With the increasing integration of uncertain distributed renewable energies(DREs)into distribution networks(DNs),communication bottlenecks and the limited deployment of measurement devices pose significant challenges ...With the increasing integration of uncertain distributed renewable energies(DREs)into distribution networks(DNs),communication bottlenecks and the limited deployment of measurement devices pose significant challenges for advanced data-driven voltage control strategies such as deep reinforcement learning(DRL).To address these issues,this paper proposes an offline-training online-execution framework for volt-var control in DNs.In the offline-training phase,a graph convolutional network(GCN)-based denoising autoencoder(DAE),referred to as the deep learning(DL)agent,is designed and trained to capture spatial correlations among limited physical quantities.This agent predicts voltage values for nodes with missing measurements using historical load data,DRE outputs,and global voltages from simulations.Furthermore,the dualtimescale voltage control problem is formulated as a multiagent Markov decision process.A DRL agent employing the multi-agent soft actor-critic(MASAC)algorithm is trained to regulate the tap position of on-load tap changer(OLTC)and reactive power output of photovoltaic(PV)inverters.In the online-execution phase,the DL agent supplements the limited measurement data,providing enhanced global observations for the DRL agent.This enables precise equipment control based on improved system state estimation.The proposed framework is validated on two modified IEEE test systems.Numerical results demonstrate its ability to effectively reconstruct missing measurements and achieve rapid,and accurate voltage control even under severe measurement deficiencies.展开更多
The high penetration and uncertainty of distributed energies force the upgrade of volt-var control(VVC) to smooth the voltage and var fluctuations faster. Traditional mathematical or heuristic algorithms are increasin...The high penetration and uncertainty of distributed energies force the upgrade of volt-var control(VVC) to smooth the voltage and var fluctuations faster. Traditional mathematical or heuristic algorithms are increasingly incompetent for this task because of the slow online calculation speed. Deep reinforcement learning(DRL) has recently been recognized as an effective alternative as it transfers the computational pressure to the off-line training and the online calculation timescale reaches milliseconds. However, its slow offline training speed still limits its application to VVC. To overcome this issue, this paper proposes a simplified DRL method that simplifies and improves the training operations in DRL, avoiding invalid explorations and slow reward calculation speed. Given the problem that the DRL network parameters of original topology are not applicable to the other new topologies, side-tuning transfer learning(TL) is introduced to reduce the number of parameters needed to be updated in the TL process. Test results based on IEEE 30-bus and 118-bus systems prove the correctness and rapidity of the proposed method, as well as their strong applicability for large-scale control variables.展开更多
Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity conversion.The existin...Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity conversion.The existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables,which in turn increases the optimization difficulty or even leads to optimization failure.This paper first proposes a deterministic VVC method based on convex deep learning power flow(DLPF).This method uses the input convex neural network(ICNN)to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment,which can ensure the global optimum with extremely fast computation speed.To cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC,this paper proposes robust VVC method based on convex deep learning interval power flow(DLIPF),which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage interval.Combining DLIPF with DLPF,this method decreases the modeling and optimization difficulty of robust VVC significantly.Test results on 30-bus,118-bus,and 200-bus systems prove the correctness and rapidity of the proposed methods.展开更多
较为全面地综述了国内外学术界对电力系统无功电压调控配合的研究现状。归纳并定义了无功电压调控的平衡状态,提出无功电压调控失配与适配的概念,建立电力系统无功均衡适配调度模型(equilibrium and coordinated reactivepower dispatch...较为全面地综述了国内外学术界对电力系统无功电压调控配合的研究现状。归纳并定义了无功电压调控的平衡状态,提出无功电压调控失配与适配的概念,建立电力系统无功均衡适配调度模型(equilibrium and coordinated reactivepower dispatch,ECRPD),指出了现行无功电压调控配合研究的关键问题与难点。提出采用多智能体系统等分布式人工智能方法与博弈论相结合,用以分析和解决ECRPD问题。展开更多
分布式可再生能源的大规模接入,加剧了有源配电网(Active Distribution Network,ADN)的三相不平衡,容易导致系统电压越限与线损增加。然而,由于当前配电网量测设备安装不全,部分节点负荷数据难以准确获取,因此传统基于全局观测的ADN电...分布式可再生能源的大规模接入,加剧了有源配电网(Active Distribution Network,ADN)的三相不平衡,容易导致系统电压越限与线损增加。然而,由于当前配电网量测设备安装不全,部分节点负荷数据难以准确获取,因此传统基于全局观测的ADN电压控制方法难以满足实际控制需求。为解决上述问题,提出一种含深度学习代理模型的电压无功控制(Volt/Var control,VVC)进化算法。设计以高速公路神经网络为代理模型,精确拟合局部量测负荷信息、调压控制策略与系统性能指标之间的映射关系。将训练后的代理模型嵌入非支配排序遗传算法的迭代寻优过程中,对电压偏移率、三相不平衡度及线路损耗指标进行直接计算,实现数据驱动的配电网VVC策略快速求取。在改进的IEEE 123节点三相配电网算例上进行测试,验证了所提算法的性能优势及求解效率。展开更多
随着新能源大规模接入以及负荷的随机波动性,对配电网的电能质量提出了更高的挑战及要求。主动配电网控制无功调压设备抑制电压波动通常转化为混合整数规划问题,难以做到实时控制且需频繁进行复杂计算。从历史数据中提取源荷状态,生成...随着新能源大规模接入以及负荷的随机波动性,对配电网的电能质量提出了更高的挑战及要求。主动配电网控制无功调压设备抑制电压波动通常转化为混合整数规划问题,难以做到实时控制且需频繁进行复杂计算。从历史数据中提取源荷状态,生成基于二阶锥最优潮流模型的电压控制策略,构建以调压装置状态、系统数据与控制策略为核心实体的配电网电压控制知识图谱;在实时电压控制时,基于时间序列相似度检索算法,以当前网络状态匹配知识图谱中相似状态,进行安全校验和优化求解,并更新知识图谱中的状态策略。同时,在无功设备调节过程中增加人机交互环节,对于时间尺度、电压及设备动作及关键点电压实现精准控制。基于改进电气电子工程师学会(Institute of Electrical and Electronics Engineers,IEEE)系统算例的仿真结果表明,所提出的基于电压控制策略知识图谱的检索方法及交互策略能够有效提升配电网无功电压控制策略生成效率,并具有不同场景适用性。展开更多
基金supported by the Science and Technology Project of the State Grid Corporation of China(No.5108-202218280A-2-366-XG)。
文摘With the increasing integration of uncertain distributed renewable energies(DREs)into distribution networks(DNs),communication bottlenecks and the limited deployment of measurement devices pose significant challenges for advanced data-driven voltage control strategies such as deep reinforcement learning(DRL).To address these issues,this paper proposes an offline-training online-execution framework for volt-var control in DNs.In the offline-training phase,a graph convolutional network(GCN)-based denoising autoencoder(DAE),referred to as the deep learning(DL)agent,is designed and trained to capture spatial correlations among limited physical quantities.This agent predicts voltage values for nodes with missing measurements using historical load data,DRE outputs,and global voltages from simulations.Furthermore,the dualtimescale voltage control problem is formulated as a multiagent Markov decision process.A DRL agent employing the multi-agent soft actor-critic(MASAC)algorithm is trained to regulate the tap position of on-load tap changer(OLTC)and reactive power output of photovoltaic(PV)inverters.In the online-execution phase,the DL agent supplements the limited measurement data,providing enhanced global observations for the DRL agent.This enables precise equipment control based on improved system state estimation.The proposed framework is validated on two modified IEEE test systems.Numerical results demonstrate its ability to effectively reconstruct missing measurements and achieve rapid,and accurate voltage control even under severe measurement deficiencies.
