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
For active distribution networks(ADNs)integrated with massive inverter-based energy resources,it is impractical to maintain the accurate model and deploy measurements at all nodes due to the large-scale of ADNs.Thus,c...For active distribution networks(ADNs)integrated with massive inverter-based energy resources,it is impractical to maintain the accurate model and deploy measurements at all nodes due to the large-scale of ADNs.Thus,current models of ADNs usually involve significant errors or even unknown occurances.Moreover,ADNs are usually partially observable since only a few measurements are available at pilot nodes or nodes with significant users.To provide a practical Volt/Var control(VVC)strategy for such networks,a data-driven VVC method is proposed in this paper.First,the system response policy,approximating the relationship between the control variables and states of monitoring nodes,is estimated by a recursive regression closed-form solution.Then,based on real-time measurements and the newly updated system response policy,a VVC strategy with convergence guarantee is realized.Since the recursive regression solution is embedded in the control stage,a data-driven closedloop VVC framework is established.The effectiveness of the proposed method is validated in an unbalanced distribution system considering nonlinear loads,where not only the rapid and self-adaptive voltage regulation is realized,but also systemwide optimization is achieved.展开更多
Photovoltaic(PV)inverter-based volt/var control(VVC)is highly promising to tackle the emerging voltage regulation challenges brought by increasing PV penetration.However,PV inverter operational reliability has arisen ...Photovoltaic(PV)inverter-based volt/var control(VVC)is highly promising to tackle the emerging voltage regulation challenges brought by increasing PV penetration.However,PV inverter operational reliability has arisen as a critical concern for practical VVC implementation.This paper proposes a new PV inverter based VVC optimization model and a Pareto front analysis method for maintaining a satisfactory inverter lifetime.First,reliability of the vulnerable DC-link capacitor inside a PV inverter is analyzed,and long-term VVC impact on inverter operational reliability is identified.Second,a multi-objective PV inverter based VVC optimization model is proposed for minimizing both inverter apparent power output and network power loss with a weighting factor.Third,a Pareto front analysis method is developed to visualize the impact of the weighting factor on VVC performance and inverter reliability,thus determining the effective weighting factor to reduce network power loss with expected inverter lifetime.Effectiveness of the proposed VVC optimization model and Pareto front analysis method are verified in a case study.展开更多
When urban distribution systems are gradually modernized,the overhead lines are replaced by underground cables,whose shunt admittances can not be ignored.Traditional power flow(PF)model withπequivalent circuit shows ...When urban distribution systems are gradually modernized,the overhead lines are replaced by underground cables,whose shunt admittances can not be ignored.Traditional power flow(PF)model withπequivalent circuit shows non-convexity and long computing time,and most recently proposed linear PF models assume zero shunt elements.All of them are not suitable for fast calculation and optimization problems of modern distribution systems with non-negligible line shunts.Therefore,this paper proposes a linearized branch flow model considering line shunt(LBFS).The strength of LBFS lies in maintaining the linear structure and the convex nature after appropriately modeling theπequivalent circuit for network equipment like transformers.Simulation results show that the calculation accuracy in nodal voltage and branch current magnitudes is improved by considering shunt admittances.We show the application scope of LBFS by controlling the network voltages through a two-stage stochastic Volt/VAr control(VVC)problem with the uncertain active power output from renewable energy sources(RESs).Since LBFS results in a linear VVC program,the global solution is guaranteed.Case study exhibits that VVC framework can optimally dispatch the discrete control devices,viz.substation transformers and shunt capacitors,and also optimize the decision rules for real-time reactive power control of RES.Moreover,the computing efficiency is significantly improved compared with that of traditional VVC methods.展开更多
