Due to the increasing share of renewable energy, new requirements are placed on control room software. Such software is often exclusive to the supplier, but other suppliers could offer new and better methods. For secu...Due to the increasing share of renewable energy, new requirements are placed on control room software. Such software is often exclusive to the supplier, but other suppliers could offer new and better methods. For security reasons, external applications often have no direct data access to control room software. Such software can provide information about the power grid via a periodic file transfer in CIM (Common Information Model) format. These files are often very large, containing complete records, delivering information not always relevant to the external applications. Extracting the relevant information required by external applications can be time-consuming, thus presenting a problem for time-critical applications. This paper presents a method allowing different applications to efficiently access the relevant data from the massive data stream contained in the CIM files. This method has been tested with a distribution system operator and clearly increases performance, allowing different applications to access the relevant data.展开更多
Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment fr...Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.展开更多
文摘Due to the increasing share of renewable energy, new requirements are placed on control room software. Such software is often exclusive to the supplier, but other suppliers could offer new and better methods. For security reasons, external applications often have no direct data access to control room software. Such software can provide information about the power grid via a periodic file transfer in CIM (Common Information Model) format. These files are often very large, containing complete records, delivering information not always relevant to the external applications. Extracting the relevant information required by external applications can be time-consuming, thus presenting a problem for time-critical applications. This paper presents a method allowing different applications to efficiently access the relevant data from the massive data stream contained in the CIM files. This method has been tested with a distribution system operator and clearly increases performance, allowing different applications to access the relevant data.
基金The authors would like to thank Dr.-Ing.Nils Bornhorst for the fruitful discussion.The publication and development of this work was funded by the Hessian Ministry of Higher Education,Research,Science and the Arts,Germany through the K-ES project under reference number:511/17.001.
文摘Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.
