging EVs.Specifically,the diffusion estimator is firstly applied to estimate the probability density function of EV charging loads,which possesses both regional adaptivity and good boundary estimation performance.Then,charging load samples are produced through slice sampling.It is capable of sampling from irregularly-shaped distributions with high accuracy.The proposed approach is verified by the numerical results from the simulations on a modified IEEE 30-bus test system based on real measurement data.
基金supported by the National Key R&D Program of China (No.2021ZD0112700)the Key Science and Technology Project of China Southern Power Grid Corporation (No.090000k52210134)。
文摘With the booming of electric vehicles(EVs) across the world, their increasing charging demands pose challenges to urban distribution networks. Particularly, due to the further implementation of time-of-use prices, the charging behaviors of household EVs are concentrated on low-cost periods, thus generating new load peaks and affecting the secure operation of the medium-and low-voltage grids. This problem is particularly acute in many old communities with relatively poor electricity infrastructure. In this paper, a novel two-stage charging scheduling scheme based on deep reinforcement learning is proposed to improve the power quality and achieve optimal charging scheduling of household EVs simultaneously in active distribution network(ADN) during valley period. In the first stage, the optimal charging profiles of charging stations are determined by solving the optimal power flow with the objective of eliminating peak-valley load differences. In the second stage, an intelligent agent based on proximal policy optimization algorithm is developed to dispatch the household EVs sequentially within the low-cost period considering their discrete nature of arrival. Through powerful approximation of neural network, the challenge of imperfect knowledge is tackled effectively during the charging scheduling process. Finally, numerical results demonstrate that the proposed scheme exhibits great improvement in relieving peak-valley differences as well as improving voltage quality in the ADN.
基金the China NSFC under Grant 71871200National Natural Science Foundation China and Joint Programming Initiative Urban Europe Call(NSFC-JPI UE)under grant 71961137004。
文摘With the increasing interactions between natural gas systems(NGS)and power systems,component failures in one system may propagate to the other one,threatening reliable operation of the whole system.Due to neglect of such cross-sectorial failure propagation in integrated electricity-gas systems(IEGSs),traditional economy-oriented reserve expansion models may lead to unreasonable planning results.In order to address this,an innovative reserve expansion model is proposed to determine the allocation of energy production components through the harmonization between costs and reliability.First,novel multifactor-influenced reliability indices are defined con-sidering synthetic effects of multiple uncertainties,including failure propagation,load uncertainties and generation failures.In reliability index formulation,contribution of failure propagation on system reliability is analytically expressed.To avoid high computational complexity,the fuzzy set theory is combined with conventional methods,e.g.,Monte-Carlo simulation technique to reduce numerous contingency states.Sampled contingency states are aggregated into several clusters represented by a fuzzy number.To effectively solve the planning model,a decomposition approach is introduced and applied to decompose the original problem into a master problem and two correlated reliability sub-problems.Numerical studies show the proposed model can plan reasonable reserves to guarantee reliability levels of IEGSs considering failure propagation.
基金supported by the Science and Technology Project of State Grid Corporation of China(No.5108-202218280A-2-448-XG)。
文摘The essential task of integrated electricity-heat systems(IEHSs)is to provide customers with reliable electric and heating services.From the perspective of customers,it is reasonable to analyze the reliabilities of IEHSs based on the ability to provide energy services with a reasonable assurance of continuity and quality,which are termed as service-based reliabilities.Due to the thermal inertia existing in IEHSs,the heating service performances can present slow dynamic characteristics,which has a great impact on the service satisfaction of customers.The neglect of such thermal dynamics will bring about inaccurate service-based reliability measurement,which can lead to the inefficient dispatch decisions of system operators.Therefore,it is necessary to provide a tool which can analyze the servicebased reliabilities of IEHSs considering the impacts of thermal dynamics.This paper firstly models the energy service performance of IEHSs in contingency states.Specifically,the nodal energy supplies are obtained from the optimal power and heat flow model under both variable hydraulic and thermal conditions,in which the transmission-side thermal dynamics are formulated.On this basis,the energy service performances for customers are further determined with the formulation of demandside thermal dynamics.Moreover,a service-based reliability analysis framework for the IEHSs is proposed utilizing the timesequential Monte Carlo simulation(TSMCS)technique with the embedded decomposition algorithm.Furthermore,the indices for quantifying service-based reliabilities are defined based on the traditional reliability indices,where dynamic service performances and service satisfactions of customers are both considered.Numerical simulations are carried out with a test system to validate the effectiveness of the proposed framework.