In the present scenario,many solar photovoltaic(SPV)systems have been installed in the distribution network,most of them are operating at the unity power factor,which does not provide any reactive power support.In fut...In the present scenario,many solar photovoltaic(SPV)systems have been installed in the distribution network,most of them are operating at the unity power factor,which does not provide any reactive power support.In future distribution grids,there will be significant advances in operating strategies of SPV systems with the introduction of smart inverter functions.The new IEEE Std.1547-2018 incorporates dynamic Volt/VAr control(VVC)for smart inverters.These smart inverters can inject or absorb reactive power and maintain voltages at points of common coupling(PCCs)based on local voltage measurements.With multiple inverter-interfaced SPV systems connected to the grid,it becomes a necessary task to develop local,distributed or hybrid VVC algorithms for maximization of energy savings.This paper aims to estimate substation energy savings through centralized and decentralized control of inverters of SPV system alongside various VVC devices.Control strategies of each SPV inverter have been accomplished in compliance with IEEE Std.1547-2018.Time-series simulations are carried out on the modified IEEE-123 node test system.By utilizing smart inverters in traditional SPV systems,considerable energy savings can be obtained.These savings can be further increased by incorporating optimal intelligent VVC characteristics(IVVCC).Results show that just by allowing smart inverters on a predefined IVVCC(as per IEEE Std.1547-2018),a reduction of 11.69%in reactive demand and 5.63%in active demand have been acquired when compared with a conventional SPV system.Reactive energy demand is additionally reduced to 48.42%by considering centralized control of VVC devices alongside optimal IVVCC.展开更多
为提高静止无功补偿器(static var compensator,SVC)应对直流电弧炉等冲击性负载的闪变抑制性能,文中在改进Takagi-Sugeno(TS)模糊算法的基础上,提出一种SVC滚动预测控制方法。首先,建立直流电弧炉电气模型并仿真分析其无功特性;然后,...为提高静止无功补偿器(static var compensator,SVC)应对直流电弧炉等冲击性负载的闪变抑制性能,文中在改进Takagi-Sugeno(TS)模糊算法的基础上,提出一种SVC滚动预测控制方法。首先,建立直流电弧炉电气模型并仿真分析其无功特性;然后,针对经典TS模糊预测算法应用于波动负荷时出现的输出异常置0情况,提出一种范围自适应修正的改进方法,该方法能消除一类算法应用机理导致的异常值,从而提高TS模糊算法对波动负荷无功功率预测的可靠性和准确性;最后,基于模型训练时间约束,建立无功功率半周期滚动预测控制模型,提前10 ms预测无功功率,改善了SVC传统控制系统响应的滞后特性。仿真结果表明,相比于SVC传统控制方法,所提方法的平均闪变改善率提高了54.17%,验证了所提方法对闪变现象的抑制效果提升显著。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 Research Project of China Southern Power Grid Corporation:The demonstration and application of the virtual power plant intelligent operation and management platform with source-grid coordination,No.GDKJXM20185069 (032000KK 52180069)。
文摘For active distribution networks(ADNs)integrated with massive inverter-based energy resources,it is impractical to maintain the accurate model and deploy measurements at all nodes due to the large-scale of ADNs.Thus,current models of ADNs usually involve significant errors or even unknown occurances.Moreover,ADNs are usually partially observable since only a few measurements are available at pilot nodes or nodes with significant users.To provide a practical Volt/Var control(VVC)strategy for such networks,a data-driven VVC method is proposed in this paper.First,the system response policy,approximating the relationship between the control variables and states of monitoring nodes,is estimated by a recursive regression closed-form solution.Then,based on real-time measurements and the newly updated system response policy,a VVC strategy with convergence guarantee is realized.Since the recursive regression solution is embedded in the control stage,a data-driven closedloop VVC framework is established.The effectiveness of the proposed method is validated in an unbalanced distribution system considering nonlinear loads,where not only the rapid and self-adaptive voltage regulation is realized,but also systemwide optimization is achieved.
基金This work was supported in part by NTU Grant No.021542-00001in part by Australian Government Research Training Program Scholarship。
文摘Photovoltaic(PV)inverter-based volt/var control(VVC)is highly promising to tackle the emerging voltage regulation challenges brought by increasing PV penetration.However,PV inverter operational reliability has arisen as a critical concern for practical VVC implementation.This paper proposes a new PV inverter based VVC optimization model and a Pareto front analysis method for maintaining a satisfactory inverter lifetime.First,reliability of the vulnerable DC-link capacitor inside a PV inverter is analyzed,and long-term VVC impact on inverter operational reliability is identified.Second,a multi-objective PV inverter based VVC optimization model is proposed for minimizing both inverter apparent power output and network power loss with a weighting factor.Third,a Pareto front analysis method is developed to visualize the impact of the weighting factor on VVC performance and inverter reliability,thus determining the effective weighting factor to reduce network power loss with expected inverter lifetime.Effectiveness of the proposed VVC optimization model and Pareto front analysis method are verified in a case study.