文摘The high penetration and uncertainty of distributed energies force the upgrade of volt-var control(VVC) to smooth the voltage and var fluctuations faster. Traditional mathematical or heuristic algorithms are increasingly incompetent for this task because of the slow online calculation speed. Deep reinforcement learning(DRL) has recently been recognized as an effective alternative as it transfers the computational pressure to the off-line training and the online calculation timescale reaches milliseconds. However, its slow offline training speed still limits its application to VVC. To overcome this issue, this paper proposes a simplified DRL method that simplifies and improves the training operations in DRL, avoiding invalid explorations and slow reward calculation speed. Given the problem that the DRL network parameters of original topology are not applicable to the other new topologies, side-tuning transfer learning(TL) is introduced to reduce the number of parameters needed to be updated in the TL process. Test results based on IEEE 30-bus and 118-bus systems prove the correctness and rapidity of the proposed method, as well as their strong applicability for large-scale control variables.
文摘Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity conversion.The existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables,which in turn increases the optimization difficulty or even leads to optimization failure.This paper first proposes a deterministic VVC method based on convex deep learning power flow(DLPF).This method uses the input convex neural network(ICNN)to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment,which can ensure the global optimum with extremely fast computation speed.To cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC,this paper proposes robust VVC method based on convex deep learning interval power flow(DLIPF),which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage interval.Combining DLIPF with DLPF,this method decreases the modeling and optimization difficulty of robust VVC significantly.Test results on 30-bus,118-bus,and 200-bus systems prove the correctness and rapidity of the proposed methods.
文摘较为全面地综述了国内外学术界对电力系统无功电压调控配合的研究现状。归纳并定义了无功电压调控的平衡状态,提出无功电压调控失配与适配的概念,建立电力系统无功均衡适配调度模型(equilibrium and coordinated reactivepower dispatch,ECRPD),指出了现行无功电压调控配合研究的关键问题与难点。提出采用多智能体系统等分布式人工智能方法与博弈论相结合,用以分析和解决ECRPD问题。
文摘分布式可再生能源的大规模接入,加剧了有源配电网(Active Distribution Network,ADN)的三相不平衡,容易导致系统电压越限与线损增加。然而,由于当前配电网量测设备安装不全,部分节点负荷数据难以准确获取,因此传统基于全局观测的ADN电压控制方法难以满足实际控制需求。为解决上述问题,提出一种含深度学习代理模型的电压无功控制(Volt/Var control,VVC)进化算法。设计以高速公路神经网络为代理模型,精确拟合局部量测负荷信息、调压控制策略与系统性能指标之间的映射关系。将训练后的代理模型嵌入非支配排序遗传算法的迭代寻优过程中,对电压偏移率、三相不平衡度及线路损耗指标进行直接计算,实现数据驱动的配电网VVC策略快速求取。在改进的IEEE 123节点三相配电网算例上进行测试,验证了所提算法的性能优势及求解效率。
文摘随着新能源大规模接入以及负荷的随机波动性,对配电网的电能质量提出了更高的挑战及要求。主动配电网控制无功调压设备抑制电压波动通常转化为混合整数规划问题,难以做到实时控制且需频繁进行复杂计算。从历史数据中提取源荷状态,生成基于二阶锥最优潮流模型的电压控制策略,构建以调压装置状态、系统数据与控制策略为核心实体的配电网电压控制知识图谱;在实时电压控制时,基于时间序列相似度检索算法,以当前网络状态匹配知识图谱中相似状态,进行安全校验和优化求解,并更新知识图谱中的状态策略。同时,在无功设备调节过程中增加人机交互环节,对于时间尺度、电压及设备动作及关键点电压实现精准控制。基于改进电气电子工程师学会(Institute of Electrical and Electronics Engineers,IEEE)系统算例的仿真结果表明,所提出的基于电压控制策略知识图谱的检索方法及交互策略能够有效提升配电网无功电压控制策略生成效率,并具有不同场景适用性。