基金supported in part by the National Natural Science Foundation of China(No.51977115)。
文摘When urban distribution systems are gradually modernized,the overhead lines are replaced by underground cables,whose shunt admittances can not be ignored.Traditional power flow(PF)model withπequivalent circuit shows non-convexity and long computing time,and most recently proposed linear PF models assume zero shunt elements.All of them are not suitable for fast calculation and optimization problems of modern distribution systems with non-negligible line shunts.Therefore,this paper proposes a linearized branch flow model considering line shunt(LBFS).The strength of LBFS lies in maintaining the linear structure and the convex nature after appropriately modeling theπequivalent circuit for network equipment like transformers.Simulation results show that the calculation accuracy in nodal voltage and branch current magnitudes is improved by considering shunt admittances.We show the application scope of LBFS by controlling the network voltages through a two-stage stochastic Volt/VAr control(VVC)problem with the uncertain active power output from renewable energy sources(RESs).Since LBFS results in a linear VVC program,the global solution is guaranteed.Case study exhibits that VVC framework can optimally dispatch the discrete control devices,viz.substation transformers and shunt capacitors,and also optimize the decision rules for real-time reactive power control of RES.Moreover,the computing efficiency is significantly improved compared with that of traditional VVC methods.
文摘In the present scenario,many solar photovoltaic(SPV)systems have been installed in the distribution network,most of them are operating at the unity power factor,which does not provide any reactive power support.In future distribution grids,there will be significant advances in operating strategies of SPV systems with the introduction of smart inverter functions.The new IEEE Std.1547-2018 incorporates dynamic Volt/VAr control(VVC)for smart inverters.These smart inverters can inject or absorb reactive power and maintain voltages at points of common coupling(PCCs)based on local voltage measurements.With multiple inverter-interfaced SPV systems connected to the grid,it becomes a necessary task to develop local,distributed or hybrid VVC algorithms for maximization of energy savings.This paper aims to estimate substation energy savings through centralized and decentralized control of inverters of SPV system alongside various VVC devices.Control strategies of each SPV inverter have been accomplished in compliance with IEEE Std.1547-2018.Time-series simulations are carried out on the modified IEEE-123 node test system.By utilizing smart inverters in traditional SPV systems,considerable energy savings can be obtained.These savings can be further increased by incorporating optimal intelligent VVC characteristics(IVVCC).Results show that just by allowing smart inverters on a predefined IVVCC(as per IEEE Std.1547-2018),a reduction of 11.69%in reactive demand and 5.63%in active demand have been acquired when compared with a conventional SPV system.Reactive energy demand is additionally reduced to 48.42%by considering centralized control of VVC devices alongside optimal IVVCC.
文摘为提高静止无功补偿器(static var compensator,SVC)应对直流电弧炉等冲击性负载的闪变抑制性能,文中在改进Takagi-Sugeno(TS)模糊算法的基础上,提出一种SVC滚动预测控制方法。首先,建立直流电弧炉电气模型并仿真分析其无功特性;然后,针对经典TS模糊预测算法应用于波动负荷时出现的输出异常置0情况,提出一种范围自适应修正的改进方法,该方法能消除一类算法应用机理导致的异常值,从而提高TS模糊算法对波动负荷无功功率预测的可靠性和准确性;最后,基于模型训练时间约束,建立无功功率半周期滚动预测控制模型,提前10 ms预测无功功率,改善了SVC传统控制系统响应的滞后特性。仿真结果表明,相比于SVC传统控制方法,所提方法的平均闪变改善率提高了54.17%,验证了所提方法对闪变现象的抑制效果提升显著。
